Thinks 1515

NYTimes: “In 1977, Karen Wynn Fonstad made a long shot cold call to J.R.R. Tolkien’s American publisher with the hope of landing a dream assignment: to create an exhaustive atlas of Middle-earth, the setting of the author’s widely popular “The Hobbit” and “The Lord of the Rings.” To her surprise, an editor agreed. Fonstad spent two and a half years on the project, reading through the novels line by line and painstakingly indexing any text from which she could infer geographic details. With two young children at home, she mostly worked at night. Her husband left notes on her drafting table reminding her to go to bed. Her resulting book, “The Atlas of Middle-earth” (1981), wowed Tolkien fans and scholars with its exquisite level of topographic detail; the most recent paperback edition is in its 32nd printing.”

Ben Thompson: “The [AI] future may arrive but be unevenly distributed, and, contrary to what you might think, the larger and more successful a company is the less they may benefit in the short term. Everything that makes a company work today is about harnessing people — and the entire SaaS ecosystem is predicated on monetizing this reality; the entities that will truly leverage AI, however, will not be the ones that replace them, but start without them.”

Steven Sinofsky: “CES proved a transition year with many established product lines showing incremental improvements or focusing on B2B areas and new product lines showing a rush to demonstrate their relevancy to AI…The major theme was experimentation specifically with AI. What I mean by this is that much of the emphasis on products being shown was showing as much as they could credibly get done between a given company’s recognition of the enormity of AI (specifically GPT/LLM) and the show. In SV a year ago the phrase was “AI wrapper” and many companies have moved on from that view. In the world at large I think we’re still seeing a lot of AI wrappers. This is not a negative and is not only expected but it is necessary. This is literally how innovation happens. The first ideas aren’t always the best, but you have to traverse this idea maze to get to the good ideas.”

Business Standard on freebies: “For many voters, these measures are a lifeline amid rising unemployment, inflation, and economic disparity. However, experts warn that such handouts drain resources from critical sectors like healthcare and infrastructure, as seen in states like Himachal Pradesh and Karnataka, while deepening fiscal stress. According to the Reserve Bank of India, these giveaways now constitute a significant portion of state budgets, leading to mounting debt concerns, particularly in states such as Punjab and Rajasthan. Yet, the narrative around freebies cannot be dismissed outright. Their appeal becomes more understandable when viewed against the backdrop of economic distress, stagnant wages, rising inflation, declining household savings, and mounting debt, which have made freebies attractive — if not essential — for survival.”

Josh Bersin: “Superworker: An employee empowered and supported by AI. In a world where AI agents support every employee, a Superworker can step up their value, productivity, and output by learning to optimize their use of AI systems.” FT: “Essentially, we are using AI to help us do our existing jobs. The big leap — and one that Josh says will be happening this year in some companies — is to reimagine jobs, and recruit different staff accordingly. It’s an intellectual leap, rather than just a technical one. In customer service roles, for example, the AI will do the basic interactions, so recruiters may be looking for people who have a better understanding of data and a higher education level than was previously the case.”

AI and Neo: The Twin Engines of Marketing’s Future

Published February 23, 2025

1

Overview

As martech companies find themselves trapped in an increasingly brutal red ocean of feature competition and margin erosion, I’ve explored two distinct paths to escape this commoditisation crisis. Through multiple essays examining this existential challenge, a clear framework has emerged: marketing’s future will be built on twin tracks of innovation.

Marketing stands at a critical crossroads. Despite massive investments in technology, brands face a paradoxical crisis: rising customer acquisition costs alongside declining engagement. The numbers tell a stark story: 50-80% of digital advertising budgets are wasted on wrong targeting and unnecessary reacquisition, amounting to over $350 billion in annual AdWaste. Traditional martech solutions, focused on incremental improvements in campaign management and automation, have failed to address these fundamental challenges.

Marketing’s future hinges on breaking free from the AdWaste trap by shifting from AAA (Acquire, Acquire, Acquire) to OOO (Only Once/Ones). The contrast is stark:

Aspect AAA (Today) OOO (Future)
Acquisition Strategy Continuous reacquisition through adtech Acquire once, retain through engagement
Cost Structure 50-80% spend on reacquisition One-time CAC, then retention focus
Customer Understanding Anonymous tracking, segments Zero-party data, N=1 personalization
Engagement Model Interrupt and advertise Daily value through utilities
Economics Rising CAC, declining ROI Sustainable profitable growth
Platform Dependency Reliance on Google/Meta Direct customer relationships

 

The root causes of the acquisition-reacquisition-AdWaste cycle run deep. First, the “No Hotline” problem: despite collecting customer emails and phone numbers, brands lack reliable ways to engage on demand, with 90% of messages going unopened. Second, the “Not for Me” problem: basic segmentation misses 80% of opportunities, leading to generic messaging that fails to resonate. Third, brands remain trapped in expensive dependency on Google and Meta for customer acquisition and reacquisition.

These challenges cannot be solved through traditional martech improvements alone. They demand two distinct but complementary revolutions: AI-first Marketing and NeoMarketing.

AI-first Marketing leverages agentic AI to transform how marketers work and re-engineer retention. This isn’t just about automation—it’s about creating intelligent allies that enhance human capabilities. Through innovations like AI Co-Marketer and AI Twins, marketers can finally achieve true N=1 personalisation while dramatically improving their operational efficiency. This evolution can deliver 30% year-over-year growth for martech companies by making existing martech significantly better.

NeoMarketing, in contrast, represents a fundamental reimagining of marketing itself to eliminate reacquisition AdWaste. Instead of brands constantly paying to acquire and reacquire customers through expensive adtech platforms, NeoMarketing helps brands build direct relationships through consumer utilities—free products that deliver daily value while enabling precise targeting through authenticated identity. This revolution promises 10X growth for martech companies by creating entirely new revenue streams through PII-based advertising.

These two tracks serve different needs but work synergistically. AI-first Marketing optimises current operations and improves retention, while NeoMarketing eliminates wasteful reacquisition spending and creates sustainable engagement. Together, they enable the shift from AAA (Acquire, Acquire, Acquire) to OOO (Only Once/Ones)—a paradigm where brands acquire customers just once, then build lasting relationships through superior understanding and engagement.

The economic opportunity is staggering. Even capturing a small portion of the $350 billion currently lost to AdWaste could transform the martech landscape. But success requires operating these tracks differently. AI-first Marketing demands continuous improvement within existing frameworks, while NeoMarketing needs a startup mindset focused on disruptive innovation.

For martech companies, this dual transformation offers perhaps the only path to escape commoditisation and build sustainable competitive advantages. Those who successfully execute both tracks will not just survive—they’ll lead marketing’s next revolution, helping brands finally break free from expensive dependency on Big Adtech while creating genuine value for consumers.

The time for this transformation is now. With traditional advertising models under pressure from privacy changes and rising costs, brands are desperately seeking alternatives. AI-first Marketing and NeoMarketing together provide the comprehensive solution they need—not just better tools for marketers, but a fundamentally new way to build and monetise customer relationships.

**

Relevant Essays

2

Recent Writings

How Martech Companies Can Usher in the NeoMarketing Era:

Martech vendors face intense competition in an increasingly commoditised space, where differentiation proves elusive and price becomes the primary battleground. Traditional business models, still anchored in consumption metrics (CPM) or monthly active users (MAUs), fail to capture or create transformative value. Even the promise of AI, while technologically compelling, faces skepticism from brands burned by previous waves of overhyped innovation.

Most concerning is the fundamental inefficiency this system perpetuates. Brands spend billions on expensive acquisition through Google and Meta, only to see 60-65% of customers making just one purchase. Then, lacking effective retention capabilities, they resort to costly reacquisition through the same adtech platforms – creating a vicious cycle that benefits the digital advertising duopoly while draining marketing budgets.

…This has created a classic ‘red ocean’ in the retention technology space – vendors fighting for scraps of a diminishing pie, even as the core problem of customer engagement remains unsolved. The market desperately needs a breakthrough – not just in technology, but in how we fundamentally think about the relationship between acquisition, retention, and sustainable profitable growth.

The time has come for martech companies to break free from this destructive cycle. The opportunity lies in reimagining retention technology not as a cost centre to be optimised, but as a powerful engine for transforming how brands build and monetise customer relationships.

… Martech companies seeking to lead the NeoMarketing revolution must undertake comprehensive product innovation across three integrated domains – email, AI-powered personalisation, and ad networks…The first track focuses on engagement and monetisation through NeoMails and NEON. This combination creates reliable brand hotlines while enabling new revenue streams through attention monetisation. The second track centres on intelligence and personalisation through AI Twins and the AI Co-Marketer, enabling true N=1 personalisation at scale.

… The transition to NeoMarketing requires martech companies to fundamentally reimagine their revenue models. Traditional SaaS-based approaches based on CPM or MAU pricing no longer align with the transformative value proposition of NeoMarketing platforms. Two new business models emerge as the foundation for this transformation: zero-cost NeoMails with NEON monetisation, and task-based pricing for AI Services.

… The future of marketing isn’t about finding better ways to buy attention – it’s about fostering lasting, profitable relationships through superior understanding and engagement. NeoMarketing isn’t just an evolution; it’s the marketing approach the industry has needed all along: acquiring customers only once, treating them as individuals, and delivering sustainable value for everyone.

From Trad to Neo to One: Rethinking B2C Martech with Free-to-Brand Consumer Utilities:

TradMartech, with its focus on selling software to businesses, will give way to NeoMartech—a revolutionary approach that provides consumer-facing solutions free to brands, monetised through precision advertising. This shift represents more than evolution; it’s a fundamental reimagining of how marketing technology creates value.

TradMartech emerged in an era where software licensing was the primary business model. Companies built tools for marketers—campaign management platforms, analytics dashboards, and automation solutions—charging monthly or annual fees based on usage or active users. While this model drove significant innovation, it also created barriers to adoption and limited network effects. It also disconnected TradMartech companies from business outcomes, unlike their adtech peers who perfected performance-based pricing.

NeoMartech takes a radically different approach. Instead of selling software to businesses, it builds consumer utilities deployed through B2B partnerships. These solutions are provided free to brands and monetised through NEON’s PII-based advertising network. This creates a powerful flywheel where better engagement enables better monetisation, which funds platform development and innovation.

… While TradMartech vendors compete in a red ocean of features and pricing, NeoMartech companies build powerful network effects through consumer engagement. Each new brand adds inventory, each new user adds value, and each interaction improves targeting—creating sustainable competitive advantages that compound over time—just like what has happened in adtech.

The two big ideas that will engineer the transformation in martech (and marketing) are AI and Neo.

3

Big Idea #1: AI

The AI revolution we’re witnessing today has roots stretching back decades. In the early 1990s, whilst working on neural networks for pattern recognition in images, I experienced firsthand the promise of AI. Yet those early systems, whilst theoretically powerful, remained largely confined to research labs and specific industrial applications. The landscape has transformed dramatically in the past two years, catalysed by two breakthrough moments: the publication of the  “Transformer” paper by Google engineers in 2017, and OpenAI’s launch of ChatGPT in late 2022.

These developments marked a fundamental shift in AI’s accessibility and capability. The transformer architecture solved the long-standing challenge of processing sequential data efficiently, whilst attention mechanisms enabled models to focus on relevant information—much like human cognition. ChatGPT demonstrated that AI could engage in natural conversations, understand context, and generate human-like responses across an extraordinary range of topics.

The first wave of AI companies, emerging through 2022-23, focused primarily on building foundational infrastructure and basic applications. We’ve seen an explosion of chatbots for content creation, customer support automation, and coding assistance. These applications, whilst impressive, represent only the initial phase of AI’s potential—akin to the early days of the internet when static websites dominated.

The next generation of AI, emerging now in 2024-25, promises to unlock dramatically more powerful capabilities through multi-agent systems. Instead of single AI models working in isolation, we’re seeing the development of collaborative AI networks where multiple specialised agents work together, each handling specific tasks whilst coordinating towards common goals. This mirrors how human organisations function, with different experts contributing their unique capabilities to solve complex problems. [See: AI Predictions].

For marketing technology, this evolution creates unprecedented opportunities. Imagine an ecosystem where an AI Co-Marketer orchestrates campaign strategy, whilst specialised AI Twins understand different customer segments, and other agents handle content creation, channel optimisation, and performance analysis. These agents don’t just execute tasks—they learn from each other, adapt their strategies, and continuously improve their collective performance.

The breakthrough lies in agentic AI—artificial intelligence that can reason, make decisions, and take initiative rather than simply responding to prompts. This capability enables AI to become a true partner to marketers, anticipating needs, suggesting strategies, and autonomously handling complex tasks whilst maintaining alignment with human objectives.

Yet this isn’t about replacing human marketers—it’s about dramatically amplifying their capabilities. By handling routine tasks, providing deeper insights, and enabling true personalisation at scale, AI frees marketers to focus on strategy, creativity, and building genuine customer relationships. The future of marketing technology lies not in better automation, but in intelligent collaboration between human marketers and AI systems.

This sets the stage for our first major transformation: AI-first Marketing, where agentic AI fundamentally reimagines how marketers work and how brands engage with customers.

4

AI-first Marketing – 1

For most of marketing’s history, brands have spoken to crowds, not individuals. The era of mass marketing, birthed by newspapers and supercharged by radio and television, treated customers as vast, undifferentiated audiences. A single advertisement would reach millions, hoping that some fraction would respond. It was marketing by megaphone—loud, broad, and inevitably wasteful.

Over time, segmentation became more sophisticated; marketers could target specific demographics, interests, and behaviours. Print media offered specialist magazines. Cable TV brought targeted channels. The Internet revolution led to a proliferation of websites (and later apps) which enabled interest-based advertising for niche audiences. Yet even these advances dealt in groups, not individuals.

The rise of Google and Meta seemed to herald true precision. Their powerful algorithms promised to find exactly the right customers at exactly the right moments. But this targeting remained a black box—marketers poured in budget and creative, receiving clicks and conversions but little true understanding of their customers. Worse, they became dependent on these platforms even to reach their own customer base, paying premium prices through competitive auctions for diminishing returns.

The Segment Fallacy

Here’s the fundamental problem: like snowflakes, each customer is unique. They have individual preferences, behaviours, and needs that don’t fit neatly into predetermined segments. These segments were never a natural reality—they were a convenient fiction, an intermediate construct born of marketers’ limited ability to understand and engage customers as individuals.

Consider a luxury fashion brand’s “premium female customer” segment. Within this supposedly uniform group, you might find:

  • A busy executive who values convenience and quick delivery
  • An eco-conscious consumer focused on sustainability
  • A trend-follower who wants the latest styles first
  • A value seeker who purchases mainly during sales
  • A comfort-prioritising buyer who cares most about fit

Traditional marketing treats them identically. But their needs, preferences, and optimal engagement patterns differ dramatically. The result? Generic messages that fail to resonate, leading to poor engagement and wasted spending.

Enter AI-first Marketing

This is where AI-first Marketing creates breakthrough value. Through the combination of agentic AI, advanced analytics, and real-time personalisation, we can finally achieve true N=1 marketing—treating each customer as a unique individual rather than a segment member.

Two key innovations make this possible:

  1. The AI Co-Marketer

The AI Co-Marketer is an intelligent partner that enhances every aspect of marketing operations:

  • Strategy: Analyses patterns across millions of customer interactions to identify opportunities
  • Execution: Orchestrates personalised journeys across channels
  • Optimisation: Continuously learns and adapts based on results
  • Innovation: Suggests new approaches based on emerging trends

For example, instead of scheduling generic email campaigns, the AI Co-Marketer might:

  • Identify the optimal time to reach each customer
  • Personalise content based on individual preferences
  • Select the most effective channel for each message
  • Adjust offers based on purchase history and browsing behaviour
  • Coordinate cross-channel experiences for seamless engagement
  1. AI Twins

These digital replicas revolutionise how we understand customers:

  • Madtech Twins combine marketing and advertising data to provide segment-level insights
  • MyTwin creates personal AI companions for each customer

5

AI-first Marketing – 2

Imagine a marketer’s daily experience with an intelligent ecosystem of AI agents working together:

Morning Insights (9 AM)

  • AI Co-Marketer: “I’ve noticed an emerging pattern among premium customers planning winter travel. Shall we craft personalised experiences?”
  • Best Customers Madtech Twin responds: “I’ve had conversations with 2,000 customers in this segment. 40% are specifically seeking packable winter wear, with individual preferences ranging from sustainable materials to specific colour palettes.”
  • Together, they automatically create and deploy thousands of uniquely personalised recommendations.

