Published April 17, 2025

My Writings

Some time ago, I came across “Ninety-five theses on AI” by Samuel Hammond—a powerful collection of concise statements designed to challenge conventional wisdom and provoke deeper thinking about artificial intelligence. This format, inspired by Martin Luther’s revolutionary 95 theses nailed to the church door in 1517, struck me as the perfect vehicle to distill my own thinking on marketing’s necessary transformation.

In the past five years, I have written over 180 essays exploring new ideas in marketing—examining everything from the attention recession and the AdWaste crisis to re-engineering retention and the transformative potential of AI-native martech. These writings have gradually coalesced into a cohesive vision I call “NeoMarketing.”

As Claude puts it: “A thesis, in this context, is not merely an observation but a declarative statement of principle—a stake in the ground that challenges established thinking. Each thesis should stand alone as a provocative insight while collectively forming a comprehensive framework for reimagining an entire discipline. The power of theses lies in their brevity and clarity—complex ideas distilled to their essence, without sacrificing their revolutionary potential.”

What follows are 75 NeoMarketing theses—each a single, potent point capturing a distinct aspect of marketing’s necessary transformation. Together, they constitute not merely a critique of the status quo but a blueprint for a fundamentally different approach to how brands build and maintain customer relationships in the digital age. This is my manifesto for NeoMarketing—a vision for eliminating the $500 billion AdWaste crisis while creating sustainable, profitable customer relationships that benefit both businesses and the people they serve.

Based on just the titles of my past essays, I asked Claude to summarise the key themes.

1. The AdWaste Crisis and Marketing Inefficiency: You’ve extensively explored how brands waste billions reacquiring customers they already know, creating what you call the “$200-500 Billion AdWaste Problem.” This theme is evident in essays like “Solving the $200 Billion AdWaste Problem” and “The Coming Age of Anti-Acquisition.”
2. AI-Native Marketing Transformation: A significant portion of your work examines how AI (particularly agentic AI) is fundamentally changing marketing—not just as an add-on tool but as a complete reimagining of operations. Essays like “Marketing’s AI Triad,” “AI Co-Marketer,” and “How Agentic AI will Transform Digital Marketing” showcase this focus.
3. NeoMails and Email Reinvention: Email transformation appears as perhaps your most consistent theme, with numerous essays on “Email 2.0,” “NeoMails,” “AMP technology,” and interactive elements. You’ve clearly positioned email as an underutilized channel with immense potential for reinvention.
4. AI Twins and Personalisation: The concept of AI Twins (both at segment and individual levels) features prominently, with essays like “AI Twins: The Future of Marketing Intelligence” and “MyTwin: The Agentic AI That Powers N=1 Personalisation,” exploring how digital replicas can transform customer understanding.
5. Attention Economics and Microns: You’ve deeply examined attention as a scarce resource, with essays on “Attention Messaging,” “Atomic Rewards,” and “Microns” that explore how to create brief, valuable engagements that respect customers’ time while building habits.
6. Progency and Performance-Based Marketing: The evolution of martech business models appears frequently, particularly your concept of “Progency” that combines platform capabilities with agency services and outcome-based economics, shown in essays like “Progency for Martech: The Missing Link.”
7. NEON and Authenticated Advertising: You’ve developed the concept of a new advertising ecosystem built on authenticated identity rather than cookies, with essays on “NeoAdtech,” “NEON,” and breaking free from “Big AdTech” appearing regularly in your recent work.
8. Profitability Focus and Metrics: A consistent thread explores shifting marketing from a cost center to a profit driver, with concepts like “Profipoly,” “ProfitXL,” and essays on becoming the “Chief Profitability Officer” demonstrating your focus on marketing economics.
9. The Only Once/OOO Philosophy: Your “Only Once” theory of marketing (acquiring customers just once) appears as a fundamental principle in essays like “Only Once: A Radical New Theory of Marketing” and “From AAA to OOO: The NeoMarketing Revolution.”
10. Velvet Rope Marketing (VRM): Special treatment for best customers features prominently, with several essays specifically on “Velvet Rope Marketing” exploring how to differentiate experiences based on customer value.
11. Martech Business Model Innovation: You’ve extensively explored how martech companies can evolve their business models, with essays on “Martech 2.0,” “New SaaS,” and various explorations of performance-based pricing and revenue sharing.
12. Retention Engineering: A significant theme is the systematic approach to customer retention, with essays like “The 10 Tenets for Re-engineering Retention” and “Retention Re-engineering” showing your focus on keeping customers rather than constantly acquiring new ones.

Claude continued: “These themes collectively form a comprehensive vision for transforming marketing from an acquisition-obsessed, wasteful discipline into a precision-focused, AI-powered approach centred on building lasting customer relationships. Your work explores this transformation from multiple angles—technological innovation, business model evolution, customer psychology, and economic fundamentals—creating a holistic framework for what you’ve termed “NeoMarketing.” The evolution of your thinking is also apparent when viewing these essays chronologically, with earlier works focused on foundational concepts like microns and email reinvention, while more recent essays integrate these ideas into larger frameworks like NeoMarketing, AI-native systems, and the comprehensive AdWaste solution.”

Theses

Overview

1. AdWaste—the reacquisition of customers who already know a brand—is the single greatest destroyer of profits in modern business, creating an absurd cycle where companies pay rent to access relationships they already own.
2. A staggering 70% of digital marketing budgets—approximately $500 billion annually—is squandered on reacquiring existing or lapsed customers through expensive ad platforms, creating marketing’s great tragedy: paying platforms to reach people already in the database.
3. This $500 billion transfer from brands to Big Adtech represents perhaps the largest ongoing wealth extraction in business history, systematically draining resources that should be directed toward innovation, customer experience, and shareholder value.
4. Without addressing the AdWaste crisis at its root, sustainable profitable growth remains a mathematical impossibility—brands are simply funding their own obsolescence through platform dependency.
5. The solution to AdWaste cannot emerge from optimising adtech performance (ROAS)—this merely makes inefficiency more efficient; true transformation requires reimagining retention through revolutionary martech approaches.
6. AdWaste stems directly from the Attention Recession—when customers mentally unsubscribe from a brand’s communications, forcing companies to buy back attention through increasingly expensive ad platforms.
7. The Attention Recession itself arises from two fundamental marketing failures: the “Not for Me” problem (generic messaging that fails to resonate with individual preferences) and the “No Hotlines” problem (inconsistent or low-value engagement channels).
8. Two transformative technologies now converge to solve these core problems: Agentic AI enables true N=1 personalisation at scale, while AMP for Email transforms static messages into interactive, app-like experiences that command attention.
9. This technological foundation also enables two revolutionary business models—NeoN, which creates a direct brand-to-brand marketplace for attention exchange through authenticated identity, and Progency, which combines AI-powered platform capabilities with performance-based service delivery.
10. Together, these innovations form NeoMarketing—a complete reimagining of how brands build and maintain customer relationships, transforming marketing from a cost centre driven by acquisition to a profit engine powered by retention, fundamentally altering the economics of customer relationships.

Endstate

11. The focus for marketing should be to maximise Earned Growth—revenue generated organically from existing customers and referrals—transforming marketing from an expensive acquisition machine into a self-sustaining engine of profitable expansion.
12. As Fred Reichheld powerfully articulates: “There is only one way to grow a business profitably. You make sure your customers are treated so well that they come back for more and bring their friends.”
13. This paradigm shift demands an OOO (Only Once/Ones) mindset to replace the wasteful AAA (Acquire, Acquire, Acquire) approach—acquiring each customer exactly once, then investing relentlessly in making them feel valued and understood.
14. True customer-centricity requires making each person feel individually recognised through N=1 omnichannel personalisation—delivering precisely the right message at the right time through the right channel to the right person, not as marketing rhetoric but as operational reality.
15. The twin north stars of marketing excellence thus become maximising customer Lifetime Value (LTV) while simultaneously minimising Customer Acquisition Cost (CAC)—a formula that mathematically guarantees sustainable profitable growth when executed systematically.

Marketing Today

16. The current state of marketing is dominated by acquisition addiction via Big Adtech platforms (Google/Meta), with 80-90% of budgets funnelled into driving clicks and conversions—creating a dependency that undermines long-term business health.
17. While this approach may drive short-term revenue growth, it conceals two devastating flaws: Customer Acquisition Costs (CAC) rise relentlessly year after year, while the absence of robust retention strategies results in 60-65% of customers making precisely one purchase before disappearing forever.
18. The dark, unspoken truth of digital marketing is that 7 out of 10 supposedly “new” customers are actually existing or former customers being expensively “reacquired”—a systemic inefficiency that remains largely unacknowledged in marketing boardrooms.
19. This AdWaste—the continuous cycle of paying to reacquire customers already in a brand’s database—represents the single greatest destroyer of business profitability, systematically transferring wealth from brands to adtech platforms while creating no genuine value.
20. CMOs have unwittingly transformed from growth champions into collection agents for Google and Meta—managing ever-larger budgets that primarily serve to enrich platforms rather than build sustainable, profitable customer relationships for their own businesses.

Attention Recession

21. AdWaste is the direct consequence of Attention Recession—a phenomenon where customers mentally unsubscribe from brand communications long before formally opting out, creating a silent exodus that eventually manifests as churn.
22. For marketers, push messages via email, SMS, RCS, WhatsApp, and app notifications serve as critical bridges to bring customers back to transactional properties (websites and apps)—yet when these bridges collapse, costly reacquisition becomes the only alternative.
23. The alarming reality is that 9 out of 10 push messages are ignored entirely, while only 3 of every 100 visitors to a brand’s property complete a transaction—a devastating funnel leakage that destroys marketing efficiency.
24. These dismal statistics illuminate marketing’s twin fundamental failures: the “Not for Me” problem (irrelevant, generic messaging that fails to resonate with individual needs) and the “No Hotlines” problem (the absence of consistent, valuable connection channels).
25. Unless these root causes are addressed—beginning with solving Attention Recession at its source—the AdWaste crisis will persist regardless of how much brands optimise their acquisition funnels or improve their ROAS metrics.

Breakthroughs

26. Five revolutionary ideas and innovations lay the foundation for solving Attention Recession and radically enhancing Engagement, Conversion, and Retention (EnCoRe)—beginning with the Best-Rest-Test-Next (BRTN) framework that reimagines segmentation based on actual engagement patterns: Best customers (retain), Rest customers (engage), Test customers (reactivate), and Next customers (convert).
27. Agentic AI represents perhaps the most transformative breakthrough—where Marketing Agents function as autonomous specialists handling complex tasks at scale while AI Twins create digital replicas of customers that enable unprecedented understanding and personalisation.
28. AMP in email transforms traditional one-way broadcast messages into interactive, two-way NeoMails—removing the “click-through penalty” that loses 80-90% of potential conversions by enabling complete transactions directly within the inbox.
29. NeoN, an alternative email ad network powered by authenticated identity, creates a direct brand-to-brand marketplace that slashes reacquisition costs while eliminating the platform “tax” currently claimed by Google and Meta.
30. Progency solves the persistent “Who Will Do It?” problem that plagues martech adoption—combining proprietary platform capabilities with specialist expertise and AI acceleration in a performance-based model that delivers outcomes, not just software.

BRTN Framework

31. The Best-Rest-Test-Next (BRTN) framework represents a radical departure from traditional demographic segmentation by categorising customers based on their actual engagement patterns and lifetime value (LTV) contribution—aligning marketing strategy with economic reality.
32. Best customers—the elite 10-20% who typically generate 60-80% of revenue and an astounding 200% of profits—demand Velvet Rope Marketing (VRM) that creates distinctly superior experiences, requiring marketing teams to deploy AI-native platforms that treat these individuals as unique entities rather than segment members.
33. Rest customers—the critical middle 30-50% showing declining engagement—represent marketing’s greatest untapped opportunity, yet receive insufficient attention from resource-constrained marketing teams; this neglected segment is precisely where Progency intervention delivers the highest ROI by rebuilding attention before dormancy sets in.
34. Test customers—the “One and Done” segment who have become inactive, dormant, or churned—constitute the primary source of AdWaste as brands repeatedly target them through expensive adtech platforms; NeoN provides a brand-to-brand co-operative alternative that cuts reacquisition costs while preserving authenticated relationships.
35. Next customers—genuinely new acquisitions—should ideally arrive through zero-cost referrals from delighted Best customers; for these newcomers, marketing teams must establish thoughtful welcome journeys, collect zero-party data, and facilitate rapid first and second transactions to establish the foundation for lasting, profitable relationships.

Agentic AI

36. While martech platforms have deployed predictive AI and more recently generative AI (particularly for content creation), the true transformation will come from Agentic AI—autonomous systems where marketing agents interact with customer agents to overcome human limitations in campaign orchestration, segmentation granularity, journey complexity, and content personalisation.
37. Tomorrow’s marketing departments will harness functional agents to streamline operations and create distinctive experiences for every customer; in the near-term, an AI Co-Marketer will coordinate these specialised agents, while the long-term vision embraces self-organising “emergents” forming a “Department of One” capable of managing millions of individual relationships simultaneously.
38. Customers will be represented by AI Twins—initially as Madtech (martech and adtech) Segment Twins reflecting cohort behaviours, then evolving to Singular (1:1) Twins as zero-party data accumulates; these digital replicas enable marketers to simulate conversations for insights on product preferences, offer receptivity, and optimal campaign approaches.
39. The truly revolutionary aspect emerges when customers themselves interact with their own Twins, setting preferences that govern all brand interactions, discovering products aligned with their actual needs, and effectively delegating routine purchasing decisions to trusted agents that understand their unique preferences.
40. Agentic AI’s ultimate goal is nothing less than the transition from crude N=Many segmentation to genuine N=1 personalisation—treating each customer as a unique individual with distinct preferences, behaviours, and needs—thereby maximising lifetime value through relationships that feel personally crafted rather than mass-produced.

NeoMails

41. Email—the highest ROI marketing channel—is undergoing a revolutionary transformation through AMP technology, evolving from static one-way messages into interactive two-way NeoMails that create genuine dialogue rather than broadcast monologues.
42. NeoMails solve email’s fundamental engagement crisis by incorporating three breakthrough innovations: Atomic Rewards (Mu) that gamify opens through micro-incentives, Microns that deliver 15-60 second “brain gain” experiences, and SmartBlocks that enable frictionless zero-party data collection.
43. The most transformative aspect of NeoMails is the elimination of the “click-through penalty” where 80-90% of potential conversions are lost—enabling customers to browse products, complete forms, and make purchases directly within their inbox without ever visiting external websites.
44. By transforming email from a mere communication channel into an engagement and transaction platform, NeoMails deliver conversion rates 4-10x higher than traditional emails—not through better copywriting or design, but by fundamentally eliminating the friction that kills traditional email effectiveness.
45. NeoMails create reliable daily “hotlines” between brands and customers—solving the “No Hotline” problem at the root of Attention Recession by delivering genuine value that customers actively seek out rather than promotional noise they increasingly ignore.

NeoN

46. NeoN (New Engaged and Open Network) reimagines advertising by creating a direct brand-to-brand marketplace that connects authenticated customers through trusted email channels, eliminating the expensive intermediaries that drive AdWaste.
47. Unlike cookie-based targeting that relies on probabilistic matching, NeoN leverages authenticated identity (PII) with deterministic, precise targeting—ensuring every impression reaches exactly the right person with zero waste.
48. ActionAds embedded in NeoMails enable complete transactions directly within the email—eliminating the “click-through penalty” while creating frictionless conversion experiences that deliver higher performance rates than traditional digital ads.
49. NeoN transforms the economics of reacquisition by enabling brands to simultaneously “print money” (monetising their Best customers’ attention) and “save money” (efficiently reactivating their Test customers at a lower cost than adtech platforms).
50. By creating a cooperative ecosystem where brands both contribute and benefit from attention exchange, NeoN develops powerful network effects—each new participant adds both supply (emails to their Best customers) and demand (reacquisition needs for their Test customers).

Progency

51. Progency—a fusion of platform, expert talent, AI agents, and continuous improvement (P-E-A-K)—addresses the “Who Will Do It?” problem that prevents brands from fully leveraging martech capabilities despite their significant investments.
52. Unlike traditional agencies using third-party tools or martech vendors providing just software, Progency combines deep platform expertise with specialist talent and AI acceleration in a performance-based model focused on outcomes rather than inputs.
53. By shifting from consumption-based pricing (CPM or MAU) to revenue-share models tied directly to measurable results, Progency aligns incentives perfectly: if the brand doesn’t benefit financially, neither does the Progency.
54. Progency specialises in maximising value from Rest customers—the often-neglected middle segment showing declining engagement—enabling marketing teams to focus their limited resources on Best customer retention and Next customer conversion.
55. Through AI agent orchestration in a parallel marketing department, Progency delivers the operational scale of enterprise software with the strategic expertise of specialist agencies—creating a transformative approach that turns martech from a cost centre into a measurable profit engine.

NeoMarketing

56. NeoMarketing represents the third distinct era of marketing evolution, following Traditional Marketing (1950s-1990s) and Modern Marketing (2000s-2020s)—a fundamental reimagining of how brands build and maintain customer relationships in the digital age.
57. Traditional Marketing operated through mass media to build brand awareness and recall—with marketers functioning as storytellers and market-makers who shaped consumer perceptions through creative campaigns, often accepting waste as an inevitable byproduct of reach.
58. Modern Marketing pivoted to performance metrics and digital platforms—obsessing over clicks, CPC, CPA, ROAS, and last-click attribution—yet this apparent precision had a devastating side-effect: transforming marketers into collection agents for Google and Meta while normalising AdWaste as standard practice.
59. NeoMarketing stands on three foundational principles that invert established orthodoxy: Retention before Reacquisition (prioritising relationship depth over constant acquisition), Trust before Transactions (building value-driven engagement before extracting revenue), and Individuals before Segments (recognising each customer’s uniqueness beyond crude groupings).
60. This new marketing paradigm delivers three transformative outcomes: an Antidote to AdWaste by solving the Attention Recession at its source, a Recipe for Re-engineering Retention through AI-powered personalisation and engagement, and a Flywheel for Profitable Growth that turns marketing from a cost centre into the primary engine of sustainable business success.

