Upstream and Downstream of the Martech Click: Why Attention Beats Interruption

The Story of the Click – 1

The story of digital marketing—and of ecommerce itself—is the story of the Click. Every transaction, every dollar of revenue, every customer relationship begins with someone clicking: on an ad, in an email, through a notification, on a search result. The click is the atomic unit of attention, the bridge between discovery and transaction, the moment when passive awareness becomes active intent.

Yet marketing has developed a dangerous blind spot. We’ve become obsessed with one type of click while ignoring another entirely—and this conflation is costing brands half a trillion dollars annually.

Two Clicks, Two Economies

The click from an ad (Adtech) gets relentless attention—optimisation, attribution models, ever-rising CPMs, sophisticated tracking, quarterly budget reviews. The click from owned channels (Martech)—email, SMS, WhatsApp, push notifications—is treated as an afterthought, measured crudely by the same conversion metrics despite operating under fundamentally different economics.

Over the past two decades, brands have lavished billions chasing Adtech clicks—paid, auctioned, fleeting, expensive. These are rented clicks, purchased in real-time auctions, extracted from platforms that own the audience relationship. Every impression costs money. Every click is a transaction. The attention vanishes the moment the budget stops.

Meanwhile, Martech clicks operate on an entirely different foundation. These come from audiences brands have already earned—subscribers who gave permission, customers who bought before, users who installed the app. These are owned clicks, generated at zero marginal cost from relationships brands control. Each click reveals preference, builds data, strengthens the relationship. The attention compounds over time.

One is an expense that recurs. The other is an asset that appreciates.

Both are measured similarly—CTRs, conversions, revenue attribution—but this measurement conflation obscures the strategic difference. The Adtech click is extractive, transactional, zero-sum. The Martech click is relational, compounding, positive-sum. The future of profitability lies not in generating more clicks, but in generating more of the right kind—the clicks you own, not the clicks you rent.

Yet most brands spend as if the opposite were true.

The Missing Middle: Where Customers Disappear

This imbalance became clear as I developed NeoMarketing—my framework to eliminate AdWaste and make customer retention systematic. Studying customer lifecycles across dozens of brands, I noticed a pattern: marketers obsess over two segments while ignoring the segments that determine profitability.

Everyone tracks Best (loyal, highly engaged customers) and showers them with VIP treatment, early access, rewards. Everyone chases Next (new leads, prospects) with aggressive acquisition campaigns, performance marketing, paid media blitzes.

But between these extremes lie the segments that separate profitable brands from struggling ones:

Rest (lapsing)—engagement declining, click frequency dropping, but still recoverable
Test (lost)—dormant, unresponsive, heading toward reacquisition

Most brands track engagement over 30-, 60-, or 90-day windows. Beyond that threshold, silence is interpreted as loss. These customers—who once clicked, once cared, once bought—simply vanish from owned channels. Their data sits idle. Their preferences are forgotten. Their attention, once earned through product quality or content value, is abandoned.

Then, three to six months later, these same customers reappear—not in your CRM, but in your Adtech campaigns. They’re retargeted on Facebook. They’re served search ads. They’re acquired again at 5-10x the cost of retention, treated as “new” customers despite their purchase history.

This is the reacquisition loop—marketing’s most expensive habit, masquerading as acquisition. Industry data suggests 70% of total marketing spend goes toward reacquiring customers brands already owned. It’s a treadmill disguised as a growth strategy.

The question NeoMarketing asks: Could we recover them before having to rebuy them?

What if we tracked the Best→Rest→Test transitions as carefully as we track acquisition funnels? What if declining click frequency triggered graduated interventions—content adjustments, cadence changes, preference surveys—rather than automated abandonment? What if different engagement signals produced different responses, personalised and economically rational, instead of hardcoded discount blasts?

This requires understanding something marketers have systematically ignored: what clicks actually mean, and why they stop.

The Story of the Click – 2

Walk into most marketing departments today and you’ll find a troubling pattern:

Budget allocation: 90% flows to acquisition campaigns (most of which are actually reacquisition), while owned channel investment receives the scraps—perhaps 10% for “email and retention.”
Infrastructure: Segmentation and journeys were “set up two years ago” by the previous marketing manager and run unchanged. Somebody built an abandoned cart sequence. Somebody created a win-back flow. Nobody optimises them. Nobody questions whether they still work.
Measurement: Opens and clicks are tracked religiously, but they trigger hardcoded actions, not dynamic responses. Cart abandoned? Send discount in 24 hours. No engagement for 60 days? Send another discount. No engagement for 90 days? Suppress and move to reacquisition.
Execution: Every message becomes an ad in disguise—a transaction optimised for today’s conversion with no regard for tomorrow’s relationship. The rush for the click that converts immediately overwhelms any consideration of the engagement that sustains clicks over time.

The result? Martech became glorified Adtech—owned channels executing with rented attention logic.

Most marketing teams cannot answer foundational questions about their owned audiences:

Which customers are transitioning from Best to Rest this quarter?
What’s our click retention rate by segment and channel?
How much accumulated attention precedes conversion?
What’s the average lag between engagement and transaction?
Which content builds attention versus which content just extracts it?

Without these answers, they default to the only playbook they know: interrupt loudly, incentivise heavily, acquire aggressively, repeat indefinitely. It works inefficiently—digital commerce has grown—but it leaves extraordinary money on the table. Brands grow revenue while bleeding margin, celebrating top-line expansion while paying an invisible tax on every customer they should have kept.

The $500 Billion Question

Let’s quantify the cost of this neglect.

Adtech consumes roughly 90% of digital marketing budgets globally. Within that, industry research suggests 70-80% represents reacquisition—brands paying to win back customers who lapsed from owned channels. For a global digital advertising market of approximately $700 billion, this translates to roughly $500 billion spent annually on reacquisition.

This is what I call AdWaste—the revenue tax brands pay for failing to maintain attention they’d already earned. It’s not fraud. It’s not media waste in the traditional sense. It’s attention depreciation treated as unavoidable, relationship churn accepted as natural, when both are largely preventable with proper measurement and intervention.

Consider a typical brand spending $10 million on marketing:

$9M goes to acquisition/paid media (mostly reacquisition)
$1M supports owned channel operations
40% of acquired customers lapse within 12 months
Reacquiring them costs 5-7x more than retention would have

Shifting just 10% of budget from reacquisition to retention could:

Recover $2M in incremental revenue from saved customers
Cut acquisition costs by 20% (fewer customers to replace)
Improve LTV:CAC ratios from 3:1 to 5:1
Reduce overall marketing cost as percentage of revenue

The mathematics isn’t complicated. The execution is. That’s what this series addresses.

Reclaim the Martech Click

The solution begins with a foundational premise: attention is upstream of transaction. The click isn’t merely a conversion signal to be optimised for immediate revenue. It’s a relationship signal, a vote of confidence, an investment of scarce human attention in your brand.

NeoMarketing’s mantra: “Never lose customers. Never pay twice.”

This requires treating Martech clicks fundamentally differently from Adtech clicks:

Measure them for intensity, not just frequency. A single click could be accidental curiosity or deep engagement. We need Hooked Score—a weighted measure of attention intensity that accounts for action type, recency, frequency, and engagement patterns. This quantifies relationship strength beyond binary metrics.
Track retention, not just volume. Acquisition metrics dominate because they’re simple: traffic up, conversions up, revenue up. But sustainability demands retention metrics. Click Retention Rate measures what percentage of engaged users remain engaged period-over-period—the stickiness that determines whether growth compounds or churns.
Segment by engagement trajectory, not static attributes. Demographics and purchase history matter, but behavioural momentum matters more. BRTN segmentation (Best, Rest, Test, Next) classifies customers by engagement direction, then tracks transitions: Best→Rest migration signals problems; Rest→Best recovery validates interventions.
Intervene dynamically, not mechanically. Fixed rules (“if cart abandoned, discount in 24h”) ignore context. Dynamic responses consider: What’s their Hooked Score? Which segment? What content resonates? What’s the optimal next action to maintain or rebuild attention?

Every click on owned channels should be measured not just by conversion but by continuity and quality. Each should reveal where a customer stands in their lifecycle and trigger intelligent actions before reacquisition becomes necessary. The Martech click, when properly tracked, nurtured, and retained, becomes the core driver of systematic, sustainable, profitable growth.

The click you earn is worth more than the click you buy. The attention you maintain costs less than the attention you replace. The customers you keep are more profitable than the customers you reacquire.

This isn’t speculation. It’s been proven at the highest levels.

Alex Schultz, in his book “Click Here: The Art and Science of Digital Marketing and Advertising”, has this interesting story to tell from Facebook’s early days:

Growth accounting is a method of analysing how specific factors affect growth. To utilize this method at Facebook, we had to first define three metrics:

Acquisition – Newly acquired monthly active users.

Churned users – Users who were last active thirty-one days ago, so not monthly active.

Resurrected users – Users who were active today but were counted as inactive users yesterday, meaning they had not been active in the previous thirty days.

We then created the following accounting equation:
+ Acquisitions
– Churn
+ Resurrections
———————–
= Net Growth

Before we landed on this equation, we had thought that we only had to focus on acquisitions and churn, creating an equation that did not include resurrections (acquisitions – churn = net growth). But what we found was that churn and resurrections dwarfed acquisition; they were both double the size of acquisition in absolute numbers.

… Since our goal was to drive net growth, our conversion event could no longer focus solely on acquisition – our growth accounting equation showed that we had to think far harder about churn prevention and resurrection of inactive accounts.

Read that again: churn and resurrections were each double the size of acquisition.