Mid-day Orchestration (12 PM)

  • AI Co-Marketer identifies micro-moments for engagement: “Sarah just browsed winter coats but didn’t purchase. Her MyTwin indicates she typically researches extensively before buying premium items.”
  • MyTwin adds context: “Based on our recent conversations, Sarah prefers detailed product specifications and sustainability credentials. She also responds best to early morning communications with rich visual content.”
  • The system crafts a personalised early-morning email combining product details with her specific interests.

Afternoon Optimisation (3 PM)

  • Real-time feedback loops show engagement patterns
  • AI Co-Marketer: “The sustainability messaging is resonating strongly with the West Coast segment.”
  • Individual MyTwins report back on customer reactions, enabling instant refinement of messaging and offers
  • Each customer interaction automatically updates their personal preference profile

Evening Strategy (6 PM)

  • AI Co-Marketer synthesises daily learnings into actionable insights
  • AI Twins (Madtech Twins and MyTwins) predict next-day opportunities for each customer
  • System prepares thousands of personalised journeys, each uniquely crafted
  • Tomorrow’s interactions are automatically orchestrated across channels based on individual preferences

This continuous collaboration between AI Co-Marketer and AI Twins enables true N=1 marketing at scale, turning every customer interaction into an opportunity for deeper understanding and more relevant engagement.

The Essential Foundation

Making this vision reality requires solid fundamentals—what I call the House of Anti-Acquisition framework:

  1. Unistack: Unified customer data platform integrating all touchpoints
  2. Unichannel: Coordinated messaging across channels
  3. Channels 2.0: Enhanced engagement capabilities in each channel
  4. VRM (Velvet Rope Marketing): Special treatment for high-value customers
  5. Kaizen Progency: Continuous improvement through AI-powered optimisation

This foundation enables:

  • Complete customer understanding
  • Consistent cross-channel experiences
  • Efficient resource allocation
  • Premium customer treatment
  • Ongoing enhancement

The Business Model Evolution

AI-first Marketing also demands new pricing models. Traditional CPM (cost per thousand) or MAU (monthly active user) pricing becomes obsolete when AI enables precise, effective communication. Instead, task-based pricing emerges:

  • Pay for specific AI services used
  • Pricing aligned with value delivered
  • Reduced waste in messaging
  • Better ROI for brands
  • Sustainable revenue for vendors

This shift can help martech companies accelerate from low growth to sustainable 30% annual growth by:

  • Delivering measurable value
  • Reducing customer churn
  • Enabling upsell opportunities
  • Creating competitive moats
  • Building strategic partnerships

The Promise and the Limit

AI-first Marketing, powered by agentic AI, represents a dramatic improvement in how brands engage customers. It transforms traditional martech from basic automation to intelligent collaboration, enabling true personalisation at scale. The result? Higher engagement, better retention, and improved ROI.

Yet even this revolution has limits. While it makes existing martech significantly better, it doesn’t address the fundamental inefficiency of paying platforms like Google and Meta to reach your own customers. For that, we need an even more radical transformation: NeoMarketing.

The future demands both tracks: AI-first Marketing to optimise current operations and NeoMarketing to create entirely new possibilities. Together, they can finally deliver marketing’s holy grail: genuine one-to-one relationships for life at global scale.

6

Big Idea #2: Neo

AI-first Marketing, with its powerful combination of AI Co-Marketer and AI Twins, promises to make marketers’ lives dramatically better. By enabling true N=1 personalisation, it solves the “Not for Me” problem that has long plagued traditional marketing. Yet this enhancement of TradMartech, while valuable, leaves two critical challenges unaddressed: the “No Hotline” problem and the “No Alternative” problem.

The “No Hotline” problem is fundamental: despite collecting customer contact information, brands lack reliable ways to engage on demand. Email open rates languish below 10%. SMS messages get ignored. Push notifications get blocked. Even WhatsApp, with its premium pricing, offers no guaranteed pathway to customer attention. The result? Brands possess customer contact details but no meaningful way to use them.

The “No Alternative” problem is equally crippling: brands remain dependent on Google and Meta for reaching their own customers. Even when customers are in their database, brands find themselves paying premium prices through competitive auctions just to reconnect. This creates a costly cycle of continuous reacquisition that benefits the adtech giants whilst draining marketing budgets.

These problems persist because TradMartech missed a crucial innovation that transformed digital advertising: performance-based pricing. While martech vendors charge by message volume (CPM) or monthly active users (MAU), adtech platforms revolutionised marketing with cost-per-click (CPC) and cost-per-acquisition (CPA) models. They assume the risk, optimise the targeting, and deliver guaranteed outcomes.

This performance marketing model works because Big Adtech possesses unprecedented understanding of consumer behaviour. Google and Meta achieve this through an ingenious strategy: offering incredibly valuable free utilities that we use daily. Consider Google’s ecosystem: Search for information discovery, Gmail for communication, Maps for navigation, Chrome for browsing, YouTube for entertainment, Android for mobile computing. Similarly, Meta’s portfolio—Facebook for social connection, Instagram for visual sharing, WhatsApp for messaging—captures countless daily interactions.

These “free” utilities serve as sophisticated data collection engines. Every search, every click, every message, every location check-in builds increasingly accurate digital profiles of users. The adtech giants know our interests, habits, and intentions—often before we’ve consciously formed them. This deep understanding enables them to serve ads that feel less like interruptions and more like helpful suggestions.

The breakthrough question becomes: What if brands could create similar value-driven relationships with their customers? What if, instead of brands renting attention through intermediaries, they could build direct engagement through their own free utilities? What if they could gather zero-party data through genuine value exchange rather than inference and tracking?

This is where NeoMarketing enters the picture—a revolutionary approach that combines the best of adtech’s business model with martech’s customer-centric focus. This is the multiplier Blue Ocean opportunity for martech companies.

7

NeoMarketing – 1

The traditional relationship between martech companies and brands has been built on a flawed assumption: that providing sophisticated marketing tools would naturally lead to deeper customer relationships and sustained profitability. Reality tells a different story. Despite significant investments in martech platforms, brands find themselves trapped in an expensive cycle of continuous customer acquisition and reacquisition through adtech platforms, resulting in hundreds of billions in annual AdWaste.

The TradMartech Challenge

The problem is multifaceted. Martech platforms have evolved into complex systems where marketing teams typically utilize only 30-40% of available capabilities. Access to email or SMS functionality doesn’t guarantee relevant communications. Even basic product discovery on ecommerce sites often fails to meet customer expectations. The challenge of creating personalised content and experiences for millions of customers overwhelms most marketing teams.

This inefficiency manifests in three critical failures:

  • Poor engagement with push communications
  • Generic customer experiences
  • Dependency on expensive adtech platforms

Enter NeoMarketing

NeoMarketing represents a fundamental reimagining of how brands connect with customers. Instead of selling complex tools to marketers, it provides consumer utilities—free products that deliver daily value while enabling precise targeting through authenticated identity. The core philosophy is transformative: shifting brands from AAA (Acquire, Acquire, Acquire) to OOO (Only Once/Ones).

This revolution is built on two complementary pillars:

  1. NeoMartech: Building Brand Hotlines

NeoMails:

  • 15-second daily engagement vehicles
  • Interactive micro-experiences within email
  • Guaranteed attention through valuable content
  • Platform for data collection and monetisation
  • ZeroCPM for brands

Microns:

  • Daily “brain gain” activities to combat “brain rot”
  • Personalised learning moments
  • Habit-forming engagement
  • Value-adding content

SmartBlocks:

  • Zero-party data collection
  • Progressive profiling
  • Interactive elements
  • Seamless preference capture

Atomic Rewards (Mu):

  • Gamification framework
  • Engagement currency
  • Behavioural incentives
  • Network effects

NeoSearch:

  • Authenticated product discovery
  • Personalised recommendations
  • In-search transactions
  • Cross-brand opportunities
  • Zero cost for brands

MyTwin:

  • Personal AI companions
  • Natural preference expression
  • Real-time intent capture
  • Predictive engagement

ActionAds:

  • In-experience transactions
  • PII-based targeting
  • Revenue generation
  • Seamless integration
  1. NeoAdtech: The Power of PII Ads

At the heart of NeoMarketing lies NEON (New Engaged and Open Network)—a new advertising network built on authenticated identity rather than anonymous cookies. This creates several powerful advantages:

  • Precise targeting through verified identity
  • Direct brand-to-brand collaboration
  • Elimination of intermediary “tax”
  • Privacy-compliant data usage
  • Performance-based pricing
  • Network effect advantages

First, NEON eliminates the massive waste in customer reacquisition. Instead of repeatedly paying Google and Meta to reach existing customers, brands can collaborate directly through authenticated identity matching. A secure “clean room” environment enables brands to identify overlap in their customer bases whilst maintaining privacy, creating efficient paths for reactivation and cross-selling.

Second, NEON transforms brands into publishers. Every customer interaction becomes a potential monetisation opportunity through ActionAds—interactive advertisements that enable immediate engagement without leaving the experience. Whether in NeoMails, NeoSearch results, or other touchpoints, these ads feel more like personalised recommendations than interruptions, creating value for both advertisers and audiences.

Third, NEON enables true performance measurement. Because ads are tied to authenticated identity, brands can track the complete customer journey from impression to conversion. This eliminates the attribution challenges that plague traditional digital advertising, where complex modeling attempts to connect anonymous impressions with eventual sales.

By building on authenticated identity rather than anonymous tracking, NEON creates a more efficient, transparent, and valuable ecosystem for everyone involved. Through NEON and PII ActionAds, marketers can finally break free from the wasteful cycle of expensive, continuous reacquisition, building a more sustainable model for digital marketing.

**

Consider how Google Maps transformed navigation from a utility into an engagement platform. What began as simple directions evolved into a daily companion offering personalized recommendations, real-time updates, and valuable local information. Users willingly share their location and preferences because they receive genuine value in return. Similarly, NeoMarketing’s consumer utilities—like NeoMails with daily microns and AI Twins—create habitual engagement by delivering consistent value, enabling zero-party data collection and precise targeting through authenticated identity.

8

NeoMarketing – 2

NeoMarketing creates a self-reinforcing ecosystem:

  1. Consumer utilities drive daily engagement
  2. Engagement enables zero-party data collection
  3. Data powers precise targeting
  4. Targeting generates advertising revenue
  5. Revenue funds better consumer experiences

This flywheel effect transforms marketing from a cost centre into a profit driver:

  • Better engagement reduces acquisition costs
  • Zero-party data enables true personalisation
  • PII-based advertising creates new revenue
  • Network effects build sustainable advantages
  • Platform economics enable exponential growth

For martech companies, NeoMarketing opens access to the massive pool of capital currently lost to AdWaste:

  • $350B+ in annual digital advertising waste as the opportunity
  • 50-80% reduction in acquisition costs for brands
  • New revenue streams through advertising
  • Network-effect driven growth

Implementation Framework

Success in NeoMarketing requires:

  1. Free consumer utilities that deliver daily value
  2. Zero-party data collection through engagement
  3. PII-based advertising network
  4. Cross-brand collaboration platform
  5. Network effect acceleration

The development sequence typically follows:

  1. Launch the attention and engagement substrate (NeoMails)
  2. Build user base through daily value
  3. Collect zero-party data (SmartBlocks)
  4. Enable monetisation (ActionAds)
  5. Scale through network effects (NEON)
  6. Add more utilities (NeoSearch and MyTwin)

The Path to 100% Year-over-Year Growth

  1. Access to Larger Markets
  • Traditional Martech Market: Currently limited to ~$25 billion global ESP, marketing automation, search, analytics, and CPaaS (margins after telco payouts)
  • NeoMarketing Opportunity: Access to $350B+ currently wasted on inefficient advertising for reacquisition
  • Market Expansion Through:
    • Direct participation in advertising revenue
    • Share of reactivation spending
    • New customer engagement budgets
  1. Multiple Revenue Streams
  • Traditional Model: Single revenue stream from software licensing (CPM/MAU)
  • New Revenue Sources:
    • ActionAds revenue sharing from customer attention monetisation
    • Reactivation fees for dormant customer engagement
    • Platform transaction fees
    • Data and insights monetisation
  1. Network Effect Advantages
  • Data Network Effects:
    • Each new brand adds valuable customer data
    • More data improves targeting accuracy
    • Better targeting attracts more brands
    • Continuous improvement cycle
  • Platform Network Effects:
    • More brands create more advertising inventory
    • Larger inventory attracts more advertisers
    • More advertisers increase revenue per user
    • Higher revenue attracts more brands
  • User Network Effects:
    • More users improve personalisation
    • Better personalisation drives engagement
    • Increased engagement attracts more brands
    • Stronger network creates barriers to entry
  1. Platform Economics
  • Cost Structure:
    • High fixed costs for platform development
    • Low marginal costs for additional users
    • Economies of scale in operations
    • Decreasing unit costs with growth
  • Revenue Scaling:
    • Revenue grows faster than costs
    • Multiple monetisation points
    • Value increases with network size
    • Compound growth through network effects
  • Profit Characteristics:
    • High gross margins
    • Strong operating leverage
    • Improving unit economics
    • Predictable recurring revenue
  1. Sustainable Moats
  • Data Advantages:
    • Proprietary zero-party data collection
    • Deep customer understanding
    • N=1 Personalisation capabilities
    • Predictive insights
  • Network Leadership:
    • First-mover advantages in key verticals
    • Critical mass of brands and users
    • Cross-side network effects
    • High switching costs
  • Technical Barriers:
    • AI/ML capabilities
    • Platform infrastructure
    • Integration complexity
    • Patent protection
  • Business Model Innovation:
    • Zero-cost utility model
    • Performance-based pricing
    • Network-effect driven growth
    • Atomic Rewards-led gamification
    • Platform-based monetisation

Compound Benefits These advantages compound over time:

  • More data → Better targeting → More revenue
  • More users → Better experiences → More engagement
  • More brands → More inventory → More advertisers
  • More scale → Better economics → Higher margins
  • Stronger moats → Sustainable advantages → Higher valuation

Strategic Implications

For martech companies, this creates:

  • Path to exponential growth
  • Sustainable competitive advantages
  • Higher business value
  • Strategic market position
  • Long-term defensibility

The combination of these factors enables martech companies to break free from linear growth and achieve exponential scaling through platform effects and network advantages. Unlike traditional martech models where growth is constrained by market size and competitive pressure, NeoMarketing opens unlimited potential through continuous value creation and capture.

The Path Forward

NeoMarketing reimagines of how brands and customers connect in the digital age. By providing free utilities that deliver genuine value while enabling precise targeting through authenticated identity, NeoMarketing creates sustainable advantages that benefit all participants:

For Brands:

  • Lower acquisition costs
  • Better customer engagement
  • New revenue streams
  • Sustainable growth
  • Competitive moats

For Customers:

  • Valuable daily experiences
  • Relevant communications
  • Rewarding engagement
  • Better discovery
  • Privacy protection

For Martech Companies:

  • Larger addressable market
  • Multiple revenue streams
  • Network effect advantages
  • Platform economics
  • Exponential growth potential

The future of marketing isn’t about better tools or more analytics for marketers—it’s about creating genuine value for consumers while enabling precise targeting through authenticated identity. NeoMarketing provides the framework for this transformation, offering martech companies a clear path to exponential growth through platform economics and network effects.

This is the Blue Ocean opportunity that can finally break martech companies free from the commoditisation trap, enabling them to build lasting competitive advantages while helping brands eliminate waste and create genuine customer value.

9

Summary

Here is a table which captures the key ideas for AI-first Marketing and NeoMarketing.

