Getting first-time buyers to open the box and use what they bought, and the only kinds of loyalty that last.
Most retention work starts on the day the reorder email goes out. By then, the decision has usually been made. It was made in a bathroom cabinet or a pantry, on the day the box arrived, when the product either got used or got put away.
Those two numbers are the guide in miniature. The first says the start is cheap to change: one prompt to plan when and where, on a letter people already got, moved a real behavior. The second says the finish is slow. Making something automatic takes weeks to months of repetition in the same setting.
So the work splits in two. Get the first use to happen early, on purpose, in the place the product will live. Then keep the next uses coming long enough for the setting to take over. Along the way, build the three things that make staying easy (habit, low search costs and switching costs people don’t resent) and skip the ones that only make leaving hard.
A product that sits unused doesn’t get reordered. Nobody reorders the thing still in the cupboard.
This guide builds on The Second Order, which covers the second purchase, replenishment timing and cohorts, and on The Whole Machine, which covers the core email and SMS flows. It doesn’t repeat them. It’s about the gap between the order and the reorder: whether the product gets used at all.
Start with The First-Use Audit. Your lowest checks name the chapters to read first. Or follow a path:
Three tools run in the page, plus the scored audit. Nothing you type leaves your browser.
Examples that open with Say or Picture use made-up round numbers. Every source is listed in Appendix C.
What this guide argues, and what would prove each claim wrong.
A position says what would prove it wrong. Test each on your own customers.
Six moments between the order and the reorder. Each has a number that tells you how it’s going and a place it quietly leaks.
Many reports show the order and the reorder and nothing in between. Everything that decides the reorder happens in between.
The whole book is above and always will be. These are the same chapters addressed individually, for linking to one idea rather than to ninety.
| Moment | The question | The number | Where it leaks |
|---|---|---|---|
| 1. Delivered | Did it arrive when they could use it? | Days from delivery to first use | Arrives mid-week, goes in a drawer. Chapter 3 |
| 2. Opened | Is the first step obvious? | Share used within 7 days | A manual instead of a move; old product still open. Chapters 4 and 5 |
| 3. First use | Did they plan when and where? | Share who gave a plan; first use by day 14 | “I’ll try it this weekend.” Chapter 6 |
| 4. Second to tenth use | Is something in the setting prompting it? | Uses per week in weeks 2 to 6 | No cue where the product lives. Chapters 7 and 8 |
| 5. The first weeks | Do they know how to get the result? | Use at day 30; education clicked, by week | Everything sent in the first 72 hours. Chapters 9 and 10 |
| 6. Staying | Is staying easy, and is leaving fair? | One-tap reorder share; reorder regularity; complaints of feeling trapped | Switching costs that feel like penalties. Chapters 11 and 12 |
The moments feed each other. A first use on delivery day, in the bathroom where the product will live, starts the setting cue that moment 4 depends on. A first use three weeks later, somewhere else, starts nothing. So the first half of this guide is about speed and place, and the second about repetition and what keeps people.
The reorder is decided in the cupboard, not the inbox.
Twelve checks on whether your buyers use what they bought, and what keeps them. About forty-five minutes with your order data, a box from your own warehouse and your post-purchase flows.
This audit isn’t about your reorder rate. It’s about the steps before it: whether you know who used the product, how fast, what told them to, and whether the reasons they stay are ones they’d be glad of.
Have your order data, your post-purchase emails and texts, your survey tool and a box packed exactly as a customer gets it. Score each check 0 to 2: 0 if it failed or nobody can answer it, 1 if partly true, 2 if clean. “We think most people use it” scores 0.
If you can’t say who used it, you can’t say why they came back.
Score as you go; your band appears when all twelve are in.
| Score | What it means | Read next |
|---|---|---|
| 20–24 | Your buyers use what they buy, and you can prove it. The job now is habit and fair captivity: the moments after the first use. | How Habits Actually Form, then Captivity Without Resentment |
| 14–19 | You’re doing some of the right things without knowing which ones work. Measure first use, then fix the zeros. | Time to First Use, then the chapter linked from your lowest check |
| 8–13 | You’re selling product that may never get used, and sending reorder emails to find out. Start with the box. | The One Scripted First Move, then Cues in the Box |
| 0–7 | Nobody knows whether customers use the product. Ask fifty recent buyers this week, then follow the thirty-day plan. | Unused Product Doesn’t Come Back, then The First Thirty Days |
Checks 1 to 3 are measurement, and they decide how much the rest is worth. A brand scoring 2 on the box checks and 0 on measurement has done work it can’t defend. Fix measurement first.
Paying for something makes people use it, for a while. Then the payment fades, and so does the use. The research on gyms and theater tickets says why the first weeks decide the reorder.
Procter & Gamble has a name for the moment this guide is about. A.G. Lafley, its chief executive, called the shopper’s choice at the shelf the first moment of truth. The second moment of truth comes at home, when the customer uses the product and finds out whether it keeps the brand’s promise Reported. A DTC brand spends most of its budget on the first moment. The reorder is decided at the second.
