Diagnose a retention problem

To: Leo — Fix the retention leak before doubling acquisition

Weekly actives are flat because rising acquisition and reactivation are offsetting worsening retention. Summer could contribute, but the clearest break in the data follows the week-5 onboarding change—not the later increase in paid spend.

What’s happening

1. We are replacing more users, not building a larger active base.

Weekly active users equal retained users + new users + returning users. Equivalently, weekly net growth equals new + returning − churned.

From week 1 to week 13: - New users rose 30%, from 6,034 to 7,847. - Returning users rose from 973 to 2,635. - Weekly churn rose 44%, from 7,116 to 10,279. - Retained users fell from 44,884 to 41,636.

The result: weekly actives moved just 0.4%, from 51,891 to 52,118. The share of the previous week’s actives retained fell from 86.3% to 80.2%. This is deterioration beneath a flat headline.

2. New users look better after one week, but substantially worse afterward.

Compare signup cohorts before and after the week-5 release:

Only the week-5 post-change cohort has an observed week-8 result. Blank cells are unobserved, not zero.

The week-4 decline is roughly nine percentage points, or 27% relative, and repeats across multiple mature post-change cohorts. Earlier cohorts flatten near 30% by weeks 7–8; the first mature post-change cohort flattens near 20%. That suggests a smaller durable habit-forming base, not merely delayed engagement.

3. Win-back is masking the problem, not fixing it.

Returning users jumped immediately after win-back emails began in week 6. Lifecycle attributes about 1,600 extra weekly returns to the campaign, but only 12% of email-returned users were active two weeks later, versus 41% of earlier organic returners.

Those populations are not comparable enough to establish a causal campaign effect. Nevertheless, most campaign returners are not sustaining activity. Repeated returns and lapses can support WAU without creating lasting growth.

Likely diagnosis—and what remains uncertain

In week 5, we removed goal-setting and weekly-frequency selection. Signup completion improved from 61% to 74%, but subsequent cohorts became much less durable.

The leading hypothesis is that we removed a commitment and planning step central to a fitness habit. Alternatively—or additionally—the easier funnel admitted more low-intent users, lowering average retention. Both could be happening.

Timing alone does not prove causality. We lack randomized evidence, acquisition-source breakdowns, and a prior-year seasonal comparison. Still, the abrupt cohort break starts before the week-9 paid-social increase and persists afterward. “It’s summer” is not a sufficient diagnosis. Also, new-cohort deterioration may not explain all aggregate churn; we should examine established users separately.

What we should do before spending more

1. Hold paid spend at its current level. Don’t double distribution into a deteriorating retention curve.

1. Test lightweight goal-setting immediately. Randomize new visitors between current onboarding and a short goal/frequency step that produces a concrete weekly plan. Measure week-4 retained users per onboarding entrant, not just signup completion or retention among completed signups. This captures the conversion–retention trade-off. Track first-workout completion and second-week adherence as early signals; Premium conversion and revenue as business guardrails.

1. Localize the leak. Segment equal-age cohorts by paid/organic source, campaign, onboarding exposure, and initial workout/plan behavior. Check existing-user retention around week 5. Reconcile the differing signup totals across the two exports before joining them.

1. Evaluate win-back with a randomized holdout. Optimize for incremental sustained activity and eventual revenue—not email-driven return visits alone.

Increase acquisition only after we demonstrate improved durable retention and acceptable channel economics. Right now, more acquisition would buy more arrivals; it has not demonstrated that it will buy lasting growth.

To: Leo | From: Activation PM | Re: Why weekly actives are flat, and why not to double paid yet

Bottom line

Summer isn't the main story. Retention of new sign-ups dropped sharply in week 5, the week we shortened sign-up. Weekly actives look flat only because more sign-ups and a win-back campaign are masking a leakier base. Doubling paid now would pour more users into a worse funnel.

What's happening

Weekly actives (WAU) went from 51,891 to 52,118 over the 13 weeks, a gain of +227. The components moved very differently:

The share of last week's actives who are still active this week fell from about 86% (week 2) to about 80% (week 13). Weekly churn rose from roughly 14% to 20%. Growth in sign-ups and returners is almost exactly cancelling a shrinking retained base.

