growth: grow a product people already want

---
name: growth
description: Grows a product some people already want. Checks must-have first (the Sean Ellis survey, retention by cohort), finds the aha moment, sets a north star and a growth model, then runs a weekly test cycle. Use when someone asks "how do we grow", "why don't new people stick", "what should we test next", "set up a growth team", "should we spend on ads", "build a referral programme", "is our activation good", or plans a growth sprint or a weekly growth meeting. Puts the order before the tactics (must-have, then the leaks, then more people in), names the common mistakes (growing before must-have, too many channels at once, copying a famous company's hack, a vanity north star, tests too small to read, learning nobody wrote down) and keeps a test log the team learns from.
license: MIT
metadata:
  source: https://alcoven.co/skills/growth
  version: "3"
---

Growth: grow what people already want

Growth work multiplies what the product already does for people. If few would miss it, more people in means more people leaving. So first ask: would a group of customers be very disappointed to lose it? Then find the moment they first get the value, the number that counts it, and the few levers that move it, tested every week and written down. The person asking usually has a target, a few people who can ship, and a few weeks, and fears spending them on tactics that don't stick. The method is Sean Ellis and Morgan Brown's Hacking Growth (2017) and the practitioners who built on it; depth and sources are in references/method.md.

What you need

• The brief: who the product is for and what it does for them; ask if it is missing.
• Must-have evidence: answers to "How would you feel if you could no longer use it?", and retention by cohort (of the customers who started in a given week or month, the share still active weeks later). Ask for retention by cohort first.
• The numbers on the way in: visits, sign-ups, activation, active and paying customers, referrals, by week and by source, each with its definition.
• The test log and the plan with its targets, if they exist, and who can ship a product change this week.

Look in growth/, metrics/, numbers/, gtm/ or analytics/. If you only have what was pasted, work with it and list what is missing.

With no numbers yet, get rough ones before anything else: in an hour, from the billing records, how many customers paid each month for the last six months, who stopped, and how each current customer found the product; then, this week, three calls with customers who stayed and three who left. Ask for nothing more until those are in.

Plain words, the right customer, small numbers

• Say it in the person's words first. "Do enough customers love it" before "must-have", "the moment a new customer first gets value" before "aha moment", "the one number that shows customers getting value" before "north star". Define a term once, in a line, when it helps.
• Ask which customer. When the one who pays differs from the one who uses (a shop and its shoppers, a company and its staff), ask which side "customers" means, and check both: the payer rarely keeps paying for long once the users stop.
• With few customers, change the method. With a few dozen customers, a survey percentage means little and growth loops have too few people to turn. Count by hand, talk to the customers who stayed and the ones who left, and look for what the stayers did early. Use the 40% survey line (step 1) and the loops once there are enough customers for a share to mean something.
• If customers are leaving, start there. Hand over to the retention skill, if the project has it, before any work on bringing in more; for a broad "how do we make more money", the product-coach skill finds where it leaks first.

The method

Scale it to the ask. One tactic ("should we add a referral link?") gets step 1 as a quick check and step 6 for the test. A growth plan, a new growth team or real money on acquisition gets every step.

