Rajesh Jain

Published May 29, 2025

1

The Idea

In the previous essay, I discussed how Progency can be thought of as the Growth Alpha engine. In this series, I will explain how NeoN can be considered as delivering Beta at a lower cost. Both Progency and NeoN are the two primary anchors of NeoMarketing, a new era in marketing which focuses on retention over reacquisition, trust over transactions, and individuals over segments. NeoMarketing helps maximise LTV and minimise CAC to deliver sustainable profitable growth.

As I explained in NeoMarketing: Double the Best, Halve the Waste: “NeoN reimagines advertising through authenticated identity, creating a direct marketplace that connects brands without expensive intermediaries. Unlike cookie-based targeting, NeoN leverages first-party data with deterministic, precise matching to enable brand-to-brand collaboration. Through this approach, one brand’s inactive “Test” customers can be precisely targeted through another brand’s engaged “Best” customer channels. Interactive ActionAds within partner emails enable complete transactions without leaving the inbox, dramatically increasing conversion rates. This creates a powerful dual advantage: publishers “print money” by monetising their engaged audience while advertisers “save money” through dramatically more efficient customer acquisition—cutting costs by 30-50% compared to traditional platforms. As third-party cookies disappear, NeoN’s authenticated approach represents not just another advertising channel but a fundamental reimagining of how brands reach both dormant customers and new prospects.”

NeoN’s revolutionary approach is powered by the PACE framework—a comprehensive system designed to eliminate the inefficiencies of traditional advertising while creating a more effective, ethical ecosystem:

• PII Matching Engine: Unlike cookie-based targeting that relies on probabilistic matching, NeoN’s core innovation lies in its authenticated identity engine. This precise system enables brands to reach their dormant customers through email channels where those individuals are actively engaged with other brands. This deterministic matching ensures messages reach exactly the right people, eliminating the waste inherent in traditional adtech.
• ActionAds: Traditional digital advertising suffers from a devastating “click-through penalty” where 80-90% of potential conversions are lost when customers must navigate to external sites. NeoN’s ActionAds, powered by AMP technology, create interactive mini-applications within emails that enable browsing, form completion, and purchasing without leaving the inbox—dramatically improving conversion rates whilst reducing friction.
• Cooperative Structure: Rather than enriching platform intermediaries, NeoN creates a direct marketplace where brands simultaneously play dual roles—as publishers monetising their engaged audiences and as advertisers efficiently reacquiring their dormant customers. This cooperative approach creates powerful network effects where each participating brand strengthens the overall ecosystem.
• Ecosystem Services: Beyond core matching capabilities, NeoN develops complementary utilities that expand inventory opportunities while enhancing data capabilities, including inbox intelligence, AI-powered newsletters, an Atomic Rewards system for micro-actions, and sophisticated data management functionality for high-potential audience identification.

NeoN focuses on Test and Next customers. It provides an alternative to current ad networks like Google and Meta, using the authenticated identity (PII) available via martech channels and touchpoints like email, notifications, and search to slash acquisition and reacquisition costs, and therefore AdWaste. By creating a more efficient distribution system for reaching target audiences, NeoN delivers the same customer access as traditional platforms (Beta) but at significantly lower costs—effectively reducing the “Beta tax” brands have been forced to pay through auction-based advertising platforms.

The implications are transformative: brands can maintain or even expand their marketing reach while dramatically reducing dependency on expensive intermediaries. This shift represents not just cost optimisation but a fundamental restructuring of the marketing ecosystem—where brands collaborate directly, customers receive more relevant communications, and the massive inefficiencies of AdWaste are systematically eliminated.

2

History

I asked Claude and ChatGPT for an overview of Beta in finance and other industries.

The concept of Beta originates in the world of finance, specifically in the Capital Asset Pricing Model (CAPM) developed in the 1960s by Jack Treynor, William Sharpe, John Lintner, and Jan Mossin. Beta measures an investment’s sensitivity to market movements—essentially, its correlation with the overall market. A Beta of 1 indicates the investment moves in lockstep with the market, greater than 1 means amplified movements, and less than 1 implies dampened volatility.

Beta served as the benchmark return investors could expect simply by taking market risk without any special insights or skill. It represented the “cost of being in the game”—the return earned by passively riding the market’s ups and downs.

In contrast, Alpha emerged as the excess return generated beyond Beta. In essence, Beta represented the tide; Alpha measured the ability to swim faster than the tide. Investors paid managers for Alpha, not Beta—because Beta could be obtained cheaply through index funds.

Beta’s Expansion Beyond Finance

While its roots lie in capital markets, the concept of Beta has spread to other industries over the past few decades:

• Consulting and Professional Services: Clients expect baseline improvements (“Beta outcomes”) from hiring top consultancies—e.g., a standard uplift in productivity or cost savings that any credible firm can deliver. But consultancies earn their premium fees by generating Alpha outcomes—transformative improvements beyond the baseline.
• Legal Industry: Big law firms price routine legal work (filings, documentation) at Beta rates. But complex litigation or landmark deals—where the outcome can swing by billions—command Alpha fees tied to exceptional results.
• Technology and SaaS: Traditional software offers baseline automation (Beta) that every competitor can also deploy. But platforms that unlock proprietary data insights, operational efficiencies, or network effects create Alpha—advantages competitors cannot easily replicate.
• Marketing (implicitly): For decades, marketing operated in a Beta paradigm—benchmarked to industry-standard metrics like clickthrough rates, conversion rates, or ROAS. Agencies promised “average” returns based on historical norms. But they rarely offered Alpha—measurable outperformance that exceeded market norms or the brand’s historical baseline.

The Beta-Alpha Link: Why Both Matter

Beta is foundational—it provides the cost of participation and a minimum benchmark. Without Beta, there’s no basis for measuring Alpha. But Beta is inherently capped—it tracks systemic market returns or industry norms. Only Alpha creates upside beyond the market or category average.

In finance, this distinction became critical as index funds commoditised Beta, driving investors to seek scarce Alpha elsewhere. The same dynamic is now emerging in industries like marketing: platform-driven advertising delivers Beta returns accessible to every brand, leading to diminishing differentiation and rising costs.

This is why the framing of Progency as the “Growth Alpha Engine” and NeoN as the “Beta Efficiency Engine” is so powerful. NeoN delivers the essential customer acquisition reach every brand needs but with dramatically improved efficiency—ensuring they don’t overpay to reach customers they already know. Meanwhile, Progency generates true outperformance by maximizing customer lifetime value (LTV) through proprietary, AI-powered engagement strategies that exceed baseline expectations.

In other words: NeoN optimises the necessary market participation costs (delivering Beta with superior efficiency), while Progency creates exceptional performance beyond industry norms (delivering Alpha that competitors cannot easily replicate). Together, they form the complete NeoMarketing framework—optimizing both sides of the profit equation.

3

The How

The modern digital advertising ecosystem operates through a series of interconnected auction-based platforms that systematically drive up costs while delivering increasingly diminishing returns. At its core, this system hinges on probabilistic targeting through cookies, device IDs, and algorithmic modelling—all designed to approximate audience characteristics rather than definitively identify individuals.

This inherently flawed approach creates multiple inefficiency points. First, the auction-based pricing mechanisms ensure costs consistently trend upward as brands compete for the same attention. Second, platform intermediaries (Google, Meta, etc.) extract significant revenue—often 30-50% of spending—simply for facilitating access to audiences that brands may already have relationships with. Third, the targeting imprecision means approximately 20-40% of impressions reach incorrect audiences despite sophisticated algorithms, creating unavoidable wastage.

Perhaps most troublingly, this system fundamentally lacks transparency. Brands never know exactly whom their adverts reach, cannot verify audience quality directly, and are beholden to black-box attribution models owned by the very platforms selling the inventory. This creates an impossible situation where those selling the service also control the measurement of its effectiveness.

NeoN’s Beta Efficiency Revolution

NeoN fundamentally transforms this broken model by addressing each inefficiency point directly.

Firstly, NeoN targets the largest source of AdWaste: reacquisition spending. By focusing specifically on dormant “Test” customers (inactive 90+ days), NeoN addresses the approximately 70% of digital marketing budgets currently haemorrhaged to repeatedly reach customers brands already know. This represents the greatest efficiency opportunity in the entire marketing landscape.

Secondly, NeoN replaces probabilistic with deterministic targeting through its PII (Personally Identifiable Information) matching engine. Rather than approximating audience characteristics, NeoN creates precise matches between a brand’s dormant customers and the active audiences of non-competing brands. This eliminates the 20-40% targeting waste inherent in cookie-based approaches, ensuring messages reach exactly the intended recipients. If traditional ad platforms are like blindfolded archers shooting arrows and hoping they hit the target, NeoN hands marketers a laser pointer—directing their message precisely to known customers already waiting to hear from them.

Thirdly, just as Singular AI Twins enable true 1:1 personalisation for retention and growth, NeoN’s authenticated identity approach enables perfect precision in reacquisition. When an inactive “Test” customer of Brand A is simultaneously an engaged “Best” customer of Brand B, NeoN creates the ideal targeting environment—reaching people through channels where they’re actively engaged, with permissioned identity ensuring accuracy.

Furthermore, NeoN applies this same authenticated approach to new acquisition through its sophisticated DMP (Data Management Platform). By leveraging cross-brand intelligence and zero-party data, NeoN identifies genuinely high-potential prospects with unprecedented precision, reducing the wastage endemic to traditional lookalike modelling.

Finally, NeoN eliminates the platform intermediary “tax” by creating direct brand-to-brand collaboration. Rather than paying Google or Meta premiums through competitive auctions, brands establish mutually beneficial partnerships—monetising their engaged audiences while efficiently reacquiring their dormant customers at substantially lower costs.

Here is a table summarising NeoN’s fixes versus traditional platforms.

Problem (AdTech)	Solution (NeoN)
Auction-based inflation	Direct brand-to-brand collaboration
Probabilistic targeting (cookies)	Deterministic PII matching
Black-box attribution	Transparent identity-based reach
Click-through penalty	In-email ActionAds conversion

The result is a comprehensive Beta Efficiency Engine that delivers the same essential customer reach as traditional platforms but with dramatically improved efficiency—cutting reacquisition costs by 30-50% and simultaneously improving targeting precision and conversion rates through interactive ActionAds embedded directly within partner emails. NeoN doesn’t merely patch the inefficiencies of today’s ad ecosystem—it rewires the economics of attention for a world where identity, trust, and efficiency form the new competitive advantage.

4

Learnings from Finance

I asked Claude and ChatGPT for lessons which NeoN can learn from finance.

In the world of finance, few innovations have been as transformative as the index fund revolution pioneered by Vanguard’s John Bogle. By offering market exposure (Beta) at dramatically lower costs than actively managed funds, index funds fundamentally restructured investment economics. NeoN brings this same revolutionary approach to marketing—applying key financial principles to create unprecedented efficiency in customer acquisition.

