Every NeoMarketing play is a move on one grid — the Transaction–Attention Table (TAT). Before you can run a play you have to build the map. A series on how, from five fields to a working radar.
| The overview. The Profit You Already Own introduced the Transaction–Attention Table as the map. This series is the build guide a CMO needs to commission that map and trust it — what data it takes, how to set each axis, and where to stop. |
1
The map comes before the machine.
Every transformation needs a map, and for NeoMarketing the map is the Transaction–Attention Table. The idea is simple. Rows show how far a customer has progressed in transactions. Columns show whether the customer is still paying attention. Put the two together and a brand can see what its dashboard normally hides: which customers can be grown, which must be protected, and which have already fallen into recovery.
Most CRM teams start the other way around — with actions. Send this journey, launch this offer, run this campaign, suppress this audience, target this cohort. That is activity-first marketing: it produces motion, not necessarily profit. The TAT changes the sequence. First map. Then diagnose. Then choose one play. Then measure the outcome.
Four questions before any play
Before approving any CRM or recovery programme, a CMO should be able to answer four questions:
- Where are my customers on the TAT?
- Which cells hold the most revenue at risk?
- Which cells are moving in the wrong direction?
- Which single play should run first?
If those four cannot be answered, the brand is not ready to spend — it is ready to guess.
Why RFM is not enough
The reflex answer to where are my customers is RFM — recency, frequency, monetary value. RFM is useful and incomplete, because all three of its variables are transaction variables: recency means transaction recency, frequency means transaction frequency, monetary means transaction value. RFM cannot see a customer who has stopped opening, stopped clicking, stopped visiting — until the transactions stop too. A customer who bought three times and still opens weekly is not the customer who bought three times and has ignored you for 90 days. Same RFM score, opposite trajectory. Transaction history tells you what happened; attention tells you what is about to.
The shared view
The deeper value of the TAT is organisational. Without it, every team argues from its own dashboard — CRM from opens and clicks, performance from ROAS, product from conversion, finance from cost — and none sees the whole base by transaction depth and attention health. The TAT creates one shared view. It tells the CRM team where to grow, the retention team where to protect, Progency where to recover, and the CFO whether paid reacquisition is a genuine necessity or a failure of earlier attention. The map is not the answer. It is the place where the right answer can finally be seen — and agreed.
2
The minimal data to start.
The power of the TAT is that it does not require a perfect data lake. A brand can build the Basic version from a handful of fields, one row per identified customer: a customer ID, preferably anonymised for analysis; lifetime transaction count; last meaningful attention date; and revenue or margin per customer.
The first field identifies the customer. The second places them by transaction depth — the row. The third places them by attention recency — the column. The fourth does something the first three cannot: it turns the grid from a headcount census into a money map.
Why revenue is the field that matters
That fourth field is the one teams are tempted to skip, and it is the one that makes the table a decision tool. A TAT built only on counts can tell you that a million customers are lost. Interesting, but not actionable. A TAT enriched with revenue or margin tells you which lost customers are worth recovering, which one-time buyers are worth pushing to a second purchase, and which repeat customers must be protected before they become expensive to re-buy. Counts describe the base; money ranks the work. Without revenue in the cells, every prioritisation argument collapses back into opinion.
What you do not need yet
A CMO does not need the full engagement event stream on day one — that is for the Advanced version. Basic can start from the latest trusted attention event already sitting in the engagement systems. The instruction to the analyst is therefore short: for every identified customer, find the lifetime transaction count, the last meaningful attention event, and the customer’s value; then place them on the grid. That is enough to act. The discipline here is to resist the urge to wait for completeness. The brand that builds a rough map this month will out-decide the brand still scoping a perfect one next quarter. Completeness is the enemy of the first decision; the map only has to be good enough to choose one play and measure it.
3
What counts as attention.
The most important construction decision in the whole table is not the row logic. It is the column logic — specifically, what counts as attention. Get this wrong and the map lies; and a map finance does not trust is a map no one acts on.
The column must be set by the last meaningful attention event — not the last order, not the last send, not the last impression, and emphatically not the last open if opens are polluted. The column should reflect one thing: whether the customer has recently shown they are still listening.
The hierarchy of trust
Order the signals by how much intent they reveal. A transaction tops everything. Then a website visit or product browse, then an app open or session, then a WhatsApp reply or reliable read, then a push tap, then an email click. The exact order can vary by category and channel maturity; the principle does not. Self-initiated signals — a visit, an app open — outrank solicited ones such as a click on something you sent. Passive or machine-fired signals are treated with suspicion.
The email open problem
This matters most for email. For years, opens were treated as proof of attention. That is no longer safe. Privacy protections, inbox prefetching and machine activity now fire a large share of opens with no human behind them. A customer who opens nothing but clicks once is more valuable than one who appears to open everything and never acts. The click is a customer signal; the open may be a machine shadow. So the rule is blunt: score the click, not the open, and if you keep the open at all, floor it near zero and tag it the lowest grade. This is not an argument that email is weak — it is an argument that measurement must be honest. Email clicks, AMP interactions, form fills, quiz answers, preference updates, pay-in-email actions and product taps are powerful, identified intent signals. Opens are low-grade supporting evidence, not the foundation of the table. The reason to be strict is downstream: every machine-fired open you let into the Strong column is a customer you will fail to recover because you believed they were fine.