1. Check must-have first. The survey: the share of recently active customers answering "very disappointed" to Sean Ellis's question, where 40% is the line Ellis drew after comparing nearly a hundred startups. Retention: cohort curves that flatten at some level, which Brian Balfour rates the stronger signal. If the evidence is missing or below the line, say so and offer the must-have engine before any tactic: start from the customers who answered "very disappointed", learn what they love, find what holds back the "somewhat disappointed" who love the same thing, and split the next work between the two (Rahul Vohra at Superhuman). Output: met, not met or unknown, with the evidence and its date.
2. Find the aha moment: when a new customer first gets the value (the first order delivered, the first document a teammate opened). Compare people who stayed with people who left: which action did the stayers take early, and how soon? Ask a few of each (the customer-interviews skill helps, if the project has it). Write it as an action and a time ("sets up a standing order within 14 days"), a hypothesis until retention confirms it. The share of new customers who reach it is activation. Output: the aha moment, its activation rate, the steps before it.
3. Set the north star and the growth equation. The north star counts value delivered, grows as more customers get more of it, and leads revenue without being revenue (Ellis). Break it into the inputs that multiply or add up to it, such as new teams × share activated in week one × share still active in week eight. To choose, define and read these numbers, use the metrics skill if the project has it. Output: the north star, 3 to 5 inputs, the equation in one line.
4. Draw the growth model. Write how new customers arrive, as loops where you can: a cycle whose output brings the next input, like a shared page that brings a reader who shares the next page (Reforge). Check Brian Balfour's four fits: market and product, product and channel (products are built to fit channels), channel and model (what a customer pays must cover what the channel costs), model and market. If the product gets better as more people in a group use it, fill one atomic network (Andrew Chen: the smallest group where it works on its own) before the next. Output: the main loop, its weakest step, the weakest fit.
5. Pick one focus area. Fix the leaks before pouring more in. Ellis's order: activation, then engagement and retention, referral, revenue, and acquisition last. Choose the stage with the weakest step, one input metric, and a target with a date. In acquisition, work one or two channels at a time. Output: focus area, metric, baseline and target, each with its date.
6. Run the weekly test cycle. Analyse the focus area; collect ideas from anyone; prioritise by scoring Impact, Confidence and Ease from 1 to 10 (ICE, from Hacking Growth); test; write down what was learned. Each idea carries its evidence, the metric it should move, and a pass line set before it starts. For test design, sample size and stopping rules, hand over to the lean-experiments skill if the project has it. Output: this week's tests, each a row in the test log.
7. Hold the weekly growth meeting. Same agenda every week: the north star and inputs; tests launched against plan; what finished and what it taught; this week's tests; the idea pipeline. Someone who can ship product changes is in the room. Output: a short dated note of decisions and learnings.
8. Review the model monthly or quarterly. Channels wear out as novelty fades and competitors arrive (Andrew Chen), and the fits shift when the product, price or market changes. Re-run the must-have survey on newer cohorts; Vohra warns the score can fall as the customer base broadens. Output: changes to the model, proposed for the person's yes.

Keep it current

Hacking Growth came before three shifts (method.md has the sources). With product-led growth, people find, try and buy without a sales call, so activation and the step from free to paid carry the growth. Buyers ask AI assistants what to use, and the engines name products mentioned and cited across the web: treat that as a channel, asking the engines your buyers' questions monthly and recording who is named. Community and content work as loops: a useful post brings a reader who shares the next one.

Common mistakes

1. Growing before must-have. People sent to a product few would miss leave, and some become critics. Instead: step 1 first; below the line, work on the product for the people who love it (Ellis and Brown).
2. Pouring in while retention leaks. Paying for new customers while most of them leave buys the same loss at a larger size. Instead: fix activation and early retention first.
3. Too many channels at once. Five half-run channels give five unreadable results. Instead: test a few cheaply, then put the effort into the one that works (the Bullseye in Weinberg and Mares's Traction).
4. Copying a famous company's hack. A tactic from another market, years ago, rarely carries over. Instead: borrow the principle, test it on your customers, expect it to fade.
5. A vanity north star. Total sign-ups, page views and downloads only grow and say nothing about value (Eric Ries). Instead: count value received, such as teams that shared a document this week.
6. Tests too small or too short to read. A button colour on a trickle of visits, or a winner called on day two, teaches nothing. Instead: changes big enough to matter, pass line and run length set first.
7. Learning nobody wrote down. The team repeats a failed test. Instead: a log row for every test with one line of learning, losses included.
8. A growth team without engineering. Growth lives in the product (onboarding, invites, pricing), so a marketing-only team can change ads and emails and little else. Instead: a small cross-functional team with someone who ships code (Ellis and Brown).
9. Stages run as separate kingdoms. Acquisition brings people who never activate, and retention pays for it (Reforge). Instead: judge every test by the north star and its guardrails.
10. Reading the survey's average. The total hides the group for whom it is already a must-have (Vohra). Instead: segment the answers; build for the highest-scoring group.

Speaking up

When a step was skipped or the evidence points elsewhere, say it once, with the evidence and the cheapest check: "Before we buy ads: of September's new customers, [n] were still active in week four (product dashboard, read 22 Oct). A short survey of recent customers would tell us within a week whether to fix the product first. Shall I draft it?" Then do what the person decides, note the risk where the team will see it (the plan, the test log), and carry on. Never refuse work over a skipped step, and never repeat the warning.

What you never do

• Invent a number, a benchmark or a "typical" rate. A benchmark appears only with its source and date.
• Trick customers: fake scarcity, invites sent from someone's contacts without their clear yes, hidden cancellation, pre-ticked consent.
• Send messages, invite anyone, publish or spend money without the person's yes.
• Call a test before its pass line and run length, or move the pass line after seeing the data.
• Change the plan, the targets or the north star on your own: propose the edit.