The Index Fund Parallel: Just as index funds strip away unnecessary intermediary fees to deliver market Beta at minimal cost, NeoN eliminates the platform “tax” that currently inflates reacquisition spending. Traditional adtech platforms operate like high-fee active managers, charging premium rates for access to audiences that brands may already have relationships with. NeoN, by contrast, functions as the marketing equivalent of a low-cost index fund—delivering the same essential customer reach without the exorbitant platform premiums. In other words, NeoN is to customer reacquisition what index funds were to investing: stripping out hidden fees, intermediaries, and inefficiencies—delivering the same reach at a fraction of the cost.

Attention Arbitrage: Financial markets thrive on arbitrage—the practice of exploiting price discrepancies across different markets. NeoN creates a similar “attention arbitrage” by identifying a fundamental pricing inefficiency: the same customer can be reached as an engaged email recipient far more cheaply than through competitive ad auctions. By tapping into partner brands’ active email audiences, NeoN allows brands to reacquire their dormant customers at 30-50% lower costs—a classic arbitrage opportunity that creates immediate value.

Factor-Based Targeting: Modern finance has evolved beyond simple indexing to “Smart Beta” strategies that target specific factors (value, quality, momentum) driving returns. Similarly, NeoN replaces crude probabilistic targeting with deterministic identity factors. Where traditional platforms rely on behavioural proxies and lookalike models, NeoN uses authenticated PII as a proprietary targeting factor—turning known identity into a precision advantage that auction-based platforms cannot replicate.

Optimising the Cost Structure: The investment world clearly distinguishes between low-cost Beta (0.1-0.3% fees for index funds) and premium-priced Alpha strategies (1-2%+ for active management). NeoN brings this same cost discipline to marketing, allowing brands to secure their necessary customer reacquisition at “index fund rates.” This fundamentally transforms budget allocation, freeing resources previously wasted on platform premiums to be redeployed toward true Growth Alpha initiatives.

Disintermediation and Direct Access: Financial markets have steadily moved toward disintermediation—enabling investors to access markets directly without unnecessary middlemen. NeoN creates this same direct access in marketing, connecting brands to customers through authenticated channels rather than platform intermediaries. This isn’t merely cost-cutting—it’s restructuring the entire value chain to eliminate entities that extract disproportionate value without adding commensurate benefits.

Just as Vanguard’s index revolution permanently altered the investment landscape by demonstrating that Beta exposure needn’t come at premium prices, NeoN transforms marketing by proving that customer acquisition doesn’t require platform dependency. By unlocking attention arbitrage, eliminating hidden fees, and replacing probabilistic guessing with deterministic precision, NeoN delivers the marketing equivalent of Bogle’s greatest insight: the most reliable path to superior performance often begins with minimising unnecessary costs.

5

Objection Handling – 1

I asked Claude and ChatGPT to list out possible objections from CMOs and other stakeholders that merit thoughtful consideration.

1. Data Privacy & Consent Concerns

Objection: “NeoN’s PII-based matching seems concerning from a privacy perspective. Are we compliant with GDPR, CCPA, and other regulations? Will customers perceive this approach as invasive or creepy?”

Response: NeoN operates on a privacy-first foundation that exceeds industry standards:

• Only uses permissioned, first-party data from existing brand relationships—never buying or selling data
• Functions as a closed-loop matching system without exposing PII across brands
• Operates like a secure “clean room” where only anonymized identifiers are compared
• Provides clear opt-out mechanisms for users across all participating brands
• Represents a privacy-forward alternative to cookie-based surveillance tracking

“NeoN uses the data customers have already shared with you—ethically, transparently, and with their permission. We don’t follow customers around the web; we reconnect you in channels they already trust.”

2. “Creepiness” or Perception of Cross-Brand Targeting

Objection: “Won’t customers find it invasive if Brand A seems to know what Brand B knows about them? Is this secretly a data-sharing arrangement consumers didn’t agree to?”

Response:

• NeoN doesn’t share raw customer data between brands—it acts as a secure matching and delivery system
• Recipients only see ads from brands within channels they’ve explicitly opted into
• The approach is less intrusive than traditional web display ads or third-party cookie retargeting
• All content is contextually relevant, privacy-safe, and easily opt-out-able
• Research shows consumers prefer relevant offers in trusted channels over random targeting across the web

“NeoN never transfers your customer data. It operates like a postal sorting service: matching addresses behind the scenes, delivering relevant offers inside permissioned inboxes without exposing identities between brands.”

3. Email Attention Overload / Customer Fatigue

Objection: “Will embedding ActionAds in emails dilute attention or annoy subscribers? Won’t this reduce engagement with our own brand’s content?”

Response:

• NeoN uses non-intrusive placements (email footers, designated sections) that complement, not compete with, primary brand content
• Each ActionAd is contextually matched to recipient behaviour—creating relevance, not interruption
• Brands maintain control over placement density and frequency (e.g., limit to one ActionAd per email)
• Early implementations show well-designed ActionAds actually increase overall email engagement by providing additional value
• The approach transforms passive email space into an active revenue stream without overwhelming the audience

“NeoN turns under-utilised email real estate into a profit generator—without distracting from your message or overwhelming your audience. It’s like adding a trusted, relevant partner message that enhances rather than detracts from the recipient experience.”

4. Deliverability & Spam Risks

Objection: “Will adding third-party ActionAds trigger spam filters or harm our email deliverability score? Will inbox providers penalise us for advertising other brands?”

Response:

• NeoN ads are native to the email format (AMP/CSS), avoiding external links or attachments that trigger spam rules
• The system works within inbox platform guidelines (Gmail AMP approval, etc.)
• Comprehensive A/B testing and deliverability monitoring ensures placements don’t impact sender reputation
• Brands can limit NeoN ads to specific audience segments or email types (promotions, newsletters)
• The system follows best practices that maintain deliverability while opening new monetisation streams

5. Brand Safety & Control Concerns

Objection: “What if my brand’s email ends up showing a competitor’s ad? How do we ensure we don’t promote brands that conflict with our values?”

Response:

• NeoN includes comprehensive brand-level whitelist/blacklist controls
• Each brand approves exactly who can advertise in their inventory
• No direct competitors will be shown without explicit permission
• Vertical segmentation ensures ads only appear from non-competing categories
• Complete transparency into which brands will appear in your communications
• Regular reporting shows exactly which brands are appearing and their performance

“With NeoN, you’re in complete control: you choose exactly who can advertise in your customer communications. No unwanted competitors, no mismatched messages, no surprises.”

6

Objection Handling – 2

6. ROI Skepticism & Adoption Risk

Objection: “Sounds promising, but will this really save 30-50% on acquisition costs compared to traditional platforms? What’s the evidence this works better than Google/Meta?”

Response:

• Comprehensive case studies and benchmark data demonstrating improved CAC versus traditional ad platforms
• Risk-free pilot campaigns or guaranteed performance benchmarks to encourage adoption
• NeoN positions as an incremental, not replacement solution—doesn’t require dismantling existing ad spend initially
• Transparent measurement allows direct comparison with other channels
• Unlike black-box attribution models, NeoN provides clear visibility into exactly which dormant customers were reached and their actions

“NeoN lets you test without risk—plug it into a subset of your dormant customers and compare outcomes side by side with existing channels. The data will speak for itself.”

7. Implementation Complexity

Objection: “This sounds promising but complex to implement. Will this require retraining my team or overcomplicating workflows? Do we need to overhaul our tech stack?”

Response:

• NeoN operates as a fully managed service or via existing ESP integrations—no new dashboard overload
• Connects to existing email service providers and CRM platforms through standard APIs
• Most brands can be onboarded within 2-4 weeks with minimal technical overhead
• Dedicated implementation specialists guide the process from start to finish
• For brands concerned about complexity, managed service options handle all technical details
• Progressive adoption model allows starting with a small segment before scaling based on proven results

“NeoN doesn’t ask your team to learn new systems. We slot seamlessly into your existing email templates, automations, and processes—like adding a high-performing co-pilot that enhances your current capabilities.”

8. Reciprocity Requirements

Objection: “For this to work, we’d need to place other brands’ ads in our emails, which we’re hesitant to do.”

Response:

• NeoN’s cooperative structure offers flexible participation models
• Brands can start as advertisers only before gradually incorporating publisher capabilities
• The system is designed to provide value at each stage of adoption
• Additional revenue stream—often generating 30-40% more than the cost of email sending—creates a compelling business case
• Complete control over which partner brands can appear in your communications

9. Measurement and Attribution Skepticism

Objection: “How can we trust NeoN’s measurement when we’ve been burned by opaque attribution models before?”

Response:

• Unlike black-box adtech platforms, NeoN’s measurement is built on deterministic identity, not probabilistic models
• All interactions occur within authenticated environments, making attribution direct and transparent
• No more questionable “view-through” claims or conversion credits
• Comprehensive reporting shows exactly which dormant customers were reached and which actions they took
• Creates unprecedented accountability compared to traditional platforms where those selling inventory also control measurement

10. Consumer Opt-Out or Brand Loyalty Dilution

Objection (from end users): “Why is Brand A showing me ads for Brand B? Is this a sellout? Will I opt out?”

Response:

• NeoN targets opt-in communications and prioritises non-intrusive, contextually relevant offers
• Customers see fewer, more relevant ads in channels they already trust—no tracking across web/apps
• The approach aligns with evolving consumer expectations around personalisation
• Research shows 71% of consumers expect personalised interactions and 76% get frustrated when this doesn’t happen
• The transparency and value exchange create clarity rather than confusion
• In-email ActionAds deliver genuine convenience—allowing customers to take action without leaving their inbox

“We don’t follow you across the internet. We only show you exclusive offers from partners we trust, inside the emails you already opted into.”

The key pattern across all objections is control, transparency, relevance, and trust. NeoN represents “a more transparent, brand-controlled, privacy-safe alternative to third-party cookie retargeting and auction-based platforms—delivering Beta at a fraction of the cost, without sacrificing customer trust or experience.”

7

The Pitch – 1

I asked Claude and ChatGPT to create a role-play pitch between Maya (CMO) and John (Progency salesperson).

Claude

Scene: Maya’s office at a mid-sized B2C retailer with $200M annual revenue. She’s reviewing her digital marketing dashboard, frowning at the steadily rising acquisition costs. John, representing NeoN, enters for their scheduled meeting.

Maya: (looking up) Thanks for making the time, John. I’ve got about 30 minutes before my next meeting. Your email mentioned something about ‘Beta Efficiency’ and reducing customer acquisition costs, which honestly couldn’t come at a better time. Our CAC has increased 22% year-over-year.