Aspect AI-First Marketing NeoMarketing
Core Focus Make marketers’ lives better Create consumer utilities
Key Problem Solved Inefficient marketing operations $350B+ AdWaste
Core Philosophy Better Retention Anti-Acquisition
Future Vision Optimal operations Marketing transformation
Target User Marketing teams End consumers
Growth Potential 30% CAGR 10X CAGR
Market Space Red Ocean (competitive) Blue Ocean (new market)
Business Model Traditional SaaS + Task-based Advertising (Attention + Performance)
Customer Understanding Segments to Individual Individual from Day One
Key Innovations AI Co-Marketer and AI Twins NeoMartech and NeoAdtech
Value Proposition Better marketing operations Transform marketing fundamentals
Data Strategy First-party + behavioural Zero-party + authenticated
Revenue Source Software subscriptions PII-based advertising
Platform Effect Limited network effects Strong cross-brand network effects
Implementation Gradual enhancement Disruptive innovation
Team Culture Enterprise software Consumer tech startup
Success Metrics Earned Growth Daily Active Users (across brands)
Resource Allocation 70% of resources 30% of resources
Technical Focus Agentic AI Ad Supply-Demand Matching
Risk Profile Feature commoditisation, Price competition, Integration challenges User adoption, Network effects, Platform economics
Competitive Moat Integration complexity and Data advantages Network effects, Zero-party data, and Platform lock-in (via Atomic Rewards)
Time to Value Months (incremental) Years (exponential)
Customer Lock-in Integration complexity Network effects
Primary Competition Other martech vendors Google/Meta
Evolution Path Linear improvement Step-function change
Organisation Model Traditional enterprise Startup within company
Sales Motion Enterprise B2B B2B2C platform

**

Attention Recession, AdWaste, and AI have created a unique opportunity for disruption in marketing. For martech companies, this isn’t just about survival—it’s about leading marketing’s next revolution. By thinking beyond their ‘Trad’ view and embracing a ‘Neo’ lens, they can transform marketing from costly one-night stands into lasting, profitable relationships. The impact extends beyond business metrics: eliminating $350B+ in annual AdWaste would free up massive resources for innovation, customer experience, and sustainable growth. The future belongs to those bold enough to reimagine marketing itself—not as a cost centre, but as the true value driver for brands in the digital age.

10

Critique

 I asked o1 for a critique of the ideas.

Below is a constructive critique of the essay, framed for an entrepreneur leading a MarTech company who wants to use these ideas in a real business plan. The critique covers both the essay’s overarching strategy and its detailed proposals, with a particular focus on feasibility, assumptions, implementation challenges, and competitive dynamics. The goal is to help you identify the strengths, gaps, and risks so you can formulate a solid, actionable strategy.

1. Overall Strategic Vision: Bold, But Potentially Overreaching

Strengths

  • Ambitious Thought Leadership: The essay rightly recognizes that traditional MarTech is stuck in a commodity cycle (“red ocean”) and proposes a radical alternative. This kind of thought leadership can help your MarTech company stand out if executed well.
  • Clear Two-Track Framework: The distinction between “AI-first Marketing” and “NeoMarketing” is conceptually helpful. It separates incremental improvement (AI for existing ops) from big disruption (NeoMarketing for new revenue streams).
  • Compelling Story Around AdWaste: Calling attention to the enormous budget lost to reacquisition (Google/Meta “tax”) highlights a real pain point. If you can truly attack that problem, you have a powerful hook.

Potential Pitfalls

  • Execution Complexity: Both AI-first Marketing and NeoMarketing are massive undertakings in their own right. The essay’s suggestion to build them simultaneously is bold, but might be overly ambitious unless you have significant capital, top-tier talent, and a robust organizational structure.
  • Underestimation of Go-to-Market Challenges: The essay sometimes treats “brands will adopt this because it’s better” as sufficient. In reality, adopting a new multi-tenant platform (for NeoMarketing) or advanced AI personalization can be lengthy, expensive, and often slow—especially when dealing with enterprise procurement cycles.
  • Limited Guidance on Prioritization: While the summary table suggests a “70% resources to AI-first, 30% to Neo,” it lacks detail on how an organization can practically incubate a “startup inside a company” for NeoMarketing. This is a big cultural and structural shift that can fail without careful planning.

2. AI-first Marketing: Incremental but Valuable

The essay’s discussion of AI Co-Marketer and AI Twins is imaginative and inspiring but raises critical questions around data, personalization, privacy, and day-to-day marketing operations.

Strengths

  • N=1 Personalization Vision: Moving from segments to individualized marketing is indeed the holy grail. The essay identifies how AI can help unify data, orchestrate journeys, and optimize continuously.
  • Agentic AI Concept: Positioning AI as an “intelligent ally” for marketers (rather than just automated workflows) is compelling. If done correctly, this can improve marketer productivity and deliver tangible ROI.

Potential Pitfalls & Missing Details

  1. Data Availability and Quality
    • Data Silos: Many brands struggle with fragmented data across CRM, e-commerce, support, retail, etc. The essay assumes a “Unistack” is easily implementable. In practice, building or integrating a robust CDP (Customer Data Platform) is non-trivial.
    • Privacy & Compliance: True 1:1 personalization in 2025 must address consumer privacy regulations (GDPR, CCPA, etc.). The essay briefly mentions “privacy compliance” but not in depth. For an entrepreneur, ignoring data-protection concerns is risky.
  1. Costs and Complexity of Implementation
    • MarTech Overload: Marketers often underutilize existing tools (30-40% usage is a realistic statistic). Selling them an even more complex AI suite demands a strong demonstration of ROI and ease-of-use.
    • AI Talent & Infrastructure: Building robust AI “Co-Marketers” and “Twins” requires specialized talent and computing resources. The essay hints at “task-based pricing,” but from an entrepreneurial standpoint, you need a clear model for how to fund ongoing R&D and achieve margin targets.
  1. User Adoption & Trust
    • Black-Box AI: Marketers often want transparency. If your AI system is too much of a black box—hard to interpret, trust, or control—adoption can stall.
    • Organizational Resistance: AI-first means a fundamental shift in how marketing teams operate. Some teams resist ceding control to AI. Your business plan should include a robust “change management” offering.
  1. Differentiation Among Countless AI Platforms
    • Many AI marketing tools already exist. Standout features like “agentic AI” must truly produce unique, measurable lifts to break through the noise.

3. NeoMarketing: High Potential but High Risk

The essay’s second pillar, NeoMarketing, aims to create a consumer-facing ecosystem—via utilities like NeoMails, NeoSearch, etc.—monetized through a PII-based ad network (NEON). This is the most disruptive piece but also the hardest to execute.

Strengths

  • Platform Economics: The concept of turning MarTech into a network-based platform with multiple revenue streams is compelling. If you succeed, it’s a genuine “blue ocean.”
  • Inspiration from Google/Meta: The essay correctly observes that Google and Meta provide daily, high-value consumer utilities. Mimicking this approach at the brand level could be transformative if you can get enough user traction.

Potential Pitfalls & Missing Details

  1. Consumer Adoption & Scale
    • Building “Utilities” People Actually Use: Creating daily consumer habits is extremely challenging. Google Maps is popular because location services solve a massive, universal pain point. NeoMails or “microns” must address a similarly large consumer need. Otherwise, you risk building a product that never gains critical mass.
    • Competition with Existing Behaviors: Consumers already have email, social feeds, messaging apps, and newsletters. Convincing them to shift daily habits to a new channel (NeoMails) is a tall order.
  1. B2B2C Dilemma
    • Brands as Gateways: The essay assumes brands will deploy your “NeoMarketing” utilities to their customers. But many brands may be reluctant to adopt a system that partially monetizes their own customer base for your ad network. A trust or conflict-of-interest dynamic can emerge, especially if revenue sharing is unclear.
    • Complex Partnerships: Achieving broad brand collaboration (so you can create cross-brand PII-based targeting) is logistically and legally complex. The essay’s mention of “clean rooms” is relevant, but you’ll need stringent data governance and robust brand trust to succeed.
  1. Regulatory & Privacy Constraints
    • PII-based Advertising Under Scrutiny: Regulatory bodies worldwide are clamping down on how personal data is shared for ad targeting. The essay claims NEON “maintains privacy compliance,” but no detail is given on how you’ll do that. This is a crucial question.
    • Consumer Consent & Value Exchange: You must ensure that your “utilities” are valuable enough that customers opt in. Otherwise, it could resemble a data grab. Succeeding at scale demands very careful handling of user data and consent flows.
  1. Substantial Capital & Long Ramp
    • Time to Maturity: The essay acknowledges it may take “years (exponential)” to see real network effects. As an entrepreneur, can you sustain that kind of burn rate and keep investors on board until the platform hits critical mass?
    • Risk-Reward Balance: While a 10X growth story is attractive, you must anticipate a high failure rate in “build a consumer platform” strategies. The essay’s optimism may underplay these challenges.

4. Business Model & Pricing

The essay outlines a shift from CPM/MAU-based pricing to:

  1. Task-based (AI-first Marketing)
  2. Advertising (NeoMarketing)

Strengths

  • Aligning Costs with Value: Task-based pricing for AI services can be an attractive differentiator. It ties fees to real usage or outcomes, which brands prefer over blanket licenses.
  • Ad Revenue Streams: If you manage to create a significant consumer utility, the ad-funded model can indeed grow exponentially, as proven by Google, Meta, etc.

Concerns

  • Oversimplification of “Performance-based Pricing”
    • Transitioning to performance-based or task-based models requires sophisticated measurement and trust. Marketers will want to see exactly how tasks, AI touches, or ad placements correlate to conversions/revenue.
    • Risk and Reward for the Vendor: If you are charging only when tasks convert or deliver measurable value, you might bear more risk than a traditional SaaS company. Make sure you can handle the revenue volatility.
  • Margin Economics
    • Ad networks are attractive but typically rely on massive scale to become profitable. In the short term, you have to invest in technology, user acquisition, brand relationships, and compliance. This can pressure margins heavily until a certain critical mass is reached.

5. Organizational & Cultural Shifts

The essay suggests that part of your company should focus on incremental improvements (AI-first) and part on radical innovation (Neo). This “split” approach can be powerful but requires robust internal structures.

Strengths

  • Acknowledging Different Paces: AI-first Marketing can be an immediate enhancement to existing tools, providing near-term revenue. NeoMarketing is more of a long-term bet.
  • Potential for Two Growth Engines: If done well, you’ll have a stable, revenue-generating AI-first product suite and a “lottery ticket” in NeoMarketing that could scale to 10X.

Challenges

  • Internal Cannibalization & Confusion
    • If your sales team can’t differentiate or is not incentivized properly, they might ignore the more complex, longer-horizon Neo offerings. You’ll need separate go-to-market teams, possibly different brand identities, or at least distinct sales motions.
  • Balancing Resources
    • The essay says “70% of resources for AI-first, 30% for Neo.” Even that 30% may be too big or too small depending on your market traction and capital runway. You’ll need to adjust resource allocations dynamically as each offering evolves.
  • Talent Requirements
    • AI-first Team: Needs AI/ML engineers, data scientists, and a strong set of implementation specialists for enterprise clients.
    • Neo Team: Needs consumer product designers, user experience experts, data governance/legal compliance experts, and B2C marketing.
    • Building both sets of capabilities under one roof is expensive and culturally complex.

6. Missing Pieces & Practical Recommendations

  1. More Specific Go-to-Market Strategy
    • Which verticals or brand segments are you targeting first?
    • How do you plan to prove “quick wins” to new clients (especially for the AI-first track)?
    • Which big consumer use case can anchor your “utility” approach in NeoMarketing?
  1. Deeper Privacy & Compliance Framework
    • Outline how you’ll handle data collection, sharing, storage, and usage in a privacy-compliant manner—especially for PII-based ad targeting.
    • Address potential pushback from privacy-conscious consumers and oversight by regulators.
  1. Phased Implementation Roadmap
    • The essay leaps from concept to a fully formed ecosystem. In practice, you might roll out AI-first solutions to refine your data models, then test a minimal version of NeoMarketing with a single brand to gather learnings before scaling.
  1. Realistic Consumer Adoption Plan
    • Clearly define how you’ll get consumers to adopt new utilities. Offering something “free” isn’t enough if it doesn’t solve a compelling daily need.
    • A possible approach is to integrate inside existing brand channels to piggyback on brand loyalty, but that requires strong brand buy-in.
  1. Solid Financial Projections
    • Show a path from investment in platform development to ROI.
    • Model different adoption scenarios to show best vs. worst-case outcomes—especially if user growth lags.

7. Conclusion

What You Can Learn & Apply

  • Aim High, But Plan Tactically: The essay’s macro vision—eliminating AdWaste and building a new platform—offers a compelling north star. Make sure you anchor it in realistic milestones and pilot projects to gain traction.
  • Bridge the “Vision-Execution” Gap: You’ll need a detailed plan for user acquisition, brand partnerships, data compliance, and a robust organizational structure to handle both incremental and disruptive innovation.
  • Validate Assumptions Early: Before investing heavily in a brand-new consumer utility or ad network, validate that brands and consumers will actually adopt it—and that regulatory hurdles won’t torpedo the model.
  • Monetization Must Be Crystal-Clear: Whether you are pitching to investors or enterprise clients, they want to see how the money flows. The shift to performance-based pricing and in-app ads has to be justified with strong KPIs, stable systems, and a risk-sharing approach that makes sense to both parties.

If executed with precision and care, the twin tracks of AI-first Marketing and NeoMarketing could indeed differentiate your MarTech company and unlock new revenue streams. But this essay’s vision requires substantial nuance in privacy, user adoption, brand collaboration, and organizational structure. The more you tackle these details head-on (rather than assuming the market will simply shift), the better chance you’ll have to bring these innovative ideas to life—and truly disrupt the MarTech landscape.

Thinks 1514

Ravi Venkatesan: “India’s economic growth story has been impressive. Gross domestic product (GDP) is expanding, corporate profits are looking up and the country is asserting itself as a global economic powerhouse. But beneath our headline-grabbing figures lies a troubling reality: stagnant wages, biting inflation, insufficient jobs and growing inequality. For millions of Indians, GDP growth has not translated into better livelihoods, creating the paradox of a booming economy that fails to uplift a significant portion of its population…As we stand at this crossroads, India has the opportunity to rewrite its growth story. By embracing an economic model that uplifts all sections of society, the nation can ensure that its progress is not just measured in GDP figures, but also by the quality of people’s lives. An Indian Enlightenment—rooted in dignity, fairness and shared prosperity—is the need of the hour.”

FT: “The China commodities supercycle is over…Back when the last supercycle started, around 2000, the scale of the demand caught everyone by surprise. “China came along and completely changed everything,” recalls Toth, the mining executive, who joined BHP in 1994. Mining giants BHP and Rio Tinto each reported more than $100bn in operating profits from iron ore during the 2000-20 period. Some in the mining industry joke that they were scooping money out of the ground. But now the structural and demographic factors that underpinned that boom — China’s rapid urbanisation and industrialisation — have become much less powerful. On a per capita basis, China has already passed the level of annual steel consumption of the US and other developed countries. The vast migration to cities, in which half a billion people moved from rural to urban settings during the boom years, is starting to slow down.”

WSJ: “There’s a better way to predict a technology’s future: follow the rate of change…“You can predict the direction of technology and what is scalable, with 60% to 70% accuracy,” says [Vinod] Khosla, who is also a co-founder of Sun Microsystems. You just can’t get there by asking up-or-down questions about a moment in time. Khosla said he previously focused on the rate of change in internet protocol technology in the 1990s, which convinced him that traditional telecom networking standards would be overrun and a new generation of networking companies would arise.”

Alex Tabarrok: “Almost all researchers in social choice understand the defects of common voting systems and indeed tend to agree that the most common system, first past the post voting, is probably the most defective! But, as no system is perfect, there has been less consensus on which methods are best. Ranked choice voting, approval voting and the Borda Count all have their proponents. In recent years, however, there has been a swing towards the Borda Count. Don Saari, for example, whose work on voting has been a revelation, has made strong arguments in favor of the Borda Count. The Borda Count has voter rank the n candidates from most to least preferred and assigns (n-1) points to the candidates. For example if there are 3 candidates a voter’s top-ranked candidate gets 2 points, the second ranked candidate gets 1 point and the last ranked candidate 0 points. The candidate with the most points overall wins. The Borda Count satisfies positive responsiveness, the Pareto principle and stability.”