John Gourville and Dilip Soman studied what happens between paying and using. At a health club whose members paid dues twice a year, attendance was highest in the month after each bill and fell away over the months that followed Published. They called it payment depreciation: the sense of having paid, which pushes people to get their money’s worth, wears off with time.
It shows up in theaters too. In an older study by Hal Arkes and Catherine Blumer, people who paid full price for a season of plays attended more of the first half of the season than people who got the same tickets at a discount. By the second half, the difference had gone Published. Soman and Gourville then looked at theatergoers who bought several plays for one price. They were more likely to skip a given play than people who had bought a ticket for that play alone Published. One price for many uses blurs the link between each use and its cost.
Why it matters for renewal: in Gourville’s own summary, gym members who feel they got their money’s worth in year one are more likely to renew in year two Reported. Ruth Bolton and Katherine Lemon found the same loop in services: how much customers used a service shaped how satisfied they were, and that satisfaction shaped how much they used it next Published.
The sense of having paid is strongest on delivery day. Every day the box stays shut, it weakens.
Say a brand gets 10,000 first-time buyers a month. Its survey says 60% have used the product within 14 days of delivery. Those users reorder within 180 days at 35%; the non-users at 12%. The blended repeat rate is 25.8%. Now suppose a better first move and a cue in the box shift 10 of the 40 non-users in every 100 to early use, and suppose only half the 23-point gap is caused by use rather than by who the buyer is. That adds about 1.2 points of repeat rate: 115 more repeat customers a month. At $60 of contribution per repeat customer, that’s about $83,000 a year, or $0.69 of first-use work per buyer before it stops paying Derived.
With the defaults, the tool gives the worked example’s $82,800 a year and $0.69 per buyer: enough for a better insert, a cue, a plan prompt and a few spaced messages.
Days from delivery to first use, by monthly cohort. It moves weeks before the reorder rate does.
A reorder rate tells you in month four what went wrong in week one. Time to first use tells you in week two. It’s the earliest retention number you can change.
For each monthly cohort of first-time buyers, count the days from delivery (not the order) to the first time the customer used the product. Report two things: the median, and the share who had used it by day 7, 14 and 30. Measure from delivery because shipping speed isn’t the customer’s delay. Report the median because a few very late users drag an average around. And keep the “not yet” group visible; at day 30, they are the people most of this guide is for.
A survey has a catch. The people who answer are more likely to be the ones who used it, so the raw answers flatter you. The tool below asks how much less likely you think non-responders are to have used it and corrects for that. It also has a second catch, covered in chapter 10: asking about use changes use a little.
With the defaults, the raw survey says 52% used it within a week. Corrected for who answered, the median is about 11 days, 46% of the cohort still hasn’t used it at day 14, and about 750 of 2,000 buyers are still unused at day 30.
I don’t know of a published benchmark for time to first use in DTC, and I wouldn’t trust one if there were: a supplement and a pair of hiking boots have different natural first moments. My rule is to compare the product with its own use cycle. Anything used daily should have half its buyers started within three days of delivery. Anything used weekly, within a week. And whatever the product, the share still unused at day 30 should fall cohort by cohort.
Measure from delivery, report the median, and keep the “not yet” group in view.
Six reasons product goes unused. They need different fixes, so find out which ones are yours before you write a single message.
Nobody buys a product intending not to use it. When the box stays shut, something small is usually in the way.
| Reason | What customers say | The fix |
|---|---|---|
| The old one isn’t finished | “Still using up my old shampoo” | Give a first use that doesn’t replace the old product: a first-week trial on one routine, a travel size, a use the old product doesn’t cover |
| Saving it for the right moment | “Waiting for the weekend” | A plan with a date (chapter 6) and a reason to start now that is true |
| Too nice to open | “It’s so pretty I didn’t want to ruin it” | Make the first use the unboxing: an open-me-first sample, a seal designed to be broken |
| Not sure how to start | “Do I use it before or after?” | One scripted first move (chapter 5) |
| Setup takes effort | “Haven’t had time to set it up” | Pre-assemble, pre-fill, pre-pair; cut the first step to under two minutes |
| Bought too much at once | “Plenty left, no rush” | A first-use message for each item in the bundle, spaced, and a smaller first order if it’s the norm |
Saving it is a real pattern, not laziness. Suzanne Shu and Ayelet Gneezy found that people put off even pleasant experiences when the deadline is far off, and that gift certificates with longer expiry periods were redeemed less often than ones with short deadlines, the opposite of what people predicted for themselves Published. An open-ended “whenever you’re ready” invites the delay. A specific first moment removes it.
Too nice to open is real too. Freeman Wu and colleagues found that highly attractive products discourage use: people see the effort that went into the look and don’t want to destroy it, and when they do use it, they enjoy it less Published. That doesn’t mean ugly packaging. It means the thing you want opened first shouldn’t be the showpiece. Give the pretty jar a plain first-use sachet beside it.
An open-ended “whenever you’re ready” is an invitation to wait.