House of NeoMarketing

61. The House of NeoMarketing creates a comprehensive framework for marketing transformation with the ultimate goal of “Rule of 40” Profitable Growth (Maximise LTV, Minimise CAC)—a structured approach with each layer building upon the previous one to create a self-reinforcing system for sustainable business success.
62. The Foundation layer establishes the bedrock through Large Customer Models, Unistack integration, and Earned Growth metrics—ensuring that data, technology, and organisational alignment support retention-first marketing.
63. The Intelligence layer leverages AI Co-Marketer, autonomous “Emergents,” and AI Twins to enable true N=1 personalisation—transforming raw customer data into actionable insights that drive individualised experiences at scale.
64. The Engagement layer creates reliable daily hotlines through Channels 2.0, NeoMails, and Zero-Party Data collection—solving the “No Hotline” problem by establishing consistent, value-driven connections that combat Attention Recession.
65. The Monetisation layer converts engagement into revenue through Shoppable Channels, Omnichannel Recommendations, the NeoN network, and Progency—turning marketing from a cost centre into a profit engine that simultaneously maximises LTV and minimises CAC.

Martech Like Adtech

66. Martech must evolve from optional toolkit to mission-critical business infrastructure—just as adtech transformed from experimental channel to essential revenue driver, NeoMarketing demands that retention technology becomes as fundamental to business success as acquisition platforms.
67. The future of martech hinges on performance-based, outcomes-driven pricing that aligns vendor success directly with customer results—mirroring adtech’s revolutionary shift from media buying to guaranteed performance that transformed how brands allocate marketing investments.
68. Martech’s perceived budget constraints will dissolve when it creates measurable revenue impact—transforming from discretionary expenditure to Cost of Goods Sold (COGS), with virtually infinite budgets available for solutions that demonstrably drive profitable growth.
69. Just as lean marketing teams can manage millions in adtech spend through algorithmic scaling, NeoMarketing enables small teams to orchestrate sophisticated, personalised engagement at global scale through AI agents and autonomous systems that eliminate operational bottlenecks.
70. The companies that successfully transform martech from cost centre to profit engine will create unprecedented market value—establishing platform dominance in customer retention similar to how Google and Meta captured acquisition, potentially creating the next trillion-dollar marketing technology behemoths.

NeoMartech’s Trillion-Dollar Opportunity

71. Google and Meta built monopoly-like platforms around search and social networks—transforming how brands acquire customers while generating hundreds of billions in annual revenue and creating multi-trillion dollar market capitalisation, yet this value extraction has come at brands’ expense.
72. The next frontier of marketing value creation lies not in better acquisition but in eliminating AdWaste entirely—NeoMartech companies that engineer retention, growth, and profit flywheels for brands will capture a market opportunity of comparable magnitude to what adtech platforms seized in the previous era.
73. These emerging NeoMartech leaders will build their dominance on four revolutionary pillars: Agentic AI that enables true N=1 personalisation at scale, AMP in Email that transforms static messages into interactive hotlines, NeoN that creates an alternative to expensive reacquisition, and Progency that delivers guaranteed outcomes rather than merely software.
74. NeoMarketing represents marketing’s return to its fundamental purpose—building deep, lasting, and profitable relationships with existing customers that maximise retention, revenues, and referrals while continuously reducing customer acquisition costs through Earned Growth.
75. As adtech dominated the Internet and Mobile eras by solving acquisition, a trillion-dollar opportunity now awaits the visionaries who solve retention in the AI era—redirecting the massive wealth currently wasted on reacquisition toward creating genuine customer value and sustainable business growth.

Published April 16, 2025

1

Theses

I have been writing on new ideas in marketing for over five years now. With each essay, I refine my earlier ideas. As Heraclitus said, “A man cannot step into the same river twice, because it is not the same river, and he is not the same man.” So, even though the core ideas may seem repetitive, there are new nuances which come with every new writing. I want to begin by summarising my key theses and then discussing two moonshots that can transform marketing: NeoN, a new email-based ad network, and Progency, for performance-based martech.

1. The $500 Billion AdWaste Crisis: 70% of digital marketing budgets are squandered on reacquisition – repeatedly targeting the same customers through expensive adtech platforms. This spend is the #1 profit killer for businesses. The solution doesn’t lie in optimising adtech, but in revolutionising martech.
2. Root Causes of Marketing Inefficiency: Two fundamental problems drive this waste: the “Not for Me” problem (lack of true 1:1 personalisation) and the “No Hotline” problem (attention recession leading to inconsistent customer connections).
3. The BRTN Framework: A new customer segmentation approach based on engagement patterns rather than demographics: Best customers (top 20% who deliver 60-80% of revenue and 200% of profits) need Retention; Rest customers (the middle 50%) need Engagement; Test customers (dormant/churned 30%) need Reactivation; and Next customers need Conversion.
4. Focus on the Overlooked Middle: Brands lose money primarily through Rest and Test customers. These segments require breakthrough technologies – specifically Agentic AI and interactive AMP in email – to transform engagement and recapture value.
5. AI-Native Martech: Agentic AI (through AI Co-Marketer and Marketing Agents) combined with AI Twins enables true N=1 personalisation at scale. This creates a “Department of One for a Segment of One” – a marketer capable of managing millions of individual customer relationships simultaneously, solving the “Not for Me” problem.
6. From TradMails to NeoMails: AMP technology transforms static emails into interactive experiences through Atomic Rewards (Mu) and Microns (15-60 second “brain gain” activities). This creates reliable daily hotlines between brands and customers, solving the “No Hotline” problem.
7. Recapturing Rest Customers: The combination of NeoMails (ensuring messages break through) and Agentic AI (converting clicks to transactions) creates an efficient system for re-engaging the critical middle segment before they become dormant.
8. The Progency Solution: Most brands lack resources to maximise value from Rest customers while focusing on Best and Next segments. Progency combines Platform, Experts, AI agents, and Kaizen (PEAK) to deliver performance-based outcomes rather than merely providing software, making martech as simple as adtech.
9. NeoN: The Attention Exchange: For Test customers, brands need an alternative to expensive duopoly platforms (Google/Meta). NeoN creates a direct brand-to-brand marketplace where Brand A monetises its Best customers’ attention to help Brand B reacquire Test customers – enabling Brand A to “print money” while Brand B “saves money.”
10. The Path to Marketing Prosperity: NeoN and Progency represent marketing’s moonshots – transforming martech from cost centre to profit engine. They usher in the era of NeoMarketing, maximising LTV while minimising CAC, eliminating AdWaste, and putting brands on a sustainable “Rule of 40” path to profitability.

2

NeoN: The Attention Exchange

NeoN reimagines advertising by creating a direct marketplace that connects brands through their most valuable asset: customer attention. Here are the key principles and mechanisms that define this revolutionary approach:

1. The True Cost of Customer Reacquisition: When customers become dormant (Test customers), brands typically resort to expensive retargeting through Google and Meta, paying premium prices in competitive auctions just to reach people already in their database. This creates a vicious cycle where brands fund their own obsolescence.
2. The Power of Authenticated Identity: NeoN leverages a fundamentally different approach based on authenticated identity (PII) rather than anonymous cookies or probabilistic matching. This enables precise targeting with near-zero waste, connecting brands directly to specific individuals.
3. From Platform Dependency to Brand Collaboration: Instead of renting access to customers through intermediaries, NeoN enables direct brand-to-brand collaboration. This creates a marketplace where brands simultaneously play dual roles – as publishers monetising their active customers’ attention and as advertisers reaching their dormant customers.
4. The Economics of Attention Exchange: When Brand A hosts ActionAds from Brand B within its NeoMails, both benefit: Brand A earns revenue from attention it has already captured (its Best customers), while Brand B saves 30-50% compared to traditional reacquisition costs for reaching its Test customers.
5. Privacy-First Architecture: Unlike invasive tracking, NeoN operates through “clean room” technology that matches audiences without exposing personal data. Customers retain control through granular permissions, creating a consent-based ecosystem that thrives in post-cookie environments.
6. Interactive, In-Email Transactions: Through AMP-powered ActionAds, customers can fill forms, browse products, redeem offers, and complete purchases without leaving their inbox – all actions happen directly within the email itself, eliminating the need for clickthrough to external landing pages. This drastically reduces friction compared to traditional click-through journeys that lose 80-90% of potential conversions.
7. Cross-Brand Audience Matching: NeoN’s matching algorithm identifies where Brand A’s active users overlap with Brand B’s dormant customers, creating efficient reactivation pathways. A sporting goods retailer’s active customers might help a fitness app reconnect with lapsed users through relevant, contextual offers.
8. Network Effects and Scaling Advantages: As more brands join NeoN, its value compounds exponentially. Each new participant adds both inventory (emails to their Best customers) and demand (reacquisition needs for their Test customers), creating a self-reinforcing ecosystem.
9. Measuring True Performance: Unlike opaque adtech attribution, NeoN provides transparent, accurate measurement since all interactions occur within authenticated environments. Brands pay only for actual engagement with their specific dormant customers, eliminating wasted impressions.
10. Complementary to Traditional Channels: NeoN doesn’t replace search or social advertising for new customer acquisition (Next customers) but provides a superior alternative specifically for reactivating dormant customers (Test customers). This allows brands to optimise their media mix for each customer segment.

NeoN represents a fundamental shift from renting attention to trading it in a fair marketplace. By eliminating expensive intermediaries and enabling precision targeting through authenticated identity, it offers a compelling alternative to the wasteful status quo. This isn’t just a new advertising channel – it’s an entirely new economic model for marketing that values customer relationships over platform dependency.

3

NeoN’s Secret Sauce – 1

There are four revolutionary ideas at the core of NeoN which make it a genuine moonshot in marketing, transforming the relationship between brands and their customers while eliminating the billions in AdWaste that plague modern businesses.

1. ActionAds: The Frictionless Conversion Engine

Traditional digital ads face a fundamental challenge: the “click-through penalty.” Every time a customer must leave their current environment to complete an action, conversion rates plummet—typically by 80-90%. This friction is perhaps the greatest hidden cost in digital marketing.

ActionAds eliminate this penalty entirely by bringing the entire transaction journey inside the email itself. Through AMP technology, customers can:

• Browse product options and variants within the inbox
• Fill out forms without navigating to landing pages
• Complete purchases with saved payment information (and if needed, an OTP as second factor authentication)
• Claim offers and apply discounts instantly
• Book appointments and reservations in seconds

The impact is transformative—conversion rates typically increase 4-10X compared to traditional click-through experiences. This dramatic improvement occurs not because the offers are necessarily better, but because the friction has been eliminated. It’s akin to the difference between ordering from a menu versus having to leave a restaurant to view the selection in another building.

Additionally, ActionAds completely eliminate click fraud—a persistent problem that drains an estimated 15-30% of digital ad budgets globally. Since all interactions occur within the authenticated email environment rather than through redirects to external sites, the bots, fake clicks, and domain spoofing that plague traditional digital advertising simply cannot exist in this ecosystem. Advertisers pay only for genuine engagements from real, verified humans.

What makes ActionAds particularly powerful is their ability to maintain context. Traditional advertising disrupts the customer’s experience, taking them out of their current environment. ActionAds respect the inbox as a personal space, creating a seamless extension of the email experience rather than an unwelcome interruption. The customer remains in control, with all interactions happening within their familiar email interface.

The technology is built to scale across use cases—from simple product offers to complex multi-step processes like travel bookings or financial applications. Each ActionAd is responsive, adapting to device screens and email client capabilities while maintaining consistent branding and functionality.

2. Matching Engine: Precision Through Authentication

The NeoN Matching Engine represents a paradigm shift in targeting technology. Unlike traditional adtech platforms that rely on probabilistic matching through cookies and device fingerprinting, NeoN operates on authenticated identity—real people who have explicitly logged in and shared their information.

This creates several profound advantages:

• Guaranteed Identity Match: When Brand A wants to reach its dormant customers through Brand B’s emails, the match is deterministic and precise—not an estimate or probability.
• Zero Waste Delivery: Every impression reaches exactly the intended recipient, eliminating the 30-50% waste typical in cookie-based targeting.
• Intelligent Content Selection: The engine doesn’t just match identities; it analyses relationship context to deliver the most relevant offers at the optimal time.
• Privacy-Preserving Architecture: Customer data never leaves secure environments. The matching occurs through “clean room” technology that identifies overlaps without exposing personal information.
• First-Party Data Prioritisation: As third-party cookies disappear, NeoN’s reliance on first-party authenticated data becomes increasingly valuable.

The Matching Engine operates as a sophisticated decision-making system, considering multiple factors simultaneously:

• Recency, frequency, and monetary value of past purchases
• Explicit and implicit preference signals
• Seasonal and contextual relevance
• Historical engagement patterns
• Customer lifecycle stage

This intelligence ensures that ads feel less like interruptions and more like helpful recommendations. A customer who purchased running shoes six months ago might receive an actionable offer for athletic socks or performance insoles—precisely when replacement or complementary products would be relevant.

Perhaps most importantly, the Matching Engine solves the “cold start” problem that plagues new ad platforms. Because brands bring their own first-party data (their dormant customers), the targeting is immediately effective without needing to build predictive models from scratch.

3. Co-operative Structure: The Network Effect Accelerator

NeoN’s co-operative structure is perhaps its most ingenious element. Every participating brand plays dual roles—as both publisher and advertiser—creating a self-balancing ecosystem that scales organically.

As publishers, brands monetise their Best customers’ attention through ActionAds in their emails, generating new revenue without additional costs. As advertisers, they efficiently reacquire dormant Test customers through other brands’ emails, saving significantly compared to traditional platforms.

This structure creates powerful incentives:

• Built-in Supply-Demand Balance: Each brand contributes inventory roughly proportional to their reacquisition needs, naturally balancing the marketplace.
• Exponential Network Effects: Every new brand joining NeoN adds both email inventory (supply) and dormant customers to target (demand), creating a virtuous cycle of increasing value.
• Collaborative Advantage: Brands benefit from collective scale without sacrificing control of their customer relationships.
• Anti-Competitive Safeguards: The system is designed to prevent direct competitors from targeting each other’s customers, ensuring brand safety.
• Equitable Value Exchange: Value received correlates directly with value contributed, creating fair distribution.

The co-operative approach solves one of digital advertising’s most persistent challenges: creating sufficient quality inventory at scale. By leveraging existing email programs that brands already operate, NeoN taps into billions of premium, authenticated impressions without needing to build audiences from scratch.

This structure also creates natural defences against fraud and quality issues. Since every participant has a stake in maintaining the ecosystem’s integrity, there are strong incentives for high-quality experiences. Poor-performing ActionAds or disruptive content would damage the value for all participants, creating self-regulation within the network.

4

NeoN’s Secret Sauce – 2

4. DIY/Self-Serve Platform: Democratising Access

The DIY/Self-Serve Platform makes NeoN’s sophisticated technology accessible to businesses of all sizes. Through an intuitive interface, brands can:

• Upload their customer lists for matching
• Create ActionAds without technical expertise
• Set targeting parameters and bid strategies
• Monitor performance in real-time
• Optimise campaigns based on results
• Scale budgets based on performance

This democratisation is crucial for widespread adoption. Traditional adtech platforms often require significant expertise and resources to operate effectively, creating barriers for smaller businesses. By making NeoN accessible through a self-serve model, the platform can scale across the market spectrum—from large enterprises to independent retailers.

The platform employs intelligent automation to simplify complex processes:

• Template Library: Pre-built ActionAd formats for common use cases
• Dynamic Creative Optimization: Automatically testing and improving ad variations
• Budget Optimisation: Intelligent allocation of spend across targeting segments
• Performance Prediction: AI-driven forecasting of expected results
• Simplified Reporting: Clear metrics focused on business outcomes

5. Beyond the Core Four: Additional Differentiators

While these four elements form NeoN’s foundation, several additional factors contribute to its potential as a marketing moonshot:

Transparent Economics

NeoN’s economic model represents a dramatic improvement over traditional ad platforms:

• Performance-Based Pricing: Brands pay primarily for actual results (clicks, conversions) rather than impressions
• No Platform Tax: Direct brand-to-brand transactions eliminate the 30-50% “tax” typically captured by intermediaries
• Revenue Sharing: Publishers receive 70-80% of advertising revenue, significantly higher than typical ad networks
• Predictable Costs: Without auction dynamics driving unpredictable price inflation, costs remain stable and predictable

First-Party Data Advantage

As privacy regulations tighten and third-party cookies disappear, NeoN’s reliance on first-party authenticated data creates significant advantages:

• Future-Proof Architecture: Built for privacy from the ground up, not retrofitted
• Compliant by Design: Clear consent frameworks and transparent data usage
• Zero Dependence on Cookies: No impact from browser privacy changes
• Enhanced with Zero-Party Data: Integrates explicitly shared preferences for better targeting

Cross-Channel Potential

While initially focused on email, NeoN’s architecture can extend to other authenticated channels:

• Mobile Apps: Leveraging logged-in state for in-app ActionAds
• Customer Portals: Extending to authenticated web experiences
• Messaging Platforms: Adapting to WhatsApp, SMS, and RCS as they support interactive content

**

The Moonshot Value Proposition

What makes NeoN truly a moonshot is not just its technological innovation but its potential economic impact. The global digital advertising market spends approximately $500 billion annually on reacquisition—reaching customers who are already in brands’ databases. By eliminating this waste, NeoN addresses a massive inefficiency in the marketing ecosystem.

For individual brands, the value proposition is compelling:

• Publishers (Best Customers): New revenue stream from existing email programs without additional costs
• Advertisers (Test Customers): 30-50% cost reduction for reacquiring dormant customers compared to traditional platforms
• Overall: Transformation of email from cost centre to profit centre

At scale, NeoN could fundamentally reorganise the economics of digital marketing, redirecting billions from platform intermediaries to the brands who create actual value for customers. This isn’t merely an incremental improvement—it’s a paradigm shift in how marketing resources are allocated and optimised.

By combining ActionAds’ frictionless experiences, the Matching Engine’s precision, a Co-operative structure that aligns incentives, and an accessible Self-Serve Platform, NeoN creates a complete ecosystem for attention exchange. This isn’t just another advertising channel—it’s an entirely new model for how brands connect with customers who already know them, transforming wasted spend into valuable engagement.

The true north star of NeoN isn’t just efficiency, but effectiveness. By bringing together the right offers, to the right customers, at the right moments, through a frictionless experience, NeoN doesn’t just save money—it creates better customer experiences. That’s the ultimate secret sauce that makes it a genuine marketing moonshot.