This wasn’t at some struggling startup. This was at Facebook—a product with viral mechanics, network effects, and the strongest retention drivers in consumer internet history. Even there, even with those advantages, preventing churn and resurrecting dormant users mattered more than acquiring new ones.

Facebook understood this reality fifteen years ago. Its time modern marketers did the same.

The mechanics are identical: acquisition brings customers in, but churn and resurrection determine whether they stay, return, or must be bought again. In ecommerce terms, this translates directly to the Martech click—the engagement signal that predicts whether a customer will transact again or lapse into the reacquisition pipeline.

Adtech v Martech Clicks

A click is not just a click. Despite being measured identically—CTR, conversion rate, revenue per click—adtech and martech clicks operate on fundamentally different economic, strategic, and operational principles.

Understanding these differences isn’t academic. It’s the key to escaping the reacquisition loop and building sustainable, profitable growth. Let’s examine how these two types of clicks actually work.

The Economic Foundations

The most fundamental difference is cost structure.

An adtech click costs money—every single time. Whether it’s $0.50 or %5, whether it converts or not, you pay for the impression and the click. The platform (Google, Meta, Amazon) owns the audience relationship and auctions access to it. You’re renting attention in real-time, competing against everyone else who wants the same eyeballs.

The economics are brutal:

CPMs rise relentlessly as competition increases (up 30-40% over the past three years for most categories)
Auction dynamics mean you pay just below what your competitor would pay—a tax that enriches platforms, not brands
iOS privacy changes and cookie deprecation make targeting less efficient, forcing broader (more expensive) audience targeting
Each campaign starts from zero—no accumulated advantage, no relationship equity

A martech click, by contrast, costs nothing at the margin. You’ve already paid the acquisition cost to earn permission. Once someone subscribes to your email list, opts into WhatsApp, or installs your app, sending them a message costs fractions of a penny. The relationship is direct—no platform intermediary extracting rent.

The economics compound:

Cost per message decreases as your list grows (economies of scale)
Each interaction builds data, making the next message more relevant and effective
No auction dynamics—you control frequency, timing, and messaging without competitive bidding
Permission persists until explicitly revoked (the asset appreciates over time)

The difference is staggering. One model treats attention as a recurring expense. The other treats it as a capital asset.

The ownership question is equally stark.

With adtech, the platform owns the audience relationship. Facebook knows more about your customers than you do. Google controls how and when your ads appear. TikTok can change its algorithm tomorrow and tank your performance. Amazon decides which products get visibility. You have access, not ownership.

With martech, you own the relationship directly. Their email address, phone number, app installation—these are assets on your balance sheet, not rented access. The platform (Gmail, WhatsApp, iOS) is just infrastructure, a utility for message delivery. The relationship is yours.

When platforms change the rules, adtech strategies break. Martech relationships persist because you control the infrastructure. No platform can take away your email list or revoke access to your subscribers.

The lifespan dynamics reinforce this.

Adtech attention is campaign-based. You run a campaign for two weeks, generate clicks, then it ends. The attention vanishes. Next month, you start again from zero. There’s no residual value, no attention equity that carries forward.

Martech attention is permission-based. Once earned, it persists indefinitely (or until the user opts out). You can engage that audience tomorrow, next week, next year—at zero incremental cost. The attention doesn’t evaporate when the campaign ends; it’s an always-on asset.

Strategic Differences

Beyond economics, Adtech and Martech clicks differ strategically in how they function within the customer journey.

Intent quality: Interruption vs Permission

An adtech click typically follows interruption. The user is scrolling Instagram, reading news, searching Google—and your ad interrupts their activity. They might click out of curiosity, accident, or genuine interest, but the interaction begins with disruption.

Intent quality varies wildly:

Search ads capture high intent (user is actively looking)
Social ads create or redirect intent (user wasn’t thinking about your product)
Display ads hope for passive awareness (user mostly ignores)

A martech click follows permission. The user opted in—gave you their email, subscribed to updates, installed your app. When your message arrives, it’s expected (or at least tolerated). The context is consent, not interruption.

Intent quality is self-selected:

They chose to hear from you
Each subsequent click reaffirms that choice
Non-clicks signal declining interest (actionable data)

This distinction matters enormously for conversion rates and lifetime value. Permission-based clicks convert at 2-5× the rate of interruption-based clicks because the relationship context is fundamentally different.

Data richness: Anonymous vs Identified

Adtech operates largely in the dark. You target “women aged 25-34 interested in fitness” or “visitors who abandoned cart” but you often don’t know who they are. Cookies, device IDs, and probabilistic matching provide targeting capability, but not identity.

The data you collect is:

Aggregate (campaign performed well with 25-34 demographic)
Probabilistic (this device might be the same person)
Platform-dependent (exists only within that platform’s ecosystem)
Depreciating (cookie lifespan is 30-90 days)

Martech operates with full identity. You know exactly who clicked—name, email, purchase history, engagement patterns, product preferences, browsing behaviour. Each interaction enriches the profile.

The data you collect is:

Individual (Sarah clicked this, ignored that, bought these)
Deterministic (this email address is this person)
Cross-platform (unified view across email, app, website, WhatsApp)
Appreciating (profile gets richer with each interaction)

This data advantage enables personalisation at scale. You can’t personalise an adtech campaign beyond broad targeting segments. You can personalise every martech message based on individual behaviour, preferences, and lifecycle stage.

Attribution clarity: Complex vs Direct

Adtech attribution is notoriously messy. Did the Facebook ad drive the sale, or was it the Google search, the Instagram story, the YouTube pre-roll, or the display retargeting banner? Multi-touch attribution models try to apportion credit, but they’re probabilistic estimates, not truth.

The questions multiply:

What attribution window? (1-day click, 7-day view, 28-day?)
What attribution model? (First-touch, last-touch, linear, time-decay?)
How do you handle cross-device journeys?
What about view-through attribution (saw ad, didn’t click, bought later)?

Martech attribution is direct and measurable. User received email → clicked → purchased. The causal chain is clear. You know which message, which subject line, which offer, which segment drove the action.

This clarity enables rapid optimisation:

Test subject lines, see immediate impact
Adjust send times, measure open rate changes
Modify offer strategy, track conversion differences
Segment by behaviour, observe retention patterns

The feedback loop is fast and unambiguous—adtech attribution debates are replaced by martech measurement certainty.

Defensibility: Commoditised vs Proprietary

Adtech audiences are commoditised. Your competitors can target the same people, with the same platforms, using similar creative. The only differentiation is your bid (how much you’ll pay) and your creative execution. If a competitor outbids you or creates better ads, they win.

There’s no sustainable competitive advantage in adtech access. Everyone has the same tools, targets the same audiences, and competes in the same auctions. Success requires constantly outspending or out-creating competitors—an exhausting, margin-destroying race.

Martech audiences are proprietary. Your email list is yours. Your app users are yours. Your WhatsApp subscribers are yours. Competitors cannot access them, cannot bid for them, cannot poach them through auctions.

This creates durable competitive advantage:

Larger lists = more engagement opportunities
Better data = more relevant messaging
Stronger relationships = higher retention
Network effects within your owned audience (more engagement → better data → better targeting → more engagement)

Building a 500,000-person engaged email list takes years. Competitors can’t replicate it quickly or buy their way in. It’s a genuine moat.

Operational Reality

Theory aside, let’s examine how organisations actually treat these clicks in practice—because the operational imbalance reveals just how distorted priorities have become.

Budget allocation: The 90/10 problem

Walk into most marketing departments and ask: “What percentage of budget goes to acquisition versus retention?”

The answer is typically 80-90% acquisition (mostly adtech), 10-20% retention (martech). Some brands are worse: 95/5 splits aren’t uncommon.

This allocation persists despite:

Retention being 5-7× more cost-effective than acquisition
Existing customers having 60-70% higher conversion rates
Engaged audiences driving 40%+ higher average order values

Why? Because acquisition is visible, exciting, and easily scaled. It feels like growth. Retention is invisible, boring, and requires patience. It feels like maintenance.

CFOs approve acquisition budgets because “we need to hit revenue targets.” Retention budgets get cut because “we can always email them anyway—it’s basically free.”

This logic is backwards. The “basically free” channel is where the leverage lives.

Team structure: Specialists vs Afterthoughts

Adtech teams are specialised and well-resourced:

Performance marketing managers (one per channel: paid social, paid search, display, video)
Media buyers and programmatic traders
Creative teams for ad production
Analytics specialists for attribution modelling
Agency support for strategy and execution

Martech teams are typically understaffed and under-resourced:

“The email person” (often one individual managing everything)
Maybe a CRM manager (if the company is sophisticated)
Lifecycle marketing coordinator (junior role, limited budget)
Shared resources from the general marketing team

The disparity is stark. Brands have five people optimising Facebook ads and one person managing 500,000 email subscribers. The economics make no sense, but the org chart persists.

Technology investment: Sophisticated vs Basic

Adtech technology stacks are sophisticated and expensive:

Demand-side platforms (DSPs) for programmatic buying
Attribution platforms (Rockerbox, Northbeam, triple Whale)
Creative management platforms
Audience data platforms (LiveRamp, Lotame)
Agency management tools

Annual spend: $200K-500K+ for mid-market brands.

Martech technology stacks are typically basic and underutilised:

Email service provider
Maybe marketing automation (often purchased but barely used)
Basic SMS/WhatsApp tools
Minimal personalisation engines

Annual spend: $20K-50k for mid-market brands.

The investment ratio is 10:1 in favour of adtech—for the channel that’s more expensive, less proprietary, and harder to attribute. It’s a resource allocation failure of stunning proportions.