For consumables, the most common reason is often the first: a bottle of something else is already open. Yours waits until it runs out, and by then the pull of having paid has faded. Ask “Were you still using another product when yours arrived?” If many say yes, design a first use that doesn’t compete with the old bottle.
One action, in one sentence, with a when and a where. Not a booklet, not ten tips, not “get started.”
Open your own box as a customer would. Picture the usual pile: a welcome letter, a care guide, a QR code to a video, a discount and a request to follow on social media. What it often lacks is the answer to the only question that matters on day one: what do I do first?
Chip and Dan Heath put it as “script the critical moves”: people facing change get stuck on ambiguity, and what looks like resistance is often lack of clarity about what exactly to do Published. Their book explains this partly with ego depletion, the idea that self-control runs out like fuel. I don’t use that explanation here, because two large multi-lab replications, with 2,141 and then 3,531 participants, found little or no effect Published. The advice survives without it: a clear first step is easier to take than a vague one.
The classic evidence is older. In a 1965 study, Howard Leventhal and colleagues gave college students frightening or mild leaflets about tetanus. Fear changed attitudes but did little for behavior. What raised the number who actually went for a shot was specific instructions: a campus map with the health center marked and the hours it was open Published. The map turned an intention into a route.
Tell them the first move, not everything they could do.
A good first move passes five tests:
Picture a magnesium supplement. The old insert says “Welcome to better sleep” and lists six benefits, the dosage, the ingredients and a discount. The scripted version, printed inside the lid where it’s seen first, says: “Tonight, 30 minutes before bed: two capsules with a glass of water. Leave the jar on your nightstand.” Everything else can go on the back or into the weeks that follow. The same sentence goes into the delivery-day email and text, so it meets them twice on the day that matters.
For a dog food, the first move isn’t “switch your dog’s food.” It’s “Tonight, mix one scoop into the usual bowl. Keep the scoop in the bag.” The switch schedule arrives later (chapter 9).
Asking people when and where they’ll act is one of the best-supported prompts in behavioral science. It works best for exactly what you need here: a single first action.
A goal says what you want to do. An implementation intention, Peter Gollwitzer’s term, says when and where you’ll do it: “If it’s 7am and I’m in the kitchen, then I’ll take two capsules with my coffee.” The plan hands control of the start to the situation, so the moment itself prompts the action.
Gollwitzer and Paschal Sheeran pooled 94 independent tests in 2006 and found a medium-to-large effect on reaching goals (d = 0.65) Published. I’d treat that as an upper bound. Pooled studies from that era tend to run larger than what holds up in the field, which is why the field studies matter more.
The best known is a flu-shot trial by Katherine Milkman and colleagues at a large company. Every employee in the study got a letter with the times and places of free on-site clinics. Some letters also had a box asking the employee to write down the date they planned to go; others asked for the date and the time Published.
PublishedMilkman, Beshears, Choi, Laibson and Madrian, PNAS, 2011. The paper reports the control rate and each prompt’s difference from it (1.5 and 4.2 points); the rates shown add them. Bars start at zero.
A 4.2-point rise on 33.1% is a 12.7% relative increase, from a line of text on a letter that was going out anyway Derived. Note the detail: a date alone wasn’t enough. The more specific plan did the work.
Mariana Carrera and colleagues tried the same idea on something repeated. At a gym, 877 members were asked to pick the days and times they’d go over the next two weeks. Attendance didn’t rise; the authors call it “a tightly estimated null effect.” Their paper notes that the large field successes of planning prompts have been one-time actions: vaccinations, screenings, voting Published.
Use the plan for the first use. Use the setting for everything after it.
That’s the right split for a DTC brand. The first use is a one-time action, and a plan prompt fits it. The fortieth use is a routine, and routines come from repetition in a stable setting, which is the next two chapters.
Habits are started by the setting, not by intentions. So the reminder has to live where the product is used, attached to something the customer already does.
Email reminders live in the inbox. Nobody checks their inbox at the bathroom sink at 10pm, which is when the night cream needs to be used. The cue has to be where the moment is.
Wendy Wood and David Neal describe habits as learned tendencies to repeat past responses, set off by features of the setting that have gone along with the behavior before: a place, the action just before it in a sequence, particular people Published. The setting starts the behavior directly; the goal doesn’t have to be in mind. A good cue ties your product to something that already happens every day in the right place: brushing teeth, making coffee, filling the dog’s bowl, putting on shoes.
Procter & Gamble’s Febreze is the best-known story of a product saved by a cue. It launched in the US in 1998 as a way to remove bad smells, and sold poorly. P&G’s researchers found that the people who most needed it had stopped noticing the smells in their own homes, so nothing prompted them to use it. The relaunch tied Febreze to the end of cleaning a room, as a finishing touch, and gave it a pleasant scent to make the moment feel done. Sales took off Reported. The story was reported by Charles Duhigg in The New York Times Magazine in 2012; it rests on P&G’s account and his reporting, not on a published study, but the lesson fits the research: the product needed a moment, not a benefit.
A benefit is a reason. A cue is a moment. Customers need the moment.