5

Progency: Performance Marketing Reinvented

Progency represents a fundamental reimagining of how martech delivers value, moving beyond software provision to outcome generation. This hybrid approach combines proprietary technology with expert services and AI orchestration to transform retention marketing. Here are the core principles:

1. The Marketing Execution Gap: Most brands lack the resources, expertise, and bandwidth to fully leverage martech platforms, typically using only 30-40% of available features. This execution gap prevents martech from delivering its potential value, particularly for Rest customer segments.
2. Beyond SaaS: The PEAK Framework: Progency transcends traditional SaaS by combining Platform, Experts, AI agents, and Kaizen methodology (PEAK) into a cohesive service. Rather than just providing tools and leaving implementation to clients, Progency takes ownership of outcomes.
3. Outcome-Based Economics: While adtech platforms charge for performance (cost per click, acquisition, etc.), martech traditionally charges for software access (based on monthly active users) regardless of results. Progency aligns incentives by adopting performance-based pricing tied directly to revenue impact.
4. Focus on the Overlooked Middle: Progency specialises in maximising value from Rest customers – the 40-50% of customers who have engaged within the past 90 days but are showing signs of declining activity. This segment represents the highest ROI opportunity for intervention.
5. The Product-Agency Fusion: Unlike traditional agencies using third-party tools or martech vendors providing just software, Progency offers a unique hybrid: deep platform expertise combined with specialist talent and AI acceleration, all tied to performance guarantees.
6. AI Agent Orchestration: At Progency’s core is a sophisticated system of AI agents working in concert with the Co-Marketer: Journey Agent, Consumer Insights Agent, Segment Agent, Content Agent, Designer Agent, Scheduler Agent, Analytics Agent, and Merchandiser Agent.
7. Closed-Loop Revenue Impact: Progency doesn’t measure success by vanity metrics like opens or clicks but by tangible business outcomes: reactivation rates, incremental revenue, and improved lifetime value. This creates direct accountability for financial results.
8. Parallel Operations Model: Rather than disrupting existing marketing workflows, Progency operates alongside them, focusing specifically on Rest customer engagement while brand teams concentrate on Best customer retention and Next customer acquisition.
9. Competitive Moat Building: For martech companies, Progency creates a sustainable advantage that pure software competitors can’t easily replicate. The combination of proprietary technology, specialist expertise, and performance commitment forms a defensible market position.
10. Scalable Value Creation: Progency achieves the seemingly impossible: delivering customised, high-touch marketing at scale through AI orchestration. This enables the economics of mass marketing with the effectiveness of personalised engagement.

Progency solves the fundamental “Who Will Do It?” problem that has limited martech’s impact. By combining technology with talent and tying compensation directly to results, it bridges the execution gap between software capabilities and business outcomes. This isn’t merely a service enhancement – it’s a complete reinvention of how martech creates and captures value in the enterprise market.

6

Progency’s Secret Sauce – 1

1. The AI Agent Revolution: Marketing’s Quantum Leap Forward

The true revolutionary breakthrough in Progency lies in its orchestrated ecosystem of AI agents—a “Department of One” that collaborates seamlessly to deliver what has long been marketing’s holy grail: true N=1 personalisation at scale. This isn’t incremental automation; it’s a fundamental reinvention of how marketing expertise is deployed, enabling brands to achieve levels of precision and effectiveness previously impossible with human teams alone. These specialised agents function as a collective intelligence, each handling distinct aspects of the marketing process while working in concert toward common goals—all without the resource constraints, cognitive limitations, or operational delays of traditional marketing structures.

Consider the agents announced recently by my company, Netcore Cloud.

Co-Marketer

• Orchestrates and coordinates all other AI agents.
• Translates strategic objectives into tactical execution.
• Ensures cohesive integration across all marketing initiatives.
• Functions as the central intelligence connecting specialised agents.

Journey Agent

• Designs an ideal journey for the given use case.
• Identifies the best communication touchpoints.
• Customises wait nodes and wait times according to user behavior.
• Implements preferred channel capabilities.
• Continuously optimises campaigns based on real-time performance data—so every next campaign performs better.

Consumer Insights Agent

• Generates insights on customer behaviour and preferences.
• Identifies the best communication touchpoints.
• Customises wait nodes and wait times according to user behaviour.
• Implements preferred channel capabilities.
• Continuously optimises and adapts based on user interactions.

Segment Agent

• Creates audience segments based on activities, attributes, affinities, and channel preferences.
• Optimises segments to maximise campaign ROI and channel effectiveness.
• Defines segment-specific strategies to enhance customer relevance and engagement.

Content Agent

• Generates personalised campaign content across various platforms.
• Optimises content for engagement.
• Optimises subject lines and engagement.

Design Agent

• Designs creatives for email, web messages, and in-app messages & notifications.
• Generates templates based on brand guidelines and campaign needs.
• Dynamically adjusts creatives for maximum user engagement.

Scheduler Agent

• Schedules the campaign for optimal timing to maximise clicks, opens, and conversions.
• Implements STO (Send Time Optimisation) capabilities.
• Optimises campaigns to avoid saturation.

Analytics Agent

• Provides campaign performance analytics with key metrics & insights.
• Identifies real-time patterns and trends to find low-hanging conversion suggestions.
• Recommends data-driven optimisations.

Merchandiser Agent

• Optimises product recommendations for maximum visibility.
• Adjusts merchandising based on customer behaviour.
• Automates visual merchandising decisions.

Transforming Marketing Through Collective Intelligence

What makes this agent ecosystem truly transformative is not just the individual capabilities, but how they function as an integrated whole. Like a well-orchestrated symphony where each instrument plays its part perfectly, these agents create marketing outcomes far greater than the sum of their parts. They collaborate continuously, learning from each interaction, sharing insights across functions, and adapting strategies in real-time.

This collective intelligence enables brands to finally overcome the fundamental limitation that has plagued marketing departments for decades: the inability to scale personalisation without proportionally scaling human resources. Now, a brand can deliver millions of genuinely individual experiences simultaneously, each precisely tailored to specific customer needs and preferences.

For the first time, Progency makes it possible for brands to focus their human marketing talent on high-level strategy and creativity for their Best and Next customers, while deploying this powerful agent ecosystem to maximise value from Rest customers. This isn’t merely augmenting human capabilities—it’s fundamentally reinventing how marketing operates, creating unprecedented opportunities for brands to build deeper, more valuable relationships with every customer they serve.

7

Progency’s Secret Sauce – 2

2. Performance-Based Pricing: Aligning Incentives for Success

Progency’s transformational approach extends beyond technology to fundamentally reimagine the business model for marketing services. At its core is a performance-based pricing structure that transforms martech from a cost centre to a strategic investment with measurable returns.

Unlike traditional SaaS providers who charge flat fees regardless of outcomes, Progency adopts the adtech model of paying for performance—but applies it to the far more valuable realm of customer retention and monetisation. This creates perfect alignment between the Progency provider and the brand:

• Scientifically Measured Uplift: Using rigorous A/B testing methodology with control and test groups, Progency establishes clear causality between its efforts and business outcomes. This isn’t vague correlation—it’s statistically valid proof of incremental revenue.
• Revenue-Share Model: Rather than fixed pricing, Progency typically takes a percentage of the incremental revenue generated. If the brand doesn’t benefit, neither does the Progency.
• Multi-Metric Flexibility: Performance contracts can be structured around various KPIs depending on business objectives: revenue lift, reactivation rates, cross-sell success, or reduced churn. This adaptability ensures perfect alignment with specific brand goals.
• Risk-Sharing Approach: By taking on performance risk, Progency demonstrates confidence in their capabilities while reducing financial uncertainty for brands.
• Transparent Attribution: All performance measurement uses transparent, agreed-upon methodologies, eliminating the “black box” attribution that plagues traditional marketing services.

This model represents a complete departure from both traditional agency hourly billing and SaaS subscription pricing. By focusing exclusively on outcomes, Progency finally transforms marketing from speculative expense to predictable profit engine.

3. Specialized Focus on “Rest” Customers

Another powerful differentiator in Progency’s approach is its specialised focus on the often-neglected middle segment of customers—the “Rest” group who have engaged with the brand but aren’t fully active:

• Complementary Focus: While brands naturally dedicate internal resources to their Best customers (top revenue producers) and Next customers (new acquisitions), Rest customers frequently fall through the cracks. Progency fills this gap without disrupting existing team priorities.
• Targeted Expertise: Progency develops deep expertise specifically in converting semi-engaged customers into active ones—a specialised skill set that differs significantly from new customer acquisition or VIP management.
• Scalable Attention: The AI agent framework enables genuinely individualised attention for Rest customers—something impossible for human teams to provide at scale for this segment.

By focusing precisely where most brands have operational gaps, Progency creates immediate value without organizational disruption.

4. Domain-Specific Knowledge Accumulation

Perhaps the most underappreciated aspect of Progency’s secret sauce is its capacity for cross-client knowledge transfer while maintaining strict data privacy:

• Pattern Recognition: AI agents learn which engagement strategies work across similar customer segments, industries, and scenarios without sharing confidential data.
• Accelerated Optimisation: Every client benefits from the collective learning of the entire Progency system, achieving optimization far faster than they could in isolation.
• Vertical Specialisation: Progency providers develop deep expertise in specific industries, understanding the unique customer dynamics of retail, financial services, travel, or other sectors.
• Macro Trend Identification: By analysing patterns across the customer base, Progency can identify emerging behavioural shifts that individual brands might miss.

This accumulating expertise creates a flywheel effect: as Progency serves more clients, its effectiveness increases, which attracts more clients, further enhancing its capabilities.

5. Seamless Technology Integration

Unlike traditional agencies that struggle with technology access or martech vendors with limited services capabilities, Progency offers seamless integration that eliminates operational friction:

• Native Platform Access: Built directly on the martech platform, Progency has complete, secure access to all capabilities without the API limitations that constrain third-party agencies.
• Zero Setup Overhead: Brands can activate Progency without complex implementation projects, technical resource allocation, or long onboarding periods.
• Continuous Enhancement: As the underlying martech platform evolves, Progency immediately leverages new capabilities without integration delays.
• Unified Analytics: Performance reporting integrates seamlessly with existing brand dashboards, eliminating reconciliation headaches.

This frictionless integration dramatically reduces time-to-value compared to traditional approaches that require extensive setup and integration work.

**

The Moonshot Value Proposition

What makes Progency truly a marketing moonshot is its potential to fundamentally restructure the economics of customer relationship management. By combining AI agent technology, performance-based pricing, specialised customer focus, accumulated expertise, and seamless integration, Progency addresses the most critical challenge facing modern marketers: extracting maximum value from existing customer relationships in an increasingly privacy-focused, cost-conscious environment.

For brands, the value proposition is transformational:

• Reduced Dependency on Acquisition: By maximizing existing customer value, brands can reduce their reliance on increasingly expensive acquisition channels.
• Operational Efficiency: AI agents deliver sophisticated marketing operations without proportional headcount increases.
• Financial Predictability: Performance-based pricing converts marketing from uncertain expense to predictable profit centre.
• Competitive Advantage: Brands that effectively monetise their Rest customers gain substantial advantages over competitors still focused primarily on acquisition.

Progency represents not merely an evolution of existing martech approaches but a completely new paradigm. It combines the best aspects of specialised agencies (expertise, outcome focus) with the advantages of martech platforms (technology depth, data integration) while eliminating the weaknesses of both through AI agent orchestration and performance-based economics.

Just as SaaS transformed software from product to service twenty years ago, Progency now transforms marketing from technology to outcomes. This is a foundational shift in how marketing value is created, delivered, and measured. That’s what makes Progency a true marketing moonshot: a radical reinvention that solves the most pressing challenges facing today’s brands while unlocking previously impossible opportunities for growth.

8

Flywheel

The true power of NeoN and Progency emerges when we understand how they work together to create a self-reinforcing flywheel—a perpetual motion machine for retention, engagement, and profitability. While each represents a moonshot on its own, their combined impact fundamentally redefines marketing itself, creating the foundation for NeoMarketing and AI-native Martech.

The Retention and Profits Flywheel

The flywheel begins with a brand’s existing customer base, segmented into Best, Rest, and Test customers. These segments represent not just different value tiers but different relationship stages requiring distinct approaches:

1. Activate the Flywheel: The cycle begins with Progency deploying its AI agent ecosystem to engage Rest customers—those showing declining engagement but not yet dormant. Through N=1 Personalisation, these customers receive precisely tailored interactions that rekindle their interest and activity.
2. Acceleration Phase: As Rest customers become more engaged, some ascend to Best status, growing their lifetime value and purchase frequency. This creates immediate revenue lift while simultaneously building a more valuable email audience—the exact audience that powers NeoN’s publisher side.
3. Recovery Operation: For customers who slip into Test status despite Progency’s efforts, NeoN provides a supremely efficient reactivation pathway. Instead of expensive retargeting through adtech platforms, these dormant customers are reached through the NeoN network at 30-50% lower cost.
4. Monetisation Multiplier: As the brand’s active audience grows through both retention and reactivation, its publishing value within NeoN increases proportionally. Each engaged Best customer represents not just direct revenue but also monetisable attention that generates incremental ActionAd revenue.
5. Reinvestment Cycle: Revenue generated from both improved customer value and NeoN’s attention monetisation creates resources for further investment in Progency’s capabilities, expanding its impact to additional customer segments and use cases.

This flywheel transforms marketing from a linear funnel (acquire, convert, repeat) into a continuous cycle where each customer segment—whether actively purchasing, drifting, or dormant—contributes to overall profitability. Most importantly, it eliminates the inefficient “leaky bucket” syndrome where brands constantly pour acquisition resources into a customer base that’s simultaneously churning out the bottom.

The Foundation of NeoMarketing

Together, NeoN and Progency form the twin pillars of NeoMarketing—a fundamentally new approach that prioritises retention over acquisition, relationship over transaction, and precision over volume. This approach delivers three transformative outcomes:

1. The End of AdWaste: By providing superior alternatives to expensive reacquisition and enabling true 1:1 engagement at scale, NeoN and Progency systematically eliminate the $500 billion currently wasted on redundant targeting. This isn’t just cost cutting—it’s redirecting resources from inefficient spend to value-creating activities.
2. Attention to Relationships: Both solutions transform fleeting attention into lasting relationships. NeoN turns the inbox from a promotional dumping ground into an interactive engagement platform, while Progency converts basic customer data into meaningful, ongoing dialogues. Together, they solve marketing’s persistent challenge: maintaining connections in an increasingly fragmented media landscape.
3. Martech as Profit Centre: Perhaps most revolutionary, this combination transforms martech from cost centre to profit engine. Through NeoN’s attention monetisation and Progency’s performance-based model, marketing technology finally shifts from expense line to profits driver—the holy grail that CMOs have sought for decades.

AI-Native by Design

What makes this flywheel uniquely powerful in today’s environment is its AI-native architecture. Unlike traditional marketing approaches retrofitted with AI capabilities, both NeoN and Progency were conceived from the ground up around artificial intelligence:

• Continuous Learning: The system becomes more effective with every interaction, continuously improving targeting, personalisation, and conversion rates.
• Autonomous Execution: Daily marketing operations happen without constant human intervention, freeing teams to focus on strategy and creativity.
• Predictive Capabilities: The flywheel doesn’t just respond to customer actions—it anticipates needs and behaviours, enabling proactive engagement.

This represents the future of marketing: not just automating existing processes but reimagining them completely through the lens of what’s possible with AI. Instead of marketing departments struggling to keep pace with exploding complexity, the NeoN-Progency flywheel creates a self-optimising system that delivers superior results with dramatically less operational overhead.

By anchoring NeoMarketing in these twin moonshots, brands can finally break free from the acquisition addiction that has drained their resources for decades. Instead, they can build sustainable, profitable growth engines powered by the most valuable asset they already own—their existing customer relationships.

9

Claude Summary

The $500 billion AdWaste crisis is perhaps the most significant yet underaddressed challenge facing brands today. This staggering figure—representing 70% of digital marketing budgets—is squandered on repeatedly reacquiring customers brands already know. It’s the marketing equivalent of paying rent for a house you’ve already built.

Solving this crisis requires more than incremental improvements to existing systems. It demands revolutionary approaches—true marketing moonshots that fundamentally reimagine how brands engage customers and deploy marketing resources. Two such transformative innovations are emerging: NeoN, an attention exchange network, and Progency, a performance-driven martech model.

The AdWaste Crisis: Understanding the Problem

The AdWaste crisis stems from two fundamental marketing failures:

1. The “No Hotline” Problem: Despite having customer contact information, brands lack reliable channels to consistently engage their audience. Email open rates hover in single digits, push notifications get blocked, and social feeds are algorithmically filtered. Without dependable connections, brands resort to expensive ad platforms to reach people already in their database.
2. The “Not for Me” Problem: Generic messaging and crude segmentation fail to resonate with individual customers. When communications feel irrelevant, customers disengage, creating a vicious cycle where disengagement drives more ad spending.

These failures are particularly costly for two customer segments—what I call the “Rest” (middle 50% showing declining engagement) and “Test” (dormant 30%)—that represent the greatest opportunity for value recovery.

NeoN: The Attention Exchange Revolution

NeoN reimagines advertising by creating a direct marketplace that connects brands through their most valuable asset: customer attention. Unlike traditional ad platforms that function as expensive intermediaries, NeoN enables direct brand-to-brand collaboration through authenticated identity.

The Secret Sauce

Four revolutionary innovations make NeoN transformative:

1. ActionAds: AMP-powered interactive ads that enable complete transactions within email—eliminating the 80-90% “click-through penalty” that plagues traditional digital advertising. Customers can browse products, complete forms, and make purchases without leaving their inbox.
2. Matching Engine: Unlike cookie-based targeting that relies on probabilistic matching, NeoN’s engine uses authenticated identity for deterministic, precise targeting with near-zero waste. When Brand A wants to reach its dormant customers through Brand B’s emails, the match is exact—not an estimate.
3. Co-operative Structure: Every participating brand plays dual roles—as both publisher and advertiser—creating a self-balancing ecosystem that scales organically. Brands monetize their Best customers’ attention while efficiently reacquiring their dormant Test customers at 30-50% lower costs than traditional platforms.
4. DIY/Self-Serve Platform: An intuitive interface democratizes access, making NeoN’s sophisticated technology accessible to businesses of all sizes without requiring technical expertise.

NeoN’s economic impact is profound. By eliminating platform intermediaries and enabling precision targeting through authenticated identity, it offers brands two compelling benefits:

• Print Money: Brands monetize their Best customers’ attention through ActionAds, generating new revenue from existing email programs.
• Save Money: Brands efficiently reacquire dormant Test customers through other brands’ emails at significantly lower costs than traditional platforms.

Progency: Performance Marketing Reinvented

While NeoN addresses dormant Test customers, Progency focuses on the critical Rest segment—those showing declining engagement but not yet inactive. It represents a fundamental reimagining of how martech delivers value, moving beyond software provision to outcome generation through a hybrid approach combining proprietary technology with expert services and AI orchestration.