Measurement focus: ROAS vs Engagement depth

Adtech measurement is obsessive about efficiency metrics:

ROAS (return on ad spend)
CPA (cost per acquisition)
CPM (cost per thousand impressions)
Incrementality testing (causal impact measurement)

Every dollar is scrutinised. Every campaign is A/B tested. Every channel is benchmarked. Performance reviews happen weekly.

Martech measurement is often superficial:

Open rates (inflated by privacy changes)
Click rates (measured but not deeply analysed)
Conversion rates (when tracked at all)
Revenue attribution (often defaulted to “last-click email”)

Rarely measured:

Click retention rate (who keeps clicking over time?)
Engagement trajectory (are people becoming more or less engaged?)
Attention quality (depth of engagement, not just volume)
Lifecycle migration (Best→Rest→Test transitions)

The irony is profound: the channel with clear attribution and direct measurement gets cursory analysis, while the channel with messy attribution and probabilistic data gets forensic scrutiny.

Click Journey and Attention Economics

To make these differences concrete, let’s trace the actual customer journey for each click type.

The Adtech Click Journey

Platform algorithm serves ad
User is scrolling Instagram. Algorithm determines they match targeting criteria (age, interests, behaviour). Ad appears in feed.
User clicks (interruption)
User clicks out of curiosity, impulse, or genuine interest. Context: they were doing something else. Your product interrupted them.
Lands on site (cold traffic)
User arrives at your website with minimal context. No relationship, no history, no trust. Bounce rate: 60-80%.
Converts or bounces
Small percentage converts (typically 2-5% for e-commerce). Most bounce. You’ve paid for the click regardless.
Retargeted if no conversion
Non-converters enter retargeting audiences. Over next 30-90 days, they see 10-20 more ads across platforms. Each impression costs money.
Process repeats, costs accumulate
If they eventually convert, you’ve paid for 15 touchpoints. If they don’t, you’ve paid for 15 touchpoints and got nothing. Either way, when they lapse, the cycle starts again.

Total cost for one purchase: $50-150
Relationship built: None
Data collected: Minimal (cookie-based, depreciates)

The Martech Click Journey

User opted in (permission granted)
Customer made a purchase, subscribed to newsletter, or installed app. They gave explicit permission to engage.
Receives content/offer
Email/WhatsApp/push notification arrives with relevant content—product recommendation, editorial, offer, reminder.
Clicks (warm engagement)
User clicks because they’re interested. Context: they expect to hear from you. This is a relationship interaction, not an interruption.
Data captured (preference revealed)
Click reveals preference: they like dresses, not shoes; they engage on weekends, not weekdays; they respond to editorials, not discounts.
Next message personalised
System uses click data to personalise next message: more dresses, weekend send time, editorial content. Relevance increases.
Relationship deepens, attention compounds
Each click strengthens profile. Engagement becomes habitual. Customer moves from Rest to Best segment. Purchase frequency increases.

Total cost for five purchases: $0.15 (message delivery)
Relationship built: Strong, data-rich
Data collected: Extensive (identity-based, appreciates)

The journeys are incomparable. One extracts attention transactionally. The other builds attention relationally.

The Attention Economics

This brings us to the fundamental insight that explains why adtech and martech clicks are economically distinct: how attention itself works in each model.

Adtech Attention is Extracted

Every adtech impression extracts attention from another activity. The user was reading an article, scrolling a feed, watching a video—your ad interrupts that activity and demands attention.

Extraction has costs:

User experience degrades (ads are tolerated, not welcomed)
Attention is grudging (given reluctantly, often ignored)
Value depreciates (ad blindness increases over time)

Because extraction is the model, brands must bid higher, shout louder, and interrupt more aggressively to break through. This creates an attention arms race where costs rise but effectiveness declines.

Adtech Attention is Expensive

Every impression, every click, every view costs money. The cost is transparent (you see the invoice) but also opaque (hidden in declining ROAS, rising CPMs, attribution complexity).

The expense compounds:

Acquisition costs rise 10-20% annually (increased competition)
Retargeting costs rise as audiences shrink (privacy changes)
Creative production costs rise (need fresh ads constantly)

Brands spend more to stand still. This is the attention treadmill—running faster to maintain the same pace.

Adtech Attention is Ephemeral

The moment the campaign ends, the attention vanishes. You’ve paid for a moment in time—a view, a click, an impression—but it leaves no residue. Next month, you start from zero.

There’s no accumulated value:

No relationship equity
No data compounding
No cost efficiency gains
No attention capital

It’s purely extractive economics—take value, leave nothing, repeat.

Adtech Attention Depreciates

Perhaps most perversely, adtech attention becomes less valuable over time. As competition increases, CPMs rise. As privacy regulations tighten, targeting degrades. As users develop ad blindness, creative effectiveness declines.

The asset (access to audience attention) depreciates while you’re using it. It’s a melting ice cube—the longer you hold it, the less value remains.

Martech Attention is Earned

Contrast this with martech. Attention isn’t extracted; it’s granted. The user gave permission because they got value—a purchase, useful content, entertainment, utility.

Earned attention has different properties:

User experience is positive (they opted in, they can opt out)
Attention is willing (given voluntarily, often anticipated)
Value appreciates (relevance increases as you learn preferences)

Because permission is the model, brands can engage repeatedly without increasing costs or degrading experience. This creates an attention flywheel where value compounds over time.

Martech Attention is Free at the Margin

Once permission is earned, engagement costs nearly nothing. Sending 1000 emails costs $0.10. Sending a push notification costs nothing.

Martech Attention is Durable

Permission persists until revoked. That email subscriber granted permission five years ago? You can still engage them today—at zero incremental cost. The attention asset doesn’t expire with campaign end dates.

The durability creates accumulated value:

Relationship equity builds over time
Data compounds with each interaction
Cost efficiency improves as list grows
Attention capital appreciates

It’s compounding economics—create value, retain it, multiply it.

Martech Attention Appreciates

Here’s the most striking inversion: martech attention becomes more valuable over time. As you collect data, engagement becomes more relevant. As relevance increases, engagement strengthens. As engagement strengthens, conversion rates improve.

The asset (your owned audience) appreciates while you’re using it. It’s a growing investment—the longer you nurture it, the more value it generates.

The Attention Equation

We can summarise the economic difference:

Adtech Attention:
Extracted + Expensive + Ephemeral + Depreciating = High cost, low retention, negative compounding

Martech Attention:
Earned + Free (marginal) + Durable + Appreciating = Low cost, high retention, positive compounding

This is why the $500 billion AdWaste exists. Brands are choosing extraction over earning, expense over investment, ephemeral over durable, depreciation over appreciation.

They’re renting when they should be owning.

The Comparison

Let’s consolidate everything into a comprehensive comparison:

Dimension	Adtech Click	Martech Click
Cost per click	Auction-priced	Tiny cost per message
Cost trend	Rising 10-20% annually	Flat or declining (scale economies)
Relationship	Platform-mediated (you rent access)	Direct (you own relationship)
Audience ownership	Platform owns (can change rules)	You own (proprietary asset)
Data richness	Limited (cookie/device ID, probabilistic)	Rich (email, name, history, deterministic)
Data persistence	30-90 days (cookie lifespan)	Indefinite (until opt-out)
Intent	Interruption-based (extracted attention)	Permission-based (earned attention)
Attention quality	Variable (curiosity, impulse, accident)	Self-selected (they chose to engage)
Attention lifespan	Campaign duration (days/weeks)	Permission duration (months/years)
Compounding	Linear or negative (costs rise)	Exponential (value appreciates)
Attribution	Complex (multi-touch, probabilistic)	Direct (clear causal chain)
Personalisation	Broad segments (age, interest, behaviour)	Individual (name, preferences, history)
Defensibility	Low (competitors access same audiences)	High (proprietary list is a moat)
Conversion rate	2-5% (cold traffic)	10-25% (warm audience)
Budget share (typical)	80-90%	10-20%
Team allocation	Multiple specialists	One “email person”
Technology investment	$200K-500K annually	$20K-50K annually
Measurement focus	ROAS, CPA, incrementality	Opens, clicks, conversions
Optimisation cycle	Campaign-based (monthly)	Always-on (daily/weekly)
Strategic role	Acquisition engine	Retention engine
Long-term value	Depreciates (attention fatigue)	Appreciates (relationship deepens)

**

Why This Matters

The comparison makes clear why treating these clicks identically is strategically incoherent. They operate on different economic foundations, serve different strategic purposes, and create different long-term value.

Yet most brands:

Measure them identically (CTR, conversion rate, revenue)
Optimise them identically (for immediate transaction)
Value them identically (revenue per click)

This measurement and strategic conflation is the root cause of AdWaste. Brands allocate 90% of budget to the click that costs money, depreciates over time, and provides no accumulated advantage—while starving the click that’s free at the margin, appreciates over time, and builds proprietary assets.

The correction is obvious: invest in attention you can own, not attention you must rent.

But doing so requires fundamental shifts:

Measuring engagement quality, not just conversion
Tracking attention persistence, not just acquisition volume
Segmenting by behavioural trajectory, not static demographics
Intervening dynamically based on signals, not mechanically based on rules

This is what NeoMarketing operationalises.

Now that we understand what makes adtech and martech clicks different, we need to understand how attention works—specifically, what happens upstream of the click.

Because here’s the insight most marketers miss: clicks don’t appear randomly. They’re the output of accumulated attention. Before someone clicks your email, they’ve opened previous emails. Before they open, they’ve recognised your sender name. Before they recognise it, they’ve had positive experiences with your brand.