Much of the popular writing on cues, bowls and plate sizes comes from Brian Wansink’s lab. More than a dozen of his papers were retracted, and he resigned from Cornell in 2019 after the university found research misconduct including data falsification, so none of that work is used in this guide Reported.
Repetition, in the same setting, for longer than anyone wants. Programs lift behavior while they run. The setting is what keeps it going after they stop.
A reorder from a habit doesn’t need persuading. The product ran out, so the customer buys more, the way they buy toothpaste. That’s the kind of repeat buying worth building, and the research on how it forms is more useful, and slower, than the popular version.
In two diary studies by Wendy Wood and colleagues, a large share of the everyday behaviors people recorded were habits in the authors’ sense: done almost daily, in the same setting, often while thinking about something else Published. Wood and Dennis Rünger’s review describes how they form: an action repeated in a stable setting gradually becomes linked to that setting, until the setting starts the action on its own. People then tend to explain their habits as choices, which is why customers will tell you they buy you because they love you when part of the reason is that you’re what’s on the shelf Published.
The clearest demonstration involves popcorn. David Neal, Wood and colleagues gave cinema-goers fresh or stale popcorn. People with a strong habit of eating popcorn at the movies ate about as much stale popcorn as fresh; how hungry they were or how much they liked it barely mattered. The habit only ran in the cinema, and only when they ate with their usual hand Published. The setting carried the behavior. Change the setting, and people went back to deciding.
The setting does the remembering. Design for the setting.
The number that gets repeated is 21 days. The best study says otherwise. Phillippa Lally and colleagues had 96 volunteers repeat a new eating, drinking or exercise behavior daily in the same setting for 12 weeks. For the people whose data fit their model, the median time to reach 95% of their eventual automaticity was 66 days, and individual estimates ran from 18 to 254 days Published. The study ran 84 days, so the longest estimates are projections Derived. The authors also found that missing a single day “did not materially affect the habit formation process” Published.
Two things follow for a brand. The cue and the education have to last two to three months, not one welcome series. And your messages should say plainly that a missed day doesn’t reset anything, because customers who think they’ve failed stop.
The largest test of this was a megastudy led by Katherine Milkman, in which 30 scientists designed 54 different four-week programs to get 61,293 members of a US gym chain to work out more Published.
Nearly half the programs worked while they ran. Very few left a lasting change. The most effective program paid small rewards for coming back after a missed workout Published. That’s a useful design rule for any brand: the moment that matters most isn’t the first use, it’s the return after the first gap.
Bas Verplanken and Wood point out that habits are most open to change when life changes: a move, a new job. The old cues disappear, and new ones haven’t formed Published. That cuts both ways. It’s the best time to win a customer from a competitor’s habit. It’s also when you lose your own. When a customer changes their shipping address, their bathroom shelf just moved. That’s the moment to send the first move again.
The same product education, spread over the first weeks and timed from delivery, is remembered better than the same content sent in the first three days.
Look at the send dates of your welcome series. If the how-to email, the tips email, the founder story and the review request all go out within 72 hours of the order, often before the box has arrived, then by the time the customer needs the second tip, they’ve forgotten the first.
The spacing effect is one of the most replicated findings in psychology. Nicholas Cepeda and colleagues pooled 839 comparisons from 317 experiments and found that learning spread over time beats the same learning crammed together. They also found that the best gap between study sessions grows with how long the learning has to last Published. Those studies are mostly about recalling words and facts, not using a face serum, so applying them to product education is my extrapolation. It’s a small one: most of what a customer needs to learn in the first weeks is facts and steps.
Teach the next step when they’re about to need it, not all of them before the box arrives.
Trigger the sequence from delivery, not the order. Each message teaches the one thing the customer needs next, and the gaps get longer as the routine settles:
| When | The job | Example, for a skincare serum |
|---|---|---|
| Delivery day | The first move, word for word from the box | “Tonight, after you wash your face: two drops, pat in. Keep the bottle by the sink.” |
| Day 2 | What to expect, and when to judge it | “Texture changes in days. Tone takes about a month. Judge it then.” |
| Day 7 | The technique that makes it work | “Two drops is enough. More doesn’t absorb.” |
| Day 14 | The common problem, and a missed day | “Tingling on day one is normal. Missed a night? Just pick up again.” |
| Day 28 | The first real result, and the next use | “Take a photo in the same light as day one. Try it in the morning too, under sunscreen.” |
| Day 50 onward | Only what’s new | Seasonal changes, a second product that fits the routine |
The second message may matter most. Many products take weeks to show their main benefit, and a customer who judges on day three concludes it doesn’t work. Telling them when to judge sets the test they’ll use. Only promise results your evidence supports, and have counsel review any claim about results, especially for supplements and skincare.
Building the flow itself is in The Whole Machine; timing the reorder reminder is in The Second Order.
Asking customers about their plans nudges them toward those plans. The effect is real, smaller than the early studies suggested, and it contaminates your survey data.
A post-purchase survey is usually treated as measurement. It’s also an intervention. People who are asked what they intend to do become a little more likely to do it, which is useful for first use and a problem for anyone reading the survey as a neutral count.