John: (taking a seat) I appreciate the time, Maya. Before I dive into NeoN, might I ask what percentage of your digital marketing budget goes toward reacquiring customers you already know? People who’ve purchased before but have gone dormant?

Maya: (sighs) We’ve actually been trying to figure that out. Our best estimate is around 65-70%. I was shocked when our analytics team showed me the numbers. We’re essentially paying Google and Meta to reach people already in our database. It feels… inefficient.

John: You’re not alone. We call it the $500 billion AdWaste crisis – the industry average is indeed about 70%. The question is: why are brands forced to pay these platforms premium prices to reconnect with customers they already have relationships with?

Maya: (nodding) It’s a necessary evil, isn’t it? Once customers stop engaging with our emails or app, we have no choice but to find them elsewhere.

John: That’s exactly the problem we’ve solved with NeoN. Think of it this way: in finance, there’s a distinction between Alpha – outperformance through skill – and Beta, which is the baseline market exposure every investor needs. The issue is that brands are paying astronomical prices for what should be basic Beta – simply reaching their own customers.

Maya: (interested) I’m listening.

John: NeoN is what we call a Beta Efficiency Engine. It delivers the same customer reach you need but at 30-50% lower cost. We do this through authenticated identity targeting instead of cookie-based auctions.

Maya: (skeptical) How exactly does that work?

John: Remember when you mentioned customers who’ve gone dormant? We call them “Test” customers – inactive for 90+ days. Here’s the key insight: while they might be dormant for your brand, many of these same individuals are actively engaged with other non-competing brands.

NeoN creates a direct marketplace where brands can reach their dormant customers through the active email channels of these other brands. The magic is in our PII matching engine that makes precise connections between your inactive customers and where they’re actually paying attention.

Maya: So if I understand correctly, you’re saying my dormant customers might be opening emails from Brand X, and I could reach them there instead of through Google or Meta?

John: Exactly. And not just reach them – engage them. Traditional retargeting has another massive flaw: the “click-through penalty.” When customers click an ad, they’re taken to a landing page where 80-90% drop off before converting.

Maya: (nodding vigorously) Tell me about it. Our funnel analytics show exactly that pattern.

John: NeoN solves this with ActionAds – interactive AMP-powered units embedded directly in partner emails. Your customers can browse products, fill forms, even complete purchases without ever leaving their inbox. It’s like eliminating all the friction from the traditional customer journey.

Maya: This sounds promising, but I have concerns. First, what about privacy? Using PII to track customers across brands sounds like it could raise red flags.

John: That’s a great question. NeoN is actually more privacy-forward than traditional adtech. We never share raw customer data between brands. Think of us like a secure postal service – we match addresses behind the scenes but never expose identities. Everything happens with explicit consent, within channels customers have already opted into.

Maya: What about brand safety? I’d be worried about which other brands might appear in our emails, or where our ads might show up.

John: You maintain complete control. Our platform includes comprehensive whitelists and blacklists, ensuring no competitors or misaligned brands ever appear in your communications. You approve exactly who can advertise to your audience.

Maya: (thinking) And implementation? My team is already stretched thin.

John: NeoN slots seamlessly into your existing email service provider through standard APIs. Most brands are onboarded within 2-4 weeks with minimal technical overhead. We offer fully managed services if you prefer a hands-off approach. It’s designed to enhance your current capabilities, not create new workflows.

Maya: (nodding slowly) The cost savings sound impressive, but I’d need to see proof that this actually works better than our current channels.

John: Absolutely. We typically start with a risk-free pilot – you select a subset of your dormant customers, and we target them through NeoN while you continue reaching a control group through traditional platforms. The side-by-side comparison speaks for itself.

Maya: (intrigued) And this is where the “Beta Efficiency” concept comes in?

John: Precisely. Just as index funds revolutionized investing by providing market exposure without the hefty fees of active managers, NeoN delivers customer acquisition Beta without the platform tax. This means you can redirect those savings toward initiatives that actually generate Alpha – like using Progency to maximize customer lifetime value.

Maya: (checking her watch) This is genuinely intriguing, John. Could you send me a proposal for a pilot program? I’d want to test this with maybe 100,000 of our dormant customers – enough for a statistically significant test.

John: Absolutely. I’ll have that to you by tomorrow. One final thought – every dollar you save on reducing AdWaste is essentially pure profit. If NeoN can cut your reacquisition costs by even 30%, what would that mean for your bottom line?

Maya: (calculating mentally) Given our current spending… that would be substantial. Let’s explore this further. Send me that proposal and some case studies if you have them.

John: (standing to leave) Will do. Thank you for your time, Maya. I believe this could be transformative for your marketing economics.

Maya: We’ll see. The data will need to speak for itself. But if you can deliver what you’re promising – the same reach at half the cost – that’s certainly worth exploring.

8

The Pitch – 2

ChatGPT

Scene: Video call between Maya, CMO of a mid-sized D2C beauty brand, and John, NeoN sales executive.

John:
Maya, thanks for taking the time. I know you’re busy, so let me get straight to it. You’ve probably felt it: your acquisition costs rising every year, Google and Meta squeezing more from your budget—and yet, the returns aren’t keeping pace. Does that resonate?

Maya:
Unfortunately, yes. CAC is up 40% in the past two years, and reacquiring churned customers is burning a hole in our budget. But what’s the alternative? We need to reach them somewhere.

John:
Exactly. That’s where NeoN comes in. Think of us as the “Beta Efficiency Engine” for your marketing. Just like an index fund gives investors market exposure at the lowest possible cost, NeoN delivers the same audience reach—your dormant customers, your next customers—but at 30–50% lower acquisition costs than you’re paying through auctions.

Maya (curious):
Wait, how? If Google and Meta own the ad space, how do you get me cheaper reach?

John:
Great question. Right now, you’re paying those platforms to reacquire customers you already know. Customers who’ve bought from you before. You’re paying a toll to rent their attention again.

NeoN cuts out the middlemen. We connect you directly to other brands’ active audiences—using privacy-safe, authenticated identity matching. If one of your dormant customers is actively engaging with Brand B’s emails, NeoN lets you reach them inside those emails, where they’re already paying attention.

Maya (raising an eyebrow):
Hold on—so you’re showing my ads inside another brand’s emails? Won’t that annoy customers—or dilute their attention?

John:
Fair concern. Actually, we place ActionAds in designated, non-intrusive slots—like the email footer or a sponsored section. Research shows they don’t harm engagement. And they’re fully interactive: your customers can browse, even complete purchases, without leaving the inbox.

And here’s the key: it’s permission-based. They’ve opted into Brand B’s emails. We’re just helping you reconnect with them where they’re already present and engaged. Much less intrusive than following them around the web with cookies.

Maya (thinking):
Interesting. But… my team’s already overloaded managing campaigns. Is this going to add more complexity?

John (smiling):
Not at all. NeoN integrates directly into your existing ESP or CRM. We generate the ActionAds, we handle the matching, the reporting. For your team, it’s as simple as pasting a block into your email template—or letting us do it as a managed service.

Maya (cautiously):
I still worry about privacy. Customers are already skittish about data sharing.

John:
That’s why we’re different. NeoN never shares raw data between brands. It’s like a secure postal sorting room: addresses get matched, but no one sees each other’s lists. GDPR, CCPA compliant, opt-out mechanisms built in. And because it’s deterministic identity—not probabilistic cookie tracking—it’s actually more transparent and privacy-safe than traditional retargeting.

Maya:
What about competitive conflicts? I don’t want a rival’s ad in my emails.

John:
You won’t. NeoN gives you full whitelist/blacklist control. You decide exactly which brands can advertise in your inventory, and whose inventory you’re allowed into. No competitors slip through.

Maya (leaning forward):
Okay… let’s talk numbers. You’re claiming 30–50% lower costs. Have you proven this?

John:
We’ve piloted NeoN with brands in fashion, beauty, and health. On average, they’ve cut reacquisition CAC by 35% compared to Meta and Google. I’d be happy to set up a risk-free pilot—let’s test it on a segment of your dormant customers, side by side with your current channels, and let the data speak for itself.

Maya (smiling):
Alright, John. You’ve piqued my interest. Send me the pilot proposal. If you can back up those claims, I’ll gladly divert some budget.

John (grinning):
Maya, I look forward to helping you stop overpaying for what you already own. I’ll send over the next steps today.

As John ends the call, Maya reflects: what if customer reacquisition didn’t have to mean paying platform tolls every time? NeoN offered not just a cheaper route—but a smarter one. Maybe, just maybe, this was the index fund moment her marketing team had been waiting for.

Published May 28, 2025

1

The Idea

In today’s marketing landscape, brands face an existential crisis that threatens their profitability and sustainability. Over the past few months, I’ve been exploring five interconnected themes that offer a revolutionary solution to this challenge:

• NeoMarketing: Marketing’s third great era that inverts established priorities—retention before acquisition, relationships before transactions, and individuals before segments.
• AI Agents Collective: A sophisticated ecosystem of AI specialists that handle complex marketing operations at scale, orchestrated by an AI Co-Marketer for true N=1 (1:1) personalisation.
• Progency: A fusion of platform, expertise, AI agents, and kaizen (continuous improvement) with performance-based economics tied directly to measurable business outcomes.
• NeoMails: Interactive AMP-powered email experiences that transform static communications into engaging hotlines with 4-10X higher engagement rates.
• NeoN: An authenticated identity advertising network enabling brand-to-brand collaboration that cuts reacquisition costs by 30-50% compared to traditional platforms.

Among these ideas, Progency has emerged as the linchpin—a revolutionary approach combining proprietary platform capabilities with specialist expertise and AI orchestration in a performance-based model. The more I’ve discussed this concept with customer-facing colleagues at Netcore and clients themselves, the clearer it becomes that Progency represents an essential solution for powering business growth and profitability in an era increasingly defined by Agentic AI and the crushing burden of AdWaste.

Yet as discussions around Progency deepen, numerous questions emerge. What business model will make it sustainable? How will we define and measure key performance metrics? Will clients truly embrace outcome-based compensation with uncapped upside potential? Can we deliver consistent growth whilst avoiding implementations that become increasingly bespoke and difficult to scale? And perhaps most critically, will organisations trust an external Progency more than their in-house marketing teams who are deeply embedded in the brand’s culture and operations?

Recently, a colleague suggested a powerful analogy from the investment world: Alpha. In finance, alpha represents the excess return of an investment relative to a benchmark index—essentially, the value a fund manager creates beyond what the market naturally delivers. Funds and managers are routinely evaluated on their ability to generate this alpha consistently over time, with compensation structures often tied directly to this performance.