The Secret Sauce

Progency’s transformative power comes from several innovative elements:

1. AI Agent Ecosystem: A sophisticated “Department of One” with specialized agents handling distinct aspects of marketing—from journey orchestration and consumer insights to content creation, design, scheduling, analytics, and merchandising. These agents work in concert, enabling true N=1 personalization at scale without proportionally scaling human resources.
2. Performance-Based Pricing: Unlike traditional SaaS providers who charge flat fees regardless of outcomes, Progency adopts a revenue-share model tied directly to measurable business results. This creates perfect alignment: if the brand doesn’t benefit, neither does the Progency.
3. Specialized Rest Customer Focus: While brands naturally dedicate internal resources to their Best customers and new acquisitions, Rest customers frequently fall through the cracks. Progency fills this gap without disrupting existing team priorities.
4. Domain-Specific Knowledge Accumulation: AI agents learn which engagement strategies work across similar segments and industries while maintaining strict data privacy, creating a flywheel effect where each client benefits from the collective learning of the entire system.
5. Seamless Technology Integration: Built directly on the martech platform, Progency has complete, secure access to all capabilities without the API limitations that constrain third-party agencies, enabling frictionless implementation and immediate value delivery.

Progency transforms marketing from an unpredictable expense to a reliable profit center by focusing precisely where most brands have operational gaps: maximizing value from existing customer relationships in an increasingly privacy-focused environment.

**

The Retention and Profits Flywheel

The true power of NeoN and Progency emerges when understanding how they work together to create a self-reinforcing flywheel for retention, engagement, and profitability:

1. Activate: Progency deploys its AI agent ecosystem to engage Rest customers through precisely tailored interactions that rekindle their interest and activity.
2. Accelerate: As Rest customers become more engaged, some ascend to Best status, growing their lifetime value while building a more valuable email audience—the exact audience that powers NeoN’s publisher side.
3. Recover: For customers who slip into Test status despite Progency’s efforts, NeoN provides a supremely efficient reactivation pathway at 30-50% lower cost than traditional adtech platforms.
4. Monetize: As the brand’s active audience grows through both retention and reactivation, its publishing value within NeoN increases proportionally, generating incremental ActionAd revenue.
5. Reinvest: Revenue from improved customer value and NeoN’s attention monetization creates resources for further investment in Progency’s capabilities, expanding its impact to additional segments and use cases.

This flywheel transforms marketing from a linear funnel into a continuous cycle where each customer segment contributes to overall profitability, eliminating the inefficient “leaky bucket” syndrome where brands constantly pour acquisition resources into a customer base that’s simultaneously churning.

The Foundation of NeoMarketing

Together, NeoN and Progency form the twin pillars of NeoMarketing—a fundamentally new approach that prioritizes retention over acquisition, relationship over transaction, and precision over volume. This approach delivers three transformative outcomes:

1. The End of AdWaste: By providing superior alternatives to expensive reacquisition and enabling true 1:1 engagement at scale, these solutions systematically eliminate the $500 billion currently wasted on redundant targeting.
2. Attention to Relationships: Both solutions transform fleeting attention into lasting relationships, solving marketing’s persistent challenge of maintaining connections in an increasingly fragmented media landscape.
3. Martech as Profit Centre: Through NeoN’s attention monetization and Progency’s performance-based model, marketing technology finally shifts from expense line to profits driver—the holy grail that CMOs have sought for decades.

What makes this approach uniquely powerful is its AI-native architecture. Unlike traditional marketing approaches retrofitted with AI capabilities, both NeoN and Progency were conceived from the ground up around artificial intelligence, enabling continuous learning, autonomous execution, and predictive engagement.

The Path Forward

For brands struggling with rising acquisition costs and diminishing returns from traditional marketing approaches, these twin moonshots offer a clear path forward:

1. Segment your customer base using the BRTN framework (Best, Rest, Test, Next) to identify where your greatest opportunities lie.
2. Implement NeoN to monetize your Best customers’ attention while efficiently reacquiring your dormant Test customers.
3. Deploy Progency to maximize value from your Rest customers, preventing them from sliding into dormancy while increasing their lifetime value.
4. Redirect resources from wasteful reacquisition spending toward deepening relationships with your Best customers and acquiring genuinely new customers.
5. Measure success not by impressions or clicks but by incremental revenue, customer lifetime value, and reduced acquisition costs.

The brands that embrace these innovations will gain tremendous competitive advantages: lower marketing costs, higher customer retention, new revenue streams, and ultimately, sustainable profitable growth. Meanwhile, those clinging to traditional acquisition-focused approaches will find themselves increasingly trapped in the AdWaste cycle, paying ever-higher prices to reach customers they should already own.

The choice is clear. The future of marketing isn’t about finding better ways to buy attention—it’s about building better systems to earn it, keep it, and turn it into lasting, profitable relationships. NeoN and Progency aren’t just innovations; they’re transformations—true marketing moonshots poised to eliminate AdWaste and usher in a new era of marketing prosperity.

Published April 15, 2025

1

The Next Frontier

For people as old as me, we have had an amazing life witnessing four technological revolutions: PC, internet, mobile, and now AI. As I have been thinking about the coming age of AI and the opportunities for Netcore, my mind went back to 1994 as I saw the Internet era beginning. I wrote about the origins of my IndiaWorld ideas in a blog post (and my book):

For me, the idea of IndiaWorld came from my own frustrations in the US in getting access to news about India. While studying at Columbia University, I would go to the library to read the newspapers and magazines from India – all of which arrived about two weeks late. Those experiences stayed with me. During 1994, when I realised that my imaging software business had failed, I had to start thinking about what to do next. It was then that stories about the Internet and its potential started appearing in international business magazines.

…The dots started connecting: the World Wide Web could bridge distances, Indians in the US could get news and information without delays, the Mosaic browser with the HTML protocol could make access to this information easy, and a web server would make publishing that information simple. All of these ideas came together to create what later became IndiaWorld. I still remember my first experience of using a service called Netcruiser to connect to the Web in September 1994 during a US visit and accessing sites with a click. The excitement and potential convinced me of its power and potential to forge an “electronic informational marketplace.”

As the Internet rose and connected people worldwide, I had to find my corner to build a business as an entrepreneur. That space turned out to be amongst NRIs (Non-Resident Indians) worldwide – offering information in exchange first for a subscription fee ($20 a year) and then ads. I was there at the right time, right place, with the right product. IndiaWorld was the first Indian Internet portal, and continuous innovations with new content and portals (Khoj, Khel, Bawarchi, Samachar) helped keep us ahead of the race, to an eventual exit (sale to Satyam Infoway).

Now, as AI rises, the pace of action and innovation is many times faster. What played out then over a few years is taking place in months. What is the equivalent of IndiaWorld that Netcore can create? What is the corner we can make our own? What is the Blue Ocean (in AI’s increasingly Red Ocean) that can give us an edge?

This is what I will explore in this series – how Netcore can harness AI to transform marketing (with a specific focus on martech) by solving the Attention Recession problem, eliminating the $500 billion AdWaste problem plaguing brands worldwide, and pioneer innovations like 1:1 Personalisation, Marketing Agents, AI Twins, NeoN (Attention Exchange), NeoMails (an upgrade from traditional emails), and Progency (product/platform, experts, AI agents, Kaizen for performance martech) that will revolutionise retention and eventually acquisition. Just as I found my opportunity in the early days of the Internet, I believe there’s an equally transformative opportunity in AI-native Martech waiting to be seized.

2

AI-Native – 1

A recent Wall Street Journal article discussed “AI-native companies”—firms that treat AI not as an afterthought or a bolt-on, but as their core starting point. Unlike large incumbents scrambling to integrate new AI capabilities into existing frameworks, AI-native startups are built from the ground up to learn, adapt, and dynamically evolve in response to customer data and workflows.

The difference is more than semantics. Think of AI-native businesses as the parallel to “digital-native” disruptors from the early internet era. They don’t have to retrain staff or overhaul legacy processes simply because a new technology has arrived. By design, AI is at the heart of their products, operations, and decision-making. This allows them to:

• Operate with fewer staff, yet achieve outsized results. Some AI-native agencies, for example, report half the headcount and twice the revenue compared with a similarly staged traditional firm.
• Adapt in real time. Far from static, AI-centric systems continuously learn from ongoing usage—“compounding advantage,” in the words of venture capital insiders. As one WSJ source observed, an AI-native product can “become as good as a custom-built app” since every user interaction further refines its capabilities.
• Move from process-based to ‘reasoning-based.’ Instead of rigid workflows, these organisations use AI to handle tasks on the fly, drastically reducing the “fire drill” nature of typical brand or campaign requests.

What’s crucial here is the mindset: AI isn’t just an efficiency tool to scale what you already do; it’s a way to rethink your entire operating model. Microsoft’s Jared Spataro, quoted in the same article, distinguishes “AI native” newcomers from established companies that try to retrofit AI onto traditional processes. If you treat AI as optional, you’ll optimise your business gradually; if you treat AI as fundamental, you could reinvent your business entirely.

I asked Perplexity for what “AI-native” or “AI-first”meant. Its response:

AI-native or AI-first refers to a concept where artificial intelligence is intrinsically integrated into the core of a product, service, or strategy, rather than being an add-on feature…This approach involves designing systems and processes with AI as a fundamental component from the ground up.

Key Characteristics of AI-Native/AI-First Systems

1. Intrinsic AI Capabilities: AI is a natural part of the functionality in terms of design, deployment, operation, and maintenance.
2. Data-Driven Ecosystem: These systems leverage a data-driven and knowledge-based ecosystem, where data and knowledge are continuously consumed and produced.
3. Adaptive Learning: AI-native systems can augment or replace static, rule-based mechanisms with learning and adaptive AI when needed.
4. Strategic Priority: In an AI-first strategy, AI is perceived as a core competitive resource and is prioritized over other potential focuses.
5. Continuous Improvement: Machine learning capabilities allow AI-first systems to get smarter over time, learning from each interaction and refining strategies without manual intervention.

Differences from Traditional Systems

AI-native systems differ from those with embedded AI or traditional digital-first approaches:

1. Built from the Ground Up: Unlike embedded AI, which integrates AI functionality into existing systems, AI-native technology is designed with AI as a core component from inception.
2. Pervasive Integration: AI is pervasive across the ecosystem, not just limited to specific components or functions.
3. Dynamic Nature: AI-native systems don’t follow fixed predefined rules but adapt continuously.
4. Scalability: The underlying resources are built to scale, allowing for growth and adaptation.

Perplexity added: “In the context of martech, an AI-first approach means integrating AI into the foundation of internal operations, helping generate ideas, automate repetitive tasks, and extract deeper insights from data. This shift from digital-first to AI-first in marketing represents a fundamental change in how marketing strategies are developed and executed, with AI becoming a key driver rather than just an occasional tool.”

3

AI-Native – 2

Foundation writes: “There are two types of opportunities for capitalizing on AI’s ability to fuel innovation: AI-native and embedded AI… AI-native refers to products and companies built from the ground up with artificial intelligence at the core of their operations and services. Rather than just integrating AI into existing systems, these companies are designed to fully exploit the capabilities of AI, enhancing innovation and efficiency in their domain…Embedded AI refers to the integration of artificial intelligence technology into existing products and services, enhancing their functionality without fundamentally altering their primary nature. This strategy allows established companies to adopt AI technologies to improve performance, user interaction, and service efficiency… AI-native companies and products offer three main advantages, stemming from their fundamental approach of embedding artificial intelligence at the core of their operations and product design from the very beginning: opportunity for innovation, unrestricted branding and design, and future-proofing.”

Jared Spataro of Microsoft writes: “A new wave of companies—we call them AI natives—provide a crystal ball into what the future of work will look like for every organization. These startups embed AI in every aspect of their business, from processes and products to sales and customer service. Like the internet-native companies of a generation ago, they’re using the latest technology to slash the cost of entry and time to market. Most of these companies are run with unrelenting efficiency and creativity. Some are so scrappy that they have a single employee (though with AI, it doesn’t feel that way)… AI-native companies point to a future where businesses are less top-down, expertise is democratized, and all data is an opportunity… Making organizational gains with AI requires reimagining processes honed over years, often decades, and taking the first step can feel daunting. I tell customers: Start by finding a business problem and use AI to tackle it. Over time you’ll develop the organizational practice of asking, “Can AI help with this?” That’s what AI-native organizations do, and that’s the key to an AI-first mindset.”

Bloomberg: “Today, so-called AI wrappers are all the rage. Step into any venture capital office in Silicon Valley and you’ll hear investors buzzing about startups that offer AI chatbots, research tools and other software applications for coding, clinicians and customer service, all built at least in part on the backs of large language models (LLMs) created by other leading AI developers. These startups are seeing revenue and valuations grow at a fast clip, often while spending a fraction of the amount that top AI model developers do on chips, data centers and talent…Michael Mignano, a partner at Lightspeed Venture Partners, likens this moment in AI to the original smartphone app boom nearly two decades ago. “Just like after the iPhone launched, there were millions of new mobile apps,” said Mignano, an investor in the AI notetaking service Granola, which uses technology from OpenAI and Anthropic. “Now with AI and LLMs, there will be millions of new AI products.”…Mignano said it would be “naïve” for people “to think that these large language providers are going to build verticalized apps for every single possible use case.” Instead, bringing AI to every industry, from law to medicine, may fall to a small army of startups.” Two examples discussed are Harvey which makes tools for lawyers and Anysphere (Cursor) which makes utilities for software engineers.

I asked Claude for the top 5 characteristics of AI-native businesses:

1. Foundation in AI-first architecture: Rather than retrofitting AI into existing systems, AI-native businesses are built with AI at their core. Their data pipelines, decision-making processes, and product development are all designed around AI capabilities from inception.
2. Autonomous decision-making systems: AI-native businesses leverage agentic AI to handle increasingly complex decisions with minimal human intervention. These autonomous systems don’t just provide recommendations—they execute actions, learn from outcomes, and continuously improve their performance.
3. Hyper-personalization at scale: The ability to deliver truly individualized experiences to each customer (N=1 personalization) without proportional cost increases. This goes beyond segmentation to treat each customer as a unique entity with specific preferences and needs.
4. Emergent intelligence through multi-agent systems: Rather than relying on single AI models, AI-native businesses orchestrate specialized AI agents that collaborate, creating collective intelligence greater than the sum of its parts. Different agents handle specific tasks while coordinating toward common goals.
5. Continuous learning loops: AI-native businesses don’t just deploy static models—they implement systems that constantly learn from interactions, adapt to changing conditions, and evolve their capabilities. This creates a virtuous cycle where more engagement generates better understanding, which delivers more relevant experiences, driving further engagement.

It added: “These characteristics enable AI-native businesses to achieve unprecedented operational efficiency, customer understanding, and adaptability—creating sustainable competitive advantages that are difficult for traditional businesses to replicate simply by adopting AI tools.”

4

AI-Native – 3

I asked ChatGPT to showcase the differences between AI-native businesses and traditional businesses.

Dimension	AI-Native Business	Traditional Business
Core Mindset	AI is integral from day one, influencing every process, product, and strategic goal.	Adopts AI as an enhancement or pilot project within legacy structures.
Data-Centric Operations	Treats data as a primary asset; real-time pipelines and advanced analytics guide decisions, product roadmaps, and personalisation strategies.	Data often sits in silos or is used in reactive, periodic analyses; AI insights can be limited by uneven data quality or fragmentation.
AI-Driven Decision-Making	Relies on predictive and prescriptive algorithms for most tactical and strategic moves, enabling proactive adaptation.	Depends on human judgement, conventional analytics, or after-the-fact BI, with AI outputs often treated as optional recommendations.
Automation at Scale	Automates everything from back-office workflows to customer-facing interactions. Humans handle high-level creativity and oversight, while AI handles repetitive or data-intensive tasks.	Automation is confined to select tasks (e.g., chatbots, scheduling). Manual interventions remain the norm for many processes.
Personalisation & Customer-Centricity	Delivers hyper-personalised experiences via continuous machine learning. May deploy AI avatars or advanced recommendation engines to anticipate user needs.	Typically offers segmented or partially personalised experiences; real-time adaptation is the exception rather than the rule.
Continuous Learning & Adaptation	AI models constantly update with new data, so products and services evolve in near real-time. Built-in feedback loops ensure self-optimisation.	Changes occur in scheduled release cycles or triggered by major events; learning curves are slowed by manual processes or bureaucracy.
AI-First Product Development	Designs offerings with AI as the central feature (e.g., real-time recommendations, autonomous functionality, advanced analytics).	Integrates AI functionalities after initial launch or via incremental add-ons to legacy products.
Scalability & Agility	Rapidly scales without equal increases in overhead, thanks to AI handling bigger data loads and routine tasks. Adapts to new market opportunities quickly.	Growth often requires proportional increases in workforce and infrastructure. Changes in direction can be slow and risk-laden.
Ethical & Responsible AI	Prioritises explainability, fairness, and governance from the outset to safeguard trust and comply with ethical and regulatory standards.	May overlook or retroactively implement AI ethics frameworks; risk management is reactive, often introduced after concerns arise.
Organisation & Culture	Flatter hierarchies. Every employee “manages” or directs AI, focusing on strategic decisions. Teams form around goals or projects rather than rigid functions.	Maintains traditional departmental silos; AI tasks often reside within a data-science or IT group, separate from core decision-making.
Talent & Skills	Hires “tinkerers” comfortable with rapid AI experimentation. Fosters data-driven problem-solving and open-minded adaptation to AI innovations.	Emphasises conventional roles, credentials, and established processes; AI skills may remain siloed or outsourced.
Use of AI: Offense & Defense	Offense: Launches new offerings, gains market share through AI-driven innovations. Defense: Automates time-consuming tasks to cut costs, redirect human talent to higher-level work.	Primarily deploys AI to cut costs or handle basic tasks (e.g., chatbots, document scanning); seldom sees AI as a game-changer.
Innovation & Differentiation	Continuously introduces AI-enhanced features or services that reimagine user experiences, forging distinctive market advantages.	AI integrations can be sporadic or limited to keep up with competitors, rather than driving disruptive innovations.
Data Sharing & Integration	Seamlessly merges cross-functional and external data sources to power advanced AI models; ensures near real-time updates for consistent insights.	Data integration projects move slowly; teams often rely on batch updates, manual extracts, or partial data from legacy systems.
Speed to Market	Rapidly prototypes, tests, and refines AI-driven ideas. Iteration cycles are short, measured in days or weeks, enabling real-time market adaptation.	Operates on fixed release schedules (e.g., quarterly, annually); slower to pivot or adopt emerging AI functionalities.
Customer Experience	Anticipates user needs through advanced analytics and deep personalisation, often delivering dynamic, in-the-moment interactions.	Responds to user behaviour post hoc; personalisation is based on broad segments or manual rules, lacking real-time reactivity.
Measuring Progress	Closely tracks AI’s contributions to KPIs (e.g., campaign speed, cost savings, upsell rate), quickly iterating on any shortfalls.	Periodic performance reviews; AI gains might be hard to isolate within legacy operational metrics.
Agentic AI & Multi-Agent Systems	Employs autonomous AI “agents” with specialised roles (e.g., analytics agent, content agent). Agents collaborate in real time, orchestrated by a central AI, reducing human burden.	Adopts a single AI tool or minimal solutions, often requiring manual coordination. Rarely invests in multi-agent systems or emergent AI orchestration.