Evidence it's the sign-up change, not summer

1. The cohort cliff is a step change. Sign-up cohorts from weeks 1–4 plateau at about 30% active by weeks 7–8. Every cohort from week 5 onward plateaus at about 20%. Week-4 retention drops from about 34% to about 25%. The break lands exactly on the week we removed the goal-setting step ("what do you want to achieve, and how many days a week?"). It doesn't appear gradually.

2. Same calendar weeks, different outcomes. Seasonality hits everyone active in a given week. In calendar week 10, the week-3 cohort (seven weeks old) is at about 30% retained. The week-6 cohort (four weeks old) is at about 24%, and it was still falling. Old cohorts held steady through August and September, so users who signed up under the old flow aren't behaving "seasonally."

3. Other changes don't fit. The week-3 workout library came before the drop and didn't move retention. The week-9 paid increase didn't change cohort quality (week 9–12 cohorts look like weeks 5–8), so the problem isn't paid traffic. It's what happens after sign-up.

4. The trade-off was a bad one. Sign-up completion rose from 61% to 74%, and week-1 retention even ticked up (about 58% vs 55%). But per 100 people starting sign-up, long-run actives fell from about 18 (61% × 30%) to about 15 (74% × 20%), roughly 19% worse. My hypothesis is that the goal and days-per-week answers fed the weekly plan, which is the core of the product. Without them, users get no plan to return to. This is inference. The change wasn't randomized, so we need to test it.

The win-back campaign is propping up the number

Returning users jumped from about 1,000 to about 2,600 a week at week 6, when win-back emails started. That's about 1,600 extra returners a week, and it's what has kept WAU flat. But only 12% of win-back returners are still active two weeks later, versus 41% of people who came back on their own before the campaign. That's roughly 190 durable users a week from about 1,600 reactivations, and it costs a free week of Premium each. Without it, WAU would be around 50,000 and visibly declining.

The comparison isn't perfectly like-for-like, because self-returners are more motivated. But the campaign is clearly adding far less durable activity than the headline suggests.

What to do instead

1. Hold paid at current levels. Don't double it. At 20% plateau retention, recovering the old 30% on about 7,500 weekly sign-ups is worth roughly 750 extra long-term actives per weekly cohort. That equals what a 50% increase in sign-ups would deliver at current quality, without spending anything.

1. A/B test restoring goal-setting, starting this week. Compare the current 1-minute flow against a version with a single "goal + days per week" screen, ideally right after account creation or at the start of the first session, so it adds seconds rather than minutes. The goal is to keep most of the completion gain and recover retention. Primary metric: week-4 retained rate. Guardrail: sign-up completion. The decision metric is retained users per 100 sign-up starts, not completion alone. Week-4 data takes about a month, so I'd also watch week-1 and week-2 retention as early signals. Then use the answers to generate the weekly plan automatically.

1. Put a holdout on win-back. Withhold the email from a random 10% of eligible users so we can measure true incremental reactivation, not just "returners." Judge it on two-week-retained reactivations and on Premium cost. Test a lighter offer or one tied to a specific plan or workout.

1. Change how we report. Show WAU split into new, returning, and retained, and add cohort week-4 retention to the weekly review. Flat WAU hid this for three months.

1. Set a gate for scaling paid. Once new cohorts' week-4 retention is back near 33% (the pre-change level), scaling acquisition is a good bet. Until then, each extra dollar buys users who mostly leave.

Caveats

• Cohort data only extends 8 weeks, and recent cohorts less. The 20% plateau is well established for weeks 5–8 cohorts and extrapolated for later ones.
• Win-back reactivations may flatter recent cohorts' later-week numbers, so the real retention gap could be slightly larger than shown.
• I don't have channel-level data, so I can't say how much of the sign-up growth is paid or whether paid cohorts differ from organic. That would be worth pulling.
• Some summer effect is plausible, but it can't explain a step change that hits only post-week-5 sign-ups.