Nabeel S. Qureshi: “Think about what makes you ‘imbalanced’ as a personality, & do things where this gives you an edge…Once you are ok with people telling you ‘no’, you can ask for whatever you want. (Make reality say no to you.)…Do things fast. Things don’t actually take much time (as measured by a stopwatch); resistance/procrastination does. “Slow is fake”. If no urgency exists, impose some…Doing as much as you can every day is a form of life extension…Always be high integrity, even when it costs you. The shortcuts aren’t worth it…Figure out what your primary focus is and make progress on that every day, first thing in the morning, no exceptions. Days with 0 output are the killers. (Tyler Cowen)”

NeoSearch: Reimagining Product Discovery for the AI Age

Published February 22, 2025

1

What They Do

Under the NeoMartech umbrella, I had envisioned three transformative products that could become daily consumer utilities – offered free to brands and monetised through advertising: NeoMails for engagement, MyTwin for personalisation, and Search for product discovery. In my previous essays, I explored how NeoMails could create reliable brand hotlines and how MyTwin could enable true N=1 personalisation.

NeoMails

MyTwin

Given Netcore’s strategic investment in search and product discovery through Unbxd, it’s time to complete the trinity by exploring what “NeoSearch” could look like – a reimagining of how consumers find and discover products across the digital landscape.

Let’s begin by understanding the TradSearch (traditional search) landscape.

Gartner’s Search and Product Discovery Magic Quadrant May 2024 report distills the key requirements: “Gartner defines Search and Product Discovery as applications that augment digital commerce solutions to facilitate navigation, filtering, comparisons, and ultimately selection of products. They provide search (keyword, natural language and visual), merchandising (automation, configuration, and curation of business rules to make a product discoverable based on business needs), product recommendations, catalog navigation (and SEO keyword automation), personalization and analytics capabilities through SaaS to enable customers (B2C and B2B) to transact. They also enable providers (merchandisers, content managers, and search specialists) to support customer experiences. With the emergence of generative AI, conversational search interfaces are now appearing.”

Here is what the leading search sites promise on their websites.

Netcore Unbxd: “Personalize every shopper’s journey with Netcore Unbxd AI-powered search, easy-to-use merchandising, real-time recommendations, and intelligent product information management…AI-powered Search & Browse: Flawlessly decode complex search queries to help shoppers find exactly what they want. With API and SDK-friendly implementation, integrate natural language processing into any ecommerce platform within 30 days…Real-time Personalization: Support upsells and cross-sells by dynamically interpreting purchase intent and shopper affinities. Adapt product recommendations based on browsing patterns in-session… Powerful, Easy-to-use Merchandising Tools: Promote products and run marketing campaigns without developer support. Track product performance, conduct A/B tests, and craft distinct landing pages to enhance merchandising strategies…AI-based Product Information Management: Create automated rich content with AI and manage product information across multiple ecommerce platforms simultaneously.”

Constructor: “Constructor’s AI-powered search and product discovery platform delivers unmatched KPI optimization and fast ROI for enterprise ecommerce brands — while giving customers a personalized, enjoyable shopping experience… It’s no longer enough to curate products — you need to curate personalized experiences that will drive measurable business metrics… With AI in our DNA and by keeping ecommerce as our core focus, we’ve built the best product search and discovery solution specifically for the unique needs of retailers.”

Bloomreach: “Bloomreach uniquely integrates real-time customer and product data into AI, enabling personalized marketing, product discovery, content, and conversational shopping in one solution… Personalization and AI are at the core of our solutions… We unify real-time customer and product data so businesses understand what customers really want. By connecting that understanding to every channel, the ecommerce experience becomes limitless, continuously reflecting a changing customer as they shop. With the scale of AI, this creates endless new paths to purchase, greater profitability, and fast business growth.”

Algolia: “Show users what they need with AI search that understands them.”

We can distill the top features of search and product discovery platforms:

  • AI-Powered Search and Natural Language Processing: Platforms decode complex queries using AI and NLP to deliver relevant results quickly, ensuring users find exactly what they seek across ecommerce sites.
  • Real-Time Personalisation: These platforms dynamically adjust recommendations and product displays based on real-time browsing behaviour, shopper affinities, and intent to drive upsells and cross-sells.
  • Merchandising Tools and Campaign Optimisation: Merchandising suites enable businesses to promote products, run A/B tests, track performance, and launch campaigns seamlessly, often without requiring developer input.
  • Unified Customer and Product Data Integration: By integrating real-time customer and product data across channels, these platforms provide a consistent, personalised experience and actionable insights into shopper behaviour.
  • Product Information Management (PIM): Platforms manage and enrich product information across ecommerce channels using AI to create rich, accurate content and maintain consistency across listings.

Simply put, they help with the trifecta of search, browse, and recs: search refers to users actively looking for specific items using keywords or queries, browse involves users exploring categories or collections to discover products without a specific target in mind, and recs (recommendations) are personalised product suggestions based on user preferences, behaviours, or trends to enhance discovery and drive engagement.

2

State of the Industry

I asked Perplexity and ChatGPT for an overview of the industry trends and innovations:

  • AI and Machine Learning Innovation: AI and machine learning continue to drive innovation by enabling platforms to provide smarter, more relevant search results and hyper-personalised recommendations. Advanced algorithms use behavioural patterns and real-time data to predict user intent and deliver more intuitive product discovery experiences.
  • Personalisation at Scale: Hyper-personalisation has become a key differentiator, with platforms leveraging real-time data to tailor search results, recommendations, and user experiences to individual preferences. This shift from static search to dynamic, user-specific discovery is enhancing engagement and driving conversions.
  • Omnichannel Search Experience: Consistent search and discovery experiences across multiple touchpoints—websites, mobile apps, in-store kiosks, and even voice assistants—are becoming the norm. Omnichannel integration ensures that users encounter seamless interactions regardless of their preferred platform or device.
  • Conversational and Visual Search: The rise of conversational interfaces and voice-activated assistants has led to a growing emphasis on natural language processing (NLP) to understand and respond to complex, dialogue-based queries. Similarly, visual search capabilities, powered by image recognition, are enabling users to discover products through photos rather than text-based searches.
  • Advanced Natural Language Processing (NLP): Platforms are incorporating more sophisticated NLP models to interpret user queries with greater accuracy and context-awareness. This capability enhances the search experience, making it more conversational and user-friendly, especially for complex or ambiguous queries.
  • Voice Search Optimisation: The increased adoption of voice-activated devices has prompted platforms to optimise their search algorithms for spoken queries, which differ in structure and length from traditional text searches. This trend reflects the shift towards hands-free, convenience-driven interactions.
  • Data-Driven Insights and Analytics: Platforms are enhancing their analytics capabilities, offering businesses deeper insights into user behaviour and search patterns. This data empowers brands to optimise their discovery strategies, refine merchandising efforts, and anticipate market trends more effectively.
  • Discovery Beyond Search Engines: Younger demographics increasingly turn to social media and AI-powered discovery platforms rather than traditional search engines for finding products. This trend reflects the growing preference for visually engaging and socially integrated discovery journeys.

I then asked Perplexity and ChatGPT on the challenges that remain in search and product discovery:

  1. Overwhelming Product Volume
    Managing extensive product catalogs remains a challenge, particularly for large ecommerce sites and B2B platforms.
    • Customers often experience decision paralysis when presented with too many options.
    • Navigating and filtering through large assortments can be difficult, especially for niche or specific items.
  1. Ineffective Search Functionality
    Many platforms still fall short in delivering accurate and relevant search results due to outdated search algorithms and limited understanding of user queries.
    • Complex and nuanced queries often return irrelevant results.
    • Semantic search can misinterpret user intent, especially for ambiguous searches.
    • Handling typos, synonyms, and variations in product descriptions remains inconsistent.
  1. Lack of Personalisation
    Personalisation remains an unmet need, with many platforms offering a one-size-fits-all search experience.
    • Platforms often fail to use customer data to deliver tailored search results and recommendations.
    • This leads to missed opportunities to connect users with products that resonate personally, reducing engagement and loyalty.
  1. Data Overload Without Actionable Insights
    While ecommerce platforms collect vast amounts of customer data, they often struggle to leverage it effectively.
    • Translating raw data into meaningful improvements for search and product discovery is a persistent issue.
    • Misinterpreting analytics can lead to misguided marketing efforts and poor product focus.
  1. Mobile Experience Limitations
    As mobile shopping continues to grow, optimizing search and product discovery for smaller screens presents unique challenges.
    • Creating intuitive and user-friendly interfaces on mobile devices is difficult due to limited screen real estate.
    • Complex filtering options and advanced search tools can feel cumbersome or confusing in mobile environments.
  1. Balancing AI and User Experience
    The rise of AI in search and discovery presents both opportunities and challenges.
    • Overly aggressive or irrelevant AI-driven recommendations can confuse or frustrate users.
    • Implementing AI features without compromising usability and transparency remains a delicate balancing act.
  1. Conversion and Retention Issues
    Poor search and discovery experiences directly impact business outcomes.
    • Customers may abandon the site if they cannot find desired products quickly, leading to high bounce rates.
    • A lack of personalised recommendations reduces opportunities for upselling and cross-selling.
    • Inconsistent or generic experiences undermine customer loyalty and retention.

3

The Case for Neo

Today’s search and product discovery landscape faces a fundamental misalignment of incentives. Brands pay significant costs for platforms yet struggle with ROI. Platform vendors compete in a commoditised market with limited revenue streams. End customers face suboptimal experiences. NeoSearch reimagines this dynamic by eliminating upfront costs and creating new value through advertising.

NeoSearch is a paradigm shift from TradSearch (traditional search).

Aspect TradSearch NeoSearch
Business Model SaaS/consumption pricing; brands pay for usage Free for brands; monetised through targeted advertising
Data Usage Limited to platform data Unified data across search, marketing and customer interactions
Personalisation Based on session/historical behaviour True N=1 through AI Twins and zero-party data
Advertising Basic sponsored listings PII-based ActionAds with precise targeting
Customer Data Siloed within search platform Integrated with broader engagement data (NeoMails, MyTwin)
Implementation Complex technical integration Simplified deployment with no upfront costs
Merchandising Manual rules and basic automation AI-driven with automated optimisation
Revenue Model Fixed/variable costs for brands Revenue sharing from advertising
Scale Benefits Limited network effects Strong network effects across brands
Innovation Speed Constrained by development resources Continuous AI-powered improvement

Creating Win-Win-Win Value

  1. For Brands
  • Elimination of upfront search platform costs
  • New revenue streams through PII Ads via NEON (New Engaged and Open Network)
  • Better customer understanding through zero-party data
  • Improved conversion rates via personalisation
  • Integration with broader customer engagement (NeoMails and MyTwin)
  • Reduced dependency on external ad platforms
  • Access to cross-brand insights and intelligence
  1. For Platform Providers
  • Larger addressable market through zero-cost model
  • Sustainable revenue through advertising
  • Network effects from cross-brand data
  • Reduced sales friction and implementation complexity
  • Continuous data improvement through broader usage
  • Strategic position as commerce enabler
  • Higher customer lifetime value
  1. For End Customers
  • Better search experiences through true personalisation
  • More relevant product recommendations
  • Seamless discovery across brands
  • Value-added advertising based on actual intent
  • Consistent experiences across channels
  • Privacy-respecting data usage
  • Enhanced shopping journeys

NeoSearch transforms the traditional search and product discovery landscape by creating sustainable value for all participants. By eliminating upfront costs and creating new revenue streams through advertising, it solves fundamental challenges faced by brands, platforms, and end customers. This alignment of incentives—where brands save costs and gain revenue, platforms build sustainable businesses, and customers get better experiences—creates a powerful foundation for innovation. The next sections will explore how NeoSearch’s technical architecture, business model, and implementation framework come together to make this vision a reality, enabled by the twin powers of AI and authenticated identity.

4

Making It Happen – 1

Key Innovations

True innovation in search isn’t about incremental improvements to existing models – it’s about fundamentally reimagining how users discover and interact with information. NeoSearch’s breakthroughs go beyond simple query-response mechanisms to create an intelligent, personalised discovery ecosystem that learns and evolves with each user. Through the combination of NeoMails (15-30 second daily engagement emails with Atomic Rewards to build hotlines), MyTwin (AI-powered personal companions for personalised discovery), and NEON (PII-based advertising network enabling precise targeting), NeoSearch establishes a new paradigm in information access and engagement.

  1. Technical Architecture
  • AI-first search engine built for personalisation
  • Real-time integration with customer data
  • PII-based advertising system
  • Zero-party data collection framework
  • Cross-brand intelligence sharing
  • Privacy-preserving data architecture
  1. Business Model Innovation
  • ‘Free’ eliminating upfront costs
  • Revenue sharing from targeted advertising
  • Network-effect driven value creation
  • Cross-brand collaboration framework
  • Performance-based monetisation
  • Sustainable ecosystem economics
  1. Experience Innovation
  • True N=1 personalisation through AI Twins
  • Interactive search experiences
  • In-search ActionAds
  • Zero-party data collection
  • Real-time optimisation
  • Cross-channel consistency

These innovations collectively transform search from a reactive tool into a proactive discovery platform. By combining NeoMails for daily engagement, MyTwin for personalisation, and NEON for precision advertising, NeoSearch creates a self-reinforcing ecosystem where every interaction improves the quality of future discoveries. This marks a fundamental shift from today’s one-size-fits-all approach to truly personalised information access.

Implementation Framework

Transforming search requires more than just technological innovation – it demands a systematic approach to deployment that builds value while maintaining user trust. The implementation framework for NeoSearch follows a carefully orchestrated sequence that establishes core functionality, proves value, and progressively expands capabilities. This methodical approach ensures sustainable adoption while creating powerful network effects.

  1. For Brands
  • Simple integration process
  • Staged rollout approach
  • Revenue sharing structure
  • Data integration guidelines
  • Performance monitoring
  • Continuous optimisation (Kaizen Progency)
  1. For Platform Providers
  • Technical architecture deployment
  • Advertising network creation
  • Partner ecosystem development
  • Analytics and reporting
  • Support infrastructure
  • Innovation roadmap
  1. For End Customers
  • Enhanced search interface
  • Personalised experiences
  • Value-added advertising (which feels like content)
  • Privacy controls
  • Feedback mechanisms

The framework’s strength lies in its balance between immediate utility and long-term vision. By starting with proven engagement mechanics and progressively introducing more sophisticated features, NeoSearch can build trust and habitual usage while continuously expanding its capabilities. This measured approach creates a solid foundation for lasting transformation in how users discover and interact with information.

The Network Effect Advantage

Network effects have powered every major digital platform’s success, from social networks to marketplace businesses. NeoSearch harnesses these dynamics in unique ways, creating multiple reinforcing loops that strengthen its value proposition with each new user and interaction. Unlike traditional search engines that primarily benefit from query volume, NeoSearch builds advantage through deeper user understanding.

As more brands adopt NeoSearch:

  • Richer cross-brand insights
  • Better personalisation
  • More targeted advertising
  • Enhanced customer experiences
  • Stronger network effects
  • Sustainable value creation

These compounding network effects create sustainable competitive advantages that grow stronger over time. As more users engage with NeoSearch, its ability to deliver personalised, relevant results improves exponentially. This virtuous cycle of better understanding leading to better results makes NeoSearch increasingly valuable to both users and content creators.

5

Making It Happen – 2

The Power of Integration

True platform power comes from the seamless integration of complementary capabilities. NeoSearch achieves this through the synergistic combination of NeoMails (with Microns and Mu) for engagement, AI Twins for personalisation, and NEON for precision advertising. Together, these three pillars create an ecosystem that transforms how users discover, interact with, and monetise information.

  1. Synergy with NeoMails
  • Search behaviour informs email content
  • Email engagement influences search rankings
  • Unified customer profiles
  • Consistent personalisation
  • Shared advertising platform
  • Cross-channel optimisation
  1. Synergy with MyTwin
  • AI Twins enhance search relevance
  • Search data improves Twin understanding
  • Unified intelligence layer
  • Predictive capabilities
  • Enhanced personalisation
  • Real-time adaptation
  1. Synergy with NEON
  • World’s first PII-based advertising network enabling precision targeting through authenticated identity
  • Transforms every search into a revenue opportunity through in-search ActionAds
  • Enables direct brand-to-brand collaboration without intermediaries through a secure “clean room” environment
  • Creates sustainable monetisation through transparent revenue sharing
  • Leverages zero-party data collected through search interactions for enhanced targeting

The integration of these three pillars – engagement, personalisation, and monetisation – a creates a comprehensive platform that delivers value to all stakeholders. This unified approach ensures that every interaction strengthens the entire ecosystem, making NeoSearch more than just a better search engine – it becomes an indispensable discovery platform that grows more valuable with each use.