Vicki Morwitz, Eric Johnson and David Schmittlein found in 1993 that asking people once whether they intended to buy a car or a personal computer made them more likely to buy one. Asking repeatedly made low-intent people less likely to buy Published. Utpal Dholakia and Morwitz then followed customers of a financial services firm who had been asked to take a satisfaction survey. Compared with customers who weren’t asked, they later bought more, defected less and were more profitable, and the effect grew for months and was still there a year later Published.
The field calls this the question-behavior effect, and like much of behavioral science it looked bigger in its early years. A 2016 meta-analysis by Chantelle Wood and colleagues pooled 116 published tests and found a small effect (d = 0.24). Corrected for the likelihood that null results went unpublished, the estimate fell to d = 0.15. The effect was larger for behaviors that are easy and socially desirable, and in student samples Published.
Using a product you’ve already bought is easy and desirable, which is the favorable case. So expect a nudge, not a transformation, and don’t design a survey around a big effect you haven’t measured.
A survey is also a nudge. Hold some buyers back, or you can’t tell how much.
Customers stay for three reasons that outlast a good product: habit, the cost of searching for something else, and the cost of switching. Awareness isn’t one of them.
Bruce Greenwald and Judd Kahn’s Competition Demystified argues that real competitive advantages are rare, and that one of the few on the demand side is customer captivity. It comes from three sources: habit, search costs and switching costs Published. In their account, a famous brand isn’t an advantage by itself; it helps only insofar as it creates captivity. That’s a useful test for a DTC brand whose retention plan is “build the brand.”
| Source | What it is | DTC examples | The signal in your data |
|---|---|---|---|
| Habit | Buying and using without deciding again | Daily supplements, coffee, pet food, a skincare step | Regular reorder gaps; use tied to a time or place |
| Search costs | Finding and judging an alternative is hard or risky | A shade that matches, a fit that works, a formula the dog tolerates | Reorders of the exact same item; low browsing before reorder |
| Switching costs | Leaving loses something or takes work | A saved profile, a device that takes your refills, a learned routine | Retention that holds even when satisfaction scores dip |
Paul Klemperer’s survey of switching costs lists where they come from: the need to stay compatible with equipment you own, the transaction cost of changing supplier, the cost of learning a new brand, uncertainty about the quality of one you haven’t tried, loyalty discounts, and plain psychological attachment Published. Several of those are search costs in Greenwald’s terms; the categories overlap, and that’s fine. Klemperer also describes the pattern that makes switching costs dangerous: firms compete hard to win customers, then charge the ones they’ve won more. Customers learn to expect it.
Thomas Burnham, Judy Frels and Vijay Mahajan split switching costs into three kinds: procedural (time and effort), financial (money or benefits lost) and relational (the discomfort of breaking a bond with a brand or a person). In their survey, all three predicted whether customers intended to stay, and together they predicted it better than satisfaction did Published.
Satisfaction explains less of staying than the costs of leaving do. That’s a warning as much as an opportunity.
The scorer below rates each source with three statements, 0 to 2, and adds a fourth group that measures resentment. Be strict: score 2 only if you could show someone the evidence.
With the example scores, habit is 3, search costs 4, switching costs 2 and resentment 2, for fair captivity of 42%. The scorer sends that brand to habit first, even though switching costs score lower, because habit and search costs are the sources customers don’t resent.
Make staying easy and leaving fair. Build habit and low search costs first, and add switching costs only as value the customer keeps.
There are two ways to keep a customer. One makes staying easier than anything else. The other makes leaving harder, and works until the customer notices.
My test for any retention mechanism: would the customer thank you for it if they understood exactly how it works? A saved shade and a one-tap reorder pass. A credit balance that vanishes on cancellation fails.
Chapters 5 to 9 are the habit plan: a first move, a plan, a cue where the product lives, and twelve weeks of spaced help. Habit is the only source of captivity that makes the customer’s life easier with no catch, which is why it comes first.
Chewy is the clearest public example of a business built on removing the search. Its Autoship program reorders on a schedule the customer sets. In fiscal 2025, Autoship customers accounted for $10.5 billion of Chewy’s $12.6 billion in net sales, 83.3%, and their sales grew 11.8% Filed. Read that carefully: the figure counts everything Autoship customers bought, not only their scheduled orders, and those customers were probably Chewy’s keenest to start with. It shows where the revenue sits, not how much Autoship caused. Still, a business whose sales come mostly from customers who never search again is hard to take customers from.
Amazon went further with the Dash Button, launched in March 2015: a physical button in the home that reordered one product with one press. It was about as pure a cut in search costs as a product can offer, and a cue that lived where the product was used. Amazon discontinued it in March 2019, saying automatic reordering, subscriptions and voice ordering had made it unnecessary. Along the way, a German court ruled that the buttons broke consumer law because they didn’t give enough information about the price at the moment of purchase Reported. One-tap reorder should still show the price.
Some switching costs are the byproduct of real value: a skin profile that took ten minutes to build, a fit history, a refill system they already own. Customers accept these because they chose them and would lose something real by leaving. Others are penalties bolted on to stop people leaving. Customers resent those, and they’re right to.