In Alcoven

Pass the project's slug and cite by § address.

1. open_project: the brief, the home widgets with their readings, and what is agreed (a north star on record is settled).
2. numbers on the product's .source.md: the north star and the focus input by: "week", with compare: "the period before" or a target; by: a field such as source shows which channel brings customers who stay. The answer ends with the question as a widget file: offer to keep the north star on the home (four widgets at most there):

# Teams sharing a document each week

- Show: line
- Measure: teams sharing
- From: product.source.md
- By: week
- Compare: target [n] by 2026-12-31
- On home: yes

Why it's here: our north star; every growth test is judged by it.

3. add_rows on the test log, growth/tests.csv: a row when a test starts, one when it ends (references/test-log.md has the columns and a .source.md that turns the log into numbers).
4. add_note with day: on the north star's widget for the day a test launched or a change shipped: the reason shows in the margin at that day.
5. add_document for the growth model and meeting notes (show the text first), hand_out the north star and targets for agreement, and task an owner and due day for each test.

Elsewhere

Keep growth/model.md, growth/tests.csv and growth/meetings/YYYY-MM-DD.md. Quote every number with its source and date; git log or the tool's history shows what shipped when. In a chat with no files, ask for retention by cohort and the test log, and hand back rows to paste.

Files

• references/method.md: each framework in depth, what changed by 2026, and the sources.
• references/test-log.md: the test log and its .source.md, the idea card, the meeting note, the growth model, and a worked example.

The growth method in depth

The frameworks behind each step of the skill, in order. Where two disagree, say so and let the person choose. Sources at the end.

1. Must-have before growth

The survey

Sean Ellis's question: "How would you feel if you could no longer use [the product]?" The answers: very disappointed, somewhat disappointed, not disappointed, and "I no longer use it". The reading is the share who answer very disappointed.

• The line. Ellis compared nearly a hundred startups and found that those below 40% very disappointed mostly struggled to grow (as Rahul Vohra reports it, 2018). It is one practitioner's line from his own comparison: a strong direction, to use with judgement.
• Who to ask. People who have used the core of the product recently. Superhuman asked people who had used it at least twice in the past two weeks, never the same person twice; Vohra writes that around 40 answers start to give a direction.
• The denominator. Decide before counting whether "I no longer use it" answers are in or out, and say which.
• Its weakness. Brian Balfour (2013) ranks the survey as the weakest of three signals, because it is quick and prone to false positives. Engagement data is stronger, and the strongest is the three together: growth that isn't trivial, retention, and meaningful use.

Retention curves

Plot, for each cohort (the customers who started in one week or month), the share still active at each later week. A curve that falls and then flattens shows a group for whom the product keeps working, and the higher it flattens, the healthier the product; a curve that keeps falling toward zero calls for work on the product before growth tactics (Sequoia's data science team, around 2018). Define "active" by an action that means value, never a login alone.

The must-have engine (Rahul Vohra, Superhuman)

Vohra turned the survey into a loop the product team ran every quarter (First Round Review, 2018):

1. Ask four questions: how would you feel if you could no longer use it; what type of people would most benefit from it; what is the main benefit you get; how can we improve it for you.
2. Segment. Give each respondent a persona and keep the personas with the most very disappointed answers. Describe that core group in their own words, which Vohra calls the high-expectation customer (after Julie Supan's work). Narrowing to them raised Superhuman's score from 22% to 33% with no product change.
3. Analyse. From the very disappointed: what they love. Ignore the not disappointed. From the somewhat disappointed: keep only those whose main benefit matches what the fans love, and read what holds them back.
4. Split the work. Roughly half on more of what the fans love, half on removing what holds back the near-fans. Rank each half by cost and impact.
5. Track it. Survey new customers continually; follow the score weekly, monthly and quarterly. At Superhuman it became the product team's only key result, and it reached 58% after about three quarters. Vohra warns the score may fall as the product reaches more demanding customers.

2. The aha moment and activation

Hacking Growth calls the aha moment the point where a customer suddenly sees why the product matters to them; the job is to get as many new customers there as fast as possible.

• Find it by comparing customers who stayed with those who left (which early action, how soon?), then ask both groups. Write it as an action and a time window.
• Check it before building on it. People who were going to stay anyway do more of everything, so a correlation can point at the wrong action. Help more new customers reach the moment and see whether their retention moves.
• Map the steps from first visit to the aha moment, count where people drop, and remove, defer or do for them every step that isn't needed. Ask those who dropped why. Reminders help only when they carry the person toward the value.