Weekly actives are flat because retention broke in week 5, not because of summer

To: Leo From: Activation PM Re: Why weekly actives are flat, and why we shouldn't double paid acquisition yet

Summary

Weekly actives aren't flat because of summer. Retention dropped sharply for every cohort that signed up from week 5 onward. That is the week we removed the goal-setting step from sign-up. The win-back campaign has hidden the damage by pulling back about 1,600 lapsed users a week, and most of them leave again within two weeks. Doubling acquisition now would pour more users into a bucket that leaks about a third faster than it did in July.

What's happening

1. Churn is up 40%, and it's offset by low-quality reactivations.

Weekly churn (users active last week but not this week, divided by last week's actives):

Returning users jumped from about 1,000 to about 2,600 a week in week 6, the week win-back emails started. New users are up 30%. Together these just cancel the extra churn, which is why the headline stays near 52,000.

2. The break follows sign-up cohort, not the calendar.

The change is a clean step at the week-5 cohort, with no gradual drift.

3. The evidence argues against seasonality.

• Cohorts behave differently in the same calendar weeks. If summer were the cause, everyone would dip together. Instead, the weeks 1–4 cohorts held at about 30% through all of August, while the cohorts that joined in August decayed fast.
• July was summer too. Churn was at its lowest then.
• September didn't help. Weeks 10–13 run into late September, and churn is at its worst.

4. The week-5 sign-up change is the most likely cause.

Removing "What do you want to achieve, and how many days a week?" cut sign-up from 3 minutes to 1. Completion rose from 61% to 74%. But we stopped asking users to commit to a goal and a weekly frequency, which is the core of a weekly-plan habit app.

The pattern fits: - First-week retention went up slightly (58% vs 55%), because the initial experience is fine. - From week 3 onward, users without a plan drift away.

Part of the drop may be compositional: the extra 13 points of completers may be lower intent. Either way, the cause points to the same step. Nothing else in the log lines up. The week-3 workout library shows no effect, and paid social only started in week 9.

5. The win-back campaign flatters the topline.

Only 12% of win-back returners are still active two weeks later, against 41% of users who came back on their own before the campaign. We're also giving away a free week of Premium to get them. Without the campaign, weekly actives would be roughly 1,500 lower and still falling.

Why doubling acquisition doesn't work right now

Per 1,000 sign-ups, about 300 were still active at week 8 before week 5. Now it's about 200. That means:

• We need 50% more sign-ups just to keep the same number of retained users.
• Sign-ups are up about 28%, which isn't keeping pace.
• Cost per retained user is up roughly 50% at today's acquisition cost. That's before any rise in marginal cost from doubling spend, which usually happens.

Each extra cohort loses about 80% of its users within 8 weeks. Doubling spend would mostly buy churn.

What we should do instead

1. Fix sign-up first (start this week).

Run an A/B test with three arms: - (a) the current flow; - (b) the old flow with goal-setting; - (c) goal-setting moved to after account creation, in the first session.

Option (c) could keep most of the completion gain and restore the plan. Judge the test on week-3 retention, where the gap (about 37% vs 30%) shows up within three weeks, and confirm at week 4.

If we want to move faster, we could simply revert to (b) while testing (c). We'd give back some completion, but we'd stop losing about 10 points of week-8 retention on every new cohort.

2. Hold paid acquisition at its current level until retention recovers.

Revisit once new cohorts are back to about 33% at week 4. When we do scale, check whether paid-social cohorts retain as well as organic ones. We don't yet have cohort data split by channel.

3. Put the win-back campaign behind a holdout.

Withhold the email from 20% of eligible users to measure: - true incremental weekly actives; - Premium cannibalisation; - whether the free week leads to paid conversion.

Also try a version without the free-Premium offer, or one that sends people back into a plan: "Pick your days for this week".

4. Change how we report growth.

Make cohort retention at weeks 4 and 8, plus the weekly churn rate, our headline health metrics alongside weekly actives. Report reactivations separately. A flat topline hid a 40% rise in churn for two months.

Caveats

• Causation isn't proven. The week-5 timing is strong evidence, but the A/B test is what will prove it.
• The two data sources don't quite match. Cohort sign-up counts are slightly lower than "new users" in the growth accounting (for example, 6,000 vs 6,034 in week 1), so the two may define new users slightly differently. The gap is too small to change the conclusion.

Happy to walk through the cohort curves together. I'd recommend we agree on the sign-up test before any change to the paid budget.