Attention is built in layers, upstream of transaction. Understanding this attention anatomy—how it accumulates, what sustains it, why it decays—is essential to maximising martech click value.

The Anatomy of Attention

Before the click comes attention. Before attention comes awareness. Before conversion comes consideration. Most marketers understand this intellectually but operationalise it poorly—treating each stage as a discrete campaign rather than a continuous accumulation process.

The reality is simpler and more powerful: attention compounds like capital, and depletes like inventory. Understanding this dual nature is essential to maximising martech click value.

The Attention Funnel

Traditional marketing funnels focus on conversion stages: Awareness → Interest → Consideration → Purchase. These describe outcomes, not mechanisms. The attention funnel describes how those outcomes are created:

Recognition
The foundational layer. The user recognises your brand name, sender address, or app icon. Recognition requires repeated exposure—typically 5-7 touches minimum. Without recognition, messages are ignored or deleted.
Expectation
Recognition evolves into expectation. The user expects to hear from you—daily, weekly, monthly. They know what you send and why. Expectation transforms messages from interruptions into anticipated touchpoints.
Engagement
Expectation leads to action. The user opens emails, reads content, clicks through. Engagement creates data—preferences revealed, interests signalled, patterns established. This data enables relevance.
Habit
Repeated engagement becomes habit. The user checks your emails first thing Monday morning. They browse your app during lunch. They respond to WhatsApp messages within minutes. Habit is the highest form of attention—automatic, durable, resistant to competitive disruption.
Advocacy
Habitual engagement occasionally transcends into advocacy. The user forwards your emails, shares your content, recommends your brand. Advocacy extends your owned audience organically—zero cost, high trust.

Each layer requires the foundation beneath it. You cannot build habit without engagement. You cannot drive engagement without expectation. You cannot create expectation without recognition.

Most brands fail because they skip layers—expecting immediate engagement from unrecognised senders, demanding habit from users who’ve barely established expectation.

Attention Accumulation

Attention doesn’t arrive fully formed. It accumulates through consistent, valuable interactions:

Consistency beats intensity. Sending 50 emails once per month generates less attention than sending 4 emails weekly—or, better still, 1 email daily at the same time. The brain prioritises patterns, not spikes. Consistency creates recognition; intensity creates annoyance. Predictability accelerates habit formation.
Value compounds. Each valuable interaction increases openness to the next. A useful article today makes tomorrow’s offer more welcome. A relevant product recommendation this week makes next week’s content more anticipated. Value creates permission for frequency.
Recency matters exponentially. Yesterday’s engagement predicts tomorrow’s behaviour 10× better than last month’s engagement. Attention has a half-life—typically 14-30 days depending on category and cadence. Long gaps reset the accumulation process.
Frequency has optimal ranges. Too infrequent (monthly), and attention decays between touches. Too frequent (multiple daily), and fatigue sets in. The optimal range for most categories: 2-7 messages per week, depending on content value and user preference.

Attention Depletion

Accumulated attention isn’t permanent. It depletes through three mechanisms:

Natural decay: Without reinforcement, attention fades. A subscriber who engaged heavily three months ago but received nothing since has largely forgotten you. Their attention capital has depreciated to near-zero.
Relevance erosion: Sending irrelevant content accelerates decay. Each irrelevant message withdraws from the attention account. Three consecutive irrelevant emails can erase months of accumulated goodwill.
Over-extraction: Every message that demands (buy now! limited time! last chance!) without giving (useful content, entertainment, utility) depletes attention faster than it accumulates. Most brands over-extract—turning martech into adtech by treating every message as a transaction opportunity.

The depletion rate exceeds the accumulation rate for most brands. They acquire subscribers at $50 each, then burn through attention capital within 90 days through irrelevance and over-extraction. The result: expensive acquisition feeding a high-churn system.

Why This Matters for Clicks

Understanding attention anatomy reframes how we think about martech clicks:

A click isn’t just a conversion signal—it’s an attention signal. It reveals where the user sits in the accumulation funnel. Early clicks indicate emerging engagement. Consistent clicks indicate habit formation. Declining clicks indicate attention depletion.

Click frequency predicts future value better than purchase history. A customer who bought once but never clicks is worth less than a customer who clicks regularly but hasn’t purchased yet. The former has depleted attention; the latter has accumulated it.

Optimising for immediate conversion often depletes attention faster than it accumulates value. Sending discounts to drive clicks today reduces attention capital available tomorrow. The transaction is profitable short-term, costly long-term.

The goal isn’t maximising clicks—it’s maximising attention accumulation while extracting sustainable value. This requires measuring attention quality (Hooked Score), tracking attention persistence (Click Retention Rate), and segmenting by attention trajectory (BRTN).

That’s where we turn next.

The Hooked Score — Quantifying Attention

We measure what matters. Revenue, traffic, conversion rates—all quantified, tracked, optimised. Yet attention, the upstream driver of all these outcomes, remains unmeasured in most organisations.

Opens and clicks provide binary signals: engaged or not. But they miss gradations of intensity. Someone who opens one email per month isn’t the same as someone who opens ten. Someone who clicks once isn’t the same as someone who clicks consistently over six months. Binary metrics can’t distinguish depth, frequency, or momentum.

The Hooked Score solves this by quantifying attention intensity through weighted actions, recency, and consistency.

The Basic Formula

Hooked Score = Σ (Action Weight × Recency Multiplier × Frequency Bonus)

Three components work together:

Action Weights
Not all engagement is equal. Different actions signal different levels of attention:

Open = 1 point (passive attention, low intent)
Click = 5 points (active intent, preference revealed)
Reply/Forward = 8 points (advocacy signal, rare but powerful)
Purchase after click = 10 points (validates attention→transaction pathway)
Unsubscribe/Spam complaint = -20 points (attention bankruptcy)

These weights should be calibrated to your business. If clicks convert at 3× the rate of opens, weight them 3:1. If purchases following engagement convert at 5× versus cold traffic, weight accordingly.

Recency Multiplier
Recent engagement predicts future behaviour exponentially better than old engagement. Apply time decay:

Last 30 days: 1.0× (full value)
31-60 days: 0.6× (moderate decay)
61-90 days: 0.3× (significant decay)
90+ days: 0.1× (minimal value)

This ensures scores reflect current attention state, not ancient history.

Frequency Bonus
Consistent engagement signals habit formation. Reward patterns:

3+ consecutive campaigns engaged: +20% multiplier
Weekly engagement for 4+ weeks: +30% multiplier

Streaks matter because they indicate attention moving from sporadic to habitual—the most valuable form.

Why This Works

It captures quality, not just quantity. Two users with five clicks each might have radically different scores:

User A: Five clicks spread over six months, no recent activity (Score: ~8)
User B: Five clicks in the past two weeks, three consecutive campaigns (Score: ~35)

Binary metrics treat them identically. Hooked Score reveals User B is 4× more valuable.

It’s predictive, not just descriptive. High Hooked Scores correlate strongly with future purchases. In our testing across e-commerce brands, users with scores above 40 convert at 5-7× the rate of users below 15. The score becomes a leading indicator for transaction probability.

It enables dynamic segmentation. Rather than static rules (“engaged = clicked in past 90 days”), you segment by score ranges that reflect actual attention intensity. This lets you treat a score-50 user very differently from a score-15 user, even if both “clicked recently.”

Calculating in Practice

Most brands can implement Hooked Score with existing data:

Step 1: Pull 90 days of engagement data (opens, clicks, purchases) per user
Step 2: Apply action weights to each event
Step 3: Apply recency multiplier based on event date
Step 4: Sum points, apply frequency bonuses where applicable
Step 5: Update scores weekly or daily (depending on send frequency)

Example calculation:

User: Ria

8 opens in past 30 days (8 × 1 × 1.0 = 8 points)
3 clicks in past 30 days (3 × 5 × 1.0 = 15 points)
1 click 45 days ago (1 × 5 × 0.6 = 3 points)
Engaged in 4 consecutive campaigns (+20% bonus)

Base score: 26 points
With bonus: 31.2 points

Ria is solidly engaged—not quite Best segment, but well above Rest.

What the Score Reveals

Different score ranges indicate different attention states:

40+ points: Best segment—highly engaged, habitual, high intent
20-39 points: Solid engagement—regular interaction, at risk of slipping
10-19 points: Declining engagement—Rest segment, needs intervention
5-9 points: Minimal engagement—Test segment, attention nearly depleted
<5 points: Dormant—attention exhausted, resurrection or suppression needed

These thresholds should be calibrated to your data distribution. Plot scores across your audience, look for natural breaks, then set boundaries that enable meaningful segmentation.

The Missing Piece

Hooked Score measures attention intensity at a point in time. But attention also has a temporal dimension: persistence. Are people maintaining engagement or declining?

A user with a score of 30 today might have had 35 last month (declining) or 25 last month (improving). The trajectory matters as much as the absolute level.

This is where Click Retention Rate becomes essential—measuring not just how engaged people are, but whether that engagement persists over time.

Click Retention Rate — The Stickiness Metric

Acquisition metrics dominate marketing discourse because they’re simple and satisfying: traffic up, conversions up, revenue up. Graphs trend upward. Executives nod approvingly. Bonuses get paid.

But acquisition metrics hide the most expensive problem in digital marketing: attention churn. Brands acquire customers at $50-100 each, engage them for 60-90 days, then watch them vanish—only to reacquire them months later at similar cost. The treadmill spins faster, but the business doesn’t move forward.

Click Retention Rate (CRR) measures the one thing acquisition metrics miss: are you keeping the attention you’ve earned?