4
Drawing the two axes.
With what counts as attention settled, the axes themselves are quick to draw.
The columns: 30 and 90 days
Set three columns by the date of the last meaningful attention event.
- Strong: within 30 days
- Weakening: between 30 and 90 days
- Lost: more than 90 days of silence
The 30-day cut identifies current listening; the 90-day cut identifies relationship breakage. A common objection arrives here: in long-cycle categories — insurance, furniture, jewellery, auto, real estate — customers simply do not buy often, so surely long silences are normal? This confuses the two clocks. A customer may not buy a sofa every month, but they can still read, click, browse, save and reply in between. Long purchase cycles do not justify long attention silences. This is the single most important distinction in the table: transaction recency and attention recency are not the same thing. A customer may not be due to buy, but they should never be invisible. If 90 days pass with no meaningful attention, the relationship is damaged even if the next purchase is months away — and recovery then needs a different machine than retention.
The rows: transaction depth
Rows are simpler: lifetime transaction depth, in three bands.
- None — identified customers who have not yet made a first purchase: subscribers, registered users, abandoned browsers, captured marketplace buyers; known, but not yet customers in the economic sense.
- One — customers who have bought exactly once; for many brands this is the largest and most important row, because a majority of first buyers never return.
- Repeat — customers who have bought twice or more, the Best relationships a brand must never let drift into paid reacquisition.
These three rows are the depth half of the BRTN segments — Best, Rest, Test, Next. Repeat maps to Best, One to Test, None to Next. Rest is the one that is not a depth at all but a state: the slipping-away pool that cuts across every row, splitting into R1, R2 and R3 — lapsed Best, Test and Next respectively. So the R is for Rest, not Recover; the columns we set next are how a customer falls into it.
Why the boundary is two, not three
The boundary that matters is between one purchase and two — and it must be visible, not buried inside generic RFM tiers. The move from first to second purchase is not just another conversion; it changes the economics of the customer. It often roughly triples lifetime value and makes every future purchase more likely. Setting the Repeat band at two-or-more puts that first-to-second inflection directly on the grid, where the whole game is played, instead of hiding it inside a broad repeat bucket. The table then shows, at a glance, how many customers are stuck after one purchase, how many remain attentive enough to move to a second, and how many have already gone quiet. That single view tends to reorder a CMO’s priorities on the spot.
5
Reading the nine cells.
Once rows and columns are set, every identified customer lands in one of nine cells — and each cell is a job, not a label.
Three verbs
The columns create three management verbs.
- Grow the Strong
- Protect the Weakening
- Recover the Lost
This is the first language a CMO should insist on: simpler than a hundred micro-segments and more useful than a generic lifecycle chart. The rows tell you how valuable or fragile the relationship is; the columns tell you whether it is still alive.
The nine jobs
Read across the grid and the plays name themselves.
- Strong / None is the First play: convert known prospects into first-time buyers.
- Strong / One is the Second play: move trial buyers to the crucial second purchase.
- Strong / Repeat is the Repeat play: keep the Best buying.
- Weakening / None needs nurture before the first-purchase window closes.
- Weakening / One needs protection before the second-purchase window closes.
- Weakening / Repeat is the quiet danger zone — valuable customers drifting before anyone in finance can see it.
- Lost / None usually warrants a low-cost revival or suppression.
- Lost / One is classic win-back: people who tried once and disappeared.
The most painful cell
And Lost / Repeat — coded R1 — is the most painful cell on the map: customers with proven value who have stopped listening and who, left alone, will be re-bought through adtech months later at full tax. This is the cell that most directly converts a brand’s own database into someone else’s margin pool, and it is the cell the dashboard is least equipped to surface, because the revenue has not yet visibly stopped. R1 is where recovery — Progency — earns its place, and where the holdout will later prove whether the lift was real.
Reading the nine cells is, in the end, reading where profit is leaking and which verb stops it.
6
Basic is enough to act.
There is a temptation to wait for the perfect table: complete event history, cross-channel identity, margin-level product data, predictive scores, attribution models, incrementality engines. All useful; none necessary to begin.
One question
Basic TAT treats attention as a date and asks one question of every customer: when was the last trusted signal that this person was still listening? That single question, answered across the base, is enough to run the first set of plays.
Reading Basic into action
The reads are direct. If One / Strong is large, run the Second play. If Repeat / Weakening holds high value, run Protect. If Lost / Repeat contains meaningful revenue, hand it to Progency. If None / Strong is growing, optimise the First play. And if Weakening is expanding across every row, your attention system itself is decaying — a warning no revenue report would have given you in time.