Revolutionising Product Discovery

Product discovery remains one of the most challenging aspects of digital commerce, with users often struggling to find exactly what they want among endless options. NeoSearch reimagines this process through AI-powered understanding and personalised recommendations, creating a more intuitive and effective way to connect users with products that truly match their needs.

  1. From Keywords to Intent
  • Traditional search relies on keyword matching
  • NeoSearch understands true customer intent through:
    • Historical behaviour
    • Zero-party data
    • Cross-channel signals
    • AI Twin insights
    • Real-time context
    • Predicted needs
  1. Dynamic Merchandising
  • Traditional approaches use manual rules
  • NeoSearch enables:
    • AI-driven optimisation
    • Real-time adaptation
    • Personalised rankings
    • Cross-brand insights
    • Revenue optimisation
    • Automated testing
  1. Intelligent Advertising
  • Traditional sponsored listings are basic
  • NeoSearch delivers:
    • PII-based targeting
    • Intent-driven ads
    • In-search conversions
    • Cross-brand opportunities
    • Revenue sharing
    • Performance optimisation

By transforming product discovery from a keyword-based search into an intelligent, conversation-driven process, NeoSearch fundamentally changes how users find and evaluate products. This shift from passive searching to active discovery creates better outcomes for both shoppers and sellers, while generating rich data that continuously improves the platform’s effectiveness.

The Economic Impact

The economic implications of reimagining search extend far beyond advertising revenue. NeoSearch creates new value streams through more efficient matching of users with information and products, while enabling novel monetisation approaches through its engagement currency and precision advertising network. This transformation promises to redirect billions in spending toward more effective discovery solutions.

  1. For Brands
  • Elimination of search platform costs (typically 6-7 figures annually)
  • New revenue streams from advertising
  • Higher conversion rates
  • Better customer retention
  • Reduced acquisition costs
  • Improved ROI
  1. For Platform Providers
  • Larger market opportunity
  • Sustainable revenue streams
  • Higher customer lifetime value
  • Network effect advantages
  • Innovation opportunities
  • Strategic positioning
  1. For End Customers
  • Better shopping experiences
  • More relevant products
  • Time savings
  • Value-added advertising
  • Enhanced discovery
  • Personalised journeys

The economic potential of NeoSearch represents a fundamental shift in how value is created and captured in the digital economy. By aligning incentives across users, content creators, and advertisers, it creates sustainable revenue streams while reducing waste in current digital advertising models. This new paradigm promises more efficient markets and better outcomes for all participants.

**

Future Vision

NeoSearch reimagines how products are discovered and purchased online. By aligning incentives across brands, platforms, and customers, it creates sustainable value while eliminating waste.

The future will see:

  • True N=1 personalisation at scale
  • Predictive product discovery
  • Seamless cross-brand experiences
  • Value-added advertising
  • Enhanced shopping journeys
  • Sustainable economics

NeoSearch, together with NeoMails and MyTwin, forms the foundation of a new commerce paradigm—one where technology serves both business goals and customer needs in perfect harmony.

6

More Ideas

I asked Claude and ChatGPT for consumer utility product ideas that fit under the NeoSearch umbrella and are free for brands.

Claude

  1. “DiscoverDaily” – Personal Discovery Engine
  • 15-second daily NeoMails with personalized micro-discoveries
  • MyTwin-powered content curation across interests
  • Interactive SmartBlocks for preference capture
  • Gamified exploration with Atomic Rewards (Mu)
  • ActionAds for precise product recommendations
  • Zero-cost model funded through NEON advertising
  1. “TwinGuide” – AI Shopping Companion
  • Personal AI shopping advisor through MyTwin
  • Natural language product discovery
  • Conversational preference learning
  • Cross-brand recommendations
  • Price tracking and deal alerts
  • Monetization through ActionAds in NeoMails
  1. “MicroLearn” – Personalized Knowledge Engine
  • Daily micro-learning moments through NeoMails
  • AI-curated educational content
  • Interactive knowledge checks
  • Personalized learning paths
  • Mu rewards for consistent engagement
  • Revenue through targeted educational ActionAds
  1. “TrendSpotter” – Personal Trend Navigator
  • Real-time trend identification across categories
  • MyTwin-powered interest matching
  • Daily trend alerts via NeoMails
  • Interactive trend exploration
  • Community-driven insights
  • Monetization through trend-related ActionAds
  1. “WishWhisperer” – Smart Wishlist Platform
  • Cross-brand wishlist management
  • MyTwin-powered gift recommendations
  • Price drop alerts via NeoMails
  • Collaborative wishlists
  • Deal discovery engine
  • Revenue through NEON’s precision advertising

Common Elements Across All Products:

  • Free to both brands and consumers
  • Daily engagement through NeoMails
  • MyTwin for personalization
  • Atomic Rewards (Mu) for gamification
  • NEON-powered monetization
  • Zero-party data collection
  • Cross-brand intelligence sharing

ChatGPT

  1. DiscoveryLens – Visual and Voice-Powered Product Search

Idea: A multi-modal product search tool that combines image recognition, voice commands, and text search to create an intuitive discovery experience.

Key Features:

  • Visual search: Snap a picture to find similar products across categories.
  • Conversational AI: Ask product-related questions (“What’s a budget-friendly hiking jacket?”) and receive personalized suggestions.
  • Voice-activated results: Hands-free product exploration via voice interfaces in mobile apps and emails.
    Why It’s a Game-Changer:
  • Bridges text, voice, and visual inputs into one unified experience.
  • Great for mobile users who prefer visual and audio interactions over typing.
  1. Personalized Search Stories – Dynamic Product Feeds in Email and Apps

Idea: A story-like discovery feature embedded within emails and apps, presenting personalized product recommendations in interactive swipeable formats.

Key Features:

  • Curated “stories” based on browsing and purchase patterns.
  • Swipe-to-explore recommendations for seasonal items and trending collections.
  • Real-time stock updates and direct purchase actions embedded within the story.
    Why It’s a Game-Changer:
  • Adds an element of habitual engagement, similar to social media stories.
  • Helps reduce decision fatigue by narrowing down product categories based on user preferences.
  1. Search-to-Cart™ – Zero-Click Personalized Checkout Experience

Idea: An inline product search and one-tap purchase feature directly within email or app-based search results.

Key Features:

  • Personalized “quick-buy” carousels tailored to user preferences.
  • Instant checkout without navigating away from the discovery screen.
  • Pre-filled cart recommendations for bundles and related items.
    Why It’s a Game-Changer:
  • Converts passive discovery into immediate action.
  • Minimizes friction in the shopping journey by eliminating extra clicks and page loads.
  1. ExploreTogether – Collaborative Product Discovery for Group Shopping

Idea: A group-based search and discovery utility that allows users to share wishlists, polls, and product ideas with friends and family in real-time.

Key Features:

  • “Vote on it” functionality: Group members vote on suggested items.
  • Shared search sessions: Synchronized browsing experiences across devices.
  • Collaborative carts for gifting events and shared purchases.
    Why It’s a Game-Changer:
  • Adds a social element to product discovery, ideal for holidays, birthdays, and group shopping.
  • Increases session time by making search collaborative and engaging.
  1. HyperFind™ – Smart Category and Preference Profiler

Idea: An onboarding experience that builds a real-time “preference map” for users based on interactive questions and swipeable product cards.

Key Features:

  • Interactive product quizzes embedded within search interfaces.
  • Smart filters automatically set based on user preferences (e.g., price range, color, style).
  • Personalized recommendations evolve as users refine their preferences.
    Why It’s a Game-Changer:
  • Accelerates the path to relevant products by pre-filtering the catalog.
  • Builds engagement through gamified profiling that feels like a fun discovery quiz.

7

Stories from the Future – 1

I asked Claude and ChatGPT to imagine the future with NeoSearch for Maya (a marketer) and Karan (a customer).

Claude

Morning: Maya’s Marketing Command Center

Maya logs into her marketing dashboard at 9 AM, greeted by her team of Madtech Twins – AI replicas of key customer segments. Her screen immediately surfaces an insight: “Overnight search trends show 40% of premium segment customers exploring winter jackets, unusual for summer. Cross-referencing data suggests upcoming international travel plans.”

The luxury segment Twin adds, “Based on search patterns and MyTwin conversations, they’re specifically interested in packable, lightweight options. Shall we create a ‘Travel Essentials’ collection?”

With one click, Maya approves. Within minutes, NeoSearch automatically reorganizes product rankings and creates personalized landing pages for different customer segments. The platform’s AI Co-Marketer suggests, “Let’s enhance this with a NeoMail featuring travel-ready outerwear, targeting customers with confirmed flight bookings in the next 60 days.”

Morning: Karan’s Personalized Discovery

Meanwhile, Karan checks his daily NeoMail during his commute. His MyTwin has curated a perfect 15-second discovery moment: “You’ve been searching for running shoes. Based on your preference for minimalist design and previous purchases, here are three options that match your style. All available with next-day delivery.”

The email features an interactive SmartBlock where Karan can swipe through options, compare features, and even see how each shoe would look with his typical running gear. He notices small Mu rewards offered for sharing his preferences, making the exploration feel more like a game than shopping.

Afternoon: Maya’s Real-Time Optimization

Maya’s attention turns to a real-time strategy session. The Madtech Twins have detected a viral TikTok trend featuring sustainable fashion. The Gen Z segment Twin suggests, “Our eco-friendly collection aligns perfectly. Based on search patterns, users are particularly interested in production transparency. Recommend highlighting our sustainable manufacturing process.”

Through NEON’s PII-based advertising network, Maya can instantly create ActionAds targeting users who’ve shown interest in sustainable fashion across multiple brands. “We’re seeing 40% higher engagement when we place these ads within organic product discovery flows,” her dashboard reports.

Afternoon: Karan’s Seamless Journey

Karan receives a notification – not a promotional push, but a genuine value-add from his MyTwin: “Remember those running shoes? I noticed you were interested in the blue ones. A similar pair just launched in your preferred colorway. Would you like to see them?”

The notification opens to an interactive search experience where Karan can explore the new collection while his MyTwin provides context based on his previous preferences: “These have the same minimalist design you love, but with enhanced cushioning – something you mentioned wanting in your last pair.”

Evening: Maya’s Data Symphony

As day winds down, Maya reviews an automated summary. NeoSearch has identified micro-trends, optimized product placements, and even predicted tomorrow’s shopping patterns. The system shows how customers who engaged with today’s travel collection have a 70% higher likelihood of making related purchases within the next week.

The AI Co-Marketer suggests, “Based on search patterns and MyTwin conversations, shall we create a ‘Complete Travel Wardrobe’ guide? Data indicates customers are looking for full-outfit inspiration, not just individual pieces.”

Evening: Karan’s Perfect Find

During his evening workout, Karan uses voice search to ask his MyTwin about running shoes again. Instead of a generic list, he gets hyper-personalized results that factor in his recent browsing, previous purchases, and even his running style (captured through earlier conversations).

His MyTwin notes, “I see you’re running more frequently lately. These shoes are particularly good for daily training. Would you like to see some user reviews from runners with similar patterns?”

The search results include ActionAds that feel more like personalized recommendations – complementary products like moisture-wicking socks and running shorts, all matching his style preferences and price range.

Late Night: The System at Work

While Maya sleeps, NeoSearch continues optimizing. It processes thousands of interactions, updates product rankings, and refines its understanding of customer segments. The system prepares for tomorrow, identifying opportunities for personalized discovery moments.

For Karan, his MyTwin is quietly planning tomorrow’s NeoMail, combining his recent running gear interest with his upcoming weekend plans to create perfectly timed, relevant discoveries.

**

This is the new world of product discovery – where AI Twins enable true N=1 personalization at scale, transforming both the marketer’s workflow and the customer’s experience. No mass messaging, no generic searches, just genuine understanding and relevant engagement, powered by the seamless collaboration of NeoSearch, NeoMails, and MyTwin, all monetized through NEON’s precision advertising network.

The future of search isn’t about better algorithms – it’s about perfect understanding and engagement, powered by a trinity of innovations that make every discovery moment feel magical.

8

Stories from the Future – 2

ChatGPT

Maya’s Day – The Marketer

8:30 AM – Morning Insights Over Coffee
Maya starts her day by opening the NeoSearch Insights Dashboard. Her screen is alive with real-time data: search trends, product discovery performance, and customer interactions. NeoSearch’s AI Co-Marketer provides her with a summary:
“Good morning, Maya. Yesterday, 6,400 users interacted with personalized search stories. Our ‘Winter Wardrobe Refresh’ collection drove 18% more conversions due to real-time trend adjustments.”

Maya notices a spike in search for eco-friendly athleisure. With one click, she uses Smart Merchandiser to boost these products across search pages and recommendations for relevant users.

10:00 AM – Campaign Creation in Minutes
Maya has a mid-day meeting, so she quickly crafts a new email and push notification campaign using NeoSearch’s DiscoverDaily Module. She adds swipeable stories showcasing trending items based on customers’ “Style Twins.” Thanks to generative AI-powered content suggestions, the campaign includes engaging copy:
“Get cozy in sustainable style – handpicked just for you.”

The ActionAds Embed allows customers to browse by category directly within the email, making it a frictionless shopping experience.

12:00 PM – Testing New Ideas
After her lunch break, Maya tests her campaign variations using the A/B Smart Engine. NeoSearch simulates user reactions and predicts the best-performing version: “Version B, featuring brighter colors and a ‘Limited Edition’ tag, is 20% more likely to convert Gen Z users.”
Confident, she schedules the campaign for peak engagement hours.

2:30 PM – Collaborating with the Merchandising Team
The merchandising team suggests adding curated bundles to increase average order value. Maya uses ExploreTogether to collaborate with them, sharing search data and trends in real time. They agree to feature “Eco-Friendly Winter Packs” as a top recommendation for returning users.

4:00 PM – Cross-Brand Partnerships
Maya leverages the NEON Cross-Brand Exchange to embed a recommendation carousel for a partnered brand selling sustainable backpacks.
“Great job, Maya. This partnership could increase your monthly ad revenue by 12%,” NeoSearch’s assistant notifies her.

5:00 PM – End-of-Day Success Snapshot
Before wrapping up, Maya checks the Customer Engagement Summary. She smiles at the metrics: open rates have increased by 35%, and 8,000 customers added items directly from search-to-cart experiences. NeoSearch has helped her turn customer curiosity into conversions—and kept her workflow effortless.

Karan’s Day – The Customer

8:15 AM – Morning Email Surprise
As Karan sips his chai, he opens his inbox and sees a NeoMail Story with a Mu reward balance: “Earn 250 Mu points: Discover Your Perfect Winter Jacket!” The interactive email features a visual search quiz that suggests styles based on his location and preferences.

He swipes through personalized recommendations. “That navy parka looks perfect,” he thinks and taps to view more details—all without leaving the email.

12:00 PM – Break-Time Browsing
During lunch, Karan uses his phone to check the ecommerce app. He clicks on “Top Picks for You”, a collection powered by MyTwin. The AI remembers Karan’s affinity for minimalist designs and suggests matching shoes and backpacks.

An “Offer Whisper” pops up: “Complete your look and get 10% off.” Encouraged, Karan adds the shoes to his cart with one tap.

3:00 PM – Collaborative Wishlist
Karan receives a ping from his sister: “I’m adding items to our holiday wishlist!” Through the ExploreTogether feature, they browse together virtually, voting on what to buy for their upcoming family trip. They agree on a travel kit from Karan’s favorite store.

6:00 PM – Voice-Activated Search
Back home, Karan remembers he needs eco-friendly gloves. He speaks into the app, “Show me sustainable winter gloves under ₹2,000.” The app’s conversational search quickly responds with tailored options and even highlights matching beanies.