Keurig learned the difference in public. In late 2014 it launched the Keurig 2.0 brewer with technology that rejected pods it hadn’t licensed. Rival pod makers soon offered workarounds, including a clip that let unlicensed pods run Reported. In the quarter to March 28, 2015, the company reported brewer and accessory sales down 23% and pod sales up 7%, and said growth was below expectations “primarily due to the slower than expected transition to the Keurig 2.0 system” Filed. In May 2015 it said it would bring back its reusable My K-Cup filter, which the 2.0 brewers had shut out, as reported by The Washington Post Reported. Keurig’s pods already had habit and search-cost captivity. The lockout added a switching cost that took something away from people who had paid for the machine, and customers treated it as a penalty.
A switching cost the customer built is loyalty. One you imposed is a grievance with a timer on it.
The strongest objection to everything in this guide: loyalty mostly follows size. It’s right, and it tells you what first-use work can and can’t do.
Researchers at the Ehrenberg-Bass Institute have spent decades showing that buying behavior follows a few stubborn patterns across categories and countries. The one that matters most here is double jeopardy: smaller brands have far fewer buyers, and those buyers are also a little less loyal Published.
The pattern was named by the sociologist William McPhee in 1963 and documented across many categories by Andrew Ehrenberg, Gerald Goodhardt and Patrick Barwise in 1990 Published. Byron Sharp’s How Brands Grow built a growth argument on it: because loyalty mostly follows penetration, brands grow mainly by reaching more buyers, most of whom buy the brand only occasionally Published. Sharp and Anne Sharp’s study of a large Australian loyalty program found it produced little of the extra loyalty it was designed to create Published.
Say one brand has ten times another’s buyers in the same category. Double jeopardy predicts the big brand’s buyers will also buy it a little more often. The small brand shouldn’t expect a much higher repeat rate than brands its size, however good its welcome series.
First use won’t make you an exception to double jeopardy. It stops you falling below it.
Compare your repeat rate with brands of your size in your category: peers who share numbers, public companies’ investor materials, panel data if you can buy it. Well below peers usually means a product or usage problem, which this guide addresses. Well above usually means a niche of heavy users: a strength, and a ceiling. Roughly level means your next gains come mostly from more buyers.
One page, every week, for the numbers between the order and the reorder. Each one moves before revenue does.
The reorder rate is a lagging number. By the time it moves, the cohort that caused it is three months old. The page below shows the same story weeks earlier, while you can still change it for the next cohort.
| Number | Defined as | What it catches |
|---|---|---|
| Used by day 7 and day 14 | Share of first-time buyers delivered in the cohort, corrected for who answered | A box, shipping or first-move change that slowed the start |
| Median time to first use | Days from delivery, by monthly cohort | The trend, without the late tail pulling it around |
| Still unused at day 30 | Count and share, by cohort | The people the next fix is for |
| Repeat gap | 180-day repeat rate of early users minus non-users, for cohorts old enough | Whether use still drives reorders, and the size of the prize |
| Plan capture | Share of orders where the buyer gave a when-and-where plan | A broken confirmation-page prompt |
| Education reach by week | Share of the cohort who opened or clicked each spaced message | A sequence that stopped firing or lost its audience |
| One-tap reorder share | Reorders placed from a saved cart, reorder link or subscription, over all reorders | Search costs creeping back in |
| Reorder regularity | Share of repeat buyers whose last gap was within a quarter of their usual gap | Habit forming, or not |
| Resentment signals | Cancel reasons, reviews and tickets mentioning “trapped,” “can’t cancel” or “tricked,” per 1,000 orders; chargebacks | Captivity turning into grievance |
| Holdout differences | Plan prompt and survey holdouts versus everyone else, on first use and reorders | What your interventions caused, not just what happened |
Put the first-use numbers next to revenue, or nobody will look at them.
First, every rate carries its cohort and its denominator. “68% used it” means nothing without “of 2,140 first-time buyers delivered in August, corrected for survey response.” Second, the holdout row is not optional. Without it, the page tells you what happened and lets everyone claim credit. With it, you know which of the chapters you acted on did the work.
Read first use by cohort, never blended: a promotion that brings in less committed buyers slows first use for that cohort without any fault in the box. And expect the repeat gap to shrink as first use improves. The people you move into early use were the less keen ones, which is why chapter 2’s tool asks for the causal share.
Measurement, then the box, then the plan and the sequence, then captivity. Four weeks, in that order.
Whether you’re starting this as the founder or you’ve just been handed retention, the order is the same. Find out who uses the product. Fix the first thing they see. Ask for a plan and space the help. Then look hard at why people stay.
At day thirty you won’t have reorder results; they need a quarter. You’ll have a first cohort measured, a box that tells people what to do, and the holdouts in place to show what worked. The first number to watch is the median time to first use for the next cohort.
Measure first use before you try to change it. Then change the box before the inbox.
Six things whoever owns first use needs on the first day.
Whoever owns first use, a retention hire, an agency or you, needs six things on day one.
