Consumer behaviour is the basis of marketing strategy, not the afterthought

Three founders, three stories about the customer

Werkbank is a fictional Rails SaaS for craft businesses: joiners, electricians, tilers, the one van and two apprentices kind of firm. It does scheduling, quotes and invoices, and its three founders agree on everything except the one thing that matters. Ask them why customers sign up and you get three confident answers. Lena, who built the product, says people come for the branded quotes because they want to look as professional as the big firms. Tobias, who does sales, says people come because double bookings are costing them jobs and their diary is a whiteboard in the van. Nadia, who runs marketing, says people come because their accountant has told them to stop invoicing from a spreadsheet.

All three stories are plausible. All three have been used to write landing pages, price plans and ad copy. None has ever been checked against what customers do inside the product. The hamper shop has the same problem in a different coat: the owner sees one crowd of "people who like nice food", when the orders are shouting that a gift buyer in December and a self buyer in March are two different animals who need two different shops.

Strategy is supposed to be built on how people decide. Analytics is supposed to be how you find out how people decide. If the strategy is built on founder folklore and the analytics is reporting last week's clicks, neither is doing its job.

Where this sits in the series

This is chapter 2 of 21 and belongs to Part one, how analytics helps. The previous chapter, The statistics every marketer actually needs, gave you the vocabulary: means, variance, correlation, significance, and why a number without an error margin is a guess with good posture. This chapter supplies the questions those tools answer. The next one, What is an insight, and why most reports do not contain one, takes the answer to a behaviour question and asks whether it changes a decision, the only test of an insight I accept.

What consumer behaviour actually covers

Consumer behaviour is the study of how people recognise a need, search, weigh options, buy, use and judge what they bought. Every model later in this series is a formal way of asking one of its questions, and each building block hides a measurable variable.

Needs and motivation. A need is the gap between where someone is and where they want to be; motivation is the pressure that gap creates. A tiler with three jobs booked for the same Tuesday has a need that is urgent and ugly. A tiler who vaguely wants to "look more professional" has a need that is real but slow. Urgency shows up in data as time to first action: the first tiler creates a job in the calendar within ten minutes of signing up, the second browses quote templates for a week.

Perception. People do not respond to your product, they respond to what they think it is. If Werkbank's homepage leads with invoicing, a visitor with a scheduling problem sees an accounting tool and leaves. Perception is measurable through what people click first and where they bounce.

Learning. Customers learn from experience, from other people and from your onboarding. It shows up as habit: the weekday a hotel guest books, the second hamper order that arrives exactly a year after the first because Grandma liked it.

Attitudes. An attitude is a stored evaluation, "booking platforms are a rip off" or "subscriptions are for big firms". Attitudes are stubborn and invisible in transaction data, which is why surveys exist, and why surveys mislead, as we will see.

The decision process. The textbook version runs from problem recognition through search, evaluation, purchase and post purchase judgement. Here it is as a flow, with the loops the tidy version leaves out.

The decision process with its loops: most real journeys stall at the postpone step and restart when a trigger returns.

Every arrow is a place where data can be collected and where the customer can be lost. The dotted lines matter most: the postpone loop is where Werkbank's "after the summer" trials live, and the switch loop is where a hotel loses a satisfied guest to a lower platform price.

Reference groups and culture. Nobody decides alone. A joiner asks the other joiners on the WhatsApp group what they use. An Austrian corporate buyer ordering Christmas hampers for a UK subsidiary has ideas about what a gift looks like that a British buyer does not share. Reference groups show up as referral sources, clustered adoption by region or trade, and the phrases people paste into support chats ("my mate said you do...").

The jobs to be done lens

The most useful reframing I know for all of the above is jobs to be done: people do not buy products, they hire them to make progress in a situation. The joiner does not want scheduling software, he wants to stop losing Tuesdays. I have written about this at length in Jobs to be done in growth strategy, so here only the analytical point.

A job is a behaviour hypothesis with a built in test. "The job is winning the work and getting it into the diary in one motion" predicts that users who create a quote and schedule the job in the same session convert to paid at a higher rate than users who do only one of the two. That prediction can be wrong. Founder folklore is unfalsifiable: "people want to look professional" survives any data, because "professional" can mean anything.

Stated versus revealed preference

This distinction does more work than any other idea in this chapter. Stated preference is what people say they want, in surveys, interviews and the "why did you sign up" dropdown. Revealed preference is what they do with their time and money. The two disagree constantly, not because people lie, but because people are poor witnesses to their own motives and very good at giving the answer that sounds sensible.