8:00 PM – Flash Deal Alert
Karan gets a personalized notification: “Mu Jackpot! 500 bonus points if you check out tonight.” He opens the Search-to-Cart™ view and checks out in seconds. The experience is so seamless, he never even needs to visit the homepage.

**

The Future of Product Discovery with NeoSearch

Maya’s day illustrates how marketers can harness NeoSearch’s AI-driven capabilities to enhance customer engagement, optimize merchandising, and unlock new revenue streams. Karan’s journey showcases how personalized discovery transforms shopping into an effortless, delightful experience filled with rewarding interactions.

NeoSearch isn’t just search—it’s an ecosystem where discovery becomes dynamic, conversations feel personal, and brands build lasting relationships with consumers.

Thinks 1513

Monika Halan: “Personal income taxes, paid by less than 5% of the total [Indian] population, pulled 19% of the almost ₹45 trillion total budget burden in 2023-24, or just over 30% of the total tax revenue and over half the direct taxes collected…Between the rich and the poor, the middle is stuck…The income-tax-paying Indian has become the cash cow for the government.”

David Chapman: “Some venture capitalists say that startup success depends more on the cofounder relationship than any other factor. It can be their main reason for choosing to invest, or not, in a founding team. Conflicts between cofounders may be the most common reason for startup failure.” [via Arnold Kling]

WSJ on Skechers: “The shoe company—known for its hands-free slip-in styles—has grabbed the attention of enough people to become the third-largest footwear company in the world by sales. It is on track to net $10 billion in revenue by 2026, without achieving the coolness status that can juice demand for a brand. Skechers did it by capturing parts of the market that are largely neglected by its competitors. Nike has superstars. Hoka has tapped into hardcore runners. Tech bros are willing to pay up for On shoes. Skechers thrives on retirees looking for comfortable kicks and families looking for something more affordable for their children. “It’s almost the complete opposite of what the bigger brands do,” John Vandemore, Skechers finance chief, said in an interview. “We’re just a different player.””

Ethan Mollick: “How seriously should we take the claims of the AI labs that a flood of intelligence is coming? Even if we only consider what we’ve already seen – the o3 benchmarks shattering previous barriers, narrow agents conducting complex research, and multimodal systems creating increasingly sophisticated content – we’re looking at capabilities that could transform many knowledge-based tasks. And yet the labs insist this is merely the start, that far more capable systems and general agents are imminent. What concerns me most isn’t whether the labs are right about this timeline – it’s that we’re not adequately preparing for what even current levels of AI can do, let alone the chance that they might be correct…The flood of intelligence that may be coming isn’t inherently good or bad – but how we prepare for it, how we adapt to it, and most importantly, how we choose to use it, will determine whether it becomes a force for progress or disruption. The time to start having these conversations isn’t after the water starts rising – it’s now.”

Bloomberg: “Over the past month we have started testing AI-driven summaries for some longer stories on the Bloomberg Terminal. The software reads the story and produces three bullet points. Customers like it — they can quickly see what any story is about. Journalists are more suspicious. Reporters worry that people will just read the summary rather than their story. To which the honest answer is: Yes, a reader might well do that, but would you prefer that they wasted their time skimming through paragraphs on a topic that they are not actually interested in? To me it’s pretty clear; these summaries, used correctly, both help readers and save time for editors.”

From Trad to Neo to One: Rethinking B2C Martech with Free-to-Brand Consumer Utilities

Published February 21, 2025

1

Neo Thinking

The business model for B2C marketing technology has remained largely unchanged for decades. ESPs charge by email volume, CPaaS players bill based on message consumption, and marketing automation platforms lock brands into annual contracts with monthly platform fees. This has created a red ocean of competition where vendors fight primarily on price and features, whilst brands struggle with rising costs and diminishing returns.

But what if we could fundamentally reimagine this model? What if, instead of charging brands for software, we could provide marketing technology as free utilities, monetising through precision advertising? This isn’t just another pricing innovation—it’s a complete rethinking of how marketing technology creates and captures value.

Think about how free B2C platforms have transformed our personal lives:

  • Google made information universally accessible
  • Meta connected billions through social networks
  • YouTube democratised video distribution

Could a similar transformation happen in B2B marketing technology? I believe it can, and it starts by recognising that B2C martech solutions fall into two distinct categories:

  1. Marketer Tools
    • Campaign management platforms
    • Analytics and reporting
    • Performance optimisation
    • These will likely remain traditional SaaS
  2. Customer-Facing Solutions
    • Email and messaging
    • Search and discovery
    • Chatbots and engagement
    • These are prime candidates for “free-to-brand” models

The second category is particularly interesting because it creates something invaluable: customer attention. And where there’s attention, there’s potential for precision advertising and data monetisation.

This reimagining comes at a critical moment. Today’s marketing is fundamentally broken, with brands spending 80-90% of their budgets on acquisition and reacquisition through Big Adtech platforms. Marketers have essentially become collection agents for Google and Meta, paying premium prices to reach their own customers through competitive auctions. The result? Massive AdWaste—an estimated $350-560 billion annually lost to wrong targeting and unnecessary reacquisition.

This is where NeoMartech enters—not as better software, but as transformative consumer utilities deployed through B2B partnerships. By providing these solutions free to brands and monetising through NEON (New Engaged and Open Network for PII-based advertising), NeoMartech companies can help brands escape the costly cycle of continuous acquisition whilst building lasting, profitable customer relationships.

The opportunity lies in thinking beyond just better technology. It’s in solving marketing’s three fundamental challenges:

  1. The “No Hotline” crisis of unreliable customer connections
  2. The “Not for Me” problem of irrelevant messaging
  3. The “No Alternative” challenge to Big Adtech’s dominance

In this series, we’ll explore how NeoMartech can redirect billions in AdWaste toward meaningful engagement whilst creating a new category of technology companies that build consumer platforms through B2B partnerships. The future of marketing technology isn’t in better software—it’s in reimagining how brands connect with customers in the digital age.

2

Trad to Neo

The marketing technology landscape stands at a pivotal moment of transformation. TradMartech, with its focus on selling software to businesses, will give way to NeoMartech—a revolutionary approach that provides consumer-facing solutions free to brands, monetised through precision advertising. This shift represents more than evolution; it’s a fundamental reimagining of how marketing technology creates value.

TradMartech emerged in an era where software licensing was the primary business model. Companies built tools for marketers—campaign management platforms, analytics dashboards, and automation solutions—charging monthly or annual fees based on usage or active users. While this model drove significant innovation, it also created barriers to adoption and limited network effects. It also disconnected TradMartech companies from business outcomes, unlike their adtech peers who perfected performance-based pricing.

NeoMartech takes a radically different approach. Instead of selling software to businesses, it builds consumer utilities deployed through B2B partnerships. These solutions are provided free to brands and monetised through NEON’s PII-based advertising network. This creates a powerful flywheel where better engagement enables better monetisation, which funds platform development and innovation.

Consider email marketing. TradMartech providers charge brands for every message sent, turning email into a cost centre that requires constant ROI justification. In contrast, NeoMails—a NeoMartech solution—provides sophisticated email infrastructure for free, enabling brands to monetise customer attention through ActionAds. This transforms email from an expense into a revenue generator.

The same principle applies across the engagement stack. MyTwin (a personalised chatbot), search and product discovery, interactive web and app messaging—all can be provided free to brands, monetised through precision advertising. This isn’t just about making software free; it’s about creating true consumer utilities that generate sustained engagement and attention.

The implications are profound. While TradMartech vendors compete in a red ocean of features and pricing, NeoMartech companies build powerful network effects through consumer engagement. Each new brand adds inventory, each new user adds value, and each interaction improves targeting—creating sustainable competitive advantages that compound over time—just like what has happened in adtech.

Here’s a clear comparison:

Aspect TradMartech NeoMartech
Primary User Marketers End Consumers
Business Model Software licensing Advertising revenue
Pricing Usage/user-based fees Free to brands
Value Creation Better tools Consumer utilities
Network Effects Limited Strong
Data Assets Brand-specific Cross-brand insights
Monetisation Direct fees Attention & data
Innovation Focus Features & automation Engagement & experience
Competitive Moat Product capabilities Network effects
Scaling Economics Linear Exponential

This transformation isn’t just about better economics—it’s about solving fundamental marketing challenges. While TradMartech helps brands execute campaigns more efficiently, NeoMartech enables them to build lasting customer relationships whilst generating new revenue streams.

The future belongs to companies that embrace this new paradigm. By providing powerful consumer utilities through B2B partnerships, NeoMartech companies can help brands escape the costly cycle of continuous reacquisition whilst building sustainable competitive advantages through network effects.

The shift from Trad to Neo is coming; it is the future of marketing technology.

3

Neo-fication

Traditional B2B SaaS pricing models in martech are fundamentally broken. Companies pay for software based on message volume or monthly active users, creating misaligned incentives and artificial barriers to adoption. The result? Many brands underinvest in customer engagement technology whilst overspending on acquisition through performance-driven and outcome-focused adtech platforms. This needs to change. Martech vendors and marketers must be aligned.

Consider email infrastructure. Today, brands pay ESPs based on message volume, turning email into a cost centre that needs constant ROI justification. But what if we flipped this model? What if brands could use sophisticated email infrastructure for free, monetising customer attention through ActionAds? This creates a virtuous cycle where better engagement drives more revenue, enabling further investment in customer experience.

The same principle applies across the engagement stack:

  • Personal AI companions
  • Product discovery and recommendations
  • Push notifications and messaging
  • Loyalty programme infrastructure
  • Customer service platforms

All these capabilities could be provided free to brands, monetised through NEON’s PII-based advertising network. This isn’t just about making software free—it’s about creating true consumer utilities that generate sustained engagement and attention.

The breakthrough insight? NeoMartech companies can potentially create better consumer experiences than brands themselves. Why? Because these companies:

  • Focus solely on engagement and experience
  • Leverage cross-brand data and insights
  • Deploy advanced AI and personalisation
  • Create network effects through scale
  • Build direct consumer relationships

Just as Google and Meta have mastered acquisition-side consumer experiences through search and social, NeoMartech companies can excel at retention-side experiences through these utilities. The parallel is striking:

  • Google knows user intent through search
  • Meta understands interests through social
  • NeoMartechs capture preferences through direct engagement

This creates an unprecedented opportunity: building consumer platforms through B2B partnerships. Instead of competing in the crowded martech SaaS space, NeoMartechs are creating an entirely new category that delivers value to all stakeholders:

For Brands:

  • Zero upfront costs
  • Immediate deployment
  • Built-in monetisation
  • Rich customer data
  • Network benefits

For Consumers:

  • Better experiences
  • Relevant content
  • Personal AI assistance
  • Unified interactions
  • Value-added services

For the NeoMartech Platform:

  • Rapid adoption
  • Network effects
  • Multiple revenue streams
  • Deep data assets
  • Sustainable advantages

The key is focusing on solutions with:

  • Zero marginal cost delivery
  • Natural engagement mechanics
  • Clear monetisation paths
  • Network effect potential
  • Scalable infrastructure

The rise of NeoMartechs and their consumer utilities represents more than just a new business model. It’s a fundamental reimagining of how brands engage with customers in the digital age. By removing cost barriers and aligning incentives, these companies can help brands build deeper relationships whilst creating new opportunities for growth and monetisation.

4

Paid to Free

Which existing martech solutions can transition from paid to free? Which new solutions can be created? Let’s explore how traditional marketing technology can be “neo-fied” whilst creating entirely new engagement channels.

NeoMails: The First Breakthrough

NeoMails exemplifies the perfect transition strategy. Rather than disrupting existing marketing emails, it creates a parallel engagement track through daily 15-30 second interactions powered by “brain gain” microns. These brief but valuable touchpoints build habitual engagement whilst delivering genuine cognitive benefits to consumers.

The genius lies in its layered value creation:

  • Daily attention through micro-content
  • Zero-party data collection via SmartBlocks
  • Revenue generation through ActionAds
  • Gamification via Atomic Rewards (Mu)
  • Trust building through consistent value delivery

This parallel track approach means brands can maintain their existing email marketing programmes whilst building this new engagement channel. The sustained attention and interaction create valuable advertising inventory that offsets email sending costs, effectively transforming email from a cost centre into a profit driver.

Existing Solutions Ready for Neo-fication

Several current martech capabilities are prime candidates for transformation:

  1. Search and Product Discovery
    • Current: Brands pay for infrastructure and algorithms
    • Neo Version: Free deployment with ad-supported monetisation
    • Enhanced by cross-brand data and AI-powered personalisation
    • Revenue through sponsored product placement and ActionAds
  2. Web and App Push Notifications
    • Current: Usage-based pricing models (as part of automation solutions)
    • Neo Version: Free delivery with interactive advertising
    • Enhanced engagement through personalisation
    • Monetisation via in-notification commerce

New Solutions Born Neo

Some solutions can be created specifically for the NeoMartech era.

  1. MyTwin
    • Personal AI companion for every customer
    • Natural language preference expression
    • Real-time intent capture and fulfilment
    • Advertising based on authentic interests
    • Cross-brand learning and personalisation

Additional Neo Opportunities

Other martech capabilities ripe for transformation include:

  1. Loyalty Programmes
    • Free infrastructure deployment
    • Cross-brand rewards integration
    • Monetisation through partner offers
    • Network effects across programmes
  2. Ratings and Reviews
    • Free platform for brands
    • Enhanced by AI-powered analysis
    • Monetisation via sponsored visibility
    • Cross-brand insight generation
  3. Customer Service Platforms
    • AI-powered support infrastructure
    • Free deployment for brands
    • Revenue through relevant recommendations
    • Learning across brand implementations
  4. Content Management
    • Free publishing infrastructure
    • AI-powered personalisation
    • Monetisation through native advertising
    • Cross-brand content optimisation

The key to successful neo-fication lies in:

  • Maintaining existing capabilities whilst building new ones
  • Creating genuine value for consumers
  • Enabling precise targeting through PII
  • Building network effects across brands
  • Generating sustainable revenue streams

This transformation isn’t just about making existing solutions free—it’s about reimagining how marketing technology can better serve both brands and consumers. By focusing on solutions with zero marginal cost delivery and clear monetisation paths, NeoMartech companies can create sustainable advantages whilst helping brands build stronger customer relationships.

The future of marketing technology will be increasingly “free to brand, paid by ads”—and the companies that master this transition will define the next era of customer engagement.

5

The Neo to One Future

A profound transformation is coming to marketing. The future lies in combining a “Neo” business model with “One” personalisation—creating unique experiences, recommendations and ads that feel like valued content for each customer. This elegant evolution from NEO to ONE captures the essence of marketing’s next revolution.

The martech industry made a historical blunder: unlike adtech, it failed to link spending with outcomes. This fundamental mistake has created a massive imbalance, with adtech commanding budgets ten times larger than martech. Most telling? Brands now pay Google and Meta premium prices to reach their own customers—a tragic indictment of martech’s failure to build lasting relationships.

The emergence of new AI capabilities offers a path to redemption. Generative AI enables crafting unique content and journeys for every customer at the N=1 level, whilst Agentic AI—through the AI Co-Marketer—orchestrates this personalisation at scale without constant marketer intervention. This combination finally makes true individualisation both possible and practical.

NeoMarketing takes us back to marketing’s essential purpose: creating customers for life. It restores the “R” in CRM—because where is the “relationship” when:

  • 60-65% of customers are “one and done”
  • 70-80% of ad budgets are spent remarketing to existing/ex-customers
  • 9 of 10 messages sent are ignored
  • 7 of 10 customers are dormant or inactive

These statistics aren’t just numbers—they represent marketing’s systemic failure to build meaningful customer relationships. NeoMarketing offers a way forward that benefits all stakeholders:

For Brands:

  • Lower customer acquisition costs
  • Higher customer lifetime value
  • New revenue streams through attention
  • Deeper customer relationships
  • Sustainable competitive advantages

For Martech Companies:

  • Larger addressable market
  • Multiple revenue streams
  • Network effect benefits
  • Deeper client relationships
  • Sustainable growth model

For Customers:

  • More relevant experiences
  • Personal AI companions
  • Valuable daily interactions
  • Better engagement
  • True personalisation

This transformation is the disruption and leapfrog marketing needs. By shifting from software fees to advertising revenue (which is an outcome of attention, engagement, experiences, and value delivery), NeoMartech companies can finally align their success with marketing outcomes. By focusing on N=1 personalisation, they can help brands build the lasting customer relationships that eliminate wasteful reacquisition spending.