Could this be the perfect framework for conceptualising Progency? Rather than selling time, features, or vague promises, a Progency would position itself as an “Alpha Generator” for marketing—delivering measurable, superior outcomes compared to industry benchmarks or the brand’s historical performance. Compensation would be tied directly to this demonstrable outperformance, creating perfect alignment between the Progency’s success and the client’s business results.

This alpha-based approach addresses many of the fundamental questions surrounding the Progency model. It provides a clear metric for success, establishes a compelling value proposition, and creates a sustainable business model with unlimited upside potential for both parties. Most importantly, it moves beyond the traditional agency or platform model to create a true partnership focused exclusively on generating measurable growth beyond what in-house teams or conventional approaches can deliver.

In this series, I’ll explore how the Progency model can systematically generate marketing alpha, transforming customer relationships whilst eliminating the staggering reacquisition AdWaste that currently plagues digital marketing budgets.

2

History

I asked Claude and ChatGPT for an overview of alpha in finance and other industries.

The concept of alpha originated in the investment world as a measure of outperformance. In its simplest form, alpha represents the excess return an investment generates compared to a market benchmark. First formalised by Michael Jensen in his seminal 1968 paper, alpha became the definitive metric for evaluating investment manager skill—separating true value creation from market-driven returns (beta).

In finance, the calculation is straightforward: if a fund returns 12% when its benchmark index delivers 10%, the manager has generated 2% alpha. This seemingly small difference compounds dramatically over time, explaining why top hedge funds and asset managers can command performance fees of 20% or more on alpha generated. The pursuit of alpha has driven the entire active management industry, with performance-based compensation structures aligning manager incentives directly with client outcomes.

What makes alpha particularly powerful is its clarity—it measures value creation in precise, quantifiable terms that cannot be easily disputed. Either the manager outperforms the benchmark, or they don’t. This transparency has made alpha the gold standard for evaluating investment skill for over five decades. At its core, alpha also represents a system of aligned incentives: rewarding providers not for effort or inputs, but for outcomes that exceed market norms.

Alpha Beyond Finance

Over the past two decades, the alpha concept has gradually transcended financial markets to influence other industries where outperformance can be measured against established benchmarks:

• Management Consulting: Firms like McKinsey and BCG increasingly structure engagements around measurable performance improvement rather than time-based billing. “Impact fees” tied to specific business outcomes (cost reduction, revenue growth, margin improvement) mirror the alpha model, with consultancies earning premiums for delivering results that exceed industry norms.
• Legal Services: Alternative fee arrangements have emerged where law firms share risk and reward by tying compensation to case outcomes rather than billable hours. Some litigation firms now operate entirely on contingency models, effectively betting on their ability to generate “legal alpha” through superior outcomes.
• Private Equity: In private equity, ‘operational alpha’ describes the incremental value created through improving acquired companies’ operations—capturing gains beyond financial leverage or market growth.
• Venture Capital: Alpha in VC stems from access: sourcing proprietary deal flow, identifying outlier founders early, or nurturing startups before the market catches on.
• Sports Management: Professional teams employ data-driven performance analysts to identify statistical advantages that translate to competitive edge—essentially seeking “alpha” in athletic outcomes. This approach has revolutionised talent evaluation in baseball, basketball, and football.
• Technology Solutions: Software-as-a-Service (SaaS) providers are increasingly exploring value-based pricing, where fees scale with measurable business impact rather than seat counts or feature sets. This represents an early attempt to tie technology costs directly to the “alpha” generated for customers.

Across industries, alpha signals proprietary advantage—an edge rooted in insight, access, execution, or innovation that cannot be easily replicated or commoditised by markets. Yet notably, while alpha-based models have reshaped industries from finance to consulting, marketing has remained largely tethered to input-based pricing and platform-driven benchmarks—paying for impressions, clicks, or reach without clear linkage to incremental business outcomes.

If alpha has transformed finance, consulting, and even sports, could marketing—long anchored to platform norms—finally build its own path to alpha?

3

Marketing’s Opportunity

The marketing industry stands uniquely positioned to adopt an alpha-based framework, yet has largely remained tied to input-based compensation models (billable hours, media commissions, platform subscriptions). This disconnect creates a significant opportunity for disruption through a Progency model that redefines value creation.

Marketing alpha can be conceptualised as measurable outperformance against established baselines—whether industry benchmarks, historical performance, or control group outcomes. For example, converting “Rest” customers to “Best” status at rates exceeding historical averages, driving retention metrics beyond industry standards, increasing engagement frequencies, or expanding average order values amongst existing customers.

Surprisingly, marketing never fully embraced Alpha in its vocabulary or metrics. Despite substantial budgets, sophisticated tools, and extensive jargon, marketing remained largely tethered to “Beta-priced” outcomes—accepting industry benchmarks as “good enough” (2% CTR, 5% open rates, 3x ROAS) and focusing primarily on acquisition rather than systematically extracting greater value from existing customer relationships.

Traditional marketing lacked the mechanisms to build proprietary, unreplicable advantages because it outsourced customer engagement to generic platforms, spoke in segments rather than to individuals, and failed to truly personalise experiences at scale. Without sophisticated systems for maximising customer lifetime value, marketing teams defaulted to acquisition-based growth rather than depth-based monetisation.

However, marketing can now generate its own Alpha—not through incremental tactical improvements, but by creating proprietary advantages that dramatically increase effectiveness beyond market norms. Just as investing alpha came from information asymmetry and proprietary models, marketing’s growth alpha can emerge from AI-powered hyper-personalisation, interactive engagement mechanisms (NeoMails), sophisticated propensity modelling, and retention-first strategies focused on existing customers.

Alpha in marketing isn’t about more spend—it’s about smarter engagement. It’s about architecting systems where revenue from existing customers compounds through deeper relationships, higher purchase frequency, and increased average order values. In this framing, marketing’s “growth Alpha” (focused on martech and existing customers) can be defined as: Actual Growth Uplift – Baseline Growth Expected from Standard Martech Investments.

The Progency model capitalises on this opportunity by establishing clear baselines, implementing sophisticated AI-driven engagement systems, and tying compensation directly to the measurable growth outperformance delivered. This creates the perfect alignment that has made alpha so powerful in finance—the Progency succeeds only when clients achieve exceptional revenue expansion from their existing customer base.

In a landscape where marketing teams struggle with maximising customer lifetime value and systematically monetising their “Rest” customers, Progency acts as an Alpha Engine—turning underutilised platform features, dormant customer segments, and execution gaps into measurable revenue gains. It creates a private playground of advantages that competitors cannot easily replicate, allowing brands to escape one-size-fits-all marketing approaches and unlock the full revenue potential of their existing customer relationships.

4

Internal Baseline or Industry Benchmark?

A central question emerges as we operationalise the Progency model: against what should Progency’s Growth Alpha be measured? Should we assess performance relative to external industry benchmarks, or base it on the client’s own historical performance?

At first glance, benchmarking against external industry standards might appear attractive. Marketing publications routinely publish average engagement metrics by sector, seemingly providing an objective yardstick for success. However, this approach faces significant limitations that make it impractical for a true Alpha model.

Unlike financial markets with universally accepted indices like the S&P 500, marketing lacks standardised benchmarks with genuine comparability. Even within narrow categories such as ecommerce apparel, performance metrics vary dramatically based on geography, brand positioning, price points, and customer demographics. A premium fashion retailer with a curated customer base naturally exhibits different engagement patterns from a mass-market discount brand, making comparisons to generic industry averages fundamentally flawed.

Measuring Alpha against the client’s own historical baseline offers a substantially cleaner, fairer, and more defensible approach. This model defines Progency’s success by its ability to deliver measurable outperformance relative to what the brand previously achieved with its in-house marketing team. When a brand’s retention rate rises from 40% to 55%, or average order value increases from $50 to $60, or the number of active “Best” customers doubles over 12 months, these improvements are unambiguously quantifiable and contextually relevant.

This internal-baseline approach creates perfect incentive alignment. Progency is rewarded specifically for helping the brand outperform itself, not for beating arbitrary industry averages that may bear little relevance to the brand’s specific circumstances. It focuses the engagement on continuous improvement and growth from within, rather than chasing external comparisons that may not apply to the brand’s unique context or strategic objectives.

Crucially, measuring Alpha against internal baselines positions Progency as a collaborative partner augmenting existing capabilities rather than creating an adversarial dynamic that might arise if performance were compared to industry standards suggesting the internal team was “below par.” It establishes a foundation of trust and collaboration essential for long-term partnership.

With compensation tied directly to growth uplift above the brand’s existing trajectory, Progency’s value creation becomes unambiguously demonstrable through actual business outcomes rather than theoretical benchmarks or market averages. This internal-benchmark Alpha creates the clearest path to aligning incentives, building trust, and proving Progency’s worth—one measurable outperformance at a time.

5

Lessons from Finance

This section written with inputs from ChatGPT and Claude.

If Alpha represents outperformance, the natural question is: how does one consistently generate Alpha in investing? Over decades, the world’s top fund managers have converged on several core strategies that separate them from the passive Beta crowd. These winning approaches share an underlying DNA: seeking proprietary advantage, exploiting market inefficiencies, and compounding small edges over time.

One foundational strategy is information asymmetry. Elite investors gain access to data, insights, or signals before others, allowing them to position ahead of market movements. This isn’t merely about having more data—it’s about having better, faster, more relevant information that others haven’t yet priced in. Renaissance Technologies built its legendary Medallion Fund partly on detecting subtle market patterns invisible to conventional analysis.

A second strategy is access advantage. Venture capitalists generate Alpha by securing entry into deals that never reach the open market. Private equity firms create proprietary deal flow through relationships that unlock off-market acquisitions. Even in public markets, activist investors use boardroom influence to drive outcomes inaccessible to ordinary shareholders. Access becomes an Alpha engine because not everyone plays the same game.

Third is operational Alpha—improving an asset’s intrinsic value beyond market expectations. In private equity, this involves overhauling management, optimising operations, or unlocking new revenue streams within portfolio companies. Firms like KKR and Blackstone have mastered this approach, engineering value from within rather than relying on market timing.

Time arbitrage represents another powerful strategy. Many investors operate on compressed timeframes—quarterly earnings, monthly reports, daily liquidity demands. Investors with patient capital can exploit the tendency of markets to underprice long-term compounding opportunities. Warren Buffett’s extraordinary track record stems partly from his willingness to hold undervalued but fundamentally strong assets while others chase faster results.

Finally, there’s structural inefficiency exploitation. Some market segments remain less covered, less liquid, or less competitive—creating fertile ground for Alpha. Micro-cap stocks, frontier markets, complex credit instruments—investors who specialise in overlooked niches often find excess returns by fishing where others refuse to venture.