Economists formalise revealed preference simply: if someone chose A when B was available and affordable, A was worth at least as much to them as B. In a random utility model, the utility a person $i$ gets from option $j$ is

$U_{ij} = V_{ij} + \varepsilon_{ij} = \sum_{k=1}^{K} \beta_k x_{ijk} + \varepsilon_{ij}$

where $U_{ij}$ is the total utility, $V_{ij}$ is the part we can explain, $x_{ijk}$ are the $K$ observable attributes of option $j$ for person $i$ (price, delivery time, whether a quote template exists), $\beta_k$ is the weight the population puts on attribute $k$, and $\varepsilon_{ij}$ is everything we did not measure, treated as random. The person picks the option with the highest $U$. We never see $U$; we see the choice, and infer the weights $\beta$ from thousands of choices. That is revealed preference turned into arithmetic.

Under the usual assumption about the random part, the probability that a person buys rather than waits takes the logistic form

$P(\text{buy}) = \frac{e^{V_{\text{buy}}}}{e^{V_{\text{buy}}} + e^{V_{\text{wait}}}}$

where $V_{\text{buy}}$ and $V_{\text{wait}}$ are the explainable utilities of buying now and of postponing. Notice that postponing has a utility too. Most marketing treats "did not buy" as an absence. It is a choice, usually a rational one, and the model gives it a seat at the table. Chapter 7 builds this into a full logistic regression; here I only want you to see that the flowchart and this formula are the same object.

For a single purchase under uncertainty, the older expected utility view is also worth having in your head:

$EU(\text{buy}) = p \cdot u(\text{it works}) + (1 - p) \cdot u(\text{it disappoints}) - c$

where $p$ is the buyer's belief that the product will do the job, $u(\cdot)$ is the value of each outcome, and $c$ is the cost in money and effort. Every marketing lever maps onto a term: testimonials and a free trial raise $p$, a clearer promise raises $u(\text{it works})$, a guarantee lifts $u(\text{it disappoints})$, a simpler signup lowers $c$. When a founder says "we need more awareness", I ask which term they think is the problem. Usually it is not awareness.

What marketing strategy actually covers

Strategy, stripped of the slideware, answers four questions: whom do we serve, what do we offer them that others do not, how do we make that visible, and how do we make money doing it. The vocabulary is segmentation, targeting, positioning, the marketing mix and competitive advantage.

Segmentation divides the market into groups that behave differently and would respond to a different offer. The key word is behave. Segmenting by company size or age is only useful if size or age predicts behaviour, and it often does not. Chapters 13 and 14 go deep on this; the short version is that a good segmentation is one where you would happily run a different campaign for each group.

Targeting is choosing which of those groups to serve and, harder, which to ignore. Werkbank cannot be the best tool for a solo plumber and a forty person building firm at once.

Positioning is the place you want to occupy in the customer's head relative to the alternatives. A map makes it visible. Here is Werkbank's, on the two dimensions its buyers actually use when comparing, which I would take from support chats and lost deal notes, not from the founders.

A positioning map on the two axes buyers use. The empty top right is only valuable if enough buyers want it.

The marketing mix is the set of levers you pull to occupy that position: product, price, place and promotion in the old formulation. For a SaaS business it is the roadmap, the plans, the channels and the message; for the hamper shop the range, the price points, the delivery promise and the December ad budget. The mix is downstream of positioning, which is downstream of behaviour. Most companies work upward from the mix, which is why they end up optimising ad copy for a promise nobody wanted.

Competitive advantage comes in two flavours: be different in a way people pay for, or deliver the same thing at a lower cost. Differentiation is the natural home of a small firm, because cost leadership needs scale. But it is only worth money if it lands on an attribute with a large $\beta$ in the utility model above. Being different on something nobody weighs is just being odd.

How analytics connects the two

The claim of this whole series in one sentence: every analytics technique answers a consumer behaviour question, and every strategic choice depends on the answer to at least one of them. The table below is the map I keep on my wall, with the chapter where each question gets its proper treatment.

Behaviour question	Technique	Chapter
How much less will they buy if we raise the price?	Regression, elasticity	4
How long does a campaign keep working after it stops?	Polynomial distributed lags	5
How many times a year will a guest come back?	Poisson regression	6
Who is likely to buy, and what goes with what?	Logistic regression, market basket	7
When will this customer leave and what is she worth until then?	Survival analysis, CLV	8
Did the new layout lift sales or was it the season?	Panel regression	9
How much will we sell next quarter?	Forecasting, seasonality	10
Does marketing drive sales, or sales drive the marketing budget?	Simultaneous equations	11
Which of these forty survey items measure the same thing?	Factor analysis, PCA	12
Who are our customers, in groups that behave differently?	Segmentation, k means, latent class	13, 14
Where should the next pound go?	Media mix modelling	15, 16
Why do they stay, and what makes them leave?	Loyalty models, SEM	17 to 19
Did the change work, or did we get lucky?	Testing, factorial designs	20

Not one question in the left column mentions a dashboard metric. Nobody's behaviour is "sessions". The metric is a shadow of the behaviour, and the technique is the tool for reading the shadow.