The journey from NEO to ONE isn’t just wordplay—it’s the path to marketing’s renaissance, where every customer feels special, every interaction creates value, and every relationship lasts.

The next questions to consider: Why will marketers agree for “free”? Who will lead this transformation? What is the prize for the winners?

6

The Path Forward

Why

For marketers, “free” is a compelling proposition because it eliminates one of the greatest barriers to technology adoption: cost justification. Unlike traditional martech solutions that require lengthy ROI analyses and budget approvals, free solutions can be deployed instantly. When these solutions are monetised through performance-based models, they perfectly align vendor success with marketing outcomes.

From the marketer’s perspective, the advantages are clear:

  • No upfront investment: Eliminates financial risk.
  • Zero adoption friction: Easy to deploy without complex approvals.
  • Built-in monetisation: Converts engagement into measurable value.
  • Pay-for-performance: The vendor wins only when marketing succeeds.
  • Immediate value visibility: Unlike traditional martech where ROI takes months to prove, performance-based monetisation provides clear metrics from day one.

This isn’t a theoretical shift—it’s proven. Google and Meta built trillion-dollar empires by offering free platforms monetised through targeted advertising. The key difference? NeoMartech applies this model to retention and engagement rather than acquisition, helping brands build lasting customer relationships while creating new revenue streams.

Who

The companies best positioned to lead this transformation are established martech providers like Netcore that combine deep technology capabilities with proven execution. The ideal leader needs:

  • Full-Stack Capabilities
    • Email marketing and automation infrastructure
    • CPaaS for cross-channel delivery
    • Product discovery and search (like Unbxd)
    • AI/ML expertise across the stack
    • Robust technology foundation
  • Market Understanding
    • Deep martech expertise
    • Strong brand relationships
    • Global market presence
    • Innovation DNA
    • Cross-industry insights
  • Execution Excellence
    • India-based Kaizen Progency
    • Global delivery capabilities
    • Implementation expertise
    • Customer success focus
    • Continuous improvement mindset
  • Strategic Differentiation
    • “Proficorn” mindset combining innovation with profitability
    • Proven AI capabilities spanning predictive, generative, and agentic AI
    • Track record of category creation and leadership
    • Culture of continuous innovation

Companies like Netcore can leverage these strengths whilst embracing the NeoMartech model to:

  • Deploy solutions rapidly: Speed matters in capturing market share
  • Scale engagement effectively: Cross-channel delivery creates seamless experiences
  • Monetise attention precisely: PII-based targeting improves ad performance
  • Build network effects systematically: Each new user and brand increases platform value

Three compounding forces further strengthen the winner’s advantage:

  1. Data Network Effects
  • More brands mean better targeting
  • More users create richer data
  • More interactions deepen understanding
  • Higher engagement drives monetisation
  1. Platform Network Effects
  • Every brand becomes a publisher
  • Every user adds network value
  • Each transaction reinforces utility
  • Ad inventory grows exponentially
  1. Economic Network Effects
  • Zero marginal cost scaling
  • Multiple revenue streams
  • Sustained reinvestment
  • Continuous innovation

These three network effects create a virtuous cycle where each strengthens the others, making the competitive moat progressively wider and deeper. The company that moves first and executes well will establish insurmountable advantages in the race to build marketing’s next great platform.

**

What

The ultimate prize? A trillion-dollar market opportunity:

  • $350–560 billion in annual AdWaste waiting to be reclaimed.
  • Massive engagement potential to monetise through customer utilities.
  • A chance to create a category-defining platform built on direct customer relationships rather than rented access.

Consider the precedent:

  • Google and Meta reached trillion-dollar valuations by mastering acquisition.
  • NeoMartech pioneers can achieve the same by mastering retention and engagement.

NeoMartech’s edge lies in authenticated identity and direct relationships—an antidote to anonymous tracking and third-party dependency. The company that successfully builds this future will:

  • Define a new industry category.
  • Generate network effects that compound over time.
  • Establish unassailable competitive moats.
  • Drive sustainable, profitable growth.

This isn’t just about improving existing economics—it’s about category creation. The ripple effects of this transformation will be felt across the ecosystem:

  • Consumers will enjoy more relevant and personalised experiences.
  • Brands will achieve profitable growth through reduced CAC and higher LTV.
  • Marketers will evolve from acquisition-driven roles to curators of lasting relationships.
  • Technology will advance into new frontiers of AI-driven personalisation.
  • Society will benefit from trust-based, value-driven engagement rather than intrusive advertising.

**

The opportunity is clear, the timing is perfect, and the path forward is within reach. By combining proven martech strengths with revolutionary business models, companies like Netcore can lead marketing’s next great transformation—from Trad to Neo, and from Neo to One. The future of marketing technology starts now. The only question is: Who will seize this moment to build the next trillion-dollar company?

7

Critique – 1

I asked Claude and ChatGPT o1 to do an analysis and pre-mortem.

Claude

Strategic Vision Assessment

  1. Core Thesis Strengths
  • The parallel with Google/Meta’s free-to-user, ad-supported model is compelling
  • Targeting AdWaste ($350-560B) shows clear market opportunity
  • The “Neo to One” progression is elegantly conceptualized
  • PII-based targeting could be superior to cookie-based approaches
  • Alignment with broader industry trends (privacy, personalization)
  1. Potential Roadblocks

Technical Challenges:

  • Building reliable infrastructure for multiple solutions simultaneously
  • Maintaining enterprise-grade performance at scale
  • Integrating with existing brand systems
  • Ensuring privacy and security across solutions
  • Managing complex ad targeting and delivery

Market Adoption Barriers:

  • Brand resistance to third-party ads in owned channels
  • Enterprise procurement processes despite “free” model
  • Competition from established players (both martech and adtech)
  • Building sufficient scale for ad monetization
  • Overcoming “too good to be true” skepticism

Pre-Mortem Analysis: What Could Go Wrong?

  1. The Platform Risk
  • Insufficient ad revenue to cover infrastructure costs
  • Low engagement with consumer utilities
  • Poor ad performance leading to advertiser churn
  • Technical failures damaging brand relationships
  • Privacy breaches or regulatory issues
  1. The Market Risk
  • Big Tech enters the space with similar offerings
  • Brands reject the ad-supported model
  • Insufficient network effects
  • Market timing issues (recession, ad spending cuts)
  • Industry consolidation working against new models
  1. The Execution Risk
  • Trying to do too much too quickly
  • Resource constraints limiting innovation
  • Talent acquisition and retention challenges
  • Loss of focus on core business
  • Operational complexity overwhelming benefits

Critical Success Factors

  1. Phased Implementation

Start with:

  • NeoMails as proof of concept
  • Focus on specific verticals/use cases
  • Build advertiser relationships early
  • Demonstrate clear ROI for early adopters
  • Create compelling case studies
  1. Technical Excellence
  • Robust infrastructure from day one
  • Enterprise-grade security and privacy
  • Seamless integration capabilities
  • High-performance ad delivery
  • Scalable architecture
  1. Go-to-Market Strategy
  • Target progressive brands first
  • Build strong reference customers
  • Focus on measurable outcomes
  • Create clear value proposition
  • Develop strong partner ecosystem

Recommendations for Netcore

  1. Immediate Actions
  • Create dedicated innovation team
  • Begin NeoMails development
  • Build initial ad platform capabilities
  • Identify pilot customers
  • Develop measurement framework
  1. Medium-Term Focus
  • Expand beyond email to other channels
  • Build AI capabilities systematically
  • Strengthen advertiser relationships
  • Develop self-service capabilities
  • Create strong privacy framework
  1. Long-Term Vision
  • Full platform development
  • Global market expansion
  • Advanced AI implementation
  • Ecosystem development
  • Category leadership

Key Questions to Address

  1. Business Model
  • How to ensure sufficient ad revenue?
  • What’s the minimum viable scale?
  • How to price for advertisers?
  • What’s the revenue sharing model?
  • How to handle operational costs?
  1. Market Entry
  • Which verticals to target first?
  • How to position against incumbents?
  • What’s the sales approach?
  • How to build trust?
  • How to demonstrate ROI?
  1. Technology
  • Build vs buy decisions?
  • How to ensure scalability?
  • Privacy and security approach?
  • Integration strategy?
  • AI development roadmap?

Final Assessment

The vision is ambitious and potentially transformative, but success will require:

  1. Methodical execution
  2. Significant resource commitment
  3. Strong market education
  4. Technical excellence
  5. Patient capital

Netcore’s position as an established player with existing capabilities provides advantages, but the transformation will require significant focus and investment. The key will be maintaining balance between current business and future vision while building the necessary components systematically.

8

Critique – 2

ChatGPT

Below is a candid, high-level critique of the “Trad → Neo → One” concept, followed by a pre-mortem scenario analysis of where things could go wrong and how best to mitigate those risks. The intent is to illuminate both the inspiring potential and the very real pitfalls of building a free, ad-supported martech stack for B2C brands.

Critical Analysis

1. Visionary Shift vs. Execution Complexities

  • Visionary Shift
    The underlying idea—turning martech from a cost center into a free-to-brand, ad-supported utility—taps into the same dynamic that made Google and Meta so powerful. Rather than charging for tools, you give them away, then monetize the vast trove of user attention and data. This has a compelling logic, especially in a market fatigued by piecemeal SaaS fees.
  • Execution Complexities
    However, adoption does not hinge on “vision” alone; it also hinges on trust, data ownership, brand control, user opt-in, privacy compliance, and the intricacy of ad targeting in a post-cookie, privacy-sensitive era. There’s a lot more friction to B2B enterprise software adoption than consumer adoption of a free Gmail or social network. When a brand embraces a free platform that monetizes user interactions, it has to be sure that:
    1. The user experience is not overshadowed by ads.
    2. The data used to power ad targeting is handled in a secure, compliant, and brand-safe way.

One of the biggest hazards is underestimating how risk-averse enterprise buyers can be, especially when it comes to anything that might undermine brand reputation or degrade customer experience.

2. Monetization Model: Potential vs. Pitfalls

  • Potential for a New Ad Channel
    If done right, NEON (PII-based advertising) could become a powerful alternative to Google and Meta. By delivering ads in channels that consumers genuinely want to engage with—daily micro-interactions, MyTwin chatbots, loyalty platforms, etc.—the click-through and conversion rates could be impressive.
  • Pitfalls of Transitioning to “Free”
    1. Scale Requirements: An ad-based monetization model requires a large enough user base and consistent engagement to attract advertisers. If the adoption ramp is slower than expected, ad revenue may not cover the investment in building and maintaining the platform.
    2. Brand Control: Brands might be squeamish about having third-party ads embedded in their own emails or loyalty journeys. Even if the ads are contextually relevant, brand guardians can worry about brand dilution.
    3. Quality vs. Quantity: The risk of “ad overload” is real. Over time, if the platform tries to push more ads to maximize revenue, it can degrade user experience and undermine brand trust.

3. Data and Privacy Realities

  • Data Network Effects
    The text highlights cross-brand insights as a competitive advantage. In principle, your platform becomes “smarter” the more brands that adopt it, and the more user interactions it sees. This is the classic network effect formula that made big adtech unstoppable.
  • Privacy & Compliance
    Getting—and staying—compliant with global regulations (GDPR, CCPA, evolving Indian data protection laws, etc.) is no small feat. The future of PII-based advertising is murky; many jurisdictions are clamping down on how personally identifiable information can be used or shared. A data breach or negative press about privacy can sabotage the entire premise.
  • Consumer Consent
    The user must also “opt in” to having a personal AI companion or “brain gain microns,” or simply to receiving targeted ads in brand channels. In practice, that’s an additional friction point. Education and upfront value are critical so that consumers see the utility outweighing any concerns about data usage.

4. Organizational & Market Readiness

  • Resistance from Existing Martech Stakeholders
    While the document’s argument is that brands are drowning in ballooning martech costs, the legacy martech providers will not go quietly. Expect fierce pushback from incumbents (Braze, Salesforce Marketing Cloud, Adobe, etc.), each with deep pockets to either replicate or undercut your approach.
  • Culture Change Inside Brands
    Marketing teams have grown accustomed to paying for SaaS but having full control over their data and brand experience. A free tool with embedded ad monetization demands a shift in mindset and acceptance of new revenue channels. Some brands might see it as genius; others might see it as risking brand equity.
  • Filling the Entire Engagement Stack
    The concept is grand in scope—covering email, push, search, chatbots, loyalty, etc. Rolling out so many free solutions also requires a massive engineering and operational commitment. Even for a large, established martech player, ensuring top-notch reliability, support, and compliance across such a broad portfolio can be resource-intensive.

5. AI at the Heart: Opportunities vs. Hype

  • Generative & Agentic AI
    The text rightly points out that generative AI and agentic AI (autonomous marketing orchestration) could turbocharge personalisation at N=1 scale. If “Agentic AI” truly delivers brand-level orchestration with minimal overhead, that is a major differentiator.
  • Hype Cycle Risks
    The generative AI boom is also full of inflated expectations. Giants like OpenAI, Microsoft, Google, and a swarm of startups are all racing for the same generative AI brass ring. Sustaining a competitive advantage in AI-driven personalisation—especially at the infrastructure level—will require continuous heavy investment and R&D.

Pre-Mortem: If It Fails, Here’s Why

Below is a scenario set a few years in the future, examining what might have caused the “NeoMartech” model to stumble:

  1. Data & Privacy Regulatory Blow
    A large-scale privacy scandal or an abrupt change in data protection laws constrains the use of PII-based advertising. The platform finds itself unable to deliver hyper-targeted ads legally without massive compliance overhead. Ad revenues plummet because the ads are no longer as precise, undermining the entire free model.
  2. Insufficient User Adoption
    Brands adopt the free tools, but users don’t engage enough for monetization to be meaningful. The “daily 15-second micro-interactions” concept doesn’t become a habit with real consumers. Advertisers see low CTRs and ROI. They redirect budgets back to Google and Meta, leaving the platform starved of revenue.
  3. Ecosystem Mismatch
    Brands are unwilling to sacrifice the sanctity of their owned channels. The brand marketing teams resist letting “third-party” ads appear in their emails, chatbots, or app push notifications, fearing it confuses brand identity. Customer satisfaction falls, unsubscribes rise, or brand guidelines block deeper adoption.
  4. Relentless Competition
    Established martech vendors replicate the “free” model by bundling an ad platform or forging alliances with big ad networks. Alternatively, Google or Meta incorporate deeper “retention solutions” for free, leveraging their enormous user base and advertisers. NeoMartech’s unique angle is diluted.
  5. Operational Overreach
    Trying to neo-fy everything from email to loyalty to chatbots to AI companions is expensive and complex. The platform becomes a jack of all trades, master of none. Reliability dips below enterprise standards, leading to brand dissatisfaction and churn.
  6. Ad Overload Erodes Trust
    Attempting to scale revenue quickly, the platform ratchets up ad impressions. The user experience degrades: more spammy feeling “ActionAds,” less real utility. Consumers unsubscribe or skip content. Engagement metrics drop, advertisers leave, brand managers revert to the old model.

Mitigation & Success Factors

  1. Laser-Focus on Select Use Cases
    Rather than trying to “neo-fy” everything at once, pick one or two high-impact areas—like email infrastructure or loyalty—and demonstrate visible success. Prove that an ad-supported model can consistently outperform traditional paid solutions in both brand ROI and user experience.
  2. Transparent User Consent & Data Governance
    Make it easy for end-users to understand the data relationship. Provide robust privacy controls, simple opt-outs, and best-in-class security. Strong privacy will build the brand’s trust and reduce regulatory headaches.
  3. Balanced Ad Policy
    Resist the temptation to flood consumer channels with ads. Set strict frequency caps, maintain ad relevance, and ensure the brand retains some control over who can advertise, how often, and in what format. A poor user experience is fatal.
  4. Proof of Incremental Revenue
    Advertisers must see real ROI, and brands must see net-new income that offsets their marketing costs. Early brand adopters should become “case studies” with compelling data to show that the free model doesn’t just slash marketing tech fees but also generates real ad revenue in brand-owned channels.
  5. Strategic Partnerships
    Scale is the name of the game in ad-based monetization. Seek large-scale alliances or buy-in from major advertisers who are looking for “post-cookie” user engagement channels. Building your own robust demand side might be critical before Google or Meta can steamroll this space.
  6. Ruthless Execution & Reliability
    B2B clients expect near-100% uptime, tight SLAs, and immediate support. Achieving enterprise-grade reliability at scale—especially if you plan to integrate with mission-critical workflows—will make or break trust with large brands.