A few additional alpha-creating strategies:

• Factor-based investing: Many successful funds now use systematic approaches that target specific “factors” (value, momentum, quality) to generate alpha. This could reinforce how systematic, data-driven approaches outperform intuition-based decisions.
• Contrarian positioning: Some of the greatest alpha is generated by investors willing to take opposite positions from the crowd.
• Compound returns: The extraordinary power of compounding small advantages over time could be emphasized more explicitly, as it’s fundamental to investment alpha.

All these Alpha strategies share a common theme: they avoid competing for Beta-priced assets in open, efficient, transparent markets. Instead, they deliberately create, discover, or access proprietary advantages unavailable to the masses.

For Progency, this analogy proves instructive. If traditional marketing plays the Beta game—buying attention at market-clearing prices from Google or Meta—then Alpha in marketing must follow similar logic: finding proprietary edges that deliver superior returns without paying platform tax.

6

Winning in Martech

If finance has its playbook for Alpha, what would the equivalent be for Progency in marketing? How can Progency create excess, proprietary growth for a brand—outperforming both historical baselines and platform-priced market norms?

The answer lies in two critical customer segments: Best and Rest. The strategy: Profit from Best, Growth from Rest.

For Best customers—the top 20% who deliver 60-80% of revenue and up to 200% of profits—the Alpha strategy is extreme retention and expansion. Just as investors maximise returns by doubling down on high-performing assets, Progency’s mission becomes deepening loyalty, increasing share of wallet, and extending lifetime value among this elite cohort.

Winning Alpha with Best customers requires hyper-personalisation: crafting bespoke offers, exclusive access, curated experiences, and preferential treatment that strengthens both emotional and transactional bonds. It means implementing Velvet Rope Marketing—ensuring every interaction reinforces the customer’s sense of privilege, belonging, and preference for the brand. The AI Agents Collective enables this at scale by orchestrating thousands of micro-personalised segments, campaigns, and touchpoints that human teams simply cannot manage.

Additionally, Best customers function as powerful referral engines. Like investors reinvesting dividends, Progency leverages satisfied Best customers to drive organic acquisition—bringing in new high-value customers at zero or negative CAC. This referral flywheel becomes a compounding Alpha source: existing profits fuelling new growth outside expensive paid channels, creating a structural cost advantage over competitors still dependent on platform acquisition.

Meanwhile, Rest customers—the middle 40-50% showing declining interaction—represent the largest untapped Alpha opportunity. Like neglected assets in a portfolio, they’re undervalued, under-managed, and at risk of decay. Yet, with proper intervention, many can graduate into the Best cohort.

Here, Progency’s Alpha strategy focuses on converting Rest to Best through timely, tailored engagement. AI-driven segmentation identifies micro-patterns of interest and drop-off. Interactive NeoMails deliver both daily engagement and relevant nudges at precisely the right moments. Personalisation engines test offers, messaging, and timing across millions of individualised journeys. The goal: prevent churn before it happens, rekindle lapsed interest, and systematically surface hidden champions from within the middle segment.

Importantly, Rest-to-Best Alpha isn’t about mass campaigns—it’s about orchestrating individualised recovery and activation paths at unprecedented scale. This is where Progency’s AI architecture delivers transformative advantage: deploying autonomous agents that never tire, continuously experiment, and optimise engagement daily without human bottlenecks or operational constraints.

Together, these two strategies form Progency’s core Alpha playbook:

• For Best: maximise retention, deepen loyalty, unlock referrals, increase purchase frequency and average order value
• For Rest: systematically prevent dormancy, reignite engagement, and migrate high-potential customers into the Best tier

Like an investor rebalancing a portfolio, Progency continuously shifts customers up the value ladder—transforming the client’s customer base into a higher-yielding, compounding growth engine. By focusing Alpha efforts inward—on owned relationships rather than rented reach—Progency builds proprietary advantage that competitors cannot easily replicate.

This represents Alpha not from buying more traffic, but from extracting substantially more value from existing relationships. In doing so, Progency elevates marketing from cost centre to profit engine driven by retention, referral, and relationship compounding—delivering measurable Growth Alpha without dependence on external, auction-based acquisition.

7

The Choice – 1

In the world of investing, individual investors routinely underperform professional fund managers, ETFs, and structured investment vehicles. The reasons are well-documented: lack of time, expertise, tools, discipline, access, and cognitive biases that lead to suboptimal decisions. Similarly, in marketing, in-house teams often struggle to achieve what a specialised, AI-powered Progency model can deliver.

Just as funds are designed to systematically extract Alpha—excess returns above the market—Progency is engineered to systematically generate Growth Alpha from a brand’s existing customer base. It doesn’t depend on heroic effort or sporadic campaigns, but on a repeatable, scalable system integrating software, AI agents, expert guidance, and continuous improvement.

Progency’s structural advantage stems from six fundamental factors, each paralleling investment management whilst addressing the realities of martech today:

1. From Software Without Service → Software + Strategy + Success

Most brands purchase martech platforms but struggle to extract value, with 60-65% of features remaining unused. The platform exists, but not the process. Staff turnover, skill gaps, and operational silos leave capabilities idle—akin to an individual investor buying a Bloomberg terminal but using it only to check basic prices.

Progency solves this by providing “Done-for-You” execution, embedding experts who know the platform intimately, and deploying pre-configured best practices. The platform transforms from a tool needing an operator into an engine running with a dedicated pit crew, with every underutilised feature becoming an unlocked lever for incremental Alpha.

2. From Priced on Inputs → Priced on Outcomes

In-house marketing teams function as cost centres: salaries, tools, overheads, campaign budgets. Their compensation and evaluation rarely link directly to revenue outcomes. Progency flips this model by aligning compensation directly to performance—outcome-based pricing rather than input-based fees. This incentive alignment mirrors hedge fund “performance fees” tied to Alpha: we win only when the client wins.

3. The Agency of AI Agents vs Human Bottlenecks

In-house teams rely on human workflows: analysts building segments, content creators producing copy, campaign managers setting up journeys, data teams crunching reports. The result? Endless drudgery and slow iteration. As one CMO confided: “Segments, Journeys, Content, Analytics. Repeat.” It’s exhausting, error-prone, and linear.

Progency deploys an AI Agents Collective: specialised AI agents for segmentation, content, journey design, optimisation, and analytics—each working autonomously but orchestrated by an AI Co-Marketer. Where human teams can manage 8-10 segments, AI agents handle millions of micro-cohorts simultaneously.

The key advantage: AI agents perform best when configured, trained, and orchestrated by the experts who built or mastered the system. In-house teams may use generative AI via prompt engineering, but lack the systemic, multi-agent orchestration expertise that drives transformative results.

4. Continuous Kaizen vs Project-Based Marketing

In-house teams typically operate in campaign mode—launch, measure, report, pause. Optimisation happens quarterly or after failures. Progency embeds Kaizen: continuous improvement loops where every interaction, every open, every click feeds machine learning models and agent-driven tests. The system improves daily, not quarterly.

This generates Alpha via iteration speed. Markets reward compounding over time. So does marketing—if the system improves continuously. In-house teams rarely have the bandwidth, data pipelines, or automation maturity to run hundreds of concurrent micro-experiments that Progency executes as standard practice.

5. From Cost Centre → Profit Engine

In-house marketing operates on budgets. Martech expenses are sunk costs. Metrics focus on activity (campaigns sent, impressions reached, leads generated) rather than direct revenue uplift per cohort or per customer. Progency transforms marketing into a profit centre with compensation tied to incremental revenue beyond baseline. Every action becomes accountable to business outcomes, not vanity metrics.

6. Trust, Culture, and Institutional Knowledge: Addressing In-House Advantages

One natural advantage of in-house teams is their deep knowledge of brand culture, product nuance, and internal politics. They understand legacy systems, stakeholder preferences, and hidden minefields. Progency addresses this by:

• Working with internal teams rather than replacing them—positioning as augmentation, not substitution
• Embedding brand liaison roles to interface daily with client teams
• Co-designing strategy while taking over execution—leaving high-level decisions and sensitive communications within the client
• Customising AI agent rules to reflect brand tone, compliance requirements, and audience sensitivities

In effect, Progency supplements in-house advantages while solving in-house limitations, absorbing operational burden, bringing in missing expertise, and applying technology at a depth most in-house teams cannot match.

8

The Choice – 2

Additional Structural Advantages

Beyond these six core factors, Progency offers several additional structural advantages often overlooked:

• Cross-client pattern recognition: Learning from similar challenges across multiple brands creates invaluable knowledge transfer that siloed in-house teams never access
• Specialised vertical expertise: Developing deep domain knowledge in specific industries enables faster pattern recognition and solution deployment
• Risk diversification: Unlike in-house teams whose careers depend on avoiding failure, Progency’s performance-based model enables bolder experimentation within guardrails
• Tech ecosystem integration: Maintaining seamless connectivity with evolving martech, adtech, and data platforms that in-house teams struggle to keep current

**

Here’s a comparison table (generated by ChatGPT and refined by Claude) summarising the key differences between In-House Marketing Teams vs Progency.

Dimension	In-House Marketing Teams	Progency (AI Agents + Experts + Platform)
Operating Model	Campaign-driven; manual execution; reactive	Continuous; AI agent-driven; always-on optimisation; proactive
Resource Limitation	Bounded by team size and bandwidth; linear scaling	Exponential scaling via AI Agents Collective + automation
Platform Utilisation	30-40% of platform features used; feature bloat	80-90%+ of features unlocked through expert orchestration
Pricing Model	Fixed salaries; cost centre budgeting; input-based	Outcome-based pricing; shared upside on revenue uplift; growth-aligned
Incentive Alignment	Effort/activity-based KPIs; marketing metrics	Directly tied to business outcomes (growth Alpha); revenue metrics
Experimentation Capacity	2-5 A/B tests per month; sequential testing	50-500 concurrent micro-experiments via AI agents; parallel optimisation
Customer Personalisation	8-10 macro-segments; broad-based targeting	1:1 hyper-personalisation at scale (N=1); individualised journeys
AI Usage	Ad hoc; basic prompt engineering; isolated tools	Orchestrated multi-agent ecosystem by AI-native experts; integrated workflows
Iteration Speed	Weekly/monthly campaign cycles; quarterly planning	Daily continuous improvement (Kaizen loops); real-time adaptation
Customer Focus	Acquisition-biased; low activation of existing customers	Retention-first; Best & Rest strategy to maximise customer LTV
Skill Turnover Risk	High (staff churn; skill drain); knowledge gaps	Centralised expertise maintained within Progency; institutional continuity
Institutional Knowledge	Deep brand familiarity; legacy knowledge	Embedded liaison roles + cross-industry pattern recognition
Strategic Role of Marketing	Cost centre; budget-dependent; defensive posture	Measurable profit engine; C-suite aligned; growth driver
Data Integration	Siloed systems; fragmented customer view	Unified customer profiles; cross-channel orchestration
Adaptability to Market Changes	Slow response to trends; lengthy approval cycles	Rapid implementation of emerging tactics; agile deployment
Alpha Creation	Limited; constrained by in-house tools, skills, processes	Structural advantage; designed to extract Growth Alpha at scale

In a world where Growth Alpha is the new currency of marketing success, Progency isn’t competing with internal teams—it’s playing an entirely different game, at a higher level of automation, precision, and incentive alignment. Just as investment funds systematically outperform individual investors through superior tools, teams, and processes, Progency systematically outperforms in-house marketing by combining technology, expertise, and outcome-based economics into an Alpha Engine that transforms marketing from cost centre to profit driver.