The worked example: three assumptions become three questions

Back to Werkbank. The job is to turn each founder's story into something the data can contradict. My framework has four steps and fits on a napkin.

1. Write the belief as a sentence about a person in a situation. Not "customers want professionalism" but "a joiner who has just lost a quote to a bigger firm signs up to send better looking quotes".
2. Name the behaviour that would be different if the belief were true. That joiner opens the quote builder first, uses a template, and sends a quote within days.
3. Name the data that records that behaviour, and what is missing. Signup timestamp, first feature event, quote sent event, source. Missing: whether they actually lost a quote recently, which only a question at signup can capture.
4. Write the decision that changes with the answer. If the belief holds, the homepage leads with quotes and onboarding walks through a template. If it fails, it does not.

For the three founders the napkin looks like this.

Founder belief	Behaviour if true	Data question	What would falsify it
People sign up to look more professional (Lena)	Quote builder is the first feature; branded template applied early	Share of trials whose first event is a quote, and their conversion versus the rest	Quote first users convert no better than others
People sign up because double bookings cost them jobs (Tobias)	Calendar is the first feature; several jobs entered in the first session	Share of trials with three or more jobs in the first 48 hours, and their conversion	Heavy calendar users churn like light ones
People sign up because the accountant said so (Nadia)	Invoice export or accountant invite early; signups cluster before tax deadlines	Share of trials inviting an accountant in week one; seasonality of signups	No seasonal spike, no accountant invites

Now the results. Werkbank has a "why are you here" dropdown at signup, so stated and revealed preference sit side by side. The numbers are illustrative; the pattern is one I have seen in real products more than once.

Stated reason at signup	Share of trials	Most common first feature	Trial to paid rate
Look more professional	41%	Quote templates (52%)	18%
Stop double bookings	33%	Calendar (71%)	34%
Accountant or bookkeeping	26%	Invoice export (64%)	27%
All trials	100%		26%
Created a quote AND scheduled it in the first week, any stated reason	22%		51%

Reading it line by line. The first row is Lena's story: the most common thing people say and the worst converting group. People are not lying; "look professional" is the socially comfortable answer to a dropdown, and it is a slow need. The second row is Tobias's, a third of signups and nearly double the conversion, with the tightest link between stated reason and first action. The third is Nadia's, a quarter of signups, decent conversion, worth checking for the seasonal spike she predicted. The fourth is the baseline, 26%.

The last row pays for the analysis. Cutting across all three stated reasons, the 22% of trials who created a quote and put the job in the calendar in the same first week converted at 51%, almost double the baseline. None of the founders described that job. The customer's job is not "look professional" or "stop double bookings", it is "win the work and lock it into the diary before I forget". That is a positioning, an onboarding flow and a homepage headline, and it came from a napkin plus one SQL query.

Conversion by stated reason versus the behaviour defined group. The group nobody named converts best.

The second example: two kinds of hamper buyer

The Gift Bow, my Solidus demo shop, gives the same lesson in retail clothes. The owner sees one customer; the orders describe two. Again the numbers are illustrative.

Attribute	Gift buyers	Self buyers
Share of orders	63%	37%
Share of revenue	71%	29%
Average order value	£68	£47
Ships to a different address	94%	6%
Includes a gift message	81%	2%
Orders placed in November and December	58%	24%
Repeat within 12 months	19%	44%
Most common repeat interval	52 weeks	9 weeks

A gift buyer spends more, buys mostly in the last eight weeks of the year, and comes back once a year if at all, almost exactly a year later, which tells you she is buying for the same person's Christmas again. A self buyer spends less, buys all year, and comes back every couple of months. These are not two segments of one shop; they are two shops. The gift buyer needs a reminder in early November, a "send it again" button and a delivery promise she can trust. The self buyer needs a subscription or a loyalty scheme, chapter 17's territory. The behaviour told me the strategy, not the other way round. The behavioural economics reading of why gift buyers pay more is in an earlier article; the short answer is that a gift is a signal, and signals are priced differently from groceries.

How to actually run this

You need three tables and a fortnight, not a data warehouse.

Data. A customer or account table with a signup or first order timestamp and the source. An event or order table with timestamps and a type. Whatever stated preference you collect: the signup dropdown, a survey, the free text in the first support chat. If you have none, add one question at signup this week. One question, not a form.