Concluding Perspective

The journey from “Trad” to “Neo” to “One” is undoubtedly thought-provoking. It outlines a blueprint for dismantling the conventional SaaS pricing model in B2C martech—transforming cost centers into “free” consumer utilities, monetized via precision advertising. The upside is huge: If NeoMartech truly aligns engagement, personalization, and ad revenues, it can redirect hundreds of billions in AdWaste back into meaningful customer relationships.

Yet this is an all-or-nothing gamble. Success demands not just a bold concept, but also impeccable execution, ironclad privacy compliance, robust user consent frameworks, brand-safe ad integrations, and the ability to convince major advertisers that NEON can deliver ROI on par with or better than Google and Meta.

Given Netcore’s existing capabilities—email infrastructure, omnichannel CPaaS, marketing automation, a global footprint, and a reputation for reliability—it is arguably well-positioned to attempt such a leap. The question is whether the industry’s huge appetite for real ROI, combined with intensifying competitive pressures, can be harnessed to power the next trillion-dollar platform or whether the model will be nibbled to death by complexity and caution.

A disciplined, phased approach—one that validates the concept in a narrower domain and secures robust brand adoption and advertiser interest—may be the best way to mitigate risks and prove out a novel category before going all-in. If executed with care, the payoff could be transforming the entire B2C martech landscape into a more sustainable, profitable, and user-centric ecosystem.

Thinks 1512

Vasant Dhar: “In a few years, when we have mobile robots walking around and doing our shopping for us, I can imagine a robot driving another robot home with the groceries. What a bizarro brave new world that would be. For someone who started kindergarten in a horse-drawn cart in Kashmir in the early sixties, the delta between then and now just boggles my mind.”

Jason Lemkin: “Whatever you think about the competition — just realize they likely see the world differently.  Especially if they are either smaller or larger than you are…Your much bigger competition isn’t always out to get you. It often just doesn’t care.”

Derek Thompson: “Americans are now spending more time alone than ever. It’s changing our personalities, our politics, and even our relationship to reality…Day to day, hour to hour, we are choosing this way of life—its comforts, its ready entertainments. But convenience can be a curse. Our habits are creating what Atalay has called a “century of solitude.” This is the anti-social century.”

Megan McArdle: “If you want to know who wields power in a society, there’s a simple and effective test: Who supports censorship? If you see someone advocating for more suppression of dangerous speech – be it heresy, hate speech or “misinformation” – you can be sure they expect their side to have exclusive use of the ban-hammer.” [via CafeHayek]

WSJ: “Everyone is eating cottage cheese. They’re eating it from single-serve cups and plain old tubs. They’re eating it for breakfast through dessert. They’re eating it to boost their protein intake, lose weight, add muscle and extend their lives. They’re blending it with sweet and savory flavors and using it in recipes for smoothies, flatbreads, scrambled eggs and ice cream. You name it, teenagers have made it with cottage cheese—and then made TikToks about it. You might be seeing hunks of dairy all over your feeds, too. Not since the Brussels sprout renaissance has a food so reviled become so relevant again.”

AI Twins in Action: Daily Allies for Smarter Marketing and Meaningful Connections (Part 9)

Stories – 1

I asked Claude and ChatGPT to write stories to showcase the AI Twins future.

Claude

A Day in the Life with AI Twins: 2025

Morning: The Marketer’s View

Maya logs into her marketing dashboard at 9 AM. Instead of the usual flood of campaign metrics, she’s greeted by her team of Madtech Twins – AI replicas of key customer segments. “Good morning Maya,” the interface reads. “We’ve spotted some interesting patterns overnight.”

The Twin representing luxury segment customers highlights a spike in winter coat browsing from Singapore. “Unusual for our tropical customers,” it notes. “Cross-referencing travel data suggests 40% of these browsers have booked winter holidays. Recommend triggering our ‘Holiday Prep’ campaign with personalized timing based on travel dates.”

Another Twin, monitoring price-sensitive customers, flags that a competitor has just launched a sale. “But here’s the interesting part,” it adds. “Based on our segment’s past behavior, they’re more interested in our loyalty program benefits than pure discounts. I’ve drafted alternative messaging focusing on points multiplication.”

Maya initiates a quick group chat with multiple Twins to brainstorm the holiday campaign. The discussion feels natural – like a virtual room full of customer advocates, each representing different segment perspectives. Within minutes, they’ve crafted nuanced approaches for different customer groups.

Morning: The Customer Experience

Jay, a busy executive, checks his email during his morning commute. His MyTwin has curated his daily digest perfectly – tech industry news up top (he’d asked to prioritize AI and sustainability stories), followed by a subtle reminder about winter wear for his upcoming Swiss vacation.

“I noticed you’ve been browsing ski jackets,” his MyTwin messages. “Given your preference for minimalist design and previous purchases, you might like these three options. All available with express shipping before your December 15th flight.”

Meanwhile, Sarah, a food enthusiast, receives a completely different experience. Her MyTwin knows she loves discovering new restaurants but prefers detailed reviews over quick recommendations. “That new Japanese place you bookmarked? I’ve compiled insights from customers with similar taste preferences. Many mention the omakase menu’s unique fusion elements – right up your alley based on your recent dining choices.”

Afternoon: Back to the Marketing Suite

Maya’s attention turns to a real-time strategy session. The Madtech Twins have been monitoring a viral social media trend around sustainable fashion. The Twin representing Gen Z customers suggests a quick pivot in content strategy: “Our sustainable collection aligns perfectly with this conversation. Based on current sentiment analysis, authenticity is key. Recommend showcasing our production process rather than pure product features.”

The luxury segment Twin adds another layer: “My customers are particularly interested in the artisanal aspects. I’ve identified a micro-influencer whose content style matches our premium positioning.”

Afternoon: The Customer Side

Jay receives a notification – not a promotional push, but a genuine value-add from his MyTwin. “Remember how you mentioned wanting more CEO decision-making stories? There’s a fascinating piece about a tech CEO’s sustainability initiative. It aligns with both your professional interests and your personal focus on environmental impact.”

Across town, Rachel, a new mother, has a completely different interaction with her MyTwin. “I noticed you’ve been searching for baby development toys. Based on your preference for Montessori methods and natural materials, here are some options that other parents with similar philosophies have found helpful. Would you like to see detailed reviews or quick comparisons first?”

Evening: The Systems at Work

As the day winds down, the AI systems continue their orchestration. Maya reviews an automated summary from her Madtech Twins – they’ve identified micro-trends, suggested campaign optimizations, and flagged opportunities for the next day. The system is learning and adapting, becoming more precise with each interaction.

For customers, their MyTwins are quietly personalizing tomorrow’s experiences. Jay’s Twin notes his engagement with the CEO article and updates his content preferences accordingly. Sarah’s Twin observes her detailed reading of the restaurant reviews and refines its understanding of her decision-making process. Rachel’s Twin analyzes her interaction with the toy recommendations to better support her parenting journey.

Late Night: The Personal Touch

Even during off-hours, the system maintains its personal touch. When Jay checks his phone before bed, his MyTwin has a thoughtful suggestion: “Given your upcoming trip and your preference for preparing early, would you like me to put together a packing list? I can customize it based on your travel style and the forecasted weather in Switzerland.”

The Next Morning

Maya starts her day with insights that would have taken weeks to gather in the past. The Madtech Twins have processed overnight interactions, identified emerging patterns, and prepared nuanced recommendations for different customer segments. But more importantly, they’ve maintained thousands of individual conversations, each feeling personal and relevant.

For customers like Jay, Sarah, and Rachel, their MyTwins have become indispensable personal assistants – understanding their unique preferences, anticipating their needs, and making their interactions with brands more meaningful and efficient.

This is the new world of marketing – where AI Twins enable true N=1 personalization at scale, transforming both the marketer’s workflow and the customer’s experience. No mass messaging, no generic campaigns, just genuine understanding and relevant engagement, powered by the seamless collaboration of Madtech Twins and MyTwins.

Thinks 1511

Mint: “Imagine a marketing team deploying an AI agent to create an entire ad campaign—researching the target audience, drafting content, and optimizing strategy seamlessly. AI agents can also resolve customer queries, reducing response times and minimizing the need for human intervention. You may think of AI agentic systems as intelligent virtual employees that are capable of autonomous decision-making and action to achieve specific goals without human intervention.”

FT: “One tactic is to ask for advice, instead of feedback. A Harvard Business School working paper written by Hayley Blunden and colleagues finds that when people ask for advice, it tends to prompt more useful comments: critical, actionable and focused on the potential for future improvements. A second approach involves a neat two-step, demonstrated by author and psychologist Adam Grant. I interviewed him on stage a few months ago, and we had a great time. Afterwards, he asked me for marks out of 10 for our performance. Oh, nine and a half, I suggested. (There’s always room for improvement, right?) In a flash, the eager follow-up question: “What would have made it a 10?” Clever. If he’d just asked for my comments I’d have told him — truthfully — that I thought he was superb. But having persuaded me to admit that there was some fractional room for improvement, I then had to think about how.”

NYTimes has advice for heart health: “Dr. Khan’s top tip for patients, after quitting smoking, is to see how many flights of stairs they can climb without getting winded — and then start building on that. Regular exercise strengthens the heart muscle and makes the body more efficient at pulling oxygen out of the blood. It also brings down blood pressure and glucose levels and helps reduce the excess body fat that can lead to insulin resistance and other metabolic disorders.The American Heart Association recommends that adults get 150 minutes of moderate aerobic activity per week, but not everyone has 30 minutes each day to hop on a treadmill or do high-intensity training, Dr. Lara-Breitinger said. That’s where small goals and short bursts of exercise can come in.”

Jensen Huang: ““It started with perception AI — understanding images, words and sounds. Then generative AI — creating text, images and sound…Next comes an era of “physical AI, AI that can proceed, reason, plan and act.”

Andy Kessler: “Faster productivity doesn’t just happen. Government shouldn’t pick winners and losers. Instead, productivity takes tireless scientists and engineers to experiment constantly with tiny improvements until the rest of us see what feels like huge gains in what we can do. Our future depends on this model. Autonomous cars and robo-taxis, almost ready for prime time, are soaking up ever-faster chips, AI, mobile broadband and vision systems. The same is true of augmented-reality glasses, productive personal assistants, and personalized medicine. Basic research of all types should be encouraged. Let many ideas flourish as industrial policy doesn’t work. Instead, success is like throwing spaghetti against the wall, seeing what sticks.” More: “It’s always a mistake when change is discouraged. Silicon Valley is one of the free market’s few meritocracies. The other is the self-correcting money-management business. Especially hedge funds. Both live on the edge of chaos. In both, if you underperform, you’re out. New ideas from those who live at the edge are fresh shots of energy to technology and really every industry.”

AI Twins in Action: Daily Allies for Smarter Marketing and Meaningful Connections (Part 8)

Stories – 1

I asked Claude and ChatGPT to write stories to showcase the AI Twins future.

Claude

A Day in the Life with AI Twins: 2025

Morning: The Marketer’s View

Maya logs into her marketing dashboard at 9 AM. Instead of the usual flood of campaign metrics, she’s greeted by her team of Madtech Twins – AI replicas of key customer segments. “Good morning Maya,” the interface reads. “We’ve spotted some interesting patterns overnight.”

The Twin representing luxury segment customers highlights a spike in winter coat browsing from Singapore. “Unusual for our tropical customers,” it notes. “Cross-referencing travel data suggests 40% of these browsers have booked winter holidays. Recommend triggering our ‘Holiday Prep’ campaign with personalized timing based on travel dates.”

Another Twin, monitoring price-sensitive customers, flags that a competitor has just launched a sale. “But here’s the interesting part,” it adds. “Based on our segment’s past behavior, they’re more interested in our loyalty program benefits than pure discounts. I’ve drafted alternative messaging focusing on points multiplication.”

Maya initiates a quick group chat with multiple Twins to brainstorm the holiday campaign. The discussion feels natural – like a virtual room full of customer advocates, each representing different segment perspectives. Within minutes, they’ve crafted nuanced approaches for different customer groups.

Morning: The Customer Experience

Jay, a busy executive, checks his email during his morning commute. His MyTwin has curated his daily digest perfectly – tech industry news up top (he’d asked to prioritize AI and sustainability stories), followed by a subtle reminder about winter wear for his upcoming Swiss vacation.

“I noticed you’ve been browsing ski jackets,” his MyTwin messages. “Given your preference for minimalist design and previous purchases, you might like these three options. All available with express shipping before your December 15th flight.”

Meanwhile, Sarah, a food enthusiast, receives a completely different experience. Her MyTwin knows she loves discovering new restaurants but prefers detailed reviews over quick recommendations. “That new Japanese place you bookmarked? I’ve compiled insights from customers with similar taste preferences. Many mention the omakase menu’s unique fusion elements – right up your alley based on your recent dining choices.”

Afternoon: Back to the Marketing Suite

Maya’s attention turns to a real-time strategy session. The Madtech Twins have been monitoring a viral social media trend around sustainable fashion. The Twin representing Gen Z customers suggests a quick pivot in content strategy: “Our sustainable collection aligns perfectly with this conversation. Based on current sentiment analysis, authenticity is key. Recommend showcasing our production process rather than pure product features.”

The luxury segment Twin adds another layer: “My customers are particularly interested in the artisanal aspects. I’ve identified a micro-influencer whose content style matches our premium positioning.”

Afternoon: The Customer Side

Jay receives a notification – not a promotional push, but a genuine value-add from his MyTwin. “Remember how you mentioned wanting more CEO decision-making stories? There’s a fascinating piece about a tech CEO’s sustainability initiative. It aligns with both your professional interests and your personal focus on environmental impact.”

Across town, Rachel, a new mother, has a completely different interaction with her MyTwin. “I noticed you’ve been searching for baby development toys. Based on your preference for Montessori methods and natural materials, here are some options that other parents with similar philosophies have found helpful. Would you like to see detailed reviews or quick comparisons first?”

Evening: The Systems at Work

As the day winds down, the AI systems continue their orchestration. Maya reviews an automated summary from her Madtech Twins – they’ve identified micro-trends, suggested campaign optimizations, and flagged opportunities for the next day. The system is learning and adapting, becoming more precise with each interaction.

For customers, their MyTwins are quietly personalizing tomorrow’s experiences. Jay’s Twin notes his engagement with the CEO article and updates his content preferences accordingly. Sarah’s Twin observes her detailed reading of the restaurant reviews and refines its understanding of her decision-making process. Rachel’s Twin analyzes her interaction with the toy recommendations to better support her parenting journey.

Late Night: The Personal Touch

Even during off-hours, the system maintains its personal touch. When Jay checks his phone before bed, his MyTwin has a thoughtful suggestion: “Given your upcoming trip and your preference for preparing early, would you like me to put together a packing list? I can customize it based on your travel style and the forecasted weather in Switzerland.”

The Next Morning

Maya starts her day with insights that would have taken weeks to gather in the past. The Madtech Twins have processed overnight interactions, identified emerging patterns, and prepared nuanced recommendations for different customer segments. But more importantly, they’ve maintained thousands of individual conversations, each feeling personal and relevant.

For customers like Jay, Sarah, and Rachel, their MyTwins have become indispensable personal assistants – understanding their unique preferences, anticipating their needs, and making their interactions with brands more meaningful and efficient.

This is the new world of marketing – where AI Twins enable true N=1 personalization at scale, transforming both the marketer’s workflow and the customer’s experience. No mass messaging, no generic campaigns, just genuine understanding and relevant engagement, powered by the seamless collaboration of Madtech Twins and MyTwins.