5

Martech  Reimagined

So, what does AI-native Martech look like? As we discuss this, it will be useful to keep these five stages of AI from OpenAI’s Sam Altman in mind:

First-generation AI-powered martech solutions have focused primarily on using Generative AI for content creation. These implementations typically automate copywriting tasks like crafting email subject lines, social media posts, product descriptions, and basic ad copy, along with image, animations and video generation. While offering efficiency gains, these solutions represent a relatively shallow integration of AI capabilities—essentially bolting generative functions onto existing martech stacks. Marketing teams can produce more content faster, but the underlying operational model remains largely unchanged. Netcore’s AI Co-Marketer is an example of this.

These applications operate at Level 1 (conversational AI) and occasionally Level 2 (reasoning) in Altman’s framework, helping marketers scale content production but rarely demonstrating autonomous decision-making capabilities. Most first-gen AI martech still requires significant human oversight for strategy, campaign orchestration, audience segmentation, and message approval—essentially enhancing rather than reimagining traditional marketing workflows.

The next-generation martech solutions will transcend basic content generation to encompass Altman’s levels 2 and 3 – transforming experiences for both marketers and end customers. Rather than simply automating tasks, marketing agents will work collaboratively (with “Human in the Loop”) to accomplish complex, multi-step objectives.

For example, a marketer can instruct the AI Co-Marketer: “I have these 100,000 widgets that I want to sell at maximum margin in the next 10 days. Identify the customers most likely to buy, create the relevant content for them, schedule the campaigns across optimal channels, and track the outcomes. Course correct as needed.” The Co-Marketer then orchestrates specialised agents—each handling distinct functions:

• Segment Agent identifies high-probability buyers through predictive analytics
• Content Agent crafts personalised messaging variants for different contexts
• Campaigns Agent handles cross-channel orchestration and timing optimisation
• Analytics Agent monitors real-time performance metrics
• Insights Agent synthesises patterns and recommends strategic adjustments

With each interaction, these agents continuously learn and refine their capabilities through automated feedback loops, creating a self-improving marketing ecosystem.

Where it gets truly revolutionary is the introduction of customer-side agents. Imagine every customer represented by their own bi-directional AI Twin—one interface engaging with marketers, the other with the actual customer. These digital representatives create a paradigm shift in the brand-customer relationship:

• Marketers can “simulate” conversations with these agents to test messaging effectiveness, product-need fit, and optimal engagement strategies before deploying campaigns
• Customers gain personal AI advocates that filter incoming marketing to match genuine preferences, negotiate for better offers, and even customise their entire experience ecosystem

For example, Amazon’s home page currently bombards me with recommendations from dozens of categories I’ve never purchased from, cluttering valuable interface real estate. With my personalised Twin, I could instruct it to reconfigure my Amazon experience around only the categories I care about, while also proactively seeking deals in those domains.

This powerful combination of Level 2 capabilities (reasoning) and Level 3 systems (agents that take autonomous actions) defines truly AI-native Martech. The result isn’t merely improved efficiency—it’s a fundamental reinvention of the marketing relationship. Traditional marketing’s adversarial dynamic (brands trying to capture attention, customers trying to filter noise) transforms into a balanced exchange where both sides’ interests are algorithmically aligned, eliminating wasted spend and irrelevant messaging while maximizing value-driven engagement.

Perhaps most significantly, this AI-native architecture solves the Attention Recession (and therefore AdWaste) by ensuring every interaction becomes genuinely relevant—transforming marketing from interruption to anticipation.

6

A New Framework

For far too long, martech has been constrained by human limitations—operating through rigid segments, linear campaigns, and static journeys. As one marketer recently confided to me: “I have 10 million customers. But at best I can manage 8-10 segments. Because with every segment, I have to do daily planning of messaging, followed by analytics.” This forces marketers to create arbitrary groupings with random monickers like “Discount Lovers” or “Family Folks”—crude approximations that fail to capture individual preferences and contexts.

What if these artificial constraints could be eliminated entirely? What if we could move from a few static segments to infinite dynamic segments? More radically, what if every customer could become their own “segment of one,” receiving messaging so precisely tailored to their needs and preferences that it feels like a personal conversation? Would that not transform marketing from an intrusion to be avoided into a service to be valued?

Today’s marketing departments, platforms, and algorithms simply cannot deliver this vision at scale. And this is precisely where martech needs not just an AI upgrade but a complete AI reimagining—a fundamental mindset shift that makes martech truly “AI-native” from its foundation.

I propose a radically different segmentation framework based not on demographic proxies but on actual engagement patterns:

• Best customers (20%): Who account for 60-80% of revenue and are highly engaged with the brand
• Rest customers (50%): Who have engaged sometime in the past 90 days but not within the last 30 days—the critical middle ground where attention is wavering
• Test customers (30%): Who have not been engaged for 90+ days and are considered dormant, inactive, or churned
• Next customers: Who are encountering the brand’s properties for the first time

Each segment requires fundamentally different AI-native approaches:

Best customers need retention and extreme 1:1 personalisation. Here, marketing agents and customer AI Twins collaborate to maximize lifetime value through anticipatory experiences that feel custom-crafted. The focus shifts from “selling more” to “serving better,” with AI continually refining its understanding of individual preferences. This segment also becomes the growth engine through AI-facilitated referral programs that bring CAC toward zero for new customers.

Rest customers face an Attention Recession problem—they’re slipping away not because they dislike the brand but because they’re overwhelmed by competing stimuli. Establishing reliable hotlines becomes crucial, as transactions will only follow when attention is recaptured. This is where NeoMails with interactive, value-first engagement becomes transformative. Brands can either build this capability in-house or partner with a Progency that specializes in attention recapture.

Test customers need strategic reactivation. Conventionally, these customers are thrown back into the expensive adtech reacquisition funnel—often at CAC rates identical to complete strangers. The AI-native alternative is NeoN, a brand-to-brand “Attention Exchange” where dormant customers can be precisely targeted through the active channels of non-competing brands, completely bypassing Big Adtech’s costly intermediation.

Next customers require thoughtful conversion—not just the first transaction but the crucial second purchase that dramatically increases lifetime value probability. AI-powered welcome journeys that collect zero-party data through natural conversation can accelerate this process, creating deeper relationships from the very first interaction.

This BRTN framework reimagines the entire customer journey and sales funnel, with AI working to eliminate friction at every stage. Here’s where AI-native Martech demonstrates its transformative potential—orchestrating marketing agents and customer twins, powering NeoMails and NeoN, and enabling Progency partnerships that deliver outcomes rather than just software.

The ultimate vision for AI-native Martech is nothing less than the “Department of One” for the “Segment of One”—where sophisticated AI systems replace entire marketing teams while delivering truly individualised experiences to each customer. This isn’t some distant sci-fi dream; it’s within our grasp today. Making it happen will unlock hundreds of billions in profits for brands—money currently wasted on repeatedly reacquiring existing customers through increasingly expensive adtech platforms.

Beyond operational efficiency (“doing more with less”), AI-native Martech addresses the fundamental economic equation of business success: maximising LTV) while minimizing CAC to drive Earned Growth for sustainable profits. This is the essence of NeoMarketing, which AI-native Martech will bring to life in the coming months.

In a marketing landscape plagued by attention scarcity and platform dependency, AI-native Martech represents perhaps the single biggest business opportunity of our era. By redirecting even a fraction of the annual $500 billion AdWaste toward relationship-building and genuine value creation, we can transform marketing from a necessary expense into a strategic advantage—and in the process, redefine the relationship between brands and customers for the digital age.

7

Critique

Feedback from the AIs.

Claude

Conceptual Strengths

1. The BRTN Framework is a standout innovation that reframes customer segmentation based on engagement rather than demographics. This approach directly addresses how AI can tackle specific needs of different customer types, making it both novel and practical.
2. The concept of AI Twins is compelling and well-developed. The bi-directional nature (interfacing with both marketers and customers) represents a breakthrough idea that could fundamentally reshape the brand-customer relationship.
3. Connecting AdWaste to Attention Recession establishes a clear economic case for AI-native martech. Framing the $500 billion inefficiency as a solvable problem creates a powerful business imperative beyond just technological innovation.
4. The “Department of One for Segment of One” concept effectively captures the transformative potential of AI in marketing, highlighting both operational efficiency and hyper-personalization simultaneously.
5. The distinction between first-generation and next-generation AI martech helps readers understand the evolution path and positions your ideas within a clear developmental framework.

Areas for Conceptual Development

1. Technical Implementation Details: The essay presents a compelling vision but lacks specific discussion of the technical foundations required. How exactly will AI Twins be created? What data structures are needed? What capabilities are required beyond current LLMs? Adding a section on technical requirements would strengthen practical application.
2. Ethical and Privacy Implications: The concept of AI representing customers raises significant ethical questions about consent, data usage, and privacy that aren’t substantively addressed. Does MyTwin require explicit consent for every data-sharing instance? How would regulatory frameworks like GDPR impact implementation?
3. Business Model Evolution: While the essay discusses how AI-native martech impacts marketing operations, it doesn’t fully explore how business models might need to evolve. If marketing shifts from cost center to profit engine, how does this change organizational structures, revenue recognition, and resource allocation?
4. Transition Strategy: There’s limited discussion of how organizations should transition from traditional or first-gen AI martech to truly AI-native implementations. A phased approach or transition roadmap would make your vision more actionable.
5. Competitive Dynamics: The essay doesn’t address how the AI-native martech landscape might evolve competitively. Will this create new platform monopolies? How will incumbent martech vendors respond? What new competitive advantages emerge?

Additional Ideas to Consider

1. AI Governance Frameworks: Given the autonomous nature of marketing agents, a discussion of governance frameworks for ensuring brand compliance, ethical behavior, and appropriate guardrails would strengthen implementation feasibility.
2. Skills Evolution: How will marketer roles evolve in an AI-native environment? What new capabilities must marketers develop to effectively collaborate with AI systems? This could be a valuable addition bridging current reality with your vision.
3. Economic Model: A quantitative model showing the economic impact of shifting from AdWaste to attention prosperity would strengthen your argument. What percentage improvements in key metrics can companies expect? How quickly can ROI be realized?
4. Network Effects: The power of AI-native martech could be amplified through network effects as AI Twins interact across brands. Exploring this dimension could showcase even greater transformative potential.
5. Industry-Specific Applications: Different verticals (retail, finance, healthcare, etc.) will likely implement these concepts differently. Exploring sector-specific applications would enrich your framework.

Critical Perspective

While your vision is compelling, it’s worth acknowledging that:

1. The creation of truly effective AI Twins faces significant technical hurdles beyond current AI capabilities, particularly around actual preference understanding versus statistical approximation.
2. The transition costs for organizations could be substantial and may create a “valley of despair” before benefits are realized.
3. Consumer adoption of AI Twins acting on their behalf may face resistance due to trust barriers and control concerns.

Addressing these critical perspectives would strengthen your argument by demonstrating awareness of implementation challenges.

Summary

Your essay presents a genuinely innovative vision for AI-native martech that goes far beyond current thinking in the space. The BRTN framework and AI Twins concept are particularly strong ideas with transformative potential. To strengthen impact, consider adding more detail on implementation pathways, addressing ethical considerations, and developing a more explicit transition strategy for organizations. The core thesis – that AI-native martech can solve the Attention Recession and eliminate AdWaste – is compelling and well-supported by your conceptual framework.

ChatGPT

1) Conceptual Clarity and Scope

Strength

• Holistic Vision: You show how AI-native martech isn’t just a tool or a feature; it’s a ground-up reinvention of marketing, from email (NeoMails) to organisational design (Agentic AI, AI Twins). The layered approach (Foundation, Intelligence, Engagement, Monetisation) is especially strong, anchoring an otherwise big, intangible idea in a structured framework.

Areas to Improve

• Overarching Complexity: Because you cover so many distinct pieces—NeoMails, NEON, MyTwin, AI Co-Marketer, Agentic AI—it’s easy for readers to lose track of the big picture. Consider clarifying how they link together in practice with a succinct “How It All Fits” or “Putting It Together” section.
• Define “AI-Native” Precisely vs. “AI-Enhanced”: You repeatedly mention “AI-native” or “AI-first,” but in places, you also show how existing systems can layer in AI capabilities (e.g., adding Co-Marketers to existing martech stacks). To avoid confusion, emphasise that “AI-native” is an aspirational end-state, not simply layering AI on top of legacy processes.

2) Feasibility and Realistic Adoption Path

Strength

• Actionable Framework (Four Layers): By emphasising data unification, organisational readiness, AI intelligence, and eventually monetisation, you give marketers a conceptual roadmap. This helps them see a path from “point solutions” to more transformative AI usage.

Areas to Improve

• Incremental vs. Big-Bang Transformation: While you identify a stepwise approach (Foundation → Intelligence → Engagement → Monetisation), the essay largely assumes a near-complete AI re-engineering is the future. Many companies won’t have the appetite or budget for that level of overhaul. You might:
  • Acknowledge “progressive adoption,” e.g. “Begin by deploying a single AI agent (Campaign Orchestration) for one channel, then expand.”
  • Explore how partial or hybrid solutions can still yield significant gains.
• Technological Readiness & Integration Gaps: You reference zero-party data, advanced data pipelines, and authentication-based ad networks (NEON). Brands often lack robust integration, real-time data flows, or cross-functional alignment. Provide clarity or best practices on bridging these gaps in smaller, legacy-laden organisations.

3) Economics and Value Proposition

Strength

• Focus on $500B AdWaste: You consistently return to how AI-native martech can slash reacquisition costs and reduce platform dependencies. This speaks directly to a huge pain point for CMOs and CFOs.

Areas to Improve

• ROI Evidence: While you highlight the idea of multi-revenue monetisation (ActionAds, brand-to-brand collaboration, daily engagement), you rarely quantify potential ROI or present real/hypothetical metrics beyond single-digit open rate improvements.
  • Illustrative mini-case: “Brand X implemented NeoMails for 100K ‘Rest’ customers, saw reactivation rise from 5% to 20%.”
  • Possible synergy: Show how a brand can reallocate 10-20% of current reacquisition spend into building out these owned channels, leading to a specific ROI timeline.

4) Organisational & Cultural Transformation

Strength

• Department of One: The idea of using multi-agent AI to create an autonomous marketing “dept” is bold and memorable. Coupled with your “Segment of One” tagline, it helps readers visualise the endgame of hyper-personalisation at scale.

Areas to Improve

• Resistance to Change: Large organisations face cultural inertia, role redefinition, and job-security fears. The essay acknowledges this challenge briefly but doesn’t fully address how to bring stakeholders (CMOs, CFOs, brand managers) on board. A few bullet points on change management—“Identify champions, run pilot campaigns, share quick wins”—would enhance the credibility of the transformation roadmap.
• Talent & Skills: With advanced AI usage comes the need for staff who can manage AI workflows, interpret AI outputs, and design ethical AI policies. Provide more detail on the new roles needed or how existing roles transform (e.g., a campaign manager becoming a “co-pilot” to the AI Co-Marketer).

5) Privacy, Consent, and Ethical AI

Strength

• References to Privacy Concerns: You do mention GDPR, user preferences, and authenticated identity solutions (like NEON) as a user-friendly alternative to shadowy cookie tracking.

Areas to Improve

• Practical Implementation of Ethics:
  • Outline how MyTwin or AI Twins handle user data, store personal preferences, or manage potential biases.
  • Suggest an “Ethical AI Charter” or governance model, so readers know you’ve considered compliance, fairness, and transparency beyond the standard disclaimers.
• User Trust-Building: For your concept of MyTwin to catch on, consumers must trust a brand enough to share fine-grained preference data. How to cultivate that trust from day one might warrant a short “Building Trust in an AI-Native World” section (covering transparency, control over data, etc.).

6) Customer vs. Marketing Focus

Strength

• Parallel Perspectives: You occasionally show how both marketers (like Maya) and customers (like Arun) benefit. This helps ground the concept in reality—especially how repeated reacquisition (AdWaste) arises from old marketing “sins,” and how AI can solve it from both ends.

Areas to Improve

• Real-World User Journeys:
  • The narrative effectively shows how daily micro-engagement and frictionless experiences can delight customers, but how do you handle the “learning curve” for customers not used to MyTwin or advanced email interactions?
  • A short nod to “user education” or “onboarding flows” for these new experiences can solidify the argument that customers themselves will adapt.

7) Potential Competitor & Ecosystem Analysis

Strength

• Positioning vs. Current AdTech: You make a strong case against the Google-Meta duopoly in reacquisition spend.

Areas to Improve

• Acknowledging Competitors & Partners:
  • Some readers may ask, “Why can’t these large adtech platforms pivot to the same solutions?” or “Is this akin to an alternative walled garden?”
  • Clarify that your proposals revolve around brand-owned or brand-collaborative channels (NEON), and how these differ from a giant new monopoly on data.

8) Suggestion: Condensed “Key Actions” Recap

After unveiling so many ideas (Agentic AI, MyTwin, Co-Marketer, Zero-Party Data, NEON, etc.), it might help to end with a short bullet list or infographic that ties them together in a sequence, for example:

1. Unify Data & Embrace Retention: Lay the Foundation (CDP, zero-party data frameworks).
2. Co-Marketer + AI Twins: Build the Intelligence layer to enable personalised, real-time decisions.
3. Daily Engagement via NeoMails: Attack the “No Hotline” problem directly.
4. Monetise with NEON: Turn email from cost centre to profit driver, powered by authenticated identity.
5. Scale Up with Agentic AI: Introduce multi-agent orchestration, eventually creating a “Department of One” that can handle 1:1 personalisation at scale.

This final summary crystallises the distinct steps while reminding readers of the overarching synergy.