9

Objection Handling

I asked Claude to list out possible objections from CMOs and other stakeholders that merit thoughtful consideration.

“This sounds expensive. How does it fit into our budget?”

Unlike traditional platforms or agencies requiring upfront investment, Progency’s performance-based model aligns perfectly with financial constraints. There are no platform fees or retainers—payment is tied directly to incremental revenue generated above established baselines. This transforms marketing from a cost centre into a self-funding profit engine, with Progency’s compensation representing a fraction of the additional revenue created.

“We’ve invested heavily in our current martech stack. Does this replace it?”

Progency doesn’t replace your existing investments—it maximises their value. Many organisations utilise only 30-40% of their martech capabilities due to expertise and bandwidth limitations. Progency unlocks the full potential of your current stack through expert orchestration and AI agents, protecting your investment while delivering superior returns. We can operate alongside your current systems or gradually transition components as results warrant.

“How do we handle data privacy and security concerns?”

Data privacy forms the foundation of Progency’s approach. All operations adhere to GDPR, CCPA, and other regional regulations with comprehensive data processing agreements. Customer data remains within your environment or in secure, compliant cloud infrastructure with encryption, access controls, and regular security audits. Unlike cookie-based targeting that raises privacy concerns, our authenticated identity approach operates with explicit consent and transparency.

“Our brand has unique requirements. How do you maintain our voice and standards?”

Progency doesn’t apply one-size-fits-all solutions. The AI Agents Collective operates within brand-specific guardrails—incorporating your voice, compliance requirements, and quality standards. The implementation begins with comprehensive brand immersion, and all content and journeys undergo review processes aligned with your governance requirements. Rather than replacing your brand expertise, Progency amplifies it through AI orchestration.

“How do we know the reported performance improvements are real?”

Transparency drives Progency’s measurement framework. Performance is tracked through rigorous control group methodology, with incrementality clearly isolated from other marketing activities. All reporting includes comprehensive attribution logic, with shared dashboards providing real-time visibility into key metrics. Performance calculations follow mutually agreed methodologies established before implementation, eliminating ambiguity around results.

“My team will feel threatened by this. How do we address the cultural impact?”

Progency positions itself as augmentation, not replacement—freeing your team from operational drudgery to focus on strategic initiatives. Most marketing teams welcome relief from repetitive tasks like segment creation, campaign setup, and performance analysis. By handling the Rest segment, Progency allows your team to concentrate on high-value activities like brand strategy and VIP customer experiences. We’ve found that marketing teams typically become our strongest advocates once they experience how Progency amplifies their impact.

“What happens if you don’t deliver results?”

This concern highlights Progency’s core advantage: our incentives align perfectly with yours. If we don’t deliver incremental revenue, we don’t get paid. Unlike traditional vendors that collect fees regardless of outcomes, Progency bears the risk of underperformance. This creates unprecedented accountability rarely seen in marketing services or technology. We’re betting on our ability to outperform your current baseline—and only succeed when you do.

10

Pitch – 1

I asked Claude and ChatGPT to create a role-play pitch between Maya (CMO) and John (Progency salesperson).

Claude

Scene: Maya’s office at a mid-sized B2C retailer. She’s reviewing marketing performance reports with a furrowed brow when her assistant announces John’s arrival for their scheduled meeting. John represents Progency, a new marketing solution she’s curious about but sceptical of.

Maya: (looking up from her laptop) John, welcome. I’ve got 30 minutes before my next meeting, so let’s get straight to it. I’ve read some of your materials about this “Progency” concept, but I’m still trying to understand how it’s different from the dozen other martech solutions and agencies constantly pitching me.

John: (taking a seat) Thanks for making the time, Maya. Before I dive into Progency, may I ask you a quick question about your current marketing approach?

Maya: (gestures) Go ahead.

John: What percentage of your digital marketing budget would you estimate is spent on reacquiring customers who already know your brand—people who’ve purchased before but gone dormant?

Maya: (pauses, considering) We’ve never broken it down quite that way. Between Google, Meta, and our other platforms… I’d guess maybe 40-45%? Our acquisition costs have risen nearly 20% year-on-year.

John: That’s actually better than most. Industry data suggests the average is closer to 70%—what we call the $500 billion AdWaste crisis. Essentially, brands are paying platforms premium prices to reach customers they already own.

Maya: (frowns) That feels… inefficient. But what’s the alternative? Once customers stop opening emails or engaging with our app, we have to find them somewhere.

John: That’s exactly the problem we’re solving. Right now, you’re forced to choose between three bad options: keep emailing dormant customers and risk deliverability issues, stop contacting them and lose the relationship entirely, or pay Google and Meta steep prices to “rediscover” them.

Maya: (nodding) That sounds about right. Our email open rates hover around 12% for active customers, and much lower for the rest. The “rest”—that’s what my team calls those middle-tier customers who aren’t our VIPs but aren’t completely gone either.

John: (perking up) Your team’s onto something there. We call it the BRTN framework—segmenting customers into Best, Rest, Test, and Next. The “Rest” segment is precisely where the biggest opportunity lies. How many customer segments does your team actively manage today?

Maya: (sighs) Realistically? About 8-10 different segments across our campaigns. I know we should do more, but each additional segment means more creative, more management, more analytics.

John: That’s the universal constraint. Even sophisticated marketing teams can only manage a handful of segments because of human bandwidth limitations. But here’s the thing—each of your customers is unique, with specific preferences, behaviours, and needs. They feel it when messages don’t resonate personally.

Maya: (sceptical) So what’s your solution? More consultants? Another platform with AI features we’ll barely use?

John: Neither, actually. Progency is something entirely different. We call it “software with success” rather than “software as a service.” Instead of charging platform fees, we only earn money when we deliver measurable revenue gains above your current baseline.

Maya: (intrigued) Performance-based pricing? That’s refreshing, but how do you deliver that performance?

John: Through what we call the PEAK framework—combining Platform capabilities, Expert strategists, AI agents, and Kaizen continuous improvement methodology. The revolutionary element is our AI Agents Collective—a sophisticated system of specialised AI working in concert to deliver true 1-to-1 personalisation at scale.

Maya: (sceptical) Everyone claims to offer personalisation these days. What makes yours different?

John: Traditional personalisation inserts a name in an email and maybe changes a product image. Our AI agents create genuinely individualised journeys for each customer—targeting micro-moments with precisely the right message, on the right channel, at the right time. Where your team manages 8-10 segments, our system can effectively treat each customer as their own segment—what we call “N=1 personalisation.”

Maya: (considering) But don’t we need to manage all this? My team’s already stretched thin.

John: That’s the beauty of the model. You don’t manage it—we do. Think of it as your “Department of One” handling everything from segmentation to content creation to campaign orchestration. Your team focuses on high-level strategy and your Best customers, while we transform your Rest customers into future Best customers.

Maya: (intrigued but cautious) So you’re saying you could take over our middle segment—these “Rest” customers—and increase their value without my team doing the heavy lifting?

John: Exactly. And we only get paid when we deliver results above your current baseline. It’s similar to how investment alpha works—we generate excess returns from your customer base beyond what you’re already achieving.

Maya: (nodding slowly) And how do you address the dormant customer problem—the ones we’re paying platforms to reacquire?

John: Two solutions there. First, NeoMails—interactive email experiences that boost engagement 4-10X by delivering real value in the inbox. These create reliable daily hotlines that prevent Rest customers from becoming dormant in the first place.

Second, for already-dormant customers, we have NeoN—an authenticated identity network that lets you precisely target these customers through partner brands’ emails at 30-50% lower cost than Google or Meta.

Maya: (thinking) What about my team? I don’t want them feeling threatened or replaced.

John: We’re not replacing your team—we’re augmenting them. Your marketers stay focused on strategy, creative direction, and managing your VIPs. We handle the operational execution for your Rest segment, which frees your team from the daily drudgery of “segments, journeys, content, analytics, repeat.” Most importantly, we create a clear path to double your Best customers while halving your acquisition waste.

Maya: (showing more interest) Okay, assuming I’m intrigued—which I am—what would next steps look like?

John: We’d start with a simple data analysis to establish your BRTN segmentation and identify your baseline performance. Then we’d propose a 90-day pilot focused on a specific segment of your Rest customers—say 50,000 of them. We’d implement our AI Agents Collective to deliver personalised journeys while measuring uplift against your control group. You’d see exactly how much incremental revenue we generate, with complete transparency.

Maya: (nodding) And your compensation would be tied to that uplift?

John: Precisely. We only succeed when you succeed. No platform fees, no hourly billing—just a percentage of the incremental revenue we create above your baseline. We’re betting on our ability to generate what we call “Growth Alpha” for your business.

Maya: (checking her watch) This is definitely more interesting than most pitches I receive. Send me a proposal for that 90-day pilot. I want to see specific KPIs, the segment definition, and exactly how you’ll measure success. I’ll need to get buy-in from my marketing directors and potentially our CFO.

John: (standing) Absolutely. I’ll have that to you by tomorrow. One final thought—every month that passes with your Rest customers not fully monetised is revenue permanently lost. We can stop that leakage while simultaneously reducing your dependency on expensive platforms.

Maya: (smiling slightly) You’ve certainly given me something to think about. I look forward to seeing the proposal.

As John leaves, Maya reopens her marketing dashboard, scrolling to the segment analysis with new interest. The conversation has shifted her thinking—perhaps there is a way to make martech as straightforward as adtech, while delivering superior results.

11

Pitch – 2

ChatGPT

Scene:
Maya, CMO of a B2C brand, is in her office. John, the Progency salesperson, is meeting her to pitch the Progency model. Maya has heard of Progency but isn’t sure how it’s different from her internal marketing team or her existing agency.

John (smiling, slides open on tablet):
“Maya, thanks for making time. I know you’ve got a great team here—but I wanted to show you how Progency can be a multiplier, not a replacement.”

Maya (leaning forward):
“Honestly, John, I’m sceptical. We’ve invested heavily in platforms and people. Why would I need an outside team to do what my marketing department’s already doing?”

