Diagnose a retention problem

To: Leo (Head of Growth) From: PM, Activation Subject: Hold on doubling paid spend — our flat WAU is an activation and win-back issue, not summer seasonality

Leo,

Before we double paid acquisition, we need to look closely at what is happening beneath our top-line WAU. While it looks like a summer slump, the data shows our flat WAU (~52,000) is actually caused by two product and lifecycle changes from August: the Week 5 onboarding change broke new-user habit formation, and the Week 6 win-back campaign is masking that core retention drop with temporary, low-intent resurrected users.

Doubling paid spend right now will pour expensive acquisition into a leaky bucket. Here is the diagnosis and what we should do before scaling spend.

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1. Why it’s not summer seasonality

If summer weather and holidays were driving our flat WAU, we would see a broad dip across all users in July and August, followed by a recovery in September. Instead: * Pre-Week 5 cohorts are rock solid: Users who signed up in Weeks 1–4 retained at ~30% by Week 8 (hitting late August and September) with zero seasonal decay. * Retention is still worsening in late September: Our weekly churned users rose from ~7,100 in early July to 10,279 in Week 13 (Sept 28). Meanwhile, our core `retained_users` (active both this week and last week) have steadily eroded from ~45,000 in Weeks 1–4 down to 41,636 in Week 13.

2. Root Cause #1: The Week 5 shorter sign-up broke long-term activation

In Week 5 (Aug 3), we removed the goal-setting step ("What do you want to achieve, and how many days a week?") to cut sign-up time from 3 minutes to 1. Sign-up completion jumped from 61% to 74%, and Week 1 retention ticked up ~3 percentage points (from ~55% to ~58%).

However, long-term cohort retention collapsed immediately starting with the Week 5 cohort: * Week 4 Retention: Dropped from ~33.8% (Weeks 1–4 cohorts) to ~24.7% (Weeks 5–9 cohorts)—a ~9 percentage point drop. * Week 8 Retention: Dropped from ~30.0% (Weeks 1–4 cohorts) to 19.9% (Week 5 cohort)—a one-third relative loss in long-term retained users.

Why this happened: Stride’s core value is a weekly workout plan. By removing the step where users state their goals and target workout days, we likely broke the mechanism that personalizes their weekly plan and anchors their habit commitment. Furthermore, cutting friction let through lower-intent users who curious-click in Week 1, skewing Week 1 retention up while failing to build a multi-week habit.

The Math: Even with a 21% boost in sign-up completion (61% $\rightarrow$ 74%), a 33% relative drop in Week 8 retention (30% $\rightarrow$ 19.9%) means every 1,000 visitors to our sign-up flow now yields ~19% fewer long-term active users than before Week 5.

3. Root Cause #2: The Week 6 win-back campaign is inflating WAU and churn

Why hasn’t WAU fallen if core retention is down? Because in Week 6 (Aug 10), lifecycle launched the 21-day inactive win-back email offering a free week of Premium: * Resurrected users spiked: `returningusers` jumped from ~990/week (Weeks 1–5) to ~2,600/week (Weeks 6–13)—adding ~1,600 temporary users per week. * They immediately re-churn: Lifecycle data shows only 12% of win-back users remain active two weeks later (compared to 41% of organic returning users). Once their free week of Premium ends, nearly 90% (~1,400 users/week) churn right back out. * This explains why weekly `churnedusers` spiked from ~7,400 in Week 4 to >9,100 in Week 6 and >10,300 by Week 11. We are propping up headline WAU by cycling ~1,600 promo-chasers in and out of the app every week while our true core base (`retained_users`) shrinks by ~3,200 users.

(Note: When we increased paid social by 25% in Week 9, new users grew, W2–W4 cohort percentages held steady at the post-Week 5 levels, and `retained_users` still dropped by another ~500 users over the next four weeks.)