The books and papers this guide leans on, and what to take from each.
And the research: Milkman and colleagues (2011) on plans and flu shots and (2021) on the gym megastudy; Lally and colleagues (2010) on how long habits take; Neal and colleagues (2011) on popcorn; Cepeda and colleagues (2006) on spacing; Wood and colleagues (2016) on the question-behavior effect; Burnham, Frels and Mahajan (2003) and Klemperer (1995) on switching costs. Full references are in Appendix C.
Andrew Lauchner runs Growth Legend, embedding inside consumer brands to own lifecycle, email and SMS, and revenue operations. He is the author of The Second Order, on turning first-time buyers into second-time buyers, and The Whole Machine, on the fundamentals of DTC growth, along with a series of field guides for DTC operators at andrewlauchner.com.
As Senior Director of Growth and Retention Marketing at Gallery Furniture, he rebuilt the customer journey and the sales playbooks together. He has worked on growth and retention at Binance and 3Commas, and has been Head of Growth and Retention at Greatness Wins and at Nexus Agriscience.
“Andrew led retention, lifecycle, and email/SMS, but what separates him from most in this space is how deeply he understands the role retention plays in the overall growth engine.”
Akram Khan, Head of Marketing at Gallery Furniture, senior to Andrew but didn’t manage Andrew directly
Andrew answers every note from operators working on this, including those looking for someone to own it. Write to andrew@growthlegend.com or message him on LinkedIn.
The formulas behind the tools, and four queries for the scorecard.
| For | Formula | Notes |
|---|---|---|
| Blended repeat rate | R = u × ru + (1 − u) × rn | u: share used early. ru, rn: repeat rates of users and non-users. |
| Lift from moving m to used | ΔR = m × (ru − rn) × c | m: share of all buyers moved. c: share of the gap caused by use; estimate it with a holdout. |
| Break-even first-use spend | ΔR × V per first-time buyer | V: contribution per repeat customer over 12 months. Yearly value is N × 12 × ΔR × V. |
| Corrected share used by day t | pt × (q + (1 − q)(1 − k)) | q: share of the cohort you heard from. k: how much less likely the rest are to have used it. |
| Median time to first use | Linear interpolation between the two days where the corrected share crosses 50% | From (0, 0) through days 1, 3, 7, 14 and 30. |
| Fair captivity | (H + S + W) / 18 × (1 − R / 12) | Each source 0 to 6 from three statements; R is resentment, 0 to 6. A judgment weighting, not a published scale. |
-- first-time buyers, whether they used it within 14 days of delivery,
-- and whether they ordered again within 180 days of the first order.
-- usage_events: one row per signal of use (survey answer, activation,
-- QR scan), with customer_id, used_at, source
WITH first_orders AS (
SELECT o.customer_id, o.id AS order_id, o.created_at,
MIN(f.delivered_at) AS delivered_at
FROM orders o
JOIN fulfillments f ON f.order_id = o.id
WHERE o.customer_order_index = 1 -- or ROW_NUMBER() over created_at
AND o.created_at < CURRENT_DATE - INTERVAL '180 days'
GROUP BY 1, 2, 3
),
use AS (
SELECT fo.customer_id,
MIN(u.used_at) AS first_used_at
FROM first_orders fo
JOIN usage_events u ON u.customer_id = fo.customer_id
AND u.used_at >= fo.delivered_at
GROUP BY 1
),
repeaters AS (
SELECT DISTINCT fo.customer_id
FROM first_orders fo
JOIN orders o2 ON o2.customer_id = fo.customer_id
AND o2.created_at > fo.created_at
AND o2.created_at <= fo.created_at + INTERVAL '180 days'
)
SELECT CASE WHEN u.first_used_at <= fo.delivered_at + INTERVAL '14 days'
THEN 'used by day 14' ELSE 'not used / unknown' END AS grp,
COUNT(*) AS buyers,
AVG(CASE WHEN r.customer_id IS NOT NULL THEN 1.0 ELSE 0 END) AS repeat_rate_180d
FROM first_orders fo
LEFT JOIN use u ON u.customer_id = fo.customer_id
LEFT JOIN repeaters r ON r.customer_id = fo.customer_id
GROUP BY 1;
Buyers with no signal fall into the second group, which mixes non-users with users you didn’t hear from. Run it again restricted to buyers who answered the survey, and use that version in the tool; the unrestricted one understates the gap.