3. The north star, the growth equation and the inputs

• The north star, in Ellis's sense, is the one number that best captures the core value customers get. In a 2024 interview he adds: it should be actionable, able to grow as the company grows (he says it should not be a ratio), and tied to revenue without being revenue.
• Amplitude's North Star Playbook (John Cutler and colleagues) adds a checklist (it expresses customer value, leads future results, the team can move it, anyone understands it, it is no vanity number) and three games a product can play: attention, transaction or productivity. A handful of inputs drive the north star, and teams work on those.
• The growth equation. Hacking Growth asks the team to reduce the business to a basic formula of its levers, so it watches the few numbers that move the result. For a shop: visitors × share who buy × order value × orders per customer per year.
• Choosing, defining and reading these well is the metrics skill's job.

4. The stages and the order

Dave McClure's AARRR (2007, "Startup Metrics for Pirates") names the five stages: acquisition, activation, retention, referral, revenue. Hacking Growth gives a chapter to each lever:

The order matters more than the list. In a 2024 interview Ellis says to invest in activation first, then engagement, referral and the revenue model, and acquisition last. Reforge adds that running each stage as its own silo turns one team's win (cheap sign-ups) into another's loss (poor retention).

5. Loops, and the four fits

Loops. Brian Balfour, Casey Winters, Kevin Kwok and Andrew Chen (Reforge, 2018) argue that funnels describe one step of how products grow, push teams into silos, and run one way, so growth needs constant new input. A loop is a closed system whose output becomes the next input, so it compounds: a customer invites a teammate who invites the next; customers make content that others find in search or AI answers and then make content; revenue pays for ads or sales people who bring revenue. Write each loop as its steps, the step that loses most, and how long one turn takes.

The four fits (Brian Balfour, brianbalfour.com). Growth to a large company needs four fits that shape each other and keep changing:

• Market and product: a specific problem for a specific group, solved well enough that they pull the product forward.
• Product and channel: in Balfour's words, "Products are built to fit with channels. Channels do not mold to products." A product that spreads through invites is built differently from one sold by sales people.
• Channel and model: what a customer pays decides which channels you can afford; a low price with costly channels loses money on each customer.
• Model and market: the price times the customers you can reach must be big enough for the company you want.

Change one fit and check the other three.

6. Networks and the cold start (Andrew Chen, 2021)

When the product gets better as more people in a group use it, growth starts cold: early on, the network is too thin to be useful. Chen's answer is the atomic network, the smallest network that can stand on its own: a team of a few people for a chat tool, one train station at rush hour for a ride service, a whole downtown for a payment card. The first one is probably smaller and more specific than you think. Build it, then copy the playbook to the network next door. Win first the smaller group that does most of the work (hosts, sellers, creators), which Chen calls the hard side. His later stages are the tipping point, escape velocity, hitting the ceiling (spam, fraud, saturation) and the moat.

7. High-tempo testing

Hacking Growth's cycle runs every week: analyse (data and customer feedback on the focus area, shared before the meeting), ideate (anyone submits ideas on a standard card), prioritise (score Impact, Confidence and Ease from 1 to 10, combined the same way every week), test (the top ideas that fit the week), then back to analysis. A team that runs and learns from more tests finds the levers sooner.

• ICE honestly. Confidence cites evidence (a test, customer words, data); a hunch scores low. Ease includes engineering time.
• The team. A growth lead, someone from product, an engineer, an analyst and a marketer, with design when needed: people who can change the product.
• The meeting. Led by the growth lead, same agenda each week: north star and key metrics, tests launched against plan, what finished and what it taught, this week's tests, the idea pipeline.
• The record. Every test, result and learning goes into a shared log, so nobody repeats a failure and new members catch up.