The query. For the Werkbank question, a first pass in SQL is about a dozen lines.

WITH first_week AS (
  SELECT account_id,
         BOOL_OR(event = 'quote_created')   AS made_quote,
         BOOL_OR(event = 'job_scheduled')   AS scheduled_job
  FROM events e
  JOIN accounts a USING (account_id)
  WHERE e.created_at < a.signed_up_at + INTERVAL '7 days'
  GROUP BY account_id
)
SELECT made_quote, scheduled_job,
       COUNT(*)                          AS trials,
       AVG(CASE WHEN a.paid_at IS NOT NULL THEN 1.0 ELSE 0 END) AS trial_to_paid
FROM first_week f
JOIN accounts a USING (account_id)
WHERE a.signed_up_at < NOW() - INTERVAL '45 days'
GROUP BY 1, 2
ORDER BY trial_to_paid DESC;

The last WHERE clause matters: only count trials old enough to have had a fair chance to convert, otherwise recent signups drag the rate down and you conclude that your newest feature is killing conversion.

Time. Framing the beliefs takes an afternoon. Cleaning the events takes most of a week, because there is always one event that was renamed in March. The analysis is two days, the write up another two.

Checks before you trust it. Group sizes: a 51% rate on 14 accounts is a rumour, on 400 it is a finding, and chapter 1 told you how to put an interval on it. Direction: does creating a quote cause conversion, or do people who were always going to convert happen to create quotes? Behaviour data alone cannot separate the two; only a test can, which is chapter 20. Time window: try 3 and 14 days as well as 7. Instrumentation: check that the event fires on mobile, where half your joiners live.

Pitfalls I see most often

Asking people why and believing the answer. Surveys are for attitudes and for things that leave no trace in the data. They are terrible at motives. Put the stated answer next to the revealed behaviour and treat the gap as the finding. Werkbank's most popular stated reason was its worst converting group; that gap was the whole insight.

Segmenting on what is easy instead of on what differs. Company size, region and age are in every CRM, so that is where people cut. Behaviour is what you actually want to differ across groups. If two segments would get the same campaign, they are one segment.

Mistaking the founder's job for the customer's job. Lena built quote templates because she is proud of them. The customer hired Werkbank for a different job. The roadmap should follow the customer's job, and the data will tell you what that is if you let it.

Treating the decision process as a straight line. The flowchart is a thinking aid. Real journeys pause for months, restart from a trigger, or skip the search because a mate recommended you. A funnel report that assumes a straight line calls a postponed customer lost and a returning one new.

Confusing positioning with copy. Positioning is the place you occupy in the customer's comparison. Copy is how you describe it. You can rewrite the copy every week; the position only moves when the product, the price or the proof moves. The psychology of why users click or bounce is real and worth knowing, but it optimises the delivery of a promise, not the promise.

How I do this for clients

The deliverable for this method is a behaviour brief, and I want it in place before anyone touches a model, a campaign or a redesign.

It starts with the free workshop: half a day with the founders or the owner, where we write down every belief about why customers buy, in the "person in a situation" form above, and rank them by how much of the strategy rests on each. Then I take two weeks at my own risk. I need read access to your orders or events, your CRM or account table, and whatever stated preference you collect. I write the queries, put stated next to revealed, and check the group sizes so nobody gets excited about fourteen accounts.

What you get is a short document: each belief, the behaviour that would confirm it, the number we found, the interval around it, and a verdict of confirmed, contradicted or not yet testable. For the last category you get the one question to add at signup or checkout. Attached is the positioning map on the axes your buyers actually use, drawn from the data rather than the deck, and the behaviour questions from the table above that matter for you, in order. You own the queries, the document and the map.

If you then want the models built, that sits under data science. If you want the findings turned into channels and campaigns, that is growth hacking plus, and I will work on commission against the growth where it fits. The pricing page says what each of those costs in plain terms; the workshop and the two weeks cost you nothing but access and honesty.

Questions to put to your own team or agency

• Which three beliefs about why our customers buy is the current strategy built on, and when did we last check any of them against behaviour?
• For each belief, what would customers do differently if it were true, and do we record that behaviour anywhere?
• Where do our stated reasons and our revealed behaviour disagree most, and what have we done about that gap?
• Which two attributes do our buyers actually compare us on, and how do we know it is those two?
• If we split customers by what they do rather than who they are, how many groups do we get, and would each get a different offer?
• Which question in the behaviour to technique table would change our biggest decision this year, and who is answering it?

This series is inspired by Mike Grigsby's Marketing Analytics (Kogan Page). The explanations, examples and numbers here are my own.