John (nodding):
“Completely fair. Let me frame it differently. Think of your in-house team like an individual investor—they know the market, they make picks, they’ve had wins. But even the smartest individual investors underperform the best funds. Why? Not for lack of effort—but because funds have access to tools, teams, systems, and processes that an individual simply can’t replicate.”

Maya (thoughtful):
“So you’re saying Progency is… like a fund manager?”

John:
“Exactly. We’re engineered to systematically generate Growth Alpha—measurable, excess growth above what your existing team, platforms, or agencies are achieving. And just like a fund doesn’t rely on a few lucky trades, we don’t rely on one-off campaigns or talent spikes. We build a machine that extracts incremental gains every day.”

Maya:
“Okay, but my team knows the brand inside out. They’ve built these campaigns, they own the customer voice. How can you possibly replicate that?”

John (smiling):
“That’s one area where we work with them, not against. We embed liaison roles who act as the bridge. Your team stays the guardians of brand tone, compliance, product nuance. We don’t take over that role—we amplify it. But where they’re limited is scaling it.”

(John flips to a slide showing “human bottlenecks” vs “AI Agents Collective.”)

John:
“Right now, your team’s building segments, journeys, content, analysing reports. It’s endless. The best they can manage? Maybe 8–10 macro segments, a few A/B tests a month. Our AI Agents Collective manages millions of micro-cohorts, running hundreds of simultaneous experiments. The system self-optimises daily.”

Maya (raising eyebrows):
“AI’s great, but we already use AI in our team—our marketers use prompt engineering. What’s different?”

John (leans in):
“Great point. But let me ask: are your marketers orchestrating multi-agent systems, or just using isolated AI tools? Are they integrating agents across content, analytics, segmentation, journey optimisation—running in real-time, collaboratively, autonomously?”

(Maya pauses, realising the distinction.)

John (continues):
“That’s the difference between handing someone ChatGPT and giving them a fully orchestrated AI co-pilot managing an army of specialist agents. The technology isn’t just the tool—it’s the system, the architecture, the orchestrators who built it.”

Maya:
“So… you’re saying it’s not about having AI, it’s about unlocking its collective power?”

John (smiling):
“Exactly. AI works best when experts who built the system are running it. Otherwise, it’s like giving a Formula 1 car to an amateur driver—you can drive, but you won’t win races.”

Maya (crossing arms):
“Sounds promising. But what’s in it for you? How are you compensated?”

John (shows slide “from inputs → outcomes”):
“That’s the other key difference. We don’t charge for time, tools, or features. We only get paid when we generate measurable Alpha—when we move the needle beyond your baseline. No uplift? No payment. But if we drive 15% higher retention? Increase average order value? Grow your Best customers? We share in that upside.”

Maya (impressed):
“Interesting. But doesn’t that make it risky for you?”

John (grinning):
“Only if we can’t deliver. But we’re confident because we’ve designed for scale, speed, precision. The model works because the incentives align perfectly. We win only when you win.”

Maya:
“And the internal team? They won’t feel threatened?”

John (reassuringly):
“Not if positioned right. We’re not here to replace—they’re still steering strategy, owning creative, customer intimacy. We’re the engine under the hood—making the car faster, more efficient, without changing the driver.”

(John flips to table comparing in-house vs Progency.)

John:
“Bottom line, Maya: your team’s playing the Beta game—doing what every other brand does, paying platform tax, chasing industry benchmarks. We play the Alpha game—finding proprietary advantages, compounding small gains, unlocking value no one else can replicate.”

Maya (smiling):
“Okay, John. You’ve piqued my interest. Let’s explore what Alpha could look like for us.”

John (closing tablet):
“That’s all I ask. Let’s build your Alpha engine together.”

AI Take – 2

I then asked Claude and ChatGPT to create a NeoMarketing playbook for marketers.

Claude

Step 1: Audit Your Customer Engagement & AdWaste

Actions:

• Analyze your customer database using the BRTN framework: identify your Best (top 20% by revenue), Rest (wavering engagement), Test (dormant 90+ days), and Next (new acquisitions) segments
• Calculate your current reacquisition spending: what percentage of digital marketing budget goes to platforms like Google and Meta to reach existing customers?
• Measure your Rest-to-Test conversion rate: how quickly are customers becoming dormant?

Quick Win: Create a simple dashboard showing these metrics. Just quantifying the problem often reveals immediate opportunities for improvement and builds internal consensus around the need for change.

Step 2: Establish Your Baseline & Set Targets

Actions:

• Document key metrics: Best customer percentage, average Best customer value, Rest customer volume, monthly reacquisition spend
• Set specific 6-month targets: e.g., “Convert 20% of Rest customers to Best status” and “Reduce platform reacquisition spend by 30%”
• Identify one high-potential Rest customer segment for your initial pilot

Quick Win: Select a modest-sized group (5,000-10,000 customers) from your Rest segment who purchased within the last 60 days but haven’t engaged since. This becomes your initial pilot group.

Step 3: Implement Your First NeoMails Campaign

Actions:

• Create a 30-day NeoMails pilot for your selected Rest segment, incorporating:
  • Atomic Rewards (Mu) to incentivize opens
  • Microns with valuable “brain gain” content
  • Interactive elements that create daily engagement patterns
• Track engagement metrics against your control group

Quick Win: Start with just one interactive element (e.g., a quick quiz, preference selector, or micro-game) to prove the concept before scaling to more sophisticated interactions. Even basic AMP elements can increase engagement by 2-3X.

Step 4: Test NeoN Targeting for Select Test Customers

Actions:

• Identify a segment of your highest-value Test customers (dormant but previously profitable)
• Compare costs and conversion rates between traditional retargeting and NeoN’s authenticated approach
• Measure the impact of frictionless ActionAds versus standard click-through campaigns

Quick Win: Start with a limited budget test (10-15% of your normal retargeting spend) focused on your most valuable lapsed customers. This creates a clear comparison without requiring major budget reallocation.

Step 5: Build Your Dual-Track Scaling Plan

Actions:

• Based on pilot results, develop your comprehensive scaling strategy:
  • Progency implementation plan to systematically convert Rest customers to Best status
  • NeoN rollout plan to progressively replace traditional reacquisition spending
• Create a 12-month roadmap with clear financial projections
• Identify organizational readiness requirements (data integration, team alignment, etc.)

Quick Win: Start with a 90-day “Phase 1” that focuses on the quickest path to ROI. This typically means prioritizing high-value Rest customers for the Progency approach, while simultaneously redirecting spending from your most expensive retargeting channels to NeoN alternatives.

Implementation Support Tools

• BRTN Calculator: Input your customer data to instantly identify your Best-Rest-Test-Next breakdown
• AdWaste Estimator: Calculate your current reacquisition spending and potential savings
• ROI Projector: Forecast the financial impact of transforming Rest customers to Best status
• Readiness Assessment: Evaluate your technical and organizational preparedness

By following this structured approach, you can begin your NeoMarketing journey with minimal risk while establishing clear proof points for broader implementation. The key is starting with focused pilots that demonstrate value quickly, then scaling methodically based on proven results.

ChatGPT

📘 NeoMarketing 2X Best, Halve Waste Playbook

(Your Practical Starter Plan to Double Profits and Halve AdWaste)

🎯 Objective

Transform marketing from a cost centre into a profit engine by doubling Best customers and halving reacquisition AdWaste.

🚀 5-Step Action Plan

1. Segment Today: Apply BRTN

✔ Action:
Audit your customer base into 4 groups:

• Best: Active in past 30 days, high spenders
• Rest: Last engaged 31-90 days ago
• Test: No engagement past 90 days
• Next: New and recent first-time buyers

Tools: Use your CRM or email platform filters.
Time needed: 1-2 days.

Outcome:
Baseline view of the goldmine you already have but under-monetise.

2. Launch Rest Revival: Micro NeoMails Pilot

✔ Action:
Create a NeoMail campaign targeting 5,000-10,000 Rest customers.

Elements to include:

• A short Micron (brain-gain content)
• A simple Atomic Reward (Mu) offer
• One interactive AMPlet inside the email (eg., a preference quiz)

Objective:
Reignite daily attention and reverse churn before it’s too late.

Tools: Partner with an ESP/agency offering AMP-powered email if needed.

3. Reactivate Test Customers: Small NeoN Pilot

✔ Action:
Identify 2-3 brands in your network (non-competing) whose engaged customers overlap with your Test segment.

✔ Set up a NeoN ActionAd campaign in their email newsletters.

Focus:

• Bring back Test customers with special comeback offers
• 30-50% cheaper than Meta/Google reacquisition

Time needed: 2-4 weeks for pilot setup and run.

4. Track 3 Critical Metrics

✔ Every week, monitor these three KPIs:

• Rest-to-Best Migration Rate (% Rest customers becoming Best)
• Test Reactivation Cost vs Traditional Ad Costs (cost per reactivated user)
• Earned Growth % (Revenue from retained + referred customers)

✔ Bonus:
Run a small AB-test:
NeoMarketing Pilot Group vs. Business-as-usual Group
Compare revenue and engagement over 60-90 days.

5. Scale via Progency Partnering

✔ Action:
If pilots work (they likely will!):

• Expand NeoMail programs to ALL Rest customers
• Scale NeoN across all Test customers
• Formalise partnership with a Progency (product + AI + experts) to manage “Rest” and “Test” segments continuously.

Why:
You avoid internal bandwidth bottlenecks, and pay based on outcomes, not software usage.

**

📈 Playbook Summary Table

Step	Focus	Outcome
1	Segment into Best, Rest, Test, Next	Know your database opportunities
2	Micro NeoMail campaign	Revive Rest customers cost-effectively
3	Small NeoN pilot	Slash Test customer reacquisition costs
4	Track 3 KPIs	Show early proof of business impact
5	Partner with Progency	Scale results without staffing burdens

 🏆 Key Mindset Shifts Needed

• Treat Best customers like royalty (extreme retention).
• Rest is your sleeping goldmine — wake them up with love.
• Test needs surgical reactivation — not mass re-ad spending.
• Acquisition alone is death by thousand cuts. Retention is survival.

AI Take – 1

I asked Claude and ChatGPT: “What can you suggest to make it better? Evaluate the ideas critically. Will they fly with marketers? If you were a marketer receiving these ideas, what would you do?”

Claude

Strengths of the NeoMarketing Framework

The NeoMarketing approach has several compelling strengths:

1. Addresses a Genuine Pain Point: The “$500 billion AdWaste crisis” resonates because most marketers intuitively understand they’re spending too much reacquiring existing customers. Your framing of this problem is powerful and data-driven.
2. Simple, Memorable Positioning: “Double the Best, Halve the Waste” is an outstanding slogan that clearly communicates the value proposition in terms executives care about.
3. BRTN Framework’s Clarity: Segmenting by engagement patterns (Best-Rest-Test-Next) rather than demographics provides actionable clarity that most marketers will find immediately applicable.
4. Performance-Based Economics: The Progency model of paying only for incremental results aligns perfectly with C-suite expectations and addresses the “vendor vs. partner” tension.