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What we should do before scaling acquisition

1. Hold off on doubling paid acquisition (and revert the 25% Week 9 increase if CAC is inefficient): Right now, 80% of new sign-ups churn by Week 7. Doubling spend at current retention rates will burn budget for a fraction of the LTV we were getting in July. 2. Restore goal-setting to the activation flow (this week): We need to A/B test bringing back the goal and frequency questions immediately: * Variant A (Control): Current 1-minute sign-up. * Variant B (Revert): Original 3-minute sign-up with goal-setting. * Variant C (Deferred Onboarding): Keep account creation fast, but require the goal-setting & weekly schedule commitment immediately before the user starts their first workout or generates their first weekly plan. * Success metric: Net retained users at Week 4 per 1,000 top-of-funnel visitors (not sign-up completion or W1 retention). 3. Fix the win-back mechanic: Giving a free week of Premium to unactivated users doesn't build a habit. We should shift the win-back hook from a temporary paywall unlock to re-establishing a weekly plan (e.g., "Let's reset your weekly schedule—pick 2 days to work out this week"), and track `retainedusers` separately from win-back `returningusers` in our growth reporting.

Once we see Week 4 cohort retention recover back above 32%, we should turn the paid acquisition tap all the way up.

Leo — I wouldn’t double paid acquisition yet. We have a retention leak, not just a summer growth slowdown. More new users and campaign-driven returns are masking deteriorating retention. The clearest break coincides with the week-5 onboarding change, though we need an experiment to establish causality.

What’s actually happening

Weekly actives remain around 52,000 because inflows are replacing increasingly large losses:

New users rose 30%, but weekly churn rose 44%. The share of the prior week’s actives retained fell from approximately 86.3% to 80.2%. In week 13, 10,482 new and returning users barely exceeded 10,279 churned users: net growth was just 203.

Flat actives are therefore concealing a less durable user base.

The evidence points to an activation problem

1. New cohorts look better initially, then retain substantially worse. For signup cohorts 1–4, first-following-week retention averaged 55.1%; for cohorts 5–12, it averaged 58.0%. But by the fourth following week, retention falls from 33.8% for cohorts 1–4 to 24.8% for cohorts 5–9. At eight weeks, the earlier cohorts retain roughly 30%, versus 19.9% for cohort 5.

That is not simply fewer people trying Stride. More are getting through the front door, but fewer form a lasting habit.

2. The break aligns with removing goal-setting in week 5. Shorter signup raised completion from 61% to 74%, but removed the step asking users what they wanted to achieve and how often they would exercise. For a habit app built around a weekly plan, that step may have helped users commit to a relevant routine.

The timing makes this our leading hypothesis—not proven causality. We should also check acquisition mix and other changes. However, the paid-budget increase occurred in week 9, after the cohort deterioration began. “Summer” alone does not explain this sharp onboarding-aligned break.

3. Win-back is propping up the headline metric, not repairing retention. Returning users jumped from about 1,000 to 2,600 a week when emails launched in week 6. Yet only 12% of email-driven returners remained active two weeks later, versus 41% of earlier organic returners. These are different populations, so this is not a causal campaign comparison, but it clearly warns against treating every return as durable recovery.

What we should do before spending more

1. Hold acquisition spend steady while testing onboarding. Randomize the current short signup against a lightweight goal-setting step that produces a personalized weekly plan. Preserve the speed improvement where possible.

1. Optimize for durable activation, not signup completion. Make four-week retention a primary outcome, alongside first workout, plan adoption and repeat workout behavior. Measure retained users per signup and per signup starter so a longer flow cannot “win” merely by filtering people out. Track Premium conversion as a guardrail.

1. Locate the failure point. Segment cohorts by acquisition channel, campaign and onboarding exposure. Compare where users stop: before their first workout, after one workout, or when beginning the next weekly plan. Reconcile the signup and new-active counts before calculating acquisition efficiency.

1. Judge win-back on incremental lasting activity. Use a randomized holdout and measure two- and four-week retention, not email-driven returns alone. Test messages that restore a realistic plan rather than relying only on free Premium.

Decision: scale acquisition once we demonstrate improved multiweek retention and acceptable acquisition economics. Doubling spend now risks buying more users into a leaking habit loop—and making flat actives look healthier without fixing the underlying 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.