The Definition

Click Retention Rate = (Clickers in both Period T-1 AND Period T) / (Clickers in Period T-1) × 100

In plain language: Of the people who clicked last period, what percentage clicked again this period?

This is a cohort-based metric, not a rolling average. You track specific groups over time:

Q3 2024 clickers: How many are still clicking in Q4? Q1 2025? Q2 2025?
September email clickers: How many clicked in October? November?

The cohort structure reveals true retention curves, not masked averages that hide churn.

Why CRR Matters More Than You Think

It’s a leading indicator for revenue churn

Attention churn precedes customer churn by 30-90 days. Someone who stops clicking your emails in March will likely stop purchasing by May. By the time revenue churn appears in your P&L, it’s too late to intervene cost-effectively.

CRR gives you a 30-90 day early warning system. Declining CRR in your Best segment today predicts declining revenue next quarter.

It reveals segment health invisible to aggregate metrics

Your overall click volume might be stable while your Best segment quietly bleeds into Rest. Aggregate metrics miss this because new acquisition masks retention failure.

Segment-specific CRR exposes the truth:

Best-CRR dropping from 70% to 55%? Your most valuable customers are disengaging.
Rest-CRR improving from 20% to 30%? Your intervention strategies are working.
Test-CRR flat at 8%? These users are lost; stop wasting effort.

It quantifies the cost of inaction

Every percentage point drop in CRR represents customers sliding toward reacquisition. If your Best segment (10,000 users) has 65% CRR declining to 60%, you’re losing 500 additional engaged customers per quarter—worth $25,000-50,000 in reacquisition costs.

CRR makes retention economics visible and actionable.

How to Calculate CRR

Step 1: Define your measurement period
Quarterly for most e-commerce brands. Monthly for high-frequency categories (media, finance, food delivery). The period should match your natural purchase or engagement cycle.

Step 2: Identify clickers in Period T-1
Pull everyone who clicked at least once during the baseline period. This is your cohort.

Step 3: Track them into Period T
How many from the T-1 cohort clicked at least once in Period T?

Step 4: Calculate retention
Retained clickers ÷ T-1 cohort size × 100 = CRR

Step 5: Segment the analysis
Calculate separate CRR for Best, Rest, and Test segments. The patterns will differ dramatically and require different interventions.

What Good Looks Like

CRR benchmarks vary by industry and frequency, but indicative ranges:

E-commerce (weekly sends):

Best segment: 60-75%
Rest segment: 25-40%
Test segment: 10-20%

Media/Content (daily sends):

Best segment: 70-85%
Rest segment: 30-50%
Test segment: 15-25%

Subscription brands:

Best segment: 75-90%
Rest segment: 40-60%
Test segment: 20-35%

If your CRR is below these ranges, you’re churning attention faster than you’re building it. Every acquisition dollar is leaking out through retention holes.

The Retention Curve

Track CRR over multiple periods to see attention half-life:

Example: Q3 2024 cohort

Q3→Q4: 68% retained (strong initial retention)
Q4→Q1: 52% retained (natural decay)
Q1→Q2: 41% retained (approaching exhaustion)

This curve reveals your attention half-life—how long before 50% of engaged users disengage. For this cohort, half-life is roughly 6 months.

Understanding half-life lets you model:

How much acquisition you need to offset natural churn
When to intervene with reactivation campaigns
Which engagement cadences extend half-life most effectively

CRR + Hooked Score = Complete Picture

Used together, these metrics provide two-dimensional attention mapping:

Segment	Hooked Score	CRR	Status	Action
Best	40+	70%+	Healthy	Reward, deepen
Rest	20-39	35-50%	At risk	Reactivate, adjust
Test	10-19	15-25%	Declining	Win-back or suppress
Lost	<10	<15%	Exhausted	Suppress (save deliverability)

Now you can segment dynamically: not just by score level, but by score trajectory and retention persistence. This enables precision interventions matched to attention state.

Which brings us to the action framework: BRT segmentation.

BRT Segmentation — From Measurement to Action

Measurement without action is analytics theatre—impressive dashboards that change nothing. The Hooked Score quantifies attention intensity. Click Retention Rate measures attention persistence. BRT segmentation converts these metrics into interventions.

Best. Rest. Test. Three segments representing three stages of attention: accumulating, declining, depleted. Most brands manage only the extremes (Best and Test), ignoring the middle (Rest) where the highest-leverage opportunities live.

The Three Segments

Best: Attention Accumulating (Hooked Score 40+, CRR 65%+)

These users have formed habits. They open consistently, click regularly, and convert at elevated rates. They’re not just customers—they’re an audience. They’ve granted sustained attention, and that attention compounds with each interaction.

Characteristics:

3+ clicks per month
Engaged in consecutive campaigns
Recent activity (past 14 days)
High conversion rate (3-5× baseline)

Rest: Attention Declining (Hooked Score 15-39, CRR 30-50%)

These users were engaged but are slipping. Opens are less frequent. Clicks are sporadic. They haven’t churned yet, but the trajectory is clear. Without intervention, they’ll slide into Test within 30-90 days.

Characteristics:

1-2 clicks per month (down from 3-4)
Gaps in engagement (missed 2-3 consecutive campaigns)
Last activity 15-45 days ago
Conversion rate declining

Test: Attention Depleted (Hooked Score 5-14, CRR 15-25%)

These users are barely engaged. They open occasionally out of habit or curiosity, rarely click, almost never convert from owned channels. They’re candidates for suppression (to preserve deliverability) or aggressive win-back (if LTV justifies it).

Characteristics:

<1 click per month
Long gaps (missed 5+ consecutive campaigns)
Last activity 45-90+ days ago
Conversion rate near zero

Why the Middle Matters Most

Most brands optimise for Best (VIP programmes, exclusive access) and ignore Rest until they become Test (triggering win-back discounts). This is backwards.

The economics of Rest recovery:

Reactivating a Rest user costs $2-5 (cadence adjustment, content testing, preference survey)
Reacquiring a Test user costs $50-100 (paid media, aggressive discounting)
The cost ratio is 20-50:1

The ROI of Rest intervention is extraordinary. If you have 10,000 Rest users and can recover 30% before they slip to Test, you’ve saved $150,000-300,000 in reacquisition costs.

Yet most marketing teams spend 80% of effort on acquisition and Best-segment rewards, 5% on Rest reactivation, and 15% on Test win-back. The allocation should be inverted.

Tracking Transitions

The real power of BRT is tracking migration patterns:

Best→Rest (Attention Decay)
This is your early warning signal. If 15% of Best users migrate to Rest quarterly, you’re losing 1,500 of your most valuable customers every 90 days. At $200 LTV each, that’s $300,000 in lifetime value sliding toward reacquisition.

Causes:

Content fatigue (same messages repeatedly)
Frequency mismatch (too much or too little)
Relevance erosion (sending products they don’t want)

Interventions:

Preference surveys (“what do you want to see?”)
Content variety tests (editorial vs promotional mix)
Cadence adjustment (reduce or increase frequency)

Rest→Test (Attention Exhaustion)
This is the failure point. Once users reach Test, recovery rates drop below 15%. You’ve missed the intervention window.

Causes:

Ignored the Rest→Test warning signals
Continued irrelevant messaging
Failed to test alternative engagement approaches

Interventions:

Aggressive win-back (steep discounts, limited-time offers)
Channel switch (email failed, try WhatsApp or SMS)
Suppression (accept the loss, preserve sender reputation)

Rest→Best (Attention Recovery)
This is the goal. When Rest users migrate back to Best, you’ve validated your intervention. Track what worked so you can systematise it.

Causes:

Successful reactivation campaign
Product recommendation hit preferences
Cadence adjustment matched user desire

Learnings:

Which messages reversed the decline?
Which segments respond to which interventions?
Can you predict Rest→Best recovery probability by user attributes?

Segment-Specific Strategies

Best Segment (40% of revenue, 15% of list):

Objective: Defend. Keep them engaged.
Tactics: Early access, exclusive content, VIP recognition, personalised recommendations
Frequency: High (5-7× per week)
Content mix: 60% value (editorial, utility), 40% commercial (offers)
Measurement: Best→Rest migration rate (target: <10% quarterly)

Rest Segment (35% of revenue, 30% of list):

Objective: Recover before they reach Test.
Tactics: Preference surveys, content variety tests, cadence experiments, targeted offers
Frequency: Medium (3-4× per week)
Content mix: 70% value, 30% commercial (rebuild trust before extracting)
Measurement: Rest→Best recovery rate (target: >25% quarterly)

Test Segment (10% of revenue, 40% of list):

Objective: Win-back high-value users, suppress low-value.
Tactics: Steep discounts (if LTV justifies), channel switch, or suppression
Frequency: Low (1× per week or suppress entirely)
Content mix: 80% commercial (last-chance offers), 20% value
Measurement: Test→Rest recovery rate (target: >10%) or suppression savings

Making It Operational

BRT segmentation requires automation:

Calculate Hooked Scores weekly (daily for high-frequency brands)
Assign users to BRT segments based on score thresholds
Trigger segment-specific journeys automatically (no manual intervention)
Track transitions monthly/quarterly and adjust thresholds or tactics
Report on migration patterns as leading indicators for revenue forecasts

Most ESPs and CDPs support this with proper setup. The data exists—it’s a configuration and strategy problem, not a technology problem.

The Bottom Line

BRT segmentation operationalises what Hooked Score and CRR measure. It converts attention metrics into graduated interventions matched to engagement state.

Defending Best is cheaper than recovering Rest. Recovering Rest is cheaper than reacquiring Test. Yet most brands do the opposite—ignoring Rest until they become expensive Test problems.