Concluding Thoughts

From a content and ideas perspective, your essay excels in:

• Painting a unified, future-oriented vision
• Demonstrating why the $500B AdWaste problem exists and how an AI-native approach can solve it
• Bridging separate martech components (NeoMails, MyTwin, Agentic AI) into a somewhat coherent ecosystem

Where it could further improve:

1. Strengthening real-world feasibility – E.g., smaller pilot steps, integration best practices, explicit ROI / cost structures
2. Addressing organisational & cultural hurdles – Implementation readiness, skill gaps, executive buy-in
3. Deepening the privacy/ethical framework – So that the shift from third-party cookies to zero-party data isn’t just about convenience but also trust and compliance
4. Quantifying potential – Even if hypothetical, show numeric results from these transformations (open rates, reacquisition savings, LTV gains).

Overall, the essay provides a visionary blueprint for how AI-native martech can reshape the future of marketing. With a few additional layers of operational detail and clear direction on the adoption journey, it will resonate more deeply with practitioners looking to implement—or at least pilot—these transformative ideas.

DeepSeek

Strengths

1. Visionary Framework:
   • The BRTN segmentation (Best/Rest/Test/Next customers) is a compelling reimagining of traditional marketing funnels, shifting focus from broad demographics to dynamic, behavior-driven engagement. This aligns with rising demand for customer-centricity.
   • Concepts like AI Twins and NeoN Attention Exchange are innovative, proposing a two-sided marketplace for customer attention that could disrupt reliance on costly adtech platforms.
2. Addressing AdWaste:
   • The essay convincingly frames the $500B AdWaste problem as a solvable inefficiency, positioning AI-native martech as a profit engine rather than a cost center. The focus on eliminating waste through hyper-personalization and anticipatory service is timely.
3. Operational Transformation:
   • The “Department of One for a Segment of One” concept challenges legacy marketing structures, advocating for AI-driven efficiency (e.g., autonomous agents replacing siloed teams). This could resonate with brands seeking lean, agile operations.
4. Ethical Guardrails:
   • Mentioning explainable AI (XAI) and governance frameworks shows awareness of ethical risks, though deeper exploration is needed (see Gaps below).

Gaps and Challenges

1. Technical Feasibility:
   • AI Twins require seamless integration of real-time data (e.g., wearables, calendars) and advanced NLP to negotiate on behalf of customers. Current LLMs struggle with context retention and ethical reasoning, raising questions about scalability.
   • NeoN Attention Exchange assumes interoperability between non-competing brands’ data systems, which faces regulatory (GDPR/CCPA) and competitive hurdles (e.g., data-sharing trust).
2. Empirical Validation:
   • While the BRTN framework is theoretically sound, there’s no mention of pilot results or case studies (e.g., how Netcore’s AI Co-Marketer reduced CAC/LTV for clients). Without proof points, claims risk appearing speculative.
3. Ethical Risks:
   • Hyper-personalization could deepen privacy concerns (e.g., AI Twins accessing sensitive data). The essay needs a dedicated section on zero-party data strategies and opt-in transparency to address this.
   • Algorithmic bias in autonomous agents (e.g., excluding “Test customers” from high-value campaigns) could perpetuate inequities.
4. Implementation Roadblocks:
   • Transitioning to AI-native martech requires dismantling legacy systems. The essay doesn’t address change management (e.g., retraining teams, overcoming resistance to AI-driven workflows).
   • The Progency model (product + experts + AI agents) is intriguing but lacks clarity on pricing, scalability, and differentiation from existing martech consultancies.
5. Market Differentiation:
   • How do Netcore’s AI-native solutions differ from incumbents like Salesforce’s Einstein GPT or emerging startups like Rasa (conversational AI)? A competitive analysis would strengthen the positioning.

Suggestions for Improvement

1. Add Validation Layers:
   • Include case studies (even hypothetical ones) to illustrate how AI-native tactics like NeoMails or AI Twins improved metrics (e.g., “Brand X saw a 40% reduction in CAC using BRTN segmentation”).
   • Reference industry trends (e.g., Gartner’s 2023 hype cycle for AI in marketing) to ground ideas in broader momentum.
2. Deepen Ethical Frameworks:
   • Propose a “Trust Charter” for AI-native martech, outlining principles like:
     • Customer-owned AI Twins (users control data sharing).
     • Bias audits for autonomous agents.
     • Transparent reward systems for NeoN Attention Exchange participation.

3. Address Implementation:
   • Outline a 3-stage adoption plan for brands:
     1. Pilot: AI-driven email campaigns (NeoMails) for Best Customers.
     2. Scale: Integrate AI Twins and Progency partnerships.
     3. Transform: Shift budgets from adtech to NeoN exchanges.
   • Discuss partnership ecosystems (e.g., collaborations with IoT providers for real-time health data to power AI Twins).
4. Competitive Edge:
   • Highlight Netcore’s unique assets (e.g., existing customer base, data infrastructure) to position it as a leader in AI-native martech. Contrast with “AI wrapper” startups that lack end-to-end integration.
5. Risk Mitigation:
   • Acknowledge potential pitfalls (e.g., over-reliance on autonomous agents, data breaches) and propose safeguards (e.g., human-in-the-loop oversight, blockchain-based consent logs).

Conclusion

The essay presents a bold, forward-thinking vision for AI-native martech, particularly in its redefinition of customer relationships and AdWaste elimination. However, to transition from thought leadership to actionable strategy, it must:

• Ground ideas in data (e.g., pilot results, industry benchmarks).
• Address ethical and operational risks head-on.
• Clarify Netcore’s differentiation in a crowded AI-martech landscape.

By bridging these gaps, the content can shift from “aspirational” to “authoritative,” empowering brands to embrace AI-native martech with confidence.

8

Stories

I asked the AIs for two stories: Maya as the CMO of a B2C company, and Arun as a customer. How will their lives be transformed in the future with AI-native Martech?

Claude

Maya’s Story: The AI-Native CMO

Before AI-Native Martech

Maya rubbed her temples as she stared at the quarterly marketing review. Despite increasing her digital ad budget by 35% year-over-year, customer acquisition costs had risen even faster. The company had spent millions retargeting people who had already purchased from them, and engagement metrics were trending downward across all channels.

“We need to revamp our segmentation strategy,” she told her team during their Monday meeting. “Our current approach isn’t working.”

Her team looked exhausted. They’d been manually creating and updating audience segments for months, yet the eight customer personas they’d developed still felt crude and ineffective. Email open rates hovered at 7%, and their app’s push notification opt-out rate was climbing alarmingly.

“I’m drowning in reports but starving for insights,” Maya confided to her CEO. “We have mountains of customer data, but we’re still essentially guessing what people want.”

After AI-Native Martech

Maya glances at her dashboard as she sips her morning coffee. Her AI Co-Marketer has already summarized overnight performance across all channels and identified three key opportunities based on emerging customer behavior patterns.

“Good morning, Maya,” the system greets her. “I’ve identified a potential supply chain issue affecting product availability in our Northeast region. I’ve temporarily redirected campaigns for affected products to alternative offerings with 87% interest overlap. This has maintained conversion rates while our operations team addresses the inventory gap.”

Maya nods, impressed. Six months ago, this situation would have created a crisis requiring emergency meetings and days of manual campaign adjustments. Now, it’s handled before she even arrives at the office.

“What about our summer collection launch?” she asks.

“The preview campaign is exceeding projections by 23%,” the Co-Marketer responds. “Based on initial engagement data, I’ve identified highly specific interest clusters and automatically generated fifteen personalized content variations. Each customer is now receiving messaging tailored to their exact preferences rather than broad segment-based communications.”

Maya opens the campaign dashboard where she can see these micro-segments visualized in real time. What used to be “Fashion Enthusiasts” has organically evolved into dozens of precisely defined interest groups, each receiving uniquely relevant messaging.

Later, in her executive meeting, Maya shares the quarterly results that have transformed her standing with the leadership team. “We’ve reduced our dependence on paid reacquisition by 62%, saving nearly $3.5 million this quarter alone,” she explains. “Our NeoMails system has increased email engagement from 7% to 58%, and our ‘Rest’ customer segment—those who were at risk of becoming inactive—has shrunk by 40%.”

The CFO looks up from his tablet. “And the bottom line impact?”

“Customer lifetime value is up 34% year-over-year,” Maya replies confidently. “More importantly, our Earned Growth metric shows we’re generating 28% of new customers through referrals—essentially zero-cost acquisition.”

After the meeting, Maya reviews some of the AI-recommended strategies for the upcoming quarter. Rather than spending hours poring over spreadsheets and making educated guesses, she now focuses on strategic decisions while her AI Co-Marketer orchestrates execution across channels. The system has already identified subtle shifts in customer preferences and adjusted messaging accordingly.

As Maya prepares to leave for the day, she receives one final update: “Based on real-time testing of messaging variants, we’ve identified a new value proposition that’s particularly effective with our high-potential customers. Would you like to review the proposed adjustment to our core messaging strategy?”

Maya smiles. From struggling to explain rising costs and disappointing results, she’s now leading the company’s most efficient and profitable department. The “Department of One” approach has allowed her to redirect her team’s talents from routine tasks to creative strategy and innovation, making marketing a true competitive advantage rather than just a necessary expense.

Arun’s Story: The Empowered Customer

Before AI-Native Martech

Arun deleted another batch of promotional emails without opening them. His inbox was a wasteland of irrelevant offers—discounts on products he’d never buy, “exclusive” deals on items he’d already purchased, and generic messages addressing him as a “valued customer” while showing no understanding of his actual needs.

He’d purchased a pair of running shoes from SportStyle three months ago, and since then, they’d bombarded him with daily promotions for everything from golf clubs to swimming gear—products he had zero interest in. Even worse, he was now seeing their ads follow him across every website and social platform, often promoting the exact shoes he’d already bought.

“I wish these companies would just listen,” he muttered to himself after closing yet another unnecessary notification from the SportStyle app.

His loyalty program experiences weren’t much better. Despite being a “Gold Member” at his favorite electronics retailer, the rewards felt generic and the communications impersonal. The points he earned seemed designed to expire before he could use them, and the recommendations never aligned with his interests.

Online shopping had become an exercise in frustration—too many options, too little guidance, and an overwhelming sense that brands viewed him as a wallet, not a person.

After AI-Native Martech

Arun wakes up to a personalized notification from his MyTwin app: “Good morning, Arun. SportStyle has new trail running shoes that match your preferred cushioning level and terrain preference. Would you like to see them?”

He taps “Yes,” and instantly views a concise presentation of three options specifically matched to his running style, foot shape, and color preferences—information his MyTwin has learned through natural conversations rather than intrusive questioning.

Unlike the old days of endless scrolling through irrelevant products, Arun now receives precisely curated recommendations that genuinely interest him. His MyTwin has learned his preferences, understands his needs, and acts as his personal shopper and brand negotiator.

Later that day, Arun receives an interactive NeoMail from his favorite electronics retailer. Instead of a generic promotion, it contains a 30-second interactive quiz about upcoming tech innovations that earns him Atomic Rewards (Mu) points. After completing it, he’s presented with a personalized offer for a wireless charger compatible with all his devices—something he’d been considering but hadn’t actively searched for yet.

“How did they know?” he wonders, impressed by the retailer’s seemingly prescient understanding of his needs. What he doesn’t realize is that his AI Twin had analyzed his device usage patterns and engagement history to anticipate this need precisely when it became relevant.

When Arun opens his favorite shopping app, he notices how different his experience is from just a year ago. Instead of bombarding him with random products and categories, the interface adapts to his preferences in real time. The navigation prioritizes sections he cares about, and even the search functionality seems to understand his intent rather than just matching keywords.

“I’m planning a weekend hiking trip,” he tells his MyTwin. “What should I bring?”

Within seconds, his twin creates a customized packing list based on his previous purchases, the weather forecast for his favorite trails, and items he might need but doesn’t yet own. For the missing items, it presents options from his preferred brands at competitive prices, even negotiating exclusive discounts by leveraging his customer value across multiple retailers.

As Arun browses, he realizes something remarkable: he no longer feels overwhelmed by marketing messages. Instead of ignoring dozens of irrelevant communications, he actively looks forward to the personalized recommendations and interactive experiences that add value to his day. The brands he engages with seem to genuinely understand him, respecting his time and preferences rather than interrupting his life with generic promotions.

What Arun doesn’t see is how this transformation benefits both sides. Brands are no longer wasting millions trying to recapture his attention through expensive ad platforms. Instead, they’re building direct relationships through his MyTwin, providing genuine value that keeps him engaged and loyal. His favorite brands now spend far less to retain him while serving him far better—a win-win that has fundamentally transformed his relationship with marketing.

ChatGPT

Below are two parallel narratives—one from the perspective of Maya, a CMO at a consumer-focused brand, and one from Arun, an everyday customer. Each showcases how AI-native Martech might transform their day-to-day experiences, highlighting both the internal shifts for marketers and the external, customer-facing benefits.

1) Maya’s Story: A CMO’s Transformation

Maya, CMO at Glazehive
Maya used to juggle countless dashboards and spreadsheets, fighting to keep track of her brand’s campaigns. Her biggest headaches were rising customer acquisition costs, fragmented data, and mass messages that barely resonated. Yet everything changed after her organisation embraced AI-native martech.

Morning: A Whole New Dashboard

• 7:00 AM – Maya checks her phone to see Glazehive’s real-time “Co-Marketer Summary.” Instead of a typical morning scramble for sales updates, she finds a concise feed of action items curated by an AI Co-Marketer. This AI agent has already run predictive models overnight, identifying segments of “Rest” customers who are likely to buy if re-engaged today.
• She taps a single button to approve an automatically generated mini-campaign plan. The Co-Marketer dispatches various sub-agents—Content, Analytics, Budget—to execute tasks from creative iteration to channel allocation.

Midday: Micro-Campaigns on Autopilot

• 11:00 AM – Maya meets briefly with her (small but powerful) marketing ops team. Since adopting the AI-native approach, the entire department shrank from 50 to 15 people—yet output soared. Gone are days of messing with 10 different tools.
• The Campaign Agent has launched highly personalised offers to thousands of “Rest” customers, each one crafted by a Content Agent that integrates real-time feedback. The data flows seamlessly into Glazehive’s owned channels, thereby reducing her reliance on expensive adtech retargeting.

Afternoon: Agentic Insights

• 2:00 PM – A Learning Agent flags a 15% spike in cart abandonments for certain high-end products. Within minutes, the Co-Marketer spawns a Testing Agent to isolate root causes—e.g. shipping fees, slow checkout. Maya simply reviews the proposed fix: a short promotional shipping waiver for these items. She clicks ‘Yes.’
• The system automatically enacts these changes and updates the brand’s email, SMS, and in-app communications. By the end of the day, the cart abandonment rate has dropped by a third.

Evening: Celebrating True N=1

• 5:30 PM – Maya reviews the day’s “wins” on her analytics feed. Arun, for instance, purchased a new limited-edition product after receiving a playful, hyper-personalised “micron” email. Open rates soared to 30% across the board, thanks to embedded “Atomic Rewards (Mu)” that turn every open into a mini-engagement opportunity.
• Instead of playing catch-up or approving last-minute budgets, Maya heads home early, trusting her AI Co-Marketer to maintain the brand’s momentum overnight. Her final message to the team: “We’re on track for a record quarter, with 20% of our revenue now coming from reactivated customers at a fraction of our old reacquisition costs.”

In short, Maya’s life has evolved from firefighting to strategic oversight. She no longer begs for budgets just to reacquire the same customers. Real-time intelligence, multi-agent collaboration, and frictionless personalisation free her to focus on high-level strategy—while AI handles the daily drudgery and cross-channel orchestration.

2) Arun’s Story: A Customer’s Experience

Arun, Tech Enthusiast and Busy Professional
Arun’s day is packed with work tasks, social activities, and a barrage of online offers. He’s grown immune to generic ads and unsubscribed from most newsletters. He typically swipes notifications away, frustrated by marketing that rarely aligns with his actual preferences. But this year, he’s noticed a shift.

Morning: A Meaningful Inbox

• 7:15 AM – Arun scrolls through his email on the train. Usually, he’d delete marketing messages en masse, but one subject line teases “Win your next weekend getaway.”
• The email isn’t just text and images—it’s an interactive “NeoMail” with a 15-second quiz that helps him pick a short holiday. He taps through, unlocking a small “Atomic Reward” (Mu). It’s a fun mini-game rather than another dull promotion. He even sees relevant offers from Glazehive, a brand he hasn’t purchased from in weeks.

Midday: Hyper-Personalised Assistance

• 11:45 AM – Arun gets a push notification from the MyTwin companion app on his phone. It references the fact that he looked up weekend travels this morning. MyTwin proactively suggests a travel accessory from Glazehive—something he’s been thinking about for a while.
• Rather than ignoring or feeling “stalked,” Arun welcomes it. Thanks to the MyTwin’s filter, he never gets random deals on, say, baby products or sports gear he’s not into. Everything is relevant. He taps “Show me more,” and the product page loads instantly within the same interface—no additional logins or friction.

Afternoon: Seamless Engagement

• 2:30 PM – Arun takes a quick break, curious about that recommended travel accessory. He’s pleasantly surprised: the entire check-out process happens inside an interactive email or direct chat—no redirect to a cluttered website or forced app download.
• One click, and his payment details (already stored securely) confirm the purchase. Because it’s friction-free, Arun is more likely to complete the transaction.

Evening: Rewarding Loyalty Without the Noise

• 5:00 PM – Another push notification pings: “Congrats, you’ve earned a new discount.” Usually, Arun would be irritated, but he sees it’s from a brand he actually likes. The note thanks him for his loyalty and invites him to a short “Micron” puzzle for extra bonus points.
• He realises these brands no longer spam him day and night with random 30%-off codes. Instead, each outreach feels like a curated interaction that respects his time—sometimes even offering entertaining micro-rewards or quick bits of content.

In summary, Arun’s routine is vastly improved by AI-native martech. The brand interactions he does see actually align with his interests. There’s minimal friction in the process—no drawn-out forms or random pop-ups. His inbox no longer overflows with junk; it’s a curated space where content arrives just in time, not “just in case.” In short, marketing has gone from feeling like a constant intrusion to a helpful and occasionally delightful concierge.

Conclusion

By moving to AI-native Martech:

• Maya can finally step back from day-to-day firefighting, relying on multi-agent orchestration that addresses tasks from segmentation to creative design in real time. Her department’s overhead and reacquisition costs drop, while retention soars.
• Arun experiences marketing that feels genuinely personalised, purposeful, and mindful of his limited attention. Instead of ignoring or resenting the brand, he’s engaged and even looks forward to the next mini game or reward.

Both stories capture the essence of AI-native marketing: smarter workflows for marketers, more meaningful experiences for customers—and, ultimately, a more sustainable, profitable future for brands.