8. What changed by 2026

• Product-led growth. OpenView's Blake Bartlett named product-led growth around 2016: buyers adopt business software the way they sign up for a consumer app, with no sales call first. Activation and the step from free to paid become the core of growth. Benchmarks such as Kyle Poyar's yearly surveys (Growth Unhinged) are quotable only with their date and sample.
• AI answer engines. Many buyers now ask ChatGPT, Claude, Gemini or Perplexity what to use. Aggarwal and colleagues (KDD 2024) showed that content owners can measure and change how visible they are inside AI-written answers. Ethan Smith (Lenny's Podcast, September 2025) describes how products get named there: through the pages, help centres, videos and forum answers the engines read; early products can show up sooner than in classic search; AI-written filler doesn't work. Measure it as a channel: a fixed set of buyer questions, asked monthly, recording who gets named.
• Must-have moves faster. Elena Verna, head of growth at Lovable, said in 2025 that in her fast-moving AI category what counts as product/market fit shifts almost weekly (Product School, November 2025). In a fast market, re-run step 1 more often.
• Channels keep wearing out. Andrew Chen's 2012 essay argues every channel's response rate falls over time as novelty fades, competitors copy and the audience widens. A new channel works best early; keep testing the next one.

Sources

All read on 2026-10-22.

• Sean Ellis and Morgan Brown, Hacking Growth, Crown Business, 2017; summaries at readingraphics.com and shortform.com.
• Rahul Vohra, "How Superhuman Built an Engine to Find Product/Market Fit", First Round Review, 2018 (updated 2024). review.firstround.com/how-superhuman-built-an-engine-to-find-product-market-fit
• Lenny Rachitsky with Sean Ellis, "The original growth hacker reveals his secrets", Lenny's Newsletter, 5 September 2024. lennysnewsletter.com/p/the-original-growth-hacker-sean-ellis
• Brian Balfour, "The Never Ending Road To Product Market Fit", 11 December 2013. brianbalfour.com/essays/product-market-fit
• Brian Balfour, "The Four Fits" essay series. brianbalfour.com/four-fits-growth-framework
• Brian Balfour, Casey Winters, Kevin Kwok and Andrew Chen, "Growth Loops are the New Funnels", Reforge, 31 July 2018. reforge.com/blog/growth-loops
• Andrew Chen, The Cold Start Problem, Harper Business, 2021; excerpt "The Atomic Network", Lenny's Newsletter, 9 December 2021. lennysnewsletter.com/p/atomic-network
• Andrew Chen, essay on the decay of clickthrough rates, 2012. andrewchen.com/the-law-of-shitty-clickthroughs
• Dave McClure, "Startup Metrics for Pirates", talk, 2007 (as described at builtin.com/articles/aarrr).
• Amplitude, North Star Playbook (John Cutler and colleagues), amplitude.com/books/north-star; the three games, amplitude.com/blog/product-games-activity
• Sequoia Capital data science team, "Retention", around 2018. articles.sequoiacap.com/retention
• Gabriel Weinberg and Justin Mares, Traction, Portfolio, 2015 (the Bullseye framework).
• Eric Ries, The Lean Startup, Crown Business, 2011 (vanity metrics).
• OpenView, "Inventing Product-Led Growth". openviewpartners.com/blog/inventing-product-led-growth
• Kyle Poyar, Growth Unhinged, 2025 SaaS Benchmarks report, November 2025. growthunhinged.com
• Pranjal Aggarwal and others, "GEO: Generative Engine Optimization", KDD 2024. arxiv.org/abs/2311.09735
• Lenny Rachitsky with Ethan Smith, "The ultimate guide to AEO", Lenny's Podcast, 14 September 2025. lennysnewsletter.com/p/the-ultimate-guide-to-aeo-ethan-smith
• Product School podcast with Elena Verna, November 2025. productschool.com/resources/product-podcast/elena-verna-lovable-ai-native-growth-playbook

The test log, the idea card, the meeting note and the growth model

The four documents a growth team keeps, and a worked example. Keep them in one folder (growth/ unless the project has another place), so every member and every AI on the project reads the same record.

The test log

A table with one row each time a test starts or ends. Rows are only ever added, so the log is also a history: what the team tried, when, and what it learned.

The header line: date,test,event,stage,metric,pass_line,ice,owner,result,learning,link. Put a field in double quotes when it holds a comma.

A .source.md beside it, growth/tests.source.md, turns the log into numbers the team can chart (tests started a week, wins by stage):

# Growth tests

- Service: File in the project
- File: growth/tests.csv
- Days: column date
- Expect: every week
- Owner: m-4f2k9a

## Measures

- tests started: rows where event is started
- tests ended: rows where event is ended
- wins: rows where result is win
- stage: by column stage

The idea card

One per idea worth scoring, in growth/ideas/.