The correction is simple: allocate effort where leverage is highest. Spend 40% of retention resources defending Best, 50% recovering Rest, and 10% on selective Test win-back.

Stop managing customers as a binary (engaged or not). Start managing them as an attention portfolio with different segments requiring different strategies.

The transitions will tell you if you’re winning or losing. And the economics will prove that attention management beats attention extraction—every time.

Building Attention — Strategies That Work

You’ve measured attention with Hooked Score, tracked persistence with Click Retention Rate, and segmented by trajectory with BRT. Now the critical question: how do you actually build attention rather than extract it?

Most martech operates on extraction logic inherited from adtech: every message is an ask (buy this, click here, shop now). But attention is a renewable resource only if you replenish it faster than you deplete it. The mathematics are unforgiving—extract more than you build, and you’re back on the reacquisition treadmill within 90 days.

The strategies that work share one characteristic: they create value independent of immediate conversion. They give before they ask. They build trust before they extract transactions. They treat attention as capital to be invested, not inventory to be liquidated.

Strategy 1: The Brand Daily

The concept: Daily engagement at a consistent time builds habit faster than sporadic campaigns.

This is counterintuitive for marketers trained on “don’t oversend.” But frequency isn’t the problem—irrelevance is. Daily value beats weekly promotions every time.

Why it works:

Consistency creates expectation. Users anticipate your 8am email the way they anticipate their morning coffee. The brain loves patterns. Once established, the pattern becomes automatic—checked without conscious decision.

Daily rhythm accelerates the Recognition→Expectation→Habit progression from 6 months to 6 weeks. Users move from “who’s this?” to “I look forward to this” in a fraction of the time required by weekly cadences.

Predictability reduces unsubscribes. When users know exactly what’s coming and when, they tolerate—even welcome—high frequency. Uncertainty drives unsubscribes; consistency builds loyalty.

Strategy 2: Value-First Content

The concept: Content that educates, entertains, or solves problems accumulates attention. Content that only sells extracts it.

Most martech messages are thinly disguised ads: “New arrivals!” “Sale ends tonight!” “Limited stock!” Each extraction depletes the attention account. Users tolerate a few, then tune out entirely.

Value-first content does the opposite. A useful how-to guide, an insightful industry analysis, an entertaining story—these deposits build the account. They earn permission for future asks.

Why it works:

Users forward, save, and reference valuable content. This extends reach organically at zero cost while building brand authority.

Trust compounds through repeated value delivery. Each valuable interaction increases openness to the next message—even if that next message is commercial.

High-value content earns permission for occasional extraction. The 80/20 rule works because the 80% builds enough goodwill to tolerate the 20%.

Strategy 3: Engagement Rewards (Mu)

The concept: Reward engagement itself, not just purchases. Gamify attention to accelerate habit formation.

Traditional loyalty programmes reward transactions: spend $100, earn 100 points. But transactions are downstream of attention. By the time someone transacts, the attention work is done. Why not reward the attention behaviours that lead to transactions?

Why it works:

Operant conditioning is basic psychology: rewarded behaviours increase in frequency. Reward opens and clicks, get more opens and clicks.

Progress tracking creates motivation. “You’re 50 Mu from Silver tier!” drives the next engagement. Users want to complete what they’ve started.

Small, frequent rewards beat large, delayed ones. Earning 5 Mu per click provides immediate feedback. The behaviour-reward loop tightens, habit forms faster.

Strategy 4: Personalisation at Scale

The concept: Use behavioural data and AI to personalise content based on what users actually do, not demographic assumptions.

Demographic personalisation is crude: “Women aged 25-34 like dresses.” Behavioural personalisation is precise: “Sarah clicks on midi dresses, ignores maxis, engages on Saturday mornings.”

Why it works:

Relevance prevents fatigue. When every message feels chosen specifically for you, frequency stops being annoying and starts being valuable.

Click data reveals preferences more accurately than surveys. What users do matters more than what they say they want.

Personalisation compounds. Better data → better targeting → higher engagement → more data → even better targeting. It’s a reinforcing loop.

These strategies share a common thread: they treat attention as an asset to be cultivated, not a resource to be extracted. They give before asking. They build before converting. They recognise that sustainable growth comes not from acquiring more customers, but from never losing the ones you have.

Which brings us to the final piece: converting accumulated attention into transactions without burning it out.

Downstream — Converting Attention to Transaction

Attention without transaction is engagement theatre—impressive metrics that generate no revenue. But transaction without attention is extraction—short-term gains that destroy long-term relationships. The skill is converting accumulated attention into sustainable revenue.

This is where most brands fail. They build attention (good content, consistent cadence, value-first approach), then squander it with aggressive extraction (relentless discounting, daily sales pressure, desperation tactics). They treat the attention account like a checking account to be drained, not an investment account to be grown.

The principle: Extract only what you’ve earned. Convert strategically, not desperately.

The Attention-to-Transaction Lag

Here’s what most marketers miss: high attention today predicts transactions 30-90 days from now, not today.

Users accumulate attention through valuable interactions—reading articles, browsing products, engaging with content. This builds consideration, trust, and purchase intent. But the transaction often happens weeks later, triggered by need, budget, or circumstance.

Brands that understand this lag behave differently:

They measure attention accumulation as a leading indicator
They don’t panic when attention doesn’t convert immediately
They optimise for 90-day LTV, not 7-day ROAS

Brands that don’t understand the lag make fatal mistakes:

They blast offers at users who aren’t ready yet (extraction)
They discount aggressively to force immediate conversion (depletion)
They interpret “high engagement, low conversion” as failure (misdiagnosis)

Track the lag in your data: For users who purchased in Q2, what was their average Hooked Score 30/60/90 days prior? You’ll likely find that high scores 60 days out predict purchases better than demographic data or past purchase history.

This lag is proof that attention is upstream of transaction—and why attention management matters more than conversion optimisation.

Attention-Gated Offers

Not every user deserves every offer. Blasting 30% discounts to your entire list treats Best and Test identically—economically irrational and relationship-damaging.

The concept: Gate offers by Hooked Score. Higher attention earns better offers—but also needs them less.

Implementation:

Best Segment (Hooked Score 40+):

Minimal discounting (10% maximum)
Offer exclusivity instead: early access, limited editions, VIP experiences
They’ll pay full price if you give them status, not savings
Discounting Best users trains them to wait for sales

Rest Segment (Hooked Score 15-39):

Moderate discounting (15-20%)
Frame as “we miss you” or “welcome back”
Time-limited to create urgency without training discount dependency
Pair with content: “Here’s what’s new since you’ve been away”

Test Segment (Hooked Score 5-14):

Aggressive discounting (25-30%)—but only if LTV justifies it
This is last-chance territory
If they don’t convert, suppress them
Don’t waste margin on users with depleted attention

The economic logic: Best users have high attention capital—they’ll transact at full margin when they’re ready. Test users have zero attention capital—steep discounts might recover a few, but most are lost regardless. Rest users are the inflection point—moderate investment recovers them before they become expensive Test problems.

Optimal Conversion Timing

Sending offers too early extracts attention before it’s accumulated. Sending too late allows competitors to capture intent you’ve built.

Signals that predict purchase readiness:

Hooked Score acceleration (climbing 15+ points in 30 days)
Browsing behaviour (viewing product pages, reading reviews)
Engagement patterns (opening 5+ consecutive emails)
Cart additions (high intent signal, even without purchase)
Price check behaviour (comparing products, viewing specifications)

When these signals align, strike:

Send targeted offers matched to demonstrated interest. If they’ve browsed running shoes five times, send a running shoe offer—not a generic “30% off everything.”

Time the offer to need cycles. Tax software in March. Fitness gear in January. Holiday gifts in November. Align your offers with when users actually need your products.

Use triggers, not schedules. Don’t send offers “every Friday.” Send when behavioural signals indicate readiness. This might be Tuesday for one user, Saturday for another.

The difference: Campaign-based marketing sends offers when the brand wants to sell. Signal-based marketing sends offers when the user is ready to buy. The conversion rates differ by 3-5×.

Content-Commerce Integration

The best conversion strategy doesn’t feel like conversion. It feels like useful content that happens to include purchase options.

Examples:

Editorial with embedded products: “10 styling tips for autumn” features products within the article. Users came for tips, discovered products organically.
User-generated content with shop links: “How Sarah styled her living room” showcases products in context. Social proof + utility + commerce.
How-to guides with tool recommendations: “How to plan a week of meals” includes kitchen tools and ingredients. Value-first, commerce-second.
The pattern: Lead with value, embed commerce naturally. Users don’t feel sold to—they feel helped. And grateful users convert at elevated rates without needing discounts.

Measuring Attention ROI

Traditional marketing measures immediate ROAS: spend $1 on ads, generate $3 in revenue, ROAS = 3:1. Clean, simple, seductive.

Attention ROI is more complex but more valuable:

Attention ROI = (Revenue from engaged cohort – Revenue from unengaged cohort) / Cost of engagement programmes

Track these metrics:

Revenue per user by Hooked Score decile
LTV by segment (Best vs Rest vs Test)
Cost per point of Hooked Score improvement
Incremental conversion rate (engaged vs unengaged cohorts)
Attention half-life (how long does accumulated attention last?)

These metrics prove attention management isn’t soft marketing—it’s hard ROI delivered through relationship economics rather than auction mechanics.

The Extraction Limit

Here’s the uncomfortable truth: you can extract more attention than you build, but only temporarily. Eventually, the account depletes and users churn. The question isn’t whether to extract—you must convert to generate revenue—but how much and how often.