DeepSeek

Story 1: Maya, the CMO of “BloomLife” (A B2C Wellness Brand)
Maya no longer spends her days drowning in spreadsheets or debating campaign ROIs with her team. Instead, her AI Co-Marketer, “BloomMind,” orchestrates a symphony of autonomous agents:

• Segment Agent identifies “Best Customers” like Arun (a yoga enthusiast and busy parent) in real-time, tracking his engagement with BloomLife’s meditation app and recent searches for stress-relief products.
• Content Agent crafts a personalized video ad showing Arun’s AI Twin (a digital avatar trained on his preferences) demonstrating a 5-minute mindfulness routine tailored to his schedule.
• Campaigns Agent deploys the ad via NeoMails—interactive, AI-generated emails that adapt based on Arun’s real-time behavior. When he clicks, his Twin negotiates a discount bundle: a weighted blanket + a free month of premium app access.
• Analytics Agent updates Maya’s dashboard: the campaign achieved 92% engagement from “Rest Customers” like Arun, cutting CAC by 60% and boosting LTV by 3x.

Maya’s role? Strategic oversight. She reviews AI-proposed quarterly themes (e.g., “Postpartum Wellness”) and approves ethical guardrails to prevent bias in targeting. Her biggest win? Redirecting 70% of BloomLife’s AdWaste budget into AI-driven loyalty programs, earning her a seat at the CEO’s table as a profit driver—not a cost center.

Story 2: Arun, the Customer
Arun’s inbox used to overflow with spam. Now, his AI Twin acts as a gatekeeper and advocate:

• Morning: Arun’s Twin blocks generic “20% off wellness gear!” emails but flags BloomLife’s NeoMail: “Your weighted blanket is ready—claim your bundle before your toddler’s naptime ends!” The Twin knows Arun’s schedule (synced with his calendar) and even negotiates free shipping.
• Afternoon: BloomLife’s app detects stress spikes via Arun’s smartwatch. His Twin auto-books a same-day massage at BloomLife’s partner spa, using loyalty points Arun didn’t know he had.
• Evening: A notification pings: “Your Twin secured early access to BloomLife’s new ‘Dad-Bod Yoga’ class. RSVP with 3 friends, get a free protein shake.” Arun smirks—it’s exactly his humor.

Arun isn’t bombarded; he’s anticipated. His Twin learns from every interaction, turning marketing from noise into a curated service. He even earns crypto-style rewards for sharing zero-party data (e.g., “I prefer evening workouts”), which his Twin trades securely on NeoN—BloomLife’s attention exchange—for exclusive perks from non-competing brands.

The Connection
Maya’s AI-native strategy turns Arun from a “Rest Customer” to a “Best Customer.” His LTV skyrockets because BloomLife doesn’t just sell products—it solves his problems before he voices them. Meanwhile, Maya’s team shrinks from 20 to 5, but her impact grows: AI handles the grind, humans handle the heart.

The Future?
For Maya: Scaling empathy. For Arun: Feeling seen. For both: A relationship where marketing isn’t a transaction—it’s a partnership.

Published April 14, 2025

1

Overview

In my previous essays, I’ve documented a startling reality: $500 billion annually—70% of digital advertising budgets—flows from brands to Big Adtech platforms in what can only be described as “AdWaste.” This massive wealth transfer occurs not because of flawed strategies, but because brands are trapped in an endless cycle of reacquisition, paying auction-driven premiums to reach customers they already own. Imagine paying rent for a house you built—this is the absurdity of modern marketing.

While I’ve outlined how NeoMarketing innovations in Agentic AI (Marketing Agents and Madtech Twins) and interactive email (AMP-powered NeoMails and NeoN) can address this crisis, I’ve come to a deeper realisation: AdWaste isn’t the disease—it’s a symptom.

The Attention Recession: Marketing’s Root Problem

The true ailment afflicting modern marketing is the Attention Recession. Consumers today drown in a deluge of hundreds of daily brand messages—emails, push notifications, social updates, and ads—all vying for slivers of their fragmented focus. Attention, once abundant, has become a scarce commodity. When customers disengage, relationships wither, leaving brands no choice but to rent access to these same customers through increasingly expensive ad platforms.

This creates a vicious cycle: declining engagement triggers more aggressive advertising, which further erodes attention, demanding even greater ad spending. It’s a self-perpetuating doom loop where brands fund their own obsolescence.

The Anatomy of Attention Bankruptcy

Consider the typical brand-customer relationship:

• Emails languish with single-digit open rates
• Push notifications are blocked or ignored
• Social posts vanish in algorithm-driven feeds
• Loyalty programmes gather dust, their communications unread

Without reliable channels to engage customers they already know, marketers resort to the only “guaranteed” alternative: paying Google, Meta, and retail media networks to reacquire them. The economics are brutal. Each dormant customer becomes a revenue leak, forcing brands into a Faustian bargain: invest in creative reengagement strategies (slow, uncertain) or pay the adtech tax (instant, predictable).

Most choose the latter—not because it’s sustainable, but because it’s the path of least resistance. The promise of digital marketing—direct relationships, precision targeting, and personalised experiences—has collapsed under the weight of platform dependency.

Building Attention Bridges: From Renters to Owners

Breaking this cycle requires addressing the root cause: attention scarcity. To thrive, brands must rebuild “attention bridges”—reliable, value-driven channels that cut through the noise. Imagine a world where:

• Every interaction feels personalised, not transactional
• Customers seek out brand communications for genuine value
• Reacquisition costs plummet as engagement becomes habitual

This isn’t a utopian vision. Emerging innovations are already paving the way:

1. AI-Powered Personalisation at Scale: AI Twins—digital replicas of individual customers—can enable true 1:1 engagement, predicting needs and tailoring interactions in real time
2. Interactive Micro-Experiences: NeoMails transform traditional one-way communications into dialogues, allowing customers to browse products, redeem rewards, or transact within the email itself
3. Daily Engagement Vehicles: Atomic Rewards (Mu) and Microns—micro-incentives and exclusive content—turn mundane interactions into habit-forming rituals
4. Authenticated Attention Networks: The NeoN network reimagines advertising by leveraging verified customer identities (with consent), shifting from interruption to invitation

The Path Forward: Earning Attention, Not Buying It

The future of marketing isn’t about finding better ways to buy attention—it’s about earning and keeping it through superior experiences. This requires a paradigm shift:

• From Renters to Owners: Build owned channels customers want to engage with
• From Noise to Signal: Replace generic blasts with insights-driven relevance
• From Cost Centre to Profit Engine: Redirect AdWaste savings into loyalty-building experiences

In this series, I’ll explore the Attention Recession in depth and introduce several breakthrough innovations that offer real solutions. I’ll delve into Agentic AI’s role in automating personalised journeys, the science of habit-forming engagement, and the rise of “attention-first” metrics that prioritise lifetime value over clicks.

The $500 billion AdWaste crisis is a wake-up call. The brands that thrive will be those that stop renting attention and start earning it—one meaningful interaction at a time. The question is no longer how to buy attention, but how to make your brand worth paying attention to.

2

2021 Writings

Attention Messaging: Bridging Adtech, CPaaS and Martech (August 2021): “Digital marketers find themselves trapped between new customer acquisition and re-acquisition of those whom they lose. The supply of new customers is not infinite – after a time, there are diminishing returns in acquiring new customers whose spending potential is low. And yet, for the business to show growth, they need to keep new customers coming in. Where do they find such customers? From their own pool of lost or dormant customers! This leads to what I call the “doom loop” of spending: customers get acquired, some of them become inactive, they are then re-acquired, some among them become inactive again, and they are re-re-acquired…The marketing triangle’s three vertices are new customer acquisition and re-acquisition (adtech, where 90% of the budget is consumed), the transactional messages sent via communications platforms (CPaaS, or communications platform as a service), and martech platforms focused on improving customer engagement on their owned properties (websites and apps). The missing centre is the world of “Attention Messaging” – a cross between attention marketing and push messaging. This is the world marketers pay least attention to leading to businesses sacrificing a big chunk of revenues and profits…Whether the problem is attention deficit, attention recession or information overload, the solution is the same – Attention Messaging needs to be done right.”

Marketing: Disrupted and Simplified (August 2021): “By not building deep relationships with existing customers and by bombarding them with irrelevant messages, marketers have trained their customers to ignore their communications, thus reducing the efficacy of the only method of bringing existing customers back to their website or app for transactions. Once customers start ignoring the messages, the marketer has little or no choice but to spend 10X more on re-acquiring that same customer via the tech giants. With everyone doing the same, the only winners are the attention sellers (Google and Facebook), who in turn create even more powerful data hoses by giving consumers even more free utilities. The irony is that as marketers did not pay attention to their customer needs, they are paying even more dearly to the attention intermediaries to reach their own ex-customers…This has been the big blunder that marketers made: they did not value the attention of their customers. Instead of building a hotline, they cut the line…Make attention the new acquisition, and rewards the new experience.”

Stop Loss: The Power of Attention Messaging (September 2021): “Marketing needs to return to its roots. Customers have needs, time and money. Once upon a time, marketers captured attention through a combination of copy, imagery and creativity to grab attention, surface the need, and channel the money. As customers become digital, data and cohorts become the focus. So, while budgets have shifted from traditional to digital, marketers missed a step. Digital customers not only leave pheromones for tracking but also have an identity with inboxes. For the first time, real-time messaging was possible to a segment of one or few, a subset of the traditional “all”. The identity-inbox combo should have been utilised to ensure the cost of engagement becomes a fraction of that of acquisition…The core idea of Attention Messaging is simple. Pay for attention. Pay existing customers for their attention, rather than pay many times more to the tech giants for the same attention…Attention Messaging needs a currency to reward attention. This needs to work across push messaging channels and brands. No single brand can make it work because they cannot offer enough rewards on their own to make it worthwhile, even if they have a loyalty program.”

Microns and Loyalty: Gamifying and Rewarding Attention (June 2021): “Email is the best channel for brand communications and 88% emails (7 out of 8) are not opened. And despite all this, email is seen to have the best RoI across all channels. So the big takeaway is: the email inbox is one of the most powerful marketing platforms. Hundreds of billions of emails make their way to inboxes each month, all competing for our attention. And yet, most emails are ignored by the recipients. Imagine the multiplier impact if more emails could be read by their recipients. This was my thinking behind microns – short, informational and identified emails that can be consumed in 15-30 seconds. This itself should take open rates higher. What more could be done to make microns even better – towards a 100% open rate? That is where the idea of a loyalty program comes in. And as I studied both email and loyalty, it became clear that these two worlds had never intersected…I started thinking about a few questions. What if brands could incentivise their customers to open and click on emails? (Of course, this could be easily abused but that can be addressed by monitoring the time taken for actions after opening an email, or what happens after the click. And incentives could be offered on a differential basis to the best customers versus the others.) Why has a multi-brand email loyalty program never been created? (This could be because no single email service provider has a large enough market share to get the critical mass for making such a program successful.) Would consumers respond to such a program or just ignore it? (The only way to know would be to actually do it and find out!) Is paying for attention a good thing? (Of course, it is – all advertising is about paying for attention. The difference is that brands pay intermediaries rather than their consumers.)”

Imagining Mus: An Attention-Action Currency (June 2021): “Rather than target the entire universe of consumers, what if brands could just focus on their existing customers and reward the less active ones for their attention? The prize for entrepreneurs to get it right is a large chunk of the global advertising pie. For brands, it is an optimisation of their huge marketing budgets. And for consumers, their ‘delete’ mindset could be converted to a ‘delight’ mindset when it comes to dealing with the inflow of messages they get. How can a win-win platform be created for all? It was time to bring multiple strands – microns, loyalty, micronbox – together into a larger idea of “Mus” (pronounced as ‘mews’; and derived from the µ symbol that I have previously suggested as an identifier for microns). Mus can become the currency for attention and action…The combination of subscription, an incentive (Mus) wrapped in a micron and delivered to a special inbox can transform brand-to-consumer communications. Just as loyalty programs reward the Best customers, Mus in microns are the key to unlocking attention of the Rest customers – moving them from inactive or semi-active to expectantly engaged.”

Atomic Rewards: The Solution to Attention Recession (October 2021): “Atomic Rewards is an idea whose time has come. It is the answer to the problem of attention recession that confronts every marketer. Attention and engagement need as much focus as marketers are doing with customer journeys, onsite and in-app experiences, and transactions. These Mu moments can be the secret to engineering profitable customers for life.”

3

Past and Future

In reviewing my writings from 2021, I’m struck by a profound realisation: the fundamental diagnosis of marketing’s attention problem and the conceptual solutions I proposed were largely correct. The core ideas—incentives (Atomic Rewards), reliable communication channels (Attention Messaging), and 1:1 personalisation—were all present. Yet four years later, these solutions haven’t been widely implemented.

This raises two critical questions: Why did these ideas struggle to gain traction then, and what makes me believe their time has finally come now?

Why Previous Implementations Faltered

The barriers to implementing attention-focused solutions were significant:

Coordination Challenges: Atomic Rewards (Mu) needed cross-brand cooperation to create sufficient value. This created a classic chicken-and-egg problem—no brand wanted to be first when the network effect wasn’t established. Many brands had existing loyalty programmes, making bespoke integrations prohibitively complex.

Value Exchange Limitations: The “Mu burn” problem remained unsolved. Consumers needed clear, compelling ways to redeem earned rewards, but creating a unified redemption system across brands proved challenging.

Technical Complexity: Creating AMP-powered interactive emails required merging creative design with sophisticated coding—a capability gap for most marketing agencies who lacked in-house software development talent. This made implementation expensive and complicated.

Channel Fragmentation: As WhatsApp opened its platform for business messaging, attention naturally gravitated to where users were already engaged. Despite higher costs and marketing restrictions, WhatsApp offered an easier solution for bottom-of-funnel conversion—mirroring the “path of least resistance” that adtech had established.

Inbox Challenges: Email environments grew increasingly cluttered despite filtration attempts. Gmail’s filtering algorithms sometimes became overzealous, relegating even legitimate brand communications to secondary folders, further complicating attention capture.

Why The Time Is Now Right

What has fundamentally changed to make these solutions viable today? Several transformative shifts have aligned:

Technological Maturation:

• AI Revolution: Generative and agentic AI now enables truly personalised content creation at scale, making N=1 marketing finally feasible without proportional cost increases
• Interactive Technology: AMP has matured, with better tooling and many success stories across marketing campaigns from multiple brands
• Identity Solutions: Cookie deprecation has accelerated interest in authenticated identity approaches, making solutions like NeoN more relevant

Economic Imperatives:

• Rising Acquisition Costs: Continually increasing CAC has reached unsustainable levels, forcing brands to seek alternatives
• Privacy Regulations: GDPR, CCPA, and similar regulations have complicated third-party data usage, making first-party data engagement more valuable
• Post-Covid Digital Fatigue: Consumers have become increasingly selective about digital engagement, raising the stakes for attention capture

Implementation Innovations:

• Microns As Engagement Wrappers: Rather than replacing brand content, Mu and Microns now function as engagement “wrappers” that drive opens first, creating space for product messaging after attention is secured
• Gamification Elements: Interactive games, puzzles, and challenges embedded within Microns create habit-forming engagement patterns
• Simplified Reward Mechanics: The “Mu burn” problem is being addressed through straightforward mechanisms like raffles with cash prizes, creating immediate perceived value
• Progency Model: End-to-end responsibility for “Rest” customer segments through the Progency approach provides greater control over the entire messaging ecosystem
• Monetisation Through NeoN: The brand-to-brand advertising network creates additional revenue streams, effectively “printing money” for brands that successfully capture attention

The Path Forward

The confluence of these factors creates a uniquely favourable environment for attention-focused solutions. While the diagnosis and conceptual framework were sound in 2021, the implementation ecosystem wasn’t ready. Today, the technological, economic, and operational elements have aligned to make these approaches not just viable but imperative.

The $500 billion AdWaste market represents both the size of the problem and the scale of the opportunity. As acquisition costs continue to rise and privacy changes further complicate traditional targeting, brands face an existential need to rebuild direct attention channels with customers they already know.

The solutions I proposed years ago weren’t wrong—they were early. Now, with enhanced implementation approaches and a more conducive market environment, we stand at the threshold of a fundamental shift in how brands capture and maintain customer attention and thus break free from the attention recession and the AdWaste it creates.

4

Recent Books

Before we delve into possible solutions for the attention recession, it’s important to first explore what “attention” itself truly entails. I asked Perplexity for a summary of books published over the past three to four years that examine this very concept. Here is the resulting list.

1. “The Sirens’ Call: How Attention Became the World’s Most Valuable Resource” by Chris Hayes: This book explores how attention has become a commodified resource and discusses its implications on society. It offers perspectives on how to manage attention in a world dominated by digital media.
2. “Attention Span: A Groundbreaking Way to Restore Balance, Happiness and Productivity” by Gloria Mark: This book explores how technology affects our attention and offers strategies to regain focus in a digital world. It discusses topics like multitasking, social media’s impact on attention, and how to manage mental resources.
3. “Peak Mind: Find Your Focus, Own Your Attention, Invest 12 Minutes a Day” by Amishi P. Jha: This book provides insights into the science of attention and offers practical mindfulness exercises to improve focus and mental clarity. It’s based on extensive research across various professions and environments.
4. “Stolen Focus: Why You Can’t Pay Attention and How to Think Deeply Again” by Johann Hari: This book explores the societal factors contributing to our inability to focus and offers insights into how to regain deep thinking skills in a distracted world.
5. “Hyperfocus: How to Be More Productive in a World of Distraction” by Chris Bailey: This book delves into the science of focus and provides techniques to enhance attention span, helping readers boost productivity in a world filled with distractions.
6. “Deep Focus” by Cal Newport: While not specifically titled as a book on attention, “Deep Focus” is part of Cal Newport’s broader work on deep work and productivity. It emphasizes the importance of eliminating distractions to achieve deep focus and extraordinary results.
7. “The Attention Economy: A Category Blueprint” by Dr. Karen Nelson-Field: This book focuses on the attention economy, providing a comprehensive guide to understanding and measuring attention in modern media. It’s more industry-oriented but offers insights into how attention is valued and measured.