-- cumulative share used by day 1, 3, 7, 14, 30 after delivery,
-- among first-time buyers who answered the use question
-- (reuses the first_orders and use CTEs from the query above)
SELECT DATE_TRUNC('month', fo.delivered_at) AS cohort,
COUNT(*) AS respondents,
AVG(CASE WHEN u.first_used_at <= fo.delivered_at + INTERVAL '1 day' THEN 1.0 ELSE 0 END) AS by_d1,
AVG(CASE WHEN u.first_used_at <= fo.delivered_at + INTERVAL '3 days' THEN 1.0 ELSE 0 END) AS by_d3,
AVG(CASE WHEN u.first_used_at <= fo.delivered_at + INTERVAL '7 days' THEN 1.0 ELSE 0 END) AS by_d7,
AVG(CASE WHEN u.first_used_at <= fo.delivered_at + INTERVAL '14 days' THEN 1.0 ELSE 0 END) AS by_d14,
AVG(CASE WHEN u.first_used_at <= fo.delivered_at + INTERVAL '30 days' THEN 1.0 ELSE 0 END) AS by_d30
FROM first_orders fo
JOIN survey_responses s ON s.customer_id = fo.customer_id
AND s.question = 'first_use'
LEFT JOIN use u ON u.customer_id = fo.customer_id
GROUP BY 1
ORDER BY 1;
Map survey answers to a date before this runs: “today” is the response date, “1 to 3 days after it came” is delivery plus 2 days, and so on; “not yet” leaves first_used_at empty. Divide respondents by the cohort’s delivered first orders to get coverage for the tool in chapter 3.
-- share of repeat buyers whose latest gap is within 25% of their median gap
WITH gaps AS (
SELECT customer_id, created_at,
created_at - LAG(created_at) OVER (PARTITION BY customer_id ORDER BY created_at) AS gap
FROM orders
),
per_customer AS (
SELECT customer_id,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM gap)) AS median_gap,
(ARRAY_AGG(EXTRACT(EPOCH FROM gap) ORDER BY created_at DESC))[1] AS last_gap,
COUNT(gap) AS n_gaps
FROM gaps WHERE gap IS NOT NULL
GROUP BY 1
)
SELECT AVG(CASE WHEN ABS(last_gap - median_gap) <= 0.25 * median_gap THEN 1.0 ELSE 0 END) AS regular_share
FROM per_customer
WHERE n_gaps >= 3;
-- reorders from a saved cart, reorder link or subscription
SELECT DATE_TRUNC('week', created_at) AS week,
AVG(CASE WHEN landing_site LIKE '%utm_content=reorder%'
OR source_name = 'subscription'
THEN 1.0 ELSE 0 END) AS one_tap_share
FROM orders WHERE customer_order_index > 1
GROUP BY 1 ORDER BY 1;
Copy them, then rewrite them in your brand’s voice. Keep the structure.
FRONT YOUR FIRST MOVE Tonight, after you brush your teeth: two capsules with a glass of water. Then leave the jar on your nightstand. BACK Days 1 to 14 [ ][ ][ ][ ][ ][ ][ ][ ][ ][ ][ ][ ][ ][ ] Missed a day? Nothing resets. Just pick up again. When to judge it: after about [4 weeks]. Not before. Questions: [support address]
HEADLINE One quick thing so it doesn't end up in a drawer.
Q1 When will you first use it?
( ) The day it arrives ( ) Next morning
( ) Next evening ( ) Pick a day: [date]
Q2 Where will you keep it?
( ) Bathroom ( ) Kitchen ( ) Nightstand ( ) Gym bag
CONFIRM Got it. We'll remind you [day], [time of day].
HOLDOUT Random 20% of orders don't see this block.
STORE plan_day, plan_place on the customer profile.
SUBJECT Your First Move, Tonight
PREVIEW one step, two minutes, then leave it by the sink
BODY It's arrived. Here's the only thing to do first:
[first move, word for word from the card].
You said [plan_day], [plan_place]. That's the moment.
Everything else can wait. We'll send the next step
in two days.
FOOTER [Brand, postal address]
Unsubscribe from these emails: [link]
[Brand]: It's [plan_day]. Your first move: [first move, under 100 characters]. Reply HELP for help. Reply STOP to opt out.
DAY 0 The first move (card, email, text: same words)
DAY 2 What to expect, and when to judge it
DAY 7 The technique that makes it work
DAY 14 The common problem; missed a day? nothing resets
DAY 28 The first real result; the next way to use it
DAY 50+ Only what's new
TRIGGERS Address change: resend the first move and the card.
First missed gap: make coming back one tap.
RULE One job per message. Each tells them the next step.
Q1 Have you used [product] yet?
( ) Yes, the day it came ( ) Yes, 1 to 3 days after
( ) Yes, 4 to 7 days after ( ) Yes, 8 to 14 days after
( ) Not yet
IF NOT YET
Q2 What's in the way? (free text)
Q3 When do you plan to start? ( ) Tomorrow ( ) This week ( ) Pick a day
RULES Ask once. Hold out a random 20% who never get it.
Read every Q2 answer weekly and sort into the six
reasons in chapter 4.
WHY THEY STAY habit / search costs / switching costs, with evidence
WHAT THEY LOSE each item: built by them, or added by us?
BY LEAVING
ADDED BY US remove or soften by: [date] owner:
REORDER CHECK one tap, same item, price shown? Y / N
LEAVING CHECK steps to cancel or pause vs steps to join
RESENTMENT SIGNALS "trapped," "can't cancel," "tricked," per 1,000
orders, this quarter vs last
DOUBLE JEOPARDY repeat rate vs three peers our size
Every external source, by chapter. Web sources were read in September 2026.