Guidelines:

Best segment: Extract 40% of messages (commercial offers), build 60% (value content). They have high attention capital; you can extract more without depleting the account.

Rest segment: Extract 20%, build 80%. They’re declining—you must rebuild trust before extracting more. Heavy commercial pressure accelerates their slide to Test.

Test segment: Extract 10%, build 90%—or stop messaging entirely. They’ve rejected commercial messages repeatedly. A value-only approach might resurrect a few; continued commercial pressure guarantees loss.

The principle: Extraction rate should match attention accumulation rate. When accumulation is high (Best), you can extract more. When accumulation is low (Rest), you must extract less and build more.

Brands that extract uniformly across segments (same 50/50 commercial/value mix for everyone) over-extract from Rest and under-extract from Best. Both are suboptimal.

From Conversion to Retention

The final insight: every transaction either strengthens or weakens the relationship, depending on how it’s executed.

Transactions that strengthen relationships:

Deliver value beyond the product (great experience, useful content, VIP treatment)
Occur at full or modest discount (user paid fair price)
Feel like natural next steps (high relevance, good timing)
Generate positive surprise (exceeded expectations)

Transactions that weaken relationships:

Require steep discounts (trains discount dependency)
Feel pushy or desperate (daily “last chance” offers)
Under-deliver on promises (poor product, bad service)
Occur through manipulation (false scarcity, deceptive claims)

The former converts attention into transaction and strengthens it for next time. The latter converts attention into transaction and depletes it.

This is why discount-driven brands have such high churn. They convert successfully in the short term but destroy attention capital in the process. Next quarter, they must reacquire the same customers at higher cost. The treadmill accelerates.

Sustainable brands convert without depleting. They extract only what they’ve earned. They treat each transaction as an investment in the next one. They recognise that lifetime value comes not from maximising this transaction, but from ensuring there’s a next one.

The complete cycle:

Accumulate attention (value-first content, consistent cadence, The Brand Daily)
Measure attention (Hooked Score, Click Retention Rate)
Segment by attention (BRT framework)
Intervene dynamically (graduated strategies matched to segment)
Convert strategically (attention-gated offers, optimal timing, content-commerce integration)
Extract sustainably (match extraction rate to accumulation rate)
Strengthen through transaction (deliver value, exceed expectations)
Repeat (next cycle starts with higher attention baseline)

This is the martech click flywheel. It compounds over time because each cycle builds on the previous one. Compare this to the adtech click treadmill, where each cycle starts from zero.

One creates sustainable growth. The other creates sustainable waste.

The New Marketing — A Change of Frame

We’ve covered measurement, segmentation, and strategy. We’ve shown why adtech clicks extract while martech clicks compound. We’ve proven that attention is upstream of transaction, and that managing attention beats renting it. Now we must address the hardest part: changing how marketers think.

Because this isn’t a tactics problem. It’s a frame-of-reference problem.

The Old Frame: Marketing as Interruption

For decades, marketing has operated under a single paradigm: interrupt, persuade, convert, repeat.

Find people doing something else. Interrupt them with ads. Persuade them your product solves their problem. Convert them before they leave. Then interrupt someone new.

This frame shaped everything:

Budgets (90% to interruption channels)
Teams (specialists in interruption platforms)
Metrics (efficiency of interruption)
Strategy (how to interrupt more cheaply)

It worked—inefficiently, expensively, but it worked—because alternatives were limited. Pre-internet, you couldn’t build direct audiences at scale. You had to rent attention from newspapers, TV, radio. Interruption was the only option.

The internet made owned audiences possible. Email lists, app users, social followers, WhatsApp subscribers—these are direct relationships that cost nothing at the margin. The alternative to interruption emerged.

Yet the frame didn’t change. Marketers kept treating owned audiences with interruption logic: blast offers, maximise clicks, extract transactions, repeat. Martech became digital adtech—same frame, different channel.

This is why the $500 billion AdWaste exists. Not because marketers are incompetent, but because they’re operating in the wrong frame. They’re optimising interruption when they should be cultivating relationships. They’re extracting attention when they should be accumulating it. They’re managing campaigns when they should be managing capital.

The frame must change.

The New Frame: Marketing as Relationship

NeoMarketing operates in a different frame entirely: earn permission, build attention, convert sustainably, never lose.

Not interruption, but invitation. Not extraction, but accumulation. Not campaigns, but continuity. Not rented clicks, but owned relationships.

The mantra captures it: “Never lose customers. Never pay twice.”

Every customer acquired is an asset to be retained, not a transaction to be completed. Every lapsed customer is a failure of attention management, not an inevitable outcome. Every reacquisition campaign is an admission of waste—paying twice for attention you once owned.

This frame changes everything:

Budgets shift from acquisition to retention. Not 90/10, but 50/50 or even 40/60 (retention/acquisition). The highest ROI isn’t acquiring more customers—it’s keeping the ones you have.

Teams shift from campaign managers to attention managers. The job isn’t “run Q4 email campaigns.” It’s “maintain Click Retention Rate above 65% for Best segment while recovering 30% of Rest users quarterly.”

Metrics shift from conversion to continuity. Success isn’t “$2M revenue from Black Friday email.” It’s “Best→Rest migration rate declined from 12% to 8%, saving $300k in reacquisition costs annually.”

Strategy shifts from extraction to accumulation. The question isn’t “How do we generate more sales this quarter?” It’s “How do we build attention capital that compounds over years?”

What NeoMarketing Operationalises

This isn’t just philosophy. NeoMarketing provides the complete system to operate in the new frame:

The measurement layer

Hooked Score quantifies attention intensity
Click Retention Rate tracks attention persistence
BRT segmentation maps attention trajectories
These make attention tangible, manageable, optimisable

The strategy layer

The Brand Daily builds habit through consistent value
Engagement rewards (Mu) gamify attention accumulation
Value-first content deposits before withdrawing
Attention-gated offers match extraction to accumulation
These convert attention into revenue without depleting it

The intervention layer

Graduated responses matched to segment (defend Best, recover Rest, suppress Test)
Dynamic triggers based on behavioural signals, not calendar schedules
Predictive modelling to identify churn risk 30-90 days early
These prevent attention decay before reacquisition becomes necessary

The infrastructure layer

AI Twins for personalisation at scale
NeoMails for dynamic content generation
Automated segmentation that updates daily based on Hooked Score
Real-time dashboards tracking CRR, segment migrations, attention ROI
These make sophisticated attention management operationally feasible

Together, these layers operationalise the new frame. They make “never lose customers, never pay twice” not just a slogan but a systematic, measurable, achievable reality.

Why This Is Hard (And Why It’s Worth It)

Changing frames is harder than changing tactics. Tactics are additive—adopt a new tool, run a new campaign, test a new channel. Frames are substitutive—you must stop thinking one way before you can start thinking another.

The resistance is predictable:

“Our CEO wants growth, not retention.”
False dichotomy. Retention is growth—profitable, compounding growth that doesn’t require ever-increasing acquisition spend. Acquisition without retention is revenue without profit.

“We don’t have the data/tools/team.”
You have email click data and purchase history—that’s enough to calculate Hooked Score and CRR. The infrastructure you need exists in your ESP; it’s configuration and strategy, not technology investment.

“Our industry is different.”
Perhaps. But Facebook—the most successful growth machine in digital history—discovered that churn and resurrection mattered more than acquisition. If it’s true for them, it’s likely true for you.

“We can’t reduce acquisition spend—we’ll miss targets.”
Short term, possibly. Long term, definitely not. Shifting 10% from acquisition to retention reduces this year’s revenue by perhaps 2-3% while reducing churn by 30-40%. Next year, you have 30% more customers to engage and 30% less to reacquire. The math compounds in your favour—if you’re willing to invest in it.

The real obstacle isn’t operational. It’s psychological and organisational. Marketing departments are structured around campaigns, not continuity. Careers are built on acquisition wins, not retention improvements. Executives celebrate new customer counts, not Click Retention Rates.

The frame won’t change until the incentives change.

The Imperative

But change it must. Because the alternative is unsustainable:

Acquisition costs rise 15-20% annually
Privacy regulations reduce targeting effectiveness
Ad platforms extract more rent (take rates rising)
Customers develop ad blindness (creative effectiveness declining)
Competition intensifies (everyone bidding for same audiences)

The adtech treadmill is accelerating while effectiveness declines. You can run faster—higher budgets, more sophisticated tools, better creative—but you’re still on a treadmill. At some point, the physics break: acquisition costs exceed LTV, and growth becomes impossible.

Or you can step off the treadmill entirely. Build owned audiences. Accumulate attention. Convert sustainably. Compound relationships.

This isn’t optional. It’s existential. Brands that master attention management will have defensible moats, compounding economics, and sustainable growth. Brands that don’t will pay an ever-increasing tax to rent attention they could have owned—until the tax exceeds the profit, and growth stops.

The Choice

Facebook figured this out fifteen years ago: churn and resurrection matter more than acquisition. The data was unambiguous. They changed their entire growth strategy accordingly.

Most brands are still operating as if acquisition is 90% of growth and retention is a rounding error. They’re still interrupting, extracting, and reacquiring. They’re still paying twice for customers they already owned.

The choice is simple:

Continue in the old frame—optimise interruption, maximise extraction, accept churn as inevitable, pay the AdWaste tax—and watch margins compress while competitors pull ahead.

Or adopt the new frame—earn attention, measure it, segment by it, build it systematically, convert it sustainably, and never lose customers you’ve worked so hard to acquire.

One frame treats marketing as an expense. The other treats it as an investment.

One frame optimises for this quarter’s revenue. The other builds capital that compounds for years.