# [The idea in a few words]

- Stage: [acquisition | activation | retention | referral | revenue]
- Metric: [the input it should move]
- Hypothesis: If we [change], [metric] goes from [baseline, with its date] to [expected], because [evidence].
- Evidence: [customer words, data, a past test, each with its source and date]
- ICE: Impact [1 to 10], Confidence [1 to 10], Ease [1 to 10]; score [combined the team's usual way]
- Pass line: [the result that counts as a win], read on [date], with at least [n] people.
- Guardrail: [what must not get worse]
- Owner: [member id]. Builds it: [member id]

Confidence cites its evidence; a hunch scores low. Ease counts engineering time.

The weekly meeting note

# Growth meeting, [date]

Focus until [date]: [stage], moving [metric] from [baseline, date] to [target] by [date].

1. North star and inputs: [reading, newest date, source]; what moved and the likely reason.
2. Tests launched: [n] of [n] planned; what slipped and why.
3. Finished: [test]: [result]; [one line of learning].
4. This week: [tests, owners, pass lines].
5. Pipeline: [ideas added; stages with too few ideas].

Decisions: [a changed focus, target or test, and who decided].

The growth model document

# Growth model, [date]

- Must-have: [met | not met | unknown]; [evidence, date].
- Aha moment: [action and window]; activation [n of n, period].
- North star: [metric]; [reading, date]. Equation: [inputs].
- Main loop: [step 1] → [step 2] → [step 3] → back to step 1. Weakest step: [step, evidence].
- Fits: market and product [..]; product and channel [..]; channel and model [..]; model and market [..]. Weakest: [..].
- Guardrails: [metrics that must not get worse].
- Focus until [date]: [stage, metric, target].
- Next review: [date].

A worked example (invented)

Crumb lets cafés order next-morning bread and pastries from local bakeries. The team is a growth lead, an engineer and a marketer. The growth lead asks: "We want twice the orders by February. Should we run ads to cafés?"

Step 1, must-have. No survey exists, so the AI drafts one and the growth lead sends it to the 74 customers who ordered at least twice in the past two weeks. 58 answer; 6 say they no longer use Crumb and are left out, as agreed before counting. 18 of 52 are very disappointed: 35%, under Ellis's line. Split by persona: cafés with a daily order, 13 of 20; home bakers ordering for events, 5 of 32. Cohort retention says the same: café cohorts flatten from week 6, home-baker cohorts keep falling.

Speaking up, once. "Before ads: cafés are well above the line and home bakers far below it, so ads should go to cafés only. But of the cafés that signed up in September, 9 of 31 set up a standing order within 14 days. Ads would bring cafés into that leak. Four weeks on activation first?" The growth lead agrees, and asks the marketer to prepare the café ads for December.

Step 2, the aha moment. Of August's cafés, those with a standing weekly order in their first 14 days were still ordering at week 8 far more often: 11 of 13, against 4 of 15 without one. Written as a hypothesis: "sets up a standing order within 14 days".

Step 3, the north star. Standing orders delivered per week. The equation: new cafés × share with a standing order within 14 days × share of standing orders still running at week 8 × cafés brought in by each bakery. Guardrails: the share of orders delivered late, and refunds.

Step 4, the model. The main loop: a bakery joins, invites the cafés it already supplies, those cafés order and discover other bakeries, and those bakeries join to serve them. The weakest step: 3 of 11 bakeries sent any invites. The weakest fit: product and channel, as the invite is a link buried in settings.

Step 5, the focus. Activation, until 15 December: the share of new cafés with a standing order within 14 days, from 9 of 31 (September) to half of November's cafés.

Step 6, the ideas.

The first goes ahead. The log gets two rows, one at the start and one at the end:

date,test,event,stage,metric,pass_line,ice,owner,result,learning,link
2026-11-03,T7 prefilled standing order,started,activation,standing order within 14 days,at least half of new cafés by 2026-11-28 with at least 30 cafés,7.0,m-4f2k9a,,,growth/ideas/t7.md
2026-11-28,T7 prefilled standing order,ended,activation,standing order within 14 days,,,m-4f2k9a,win,17 of 33 new cafés set one (52%); cafés take a filled-in suggestion more readily than a blank form,growth/ideas/t7.md

The note on the north star's widget, on 3 November, says "T7 live: standing order suggested after the second order", so the rise that follows has its reason beside it. The meeting note on 1 December records the honest limit: a before-and-after reading is weaker than a split test; November had no holiday and nothing else shipped for cafés, so the team keeps T7 and plans a split test of the discount idea next. The growth lead's café ads start in December, with the activation rate as their guardrail.