Simplicity is the point
Basic is the map every brand can build today, and it is deliberately simple, because simplicity creates agreement. The CFO can understand it. The CMO can commission it. The CRM team can act on it. The performance team can be challenged with it. Its purpose is not analytical perfection; it is managerial alignment. If the analyst cannot explain it, it is too complex; if the CRM team cannot act on it, the map is decorative. Basic is not a lesser version to be upgraded away from — it is the floor, and for most brands the floor is enough to start moving customers up and leftward while stopping the drift to the right. A simple map that ships this quarter beats a sophisticated one still being scoped next year — the cost of waiting for perfect data is every customer who drifts to Lost while the model is built.
7
Advanced: when attention becomes a score.
Once the Basic table is trusted, the Advanced version can be built — and the order matters: trust first, sophistication second.
Attention as a score
Advanced TAT does not replace the grid; it enriches it. The rows are still transaction depth, the columns are still set by attention recency, but inside each cell every customer now carries an attention score. The score combines three things you can defend out loud: signal type (self-initiated beats solicited), recency (points decay, so the score falls on its own if nothing fresh arrives), and depth (a transaction beats a browse, a browse beats a click, a click beats an open). A practical method is a capped blend — take the top meaningful signal, add a small contribution from the next two, cap at one hundred — rather than letting forty hidden weights decide the one screen a CFO is looking at. An auditable five-input score beats a black-box forty-input one every time.
Recency sets the column; the score enriches the cell
One rule protects the whole thing from false precision: recency sets the column; the score only enriches the cell. Never let the score redraw the grid. If the score decides the column, the table becomes a black box and teams debate weights instead of acting. A customer with a strong signal 95 days ago still belongs in Lost by the column rule — the score may tell you to prioritise them within that lost cell, but it does not move them out of it. A customer with a weak signal yesterday belongs in Strong, even if the score flags them as low priority there. The column answers one question — are they still listening? The score answers a different one — how strong is the attention inside that state? Keep the two separate and the map stays legible; merge them and it turns opaque.
What the score unlocks
Held to that discipline, Advanced unlocks three things Basic cannot.
- First, within-cell ranking: not every Lost / Repeat customer deserves equal recovery spend, and the score decides who gets email, who gets WhatsApp, who gets a concierge call, who gets NeoNet routing and who gets suppressed.
- Second, trajectory: a score falling week after week matters more than one that is merely low but stable, and a falling score in the Strong column is the earliest possible drift warning — the chance to protect a customer before they cross the 90-day line.
- Third, a real Click Retention Rate: attention-score decay across the base is attention churn, measured rather than asserted, and because attention decays before revenue does, it is a far earlier warning than revenue churn.
Basic tells you where customers are; Advanced tells you which way they are moving. Basic is the map; Advanced is the radar.
8
Commissioning the map, and the first move.
A CMO does not need to specify every formula, but the commissioning brief should be clear enough to prevent the common mistakes. It comes in two stages.
The brief
Build Basic first. Use customer ID, lifetime transaction count, last meaningful attention date and customer value. Set the attention date from click-grade or self-initiated signals; floor email opens. Use 30 and 90 days as the default column cuts and lifetime count for transaction depth. Show both customer count and revenue or margin in every cell. Name the two cells with the highest money at risk. Recommend one play, not six. Only once Basic is accepted, build Advanced: add signal scoring, recency decay, a capped blend and trajectory — and use the score to rank customers inside a cell, never to move them between columns. Keep the weights visible and every output auditable.
The one screen
The output should fit on a single screen: the nine-cell table, customer count by cell, revenue or margin by cell, the attention trend by cell, the highest-risk cell, the recommended first play, and the holdout design that will measure it. The tests are practical. If the analyst cannot explain the table in five minutes, it is too complex. If the CFO cannot see why a cell is being prioritised, the model is too opaque. If the CRM team cannot act on it, the map is decorative. The TAT exists to decide, not to decorate.
The first move
The first move after building the map should not be a grand transformation. It should be one measurable play against one leaking cell. For many brands that will be Second — move attentive One-band customers to a second purchase, often the fastest route to LTV expansion. For others it will be Protect — stop valuable Repeat customers drifting from Weakening into Lost. For brands with large lapsed pools and heavy paid reacquisition it will be Recover — hand Lost / Repeat to Progency and measure the lift against a three-arm holdout of worked, untouched control and adtech arms. The choice should be driven by the table, not by organisational habit. A campaign calendar asks what we are sending next week; the TAT asks where profit is leaking and which play fixes it first. That is the difference between activity and accountability.
The discipline
The table fails two ways: over-engineered too early, or reduced to a pretty segmentation chart with no economic consequence. The discipline that avoids both is short. Transaction depth sets the rows. Attention recency sets the columns. Customer value sizes the opportunity. The attention score ranks priority inside a cell. Holdouts prove whether the play worked. Everything else is optional. This is why the TAT belongs at the very front of the Alpha Audit: before a brand buys new software, switches agency, raises spend or celebrates another ROAS report, it should know how many of its customers are still listening, how many are drifting, how many have gone dark, how much revenue sits in each state, and how many will be re-bought through adtech if nothing changes. Those are not analytics questions. They are profit questions — and they are the map your dashboard was built not to draw.