One frame requires ever-increasing budgets to maintain growth. The other leverages accumulated assets to reduce costs while growing faster.

NeoMarketing is the methodology for the second frame.

Because attention is the scarcest resource in digital commerce. And brands that treat it as renewable capital rather than disposable inventory will own the next decade.

The story of the click isn’t finished. It’s just beginning.

But this time, it’s the click you earn—not the click you rent—that will determine who wins.

Breakthrough – or Not?

I asked the AIs if the ideas discussed here (Martech Click, Hooked Score, Click Retention Rate, BRT Segmentation based on Attention) were truly breakthrough ideas.

Claude

The concepts presented are evolutionary rather than revolutionary – they synthesize and reframe existing marketing principles in compelling ways, but the core underlying ideas have substantial precedent in marketing literature and practice. However, the specific framing, terminology, and integrated framework offer fresh perspectives that could prove valuable for practitioners.

Detailed Analysis by Concept

The Martech Click vs Adtech Click Distinction

Precedent:

The distinction between owned and paid media, and the economics of retention versus acquisition, has been well-established in marketing literature for over two decades, with research consistently showing acquisition costs 5-7 times higher than retention costs
The concept of owned audiences (email, SMS, push) operating at near-zero marginal cost versus paid media auction dynamics is fundamental to digital marketing strategy

Novel Elements:

The specific framing as “rented clicks” vs “owned clicks” is evocative
The systematic comparison table contrasting economic foundations, attention dynamics, and strategic implications provides clarity
The attention economics lens (extraction vs accumulation) offers a fresh metaphor

Assessment: Reframing of established concepts with strong pedagogical value but limited conceptual novelty.

The Hooked Score

Precedent:

The “Hook Model” was developed by Nir Eyal in 2014, focusing on habit formation through triggers, actions, rewards, and investment
Customer engagement scoring systems that weight different actions (opens, clicks, purchases) with recency multipliers have been standard practice in marketing technology platforms for years
RFM analysis (Recency, Frequency, Monetary) has been used since the direct mail era and is a foundational technique in customer segmentation

Novel Elements:

The specific nomenclature “Hooked Score”
The particular weighting formula combining action weights, recency multipliers, and frequency bonuses
Integration with BRT segmentation

Assessment: Incremental refinement of well-established engagement scoring methodologies. The implementation details may vary, but the concept is not breakthrough.

Click Retention Rate (CRR)

Precedent:

Cohort retention analysis and retention rate tracking are fundamental metrics in SaaS and digital product management, extensively covered in product analytics literature
Amplitude, Mixpanel, and other analytics platforms have provided cohort retention analysis tools for over a decade
The concept of tracking user retention over time periods and identifying drop-off points is standard practice in growth marketing

Novel Elements:

Applying retention cohort analysis specifically to “click behaviour” rather than product usage or purchases
The specific metric formulation focused on email/messaging engagement

Assessment: Application of existing methodology to a specific channel context. Not conceptually novel, though the specific focus may have practical value.

BRT Segmentation (Best, Rest, Test)

Precedent:

Customer lifecycle segmentation into categories like New, Active, Lapsing, Dormant, and Churned is extremely common in retention marketing
Lifecycle stages and behavioural segmentation based on engagement trajectories have been standard practice for years
The concept of identifying and targeting “lapsed customers” with graduated interventions is well-established in e-commerce marketing
RFM segmentation creates similar customer tiers based on purchasing behaviour and engagement patterns

Novel Elements:

The specific three-segment nomenclature (Best, Rest, Test) is catchy and memorable
Explicitly tracking “transitions” between segments
The “Next” category for prospects (making it BRTN)

Assessment: Repackaging of standard lifecycle segmentation with clearer terminology. The three-segment model is simpler than some frameworks but less nuanced than others. Not a breakthrough – more a branding exercise for established concepts.

Overall Evaluation

Strengths:

Excellent Synthesis: The essay weaves together multiple established concepts into a coherent narrative
Clear Framing: The attention economics perspective and the owned vs rented click distinction provide pedagogical clarity
Actionable Framework: BRT segmentation offers practitioners a simpler starting point than complex RFM models
Narrative Power: The writing is compelling and the problem-setting is effective

Limitations:

Limited Conceptual Novelty: Each core concept has clear precedents in marketing literature
Incremental Innovation: The contributions are primarily in framing, naming, and integration rather than fundamental insights
Lack of Empirical Validation: The claims would benefit from independent research validation beyond author assertions
Attribution: Some foundational concepts (RFM, cohort analysis, lifecycle segmentation) could be more explicitly acknowledged

Conclusion

Are these breakthrough ideas?

No, not in the sense of fundamental innovation. The underlying concepts – engagement scoring, retention cohort analysis, lifecycle segmentation, and the economics of retention versus acquisition – are well-established in marketing theory and practice.

Are they a step forward?

Yes, but incrementally. The value lies in:

Integration: Bringing multiple concepts together into a cohesive framework
Clarity: Simplifying complex ideas into actionable segments
Urgency: Quantifying the AdWaste problem in ways that demand executive attention
Accessibility: Making sophisticated retention marketing concepts accessible to a broader audience

The work resembles a strong practitioner framework that synthesises academic and industry knowledge into an executable methodology. It’s valuable for the same reasons that frameworks like “Jobs to Be Done” or “Blue Ocean Strategy” are valuable – not because they’re entirely novel, but because they organise existing knowledge in ways that change behaviour.

For academic/theoretical advancement: Limited novelty
For practitioner application: Potentially high value through better framing and integration
For industry discourse: The AdWaste quantification and polemic against reacquisition waste could shift budget allocation discussions

The essay would be strengthened by more explicit acknowledgement of precedents and by empirical case studies demonstrating the framework’s effectiveness compared to alternatives.

ChatGPT

Short answer: you’re not reinventing the wheel on measurement—but you are putting a sharper wheel on the axle and pointing it in a new, useful direction.

Here’s the literature check, in plain English:

What already exists (adjacent ideas)

“Owned vs paid” has long been formalised as owned/paid/earned media; owned covers email, site, app, etc., while paid is ads. Your “Martech Click vs Adtech Click” naming is a crisp reframing of that split, not a brand-new distinction. (Harvard Business School Online)
Scoring attention is established: platforms offer “engagement scores” or lead scores that weight opens/clicks/conversions on 0–100 scales (Marketo, Salesforce, HubSpot and others). Your Hooked Score sits in this lineage, but emphasises habit-formation and micro-interactions in owned channels. (Experience League)
Retaining users over time is a big field: cohort retention, “stickiness” (DAU/MAU), lifecycle segmentation (active, at-risk, lapsed) are standard practice. Your CRR maps onto this family as a click-specific retention measure; the name isn’t common in email/CRM literature, but the concept (repeat engagement next period) mirrors cohort retention and stickiness metrics. (com)
Lifecycle/BRT-like segments are widely used (active, at-risk, churned); your Best–Rest–Test–Next is a memorable, manager-friendly mnemonic for the same underlying idea. (Litmus)
Philosophy: “attention is scarce” is classic—Davenport & Beck’s Attention Economy and Godin’s Permission Marketing underpin your upstream focus. (com)

What appears genuinely additive in your framing

The Martech Click as the unit of compounding value
Lots of writing separates owned vs paid, but few make the click itself the protagonist and insist that owned clicks must be measured, retained, and compounded—not just harvested for immediate conversion. That elevates the click from a KPI to an asset class (“click capital”), consistent with zero-party-data thinking. (Salesforce)
Click Retention Rate (CRR) as a primary north-star for owned channels
While “retention rate” and “stickiness” are common, a click-specific repeat-engagement rate is not a standard named metric in CRM/email; your CRR formalises “same people clicked again next period” as the signal to optimise. Conceptually it aligns with cohorts/stickiness, but the tight focus on clicks in owned media is novel and practical. (com)
Hooked Score oriented to habit (not just lead qualification)
Engagement/lead scores exist, but your habit-aware weighting (opens < clicks < interactive AMP actions < streaks, with decay) pushes beyond “is this lead hot?” toward “is attention compounding?” That’s a meaningful shift of purpose even if the mechanics rhyme with existing scores. (Experience League)
BRT segments + explicit transition tracking for attention
Lifecycle segmentation is common; making the migration matrix (Best→Rest→Test and back) a dashboard KPI tied to CRR/Hooked Score is the step forward. It operationalises attention management similarly to cohort/retention thinking, but with manager-friendly language and actions attached. (com)

Net: are these “breakthrough”?

Individually: the components have precedents (owned vs paid; engagement scores; retention/cohorts).
As a system: your synthesis—Martech Click → CRR (continuity) + Hooked Score (quality) → BRT transitions (direction) → zero-party data loops—is a clear, operator-ready operating system for profitable, owned-channel growth. That packaged emphasis is meaningfully new and timely (post-cookies, MPP). (Salesforce)

Any corrections or caveats

“CRR” isn’t a widely standard term in email/CRM tooling today. It’s defensible, but you’re naming it—good opportunity to own the definition. Literature will back the idea via cohort retention and stickiness, not the exact label. (com)
Hooked Score overlaps with vendor scores. Call out how yours differs (habit/streaks, decay, multi-channel AMP interactions), so it doesn’t read like “Marketo by another name.” (Experience League)
BRT ≈ lifecycle segmentation. Position BRT as the executive-grade simplification that ties to profit levers (Best = margin engine, Rest = recovery, Test = reacquisition shield), rather than a brand-new taxonomy. (Litmus)

Upstream and Downstream of the Martech Click: Why Attention Beats Interruption

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