Potential Challenges and Improvements

However, several aspects might face resistance or require refinement:

1. Complexity Concerns: While conceptually sound, the full implementation of AI Agents, Twins, NeoMails, and NeoN might overwhelm marketers who are still struggling with basic personalization. Consider creating a phased adoption roadmap that starts with simpler components.
2. Proof Points Gap: Marketers will immediately ask for case studies proving these concepts work at scale. Without substantial real-world examples, consider starting with a “pilot program” framing rather than presenting it as a fully proven system.
3. Execution Realities: The promise of AI-driven personalization at scale faces practical hurdles like data quality, integration complexity, and governance. Adding a realistic assessment of these challenges would build credibility.
4. Channel Diversification: The framework seems heavily weighted toward email. While logical given email’s ROI, today’s marketers need omnichannel solutions. Expanding how these concepts apply across channels would strengthen appeal.
5. Potential Pushback: The most likely objection will be: “If this works so well, why isn’t everyone doing it?” Addressing this head-on by acknowledging the novelty while emphasizing the competitive advantage of early adoption would be helpful.

If I Were a Marketer Receiving These Ideas

As a marketing leader, my reaction would likely be:

1. Intrigued but Cautious: The framework addresses real pain points with logical solutions, but the comprehensive nature might seem ambitious.
2. Looking for Proof: I’d immediately want to see case studies or at least pilot results. Without these, I’d be hesitant to commit fully.
3. Seeking Incremental Steps: Rather than implementing the entire system at once, I’d want a clear path to start small, prove value, and expand gradually.
4. Considering Resource Requirements: I’d have concerns about implementation complexity, data readiness, and the expertise needed to make this work.
5. Wanting Specifics on My Industry: I’d wonder how these concepts translate to my specific vertical and customer base.

Suggested Refinements

To make NeoMarketing more immediately actionable:

1. Create an Adoption Staircase: Develop a clear, phased implementation approach starting with basic components (like the BRTN framework) and progressing to more advanced elements.
2. Offer a Self-Assessment Tool: Provide a diagnostic that helps marketers quantify their current AdWaste and identify their biggest opportunities.
3. Develop Industry-Specific Versions: Tailor the approach for key verticals (retail, financial services, travel, etc.) to make it more immediately relevant.
4. Acknowledge the Change Management Challenge: Address the organizational and cultural shifts required, not just the technological ones.
5. Build a Clear ROI Model: Create a calculator that helps marketers forecast potential returns based on their specific customer base and current metrics.

The core ideas are solid and address genuine market needs. With these refinements, NeoMarketing could become a transformative framework that redefines how marketers approach customer relationships and ROI.

ChatGPT

1. Strengths

• Clear, Bold Mantra: “Double the Best, Halve the Waste” is a memorable, sharp slogan. It simplifies the complex NeoMarketing vision beautifully for marketers who often seek simple action guides.
• Strong Diagnosis of the Problem: You have pinpointed the AdWaste issue very sharply: the insane waste in reacquisition, and the two root problems — “Not for Me” (poor personalisation) and “No Hotline” (lack of daily engagement). This diagnosis resonates deeply with marketers who feel their budgets slipping away without corresponding outcomes.
• Compelling Framework: You present an actionable plan: Progency for Rest-to-Best transformation (revenue uplift), and NeoN for slashing reacquisition costs (profit improvement). This “two-problem, two-solution” structure is logical and powerful.
• Innovation with Familiarity: Marketers know the importance of segmentation and lifecycle marketing. Your BRTN segmentation is a clever upgrade that feels intuitive yet fresh.
• Focus on Economic Outcomes: Talking in the language of LTV, CAC, and “Rule of 40” profitable growth grounds your ideas solidly in business terms, not just marketing metrics.

2. Weaknesses (from a critical lens)

• Hard Work vs. Quick Wins Reality: Your story with the $6M ecommerce founder (“ABC: Agency, Budget, Clicks”)is powerful. However, it exposes a deeper truth: marketers are lazy or overburdened.
  • Progency and NeoMails/NeoN require rethinking, retraining, rebuilding.
  • Many marketers might nod but delay action because it’s harder work than throwing money at ads.

➡ Suggestion: Explicitly propose an “easy entry point” — e.g., start with 1 segment, 1 micron program, 1 NeoN campaign. Lower the initial activation energy.

• Need for Case Studies or Proof Points: There’s a gap today: you state NeoMails and NeoN’s potential impact (4-10X better engagement, 30-50% CAC cuts)— but real-world pilots or data would build credibility immensely.
  Marketers need “show me, not tell me.”

➡ Suggestion: Even hypothetical mini-case examples would make it more believable. (“Imagine a fashion brand doing X and seeing Y results.”)

• Organisational Inertia: Shifting from Acquisition Addiction (AAA) to Retention Mindset (OOO) demands cultural change. CMOs might agree intellectually but lack the will or fear failure.

➡ Suggestion: You could propose a “Parallel Pilot Approach” — run a NeoMarketing pilot alongside existing adtech tactics, and compare outcomes. This de-risks the mindset shift.

• Privacy and Consent: NeoN’s authenticated targeting model is powerful, but you gloss over privacy concerns. CMOs today are deeply wary after GDPR/CCPA penalties.

➡ Suggestion: Add a simple visual of how NeoN respects user consent at every step (eg., “customer-controlled identity exchange”).

3. If I Were a Marketer Receiving These Ideas

• Initial Reaction: “Brilliant…but tough.” I’d love the vision. I’d worry about execution complexity.
• Actions I Would Take:
  • Immediately ask my retention team to identify my current Best-Rest-Test segmentation.
  • Run a small test of NeoMails to revive Rest customers.
  • Pilot NeoN for a small Test customer batch — maybe a 2-3 week campaign.
  • Ask for an agency-like partner to “do it for me” (the Progency model fits here brilliantly).
  • Start measuring baseline metrics: Rest-to-Best conversion %, Test-to-Reactivate costs.

4. Summary: Overall Evaluation

Category	Evaluation
Ideas	Excellent, crisp, transformative for marketing
Practicality	Needs a “step 1-2-3” simpler activation plan
Reception by Marketers	Excitement mixed with caution
Next Step You Should Add	Proof points, lighter entry path, privacy reassurances

 

Cutting CAC

While transforming Rest customers into Best represents the revenue upside of NeoMarketing, addressing the AdWaste crisis by cutting Customer Acquisition Costs delivers the immediate profit impact. AdWaste is created by continuously reacquiring Test customers through auction-based adtech platforms. It’s the equivalent of a homeowner paying rent to sleep in their own bedroom. This devastatingly inefficient cycle consumes approximately 70% of digital marketing budgets globally—an estimated $500 billion annually—yet rarely receives scrutiny in boardroom discussions.

The mechanics of this waste are straightforward but pernicious. When customers slip from Rest to Test status (becoming inactive for 90+ days), brands lose the ability to reach them through owned channels. Their only recourse becomes paying premium prices to Google, Meta, and other platforms in competitive auctions to “rediscover” these customers—often at costs 5-10x higher than retention marketing. With each passing month, these platforms’ algorithms drive prices steadily upward through their auction dynamics, creating a mathematical impossibility for sustainable profitability.

What makes this situation particularly absurd is that brands are essentially paying rent to access customers they already know. These customers exist in their databases—complete with email addresses, purchase histories, and behavioural data—yet brands must pay third-party intermediaries exorbitant fees to reach them.

NeoMarketing offers a two-pronged approach to slash this wasteful spending by at least 50%.

First, solving the Hotlines problem through NeoMails prevents Rest customers from becoming Test customers in the first place. Traditional emails achieve single-digit open rates because they deliver little genuine value while demanding significant attention (clicking through to websites, completing multi-step processes). NeoMails fundamentally reinvent this dynamic through the Mu-Micron combination—Atomic Rewards (Mu) to drive opens through micro-incentives, and Microns providing 15-60 seconds of valuable “brain gain” experiences daily.

This daily engagement creates habit-forming interaction patterns. When customers expect and value these micro-experiences, they remain connected to the brand, preventing the attention recession that leads to dormancy. Even if they’re not actively purchasing, the hotline remains open—preserving the ability to reach them without expensive intermediaries when the right offer arrives.

Second, NeoN provides a revolutionary alternative for reaching Test customers who have already gone dormant. Unlike traditional retargeting that relies on probabilistic cookie matching through expensive auction platforms, NeoN creates a brand-to-brand cooperative email ad network based on authenticated identity (PII).

This approach creates a powerful win-win ecosystem:

• Publisher brands monetise their engaged email audiences through ActionAds embedded within their communications to Best customers
• Advertiser brands precisely target their dormant Test customers through these authenticated channels at 30-50% lower costs than traditional platforms
• Customers receive more relevant offers in channels where they’re already engaged, eliminating the intrusive retargeting that follows them across the web

NeoN’s precision matching eliminates the waste inherent in traditional targeting, where up to 40% of impressions reach incorrect audiences due to cookie limitations. Instead, messages reach exactly the right individuals based on email identity rather than probabilistic proxies.

Most importantly, ActionAds eliminate the devastating “click-through penalty” that typically loses 80-90% of potential conversions. By enabling customers to browse products, complete forms, and make purchases directly within partner emails, NeoN dramatically increases conversion rates while reducing friction.

NeoMails prevents customers from becoming dormant while NeoN provides a cost-effective way to reconnect with those who already have.

The financial impact is transformative. By preventing dormancy and cutting reacquisition costs by 30-50% while simultaneously creating new revenue streams from existing email programs, brands can redirect millions from wasteful platform spending back to their bottom line. This is precisely what “Halve the Waste” means: systematically eliminating the inefficient reacquisition cycle while building more valuable, sustainable customer relationships.

**

NeoMarketing’s dual strategy—Progency’s Rest-to-Best transformation alongside NeoN’s precision targeting—creates an integrated solution to marketing’s most expensive inefficiencies. By systematically doubling the Best customer base while halving AdWaste, this approach doesn’t merely optimise existing processes; it fundamentally rewires marketing economics.

For marketers, the path forward is clear. First, measure your current baseline: what percentage of customers are Best, what’s your Rest-to-Test conversion rate, and how much do you spend on reacquisition? Then implement the dual-track strategy: deploy Progency to elevate Rest customers through AI-powered personalisation while implementing NeoN to slash reacquisition costs.

As traditional marketing continues its descent into diminishing returns and rising costs, NeoMarketing offers a revolutionary alternative—one that transforms marketing from necessary expense into the primary profit engine driving sustainable business growth.