The blurb from The Siren’s Call captures the story of our modern times: “We all feel it—the distraction, the loss of focus, the addictive focus on the wrong things for too long. We bump into the zombies on their phones in the street, and sometimes they’re us. We stare in pity at the four people at the table in the restaurant, all on their phones, and then we feel the buzz in our pocket. Something has changed utterly: for most of human history, the boundary between public and private has been clear, at least in theory. Now, as Chris Hayes writes, “With the help of a few tech firms, we basically tore it down in about a decade.” Hayes argues that we are in the midst of an epoch-defining transition whose only parallel is what happened to labor in the nineteenth century: attention has become a commodified resource extracted from us, and from which we are increasingly alienated. The Sirens’ Call is the big-picture vision we urgently need to offer clarity and guidance. Because there is a breaking point. Sirens are designed to compel us, and now they are going off in our bedrooms and kitchens at all hours of the day and night, doing the bidding of vast empires, the most valuable companies in history, built on harvesting human attention. As Hayes writes, “Now our deepest neurological structures, human evolutionary inheritances, and social impulses are in a habitat designed to prey upon, to cultivate, distort, or destroy that which most fundamentally makes us human.” The Sirens’ Call is the book that snaps everything into a single holistic framework so that we can wrest back control of our lives, our politics, and our future.”

Given how saturated our attention channels have become, it’s hardly surprising that brand communications struggle to stand out. From relentless notifications to an overwhelming barrage of ads, every message competes for the same fragment of mental space. As a result, even the most carefully crafted campaigns often fail to reach us—the very customers they’re meant to engage.

5

Books Summary – 1

I asked Perplexity for a for a summary of the ideas from the recent books on Attention and applicability for marketing.

The Crisis of Attention in the Digital Age: Insights from Contemporary Thought Leaders

The accelerating fragmentation of human attention has emerged as one of the defining challenges of the 21st century. As digital platforms weaponize psychological vulnerabilities to commodify focus, individuals and institutions face unprecedented threats to productivity, mental health, and societal cohesion. Drawing from seven seminal works published between 2021-2024, this analysis synthesizes groundbreaking research on attention economics, neuroscientific foundations of focus, and pragmatic strategies for reclaiming cognitive sovereignty in an age of perpetual distraction.

1. The Commodification of Attention: A New Resource War

Chris Hayes’ The Sirens’ Call frames attention as the “new oil,” arguing that tech platforms have engineered a global attention extraction economy comparable to 19th-century resource colonialism. Through algorithmic architectures that exploit dopamine-driven feedback loops, platforms convert human focus into measurable “attention minutes” traded in advertising markets. Hayes demonstrates how this commodification creates psychological alienation, with users becoming both laborer and product in attention markets. His analysis of “attention strip-mining” reveals how constant notifications mirror industrial age exploitation, demanding policy interventions akin to environmental regulations.

Karen Nelson-Field’s The Attention Economy: A Category Blueprint quantifies this crisis through media measurement frameworks. Her “attention seconds” metric exposes the fallacy of impression-based advertising, showing premium video commands 2.3X more focused attention than social media stories. This data-driven approach challenges marketers to prioritize attention quality over reach, advocating for authenticated identity solutions like NeoN to combat platform-driven attention arbitrage.

2. Neuroscientific Foundations of Focus

Gloria Mark’s Attention Span dismantles productivity myths through biometric studies revealing the natural 23-minute attention cycle. Her discovery of “kinetic attention”—the cognitive benefits of physical movement during focus breaks—revolutionizes workplace design principles. Mark demonstrates that strategic distraction (47-second email checks every 10.5 minutes) can boost creativity without derailing flow states, challenging orthodox time management doctrines.

Complementing this, Amishi Jha’s Peak Mind leverages decade-long mindfulness research with military and medical professionals. Her “12-minute daily practice” protocol shows 27% improvement in sustained attention through focused breathing exercises that strengthen the anterior cingulate cortex. Jha’s “attention gym” metaphor reframes mental training as cognitive strength conditioning, with fMRI evidence proving mindfulness increases gray matter density in attention networks.

3. Systemic Drivers of Attention Bankruptcy

Johann Hari’s Stolen Focus adopts a sociological lens, identifying twelve interlocking factors eroding collective attention. His analysis of “surveillance capitalism’s acceleration feedback loop” reveals how platform algorithms punish deliberative content, favoring inflammatory material that hijacks limbic responses. Hari documents the 56% decline in average focus duration since 2004, tracing it to hyper-competitive work cultures enforcing constant connectivity.

Cal Newport’s Deep Work crystallizes the economic consequences, showing knowledge workers lose 2.1 hours daily to context switching. His “attention capital theory” quantifies the $785 billion annual productivity loss from shallow work in the U.S. alone. Newport’s ethnographic studies of “attention blacksmiths”—from Jung’s Bollingen Tower retreat to Bill Gates’ Think Weeks—provide a blueprint for institutional deep work policies.

4. Reclaiming Cognitive Agency: Strategies Across Scales

Chris Bailey’s Hyperfocus introduces dual-mode attention management, differentiating deliberate concentration (hyperfocus) from creative incubation (scatterfocus). His “attention inventory” technique—tracking 147 discrete cognitive states over 21 days—enables personalized focus optimization. Bailey’s “20% distraction budgeting” framework permits strategic mind-wandering while protecting core deep work blocks.

At the organizational level, Mark’s “attention-aware workplace” model proposes architectural solutions:

• Kinetic workstations with treadmill desks to leverage motor-cognitive coupling
• “Focus havens” implementing variable acoustics for different attention modes
• Meeting protocols enforcing “monotasking zones” without digital devices

Nelson-Field’s industry reforms advocate for:

• Attention-weighted advertising metrics replacing CPM/CPC models
• Privacy-preserving attention analytics through federated learning systems
• Co-regulated attention markets with platform liability for attention harm

5.  The Path Forward: From Extraction to Stewardship

Hayes’ policy blueprint in The Sirens’ Call proposes radical interventions:

• Algorithmic transparency mandates under an FDA-style regulatory body
• Attention rights frameworks classifying cognitive intrusion as workplace harassment
• Digital public infrastructure alternatives to surveillance-based platforms

Concurrently, Jha’s neuroplasticity research proves that 6 weeks of mindfulness training can increase attentional control by 39%, offering scalable solutions for education systems. When combined with Newport’s “digital minimalism” protocols—shown to reduce screen time by 31% without productivity loss—these approaches chart a course toward sustainable attention ecosystems.

The convergence of these works reveals an urgent truth: attention stewardship must become the defining skill of the digital century. As Hari concludes, “Just as we learned to filter water, we must now learn to filter focus—or drown in the cognitive pollution of our age”. The solutions exist; what remains is the collective will to implement them at scale.

6

Books Summary – 2

I also asked DeepSeek for a summary of the ideas from the recent books on Attention and applicability for marketing

The books listed offer profound insights into the modern attention crisis, blending personal strategies with societal critiques. Below are the most salient ideas relevant to addressing marketing’s attention recession:

1. Attention as a Commodity

Chris Hayes, The Sirens’ Call

• Core Idea: Attention has become the 21st century’s most exploited resource, mined by tech platforms through relentless notifications and algorithmic feeds.
• Relevance: Brands are forced to “rent” attention from adtech giants, mirroring Hayes’ critique of attention commodification. His call to reclaim agency over attention aligns with building owned engagement channels.

Dr. Karen Nelson-Field, The Attention Economy

• Core Idea: Attention must be measured not by impressions but by quality—duration, emotional resonance, and cognitive engagement.
• Relevance: Shifts the focus from “reach” to “impact,” urging brands to prioritize metrics like time-spent and recall over clicks.

2. The Fragmented Mind

Gloria Mark, Attention Span

• Core Idea: Digital multitasking reduces average focus time to 47 seconds per task, eroding productivity and satisfaction.
• Relevance: Brands compete in a landscape where consumers’ fractured attention demands hyper-relevant, bite-sized interactions (e.g., Microns).

Johann Hari, Stolen Focus

• Core Idea: Attention deficits stem from systemic issues—endless scrolling, workplace burnout, and profit-driven tech design.
• Relevance: Marketing’s reliance on interruptive ads exacerbates the problem. Hari’s solution—designing “attention-protective” environments—supports owned channels like NeoMails.

3. Restoring Focus

Amishi P. Jha, Peak Mind

• Core Idea: Mindfulness training (12 minutes/day) strengthens attentional control by rewiring neural pathways.
• Relevance: Brands can borrow this principle, creating daily “mindful moments” (e.g., gamified quizzes in emails) to deepen engagement.

Chris Bailey, Hyperfocus

• Core Idea: “Hyperfocus” occurs when we channel attention intentionally, minimizing distractions.
• Relevance: Interactive AMP emails and Atomic Rewards incentivize focused engagement, turning passive readers into active participants.

Cal Newport, Deep Focus

• Core Idea: Deep work requires eliminating shallow tasks and structuring time for uninterrupted concentration.
• Relevance: Brands must reduce “noise” in communications—fewer generic blasts, more personalized, value-driven content.

4. Systemic Solutions

Cross-Book Synthesis:

• Personal Habits: Jha and Bailey emphasize individual practices (mindfulness, time-blocking) to combat distraction.
• Structural Change: Hayes and Hari argue for redesigning tech ecosystems to prioritize human well-being over engagement metrics.

Marketing Implications:

• Owned Channels: Build “attention sanctuaries” (e.g., NeoN’s authenticated network) where engagement is voluntary and rewarding.
• Value Exchange: Atomic Rewards and Microns align with Hari’s call to “pay for attention” directly, bypassing adtech intermediaries.

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Actionable Takeaways for Marketers

1. Prioritize Quality Over Quantity: Measure attention via depth (time, interaction) rather than reach.
2. Leverage Mindfulness Principles: Design micro-engagements (e.g., daily puzzles) that reward focused attention.
3. Build Ethical Ecosystems: Use authenticated networks (NeoN) to create mutually beneficial attention exchanges.
4. Reduce Cognitive Load: Replace generic ads with hyper-personalized, AMP-powered experiences that respect users’ time.

By synthesizing these insights, brands can transition from renting attention to cultivating it—transforming the $500 billion AdWaste crisis into a loyalty-building opportunity.

7

Attention Solutions – 1

The Attention Recession represents the root cause of marketing’s AdWaste crisis. As consumers tune out brand communications, relationships deteriorate, leading to expensive reacquisition cycles. Breaking free from this pattern requires a comprehensive approach focused on rebuilding attention bridges. The following innovations offer practical solutions to reconnect with audiences, transform engagement, and eliminate wasteful spending.

1. NeoMails: Building Reliable Hotlines

The concept of NeoMails represents a fundamental reimagining of email as a reliable, daily engagement channel rather than a one-way broadcast medium. By transforming the inbox into an interactive, value-driven destination, NeoMails solve the “No Hotline” problem that forces brands into expensive reacquisition.

A. Atomic Rewards (Mu)

Atomic Rewards address a core truth: attention deserves compensation. Rather than paying intermediaries for customer attention, brands can reward consumers directly for their engagement:

• Subject Line Incentives: Small rewards (Mu) embedded in subject lines create anticipation, driving open rates from single digits to much higher numbers by triggering curiosity and perceived value
• Progressive Earning: Rewards accumulate through consistent engagement, creating habit-forming behaviour patterns
• Network Effects: As more brands adopt Mu, consumers develop a “collection” mindset, increasing overall engagement across participating brands
• Attention Currency: Mu becomes a universal “attention currency” that consumers can earn across multiple brands and redeem through raffles or other reward mechanics

The psychological power of Mu stems from immediate gratification combined with longer-term accumulation, similar to loyalty programmes but focused on attention rather than purchases.

B. Microns (Value-Driven Microexperiences)

Microns transform emails from information vehicles into interactive, enriching experiences:

• 15-60 Second “Brain Gain”: Short quizzes, puzzles, or interactive content provide genuine value, combating “brain rot” from endless scrolling
• Daily Habit Formation: Consistent delivery creates anticipation and engagement rituals
• Progressive Profiling: Interactive elements gather zero-party data while providing entertainment, creating a fair value exchange
• Wrapper Architecture: Microns serve as “engagement wrappers” around traditional brand content, securing attention before delivering commercial messages

These micro-experiences create a psychological shift: customers begin actively seeking brand communications rather than avoiding them, fundamentally altering the attention dynamic.

C. ActionAds (In-Place Advertising)

ActionAds transform email from a cost centre into a profit engine by enabling seamless in-email transactions:

• Frictionless Commerce: Customers can browse, select, and purchase products without leaving their inbox, eliminating conversion barriers
• Contextual Relevance: Powered by zero-party data, these ads feel like personalised recommendations rather than interruptions
• Revenue Generation: Brands can monetise their email engagement, hosting relevant third-party promotions from non-competing partners
• Identity-Based Targeting: Unlike cookie-based advertising, ActionAds use authenticated identity (with consent) for precise, privacy-compliant targeting

The combination of these elements transforms email from an outdated push channel into a dynamic, interactive platform that consumers actively engage with. When implemented together, open rates can climb from single digits to 40-60%, with engagement times increasing from seconds to minutes—fundamentally changing the economics of customer relationships.

2. N=1 Personalisation

Generic messaging creates the “Not For Me” problem that drives customers into dormancy. True N=1 personalisation—treating each customer as a unique individual rather than a segment member—creates relevance that commands attention.

A. AI Twins (Segment and Singular)

AI Twins create digital replicas of customers at both segment and individual levels:

• Segment (Madtech) Twins: Aggregate customer understanding at the cohort level, identifying patterns and preferences for similar groups
• Singular Twins (MyTwin): Individual-level digital replicas that understand specific preferences, behaviours, and needs
• Continuous Learning: Twins evolve with each interaction, developing increasingly accurate predictions of customer preferences
• Conversation-Based Understanding: Through natural language interactions, Twins can gather explicit preferences without intrusive questioning
• Predictive Engagement: Twins anticipate needs before customers express them, enabling proactive personalisation

AI Twins create a revolutionary shift from demographics-based segmentation to genuine individual understanding, making every communication feel personally crafted.

B. AI Marketing Agents

AI Marketing Agents automate personalisation at scale, eliminating the operational complexity that previously made individual-level engagement impossible:

• Content Agents: Create personalised messaging variants for different customer contexts
• Campaign Agents: Orchestrate multi-channel engagement sequences optimised for individual preferences
• Journey Agents: Adapt customer experiences in real-time based on behaviour signals
• Testing Agents: Continuously evaluate messaging effectiveness, optimising for engagement
• Analytics Agents: Process customer signals to identify patterns and opportunities

Together, these specialised agents form an “AI Marketing Department” that can execute thousands of personalised campaigns simultaneously without proportional staffing increases. This solves the operational bottleneck that prevented true personalisation despite its known effectiveness.

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Attention Solutions – 2

3. NeoN: The Attention Exchange

NeoN reimagines advertising by creating a direct brand-to-brand attention exchange that eliminates platform intermediaries:

• Authenticated Identity: Uses PII (with consent) for precise targeting, eliminating the waste of cookie-based approaches
• Direct Brand Collaboration: Enables brands to reach dormant customers through partners’ active engagement channels
• Privacy-Compliant Data Usage: Employs “clean room” technology to match audiences without exposing personal data
• Performance-Based Pricing: Aligns costs with actual results rather than auction-based inflation
• Reacquisition Alternative: Provides a cost-effective channel for reactivating dormant customers without platform fees

NeoN creates a superior alternative to expensive platform-based reacquisition, addressing the “No Alternative” problem that locks brands into wasteful adtech spending. By collaborating directly, brands can share audience attention in a mutually beneficial ecosystem that reduces costs while improving targeting precision.

4. Progency: Operationalising Attention Solutions

The Progency model addresses the fundamental “Who Will Do It?” problem that prevents martech adoption despite its known benefits:

A. Martech Performance Agency

• Outcome-Based Model: Compensation tied to attention and engagement metrics rather than software fees
• End-to-End Responsibility: Full accountability for customer reactivation and retention
• Focus on “Rest” Customers: Specialised attention on the overlooked middle segment between “Best” (already engaged) and “Test” (fully dormant)
• Incentive Alignment: Success fees based on incremental revenue, creating perfect alignment with brand objectives

B. PEAK Framework

The PEAK methodology creates a comprehensive system for attention optimisation:

• Product/Platform: Proprietary martech tools for attention capture and engagement
• Experts: Specialised teams with deep expertise in attention economics
• AI Agents: Autonomous systems for personalisation and optimisation at scale
• Kaizen: Continuous improvement methodology for ongoing attention enhancement

Progency transforms martech from a software expense into a performance-driven profit centre, similar to how adtech operates but focused on retention rather than acquisition.

Additional Attention Solutions

Beyond these core innovations, several complementary approaches further address the Attention Recession:

1. Attention-First Metrics

• Earned Growth: Measuring organic referrals and repeat engagement rather than paid acquisition
• Attention Quality Score: Evaluating engagement depth rather than just quantity
• Customer Lifetime Value (LTV): Prioritising long-term relationship value over short-term clicks
• Reacquisition Rate: Tracking the percentage of customers requiring paid reacquisition, and reducing this to near-zero

2. Attention-Protective Environments

• Distraction-Free Interfaces: Simplified, focused design that eliminates cognitive overload
• Value-Driven Communications: Prioritising genuine value over interruption-based messaging
• Permission-Based Engagement: Building relationships based on consent rather than intrusion
• Attention Budgeting: Respecting customers’ limited cognitive resources

3. Habit-Forming Engagement

• Daily Engagement Rituals: Creating predictable, rewarding interaction patterns
• Variable Reward Mechanics: Introducing elements of surprise to drive continued engagement
• Progress Indicators: Showing advancement toward goals or rewards
• Community Reinforcement: Using social dynamics to strengthen engagement habits

Integration: The Attention Ecosystem

While each solution addresses specific aspects of the Attention Recession, their true power emerges when implemented as an integrated ecosystem:

1. NeoMails with Atomic Rewards create reliable, daily engagement channels
2. AI Twins and Marketing Agents ensure every interaction feels personally relevant
3. NeoN provides cost-effective reactivation for dormant customers
4. Progency delivers the operational expertise to implement and optimise the entire system

This integrated approach creates a virtuous cycle: better engagement reduces dormancy, which decreases reacquisition needs, freeing resources for enhanced personalisation, which further improves engagement. The result is a sustainable alternative to the vicious cycle of attention decay and expensive reacquisition that drives AdWaste.

Conclusion: From Attention Recession to Attention Prosperity

The $500 billion AdWaste crisis stems directly from the Attention Recession. By implementing these solutions, brands can fundamentally transform their relationship with customer attention—moving from costly, intermittent acquisition to consistent, valuable engagement.

This shift represents more than just cost savings; it creates a new marketing paradigm where attention is earned through value rather than bought through interruption. Brands that master these approaches will not only eliminate AdWaste but build lasting competitive advantages through stronger customer relationships, lower acquisition costs, and new revenue streams.

The future of marketing belongs to those who solve the attention problem at its source rather than merely treating its symptoms. These innovations provide the practical pathway to break free from the attention recession and build a more sustainable, profitable marketing ecosystem.