Live cohort read OnCourse · Medical Exam Prep PostHog #61503 26 Jun 2026

Retention
Intelligence

The cliff, the core, and the features that decide who stays.

7,011 MAU 2,917 cohort users 15 features tested 9 cited sources

OnCourse doesn't have a retention problem. It has a Day-1 activation cliff — sitting on top of a core that stays like a healthy edtech product should.

~88% of new users never return after day one. Yet of the total Day 1→14 decay, 93% happens inside the first week — after that the curve goes nearly flat at a ~4–5% habitual core that beats the AppsFlyer education D30 floor and matches the cross-industry median.

The diagnosis writes the roadmap: widen the top of the funnel and manufacture a first-session "aha," then protect the core. This briefing quantifies all of it — against sourced edtech benchmarks.

01 — Vital signs

The scorecard

Seven headline metrics, each set against the best external benchmark we could source for consumer / education apps. Read it as a triage chart: where we're bleeding, where we're stable.

Verdicts: Behind · Watch · On par · Ahead. OnCourse figures from PostHog rolling-window retention; benchmarks cited per card.

02 — The retention curve

A cliff, then a flatline

New-user retention drawn like a vitals monitor. The plunge from Day 0 to Day 1 is the whole story on the left; zoom into the tail and the line you want to see appears — it stops falling.

The cliff — Day 0 → Day 30

Share of new users active on exactly day N (classic retention). The drop is near-vertical; everything after week one hugs the floor.

The core — Day 1 → Day 17 (zoom)

Same data, rescaled to 0–14%. After D7 the line is essentially horizontal — the signature of a real habitual core, not a dying cohort.

12.42%
Day 1 retention
vs 14–15% edu floor
4.78%
Day 7 retention
vs ~8% all-app median
~4.2%
D14–17 plateau
≥ edu D30 floor (2–3%)
93%
of D1→14 loss
occurs inside week 1
The slope from D7→D14 is −0.08pp/day. The curve isn't decaying to zero — it's asymptoting at ~4.2%. The leak is front-loaded into days 0–7, which is exactly where intervention pays back across the entire downstream curve.

Weekly view — W0 → W3

Smoother than daily. W1 13.8% → W2 10.4% → W3 8.5%: a gentle, non-collapsing decay. (W4+ omitted — fewer than 16 eligible users.)

W1 retention by signup cohort

Rolling 7-day-window definition, completed cohorts only. Recent large cohorts sit at a stable 14–18%.

Earlier ad-hoc figures (~35–51%) used PostHog's calendar-week first-time retention, which reads higher because a late-week signup's "week 1" window can begin a day later. This report uses the stricter rolling definition throughout.

03 — Against the field

How we stack up

Education is structurally one of the lowest-retention categories. Two credible benchmark families disagree by method — OneSignal's engaged-app dataset is the optimistic ceiling; AppsFlyer's install-cohort data is the raw floor. We plot OnCourse against both, plus the all-app median.

OnCourse Education · OneSignal 2024 (engaged ceiling) Education · AppsFlyer (install floor) All-app median · UXCam/AppsFlyer
Read 1

D1 (12.4%) and D7 (4.8%) trail every benchmark — the activation problem is real and quantified, not a category excuse.

Read 2

But the D30-proxy core (~4.2%) clears the AppsFlyer edtech floor of 2–3% and matches the ~4% all-app median. The tail is category-competitive.

Read 3

Subscription lens (RevenueCat 2025): education has the worst annual renewal (24%) but the best weekly (58%) — plan length should match the exam season.

04 — Habit formation

Stickiness & the daily loop

An exam-prep product lives or dies on the daily study habit. DAU/MAU is the north-star here — and right now it's the metric most worth watching.

The DAU ramp

Daily active users (app opens) over the last 5 weeks — the event began instrumenting in late May, so this is a ramp, not steady state.

DAU/MAU vs the field

Where the daily habit sits today against education norms and best-in-class Duolingo. The gap is the opportunity.

7,011
MAU
28-day unique openers
430
avg DAU
28-day mean
6.1%
DAU / MAU
vs 10–15% edu norm
24.8%
WAU / MAU
weekly-habit band

Cohorts are maturing — returning share is climbing

Each weekly bar splits new vs returning openers. Returning share has risen every week (2.9% → 38.7%) as early cohorts age into the product — an encouraging counter-signal beneath the headline ratios.

New openers Returning openers Label = returning share of that week

Caveat: Application Opened only began ramping ~22 May 2026, so the 28-day MAU is inflated by heavy new-user inflow, mechanically depressing DAU/MAU. Treat the 6.1% as directional until a full steady-state window exists.

05 — What makes them stay

The retaining features

For new users (cohort of 2,917), which first-week actions are associated with coming back in week one? Base W1 retention is 16.6%. Every feature lifts above it — these are activation candidates, read with adoption and sample size, not proof of cause.

Week-1 retention by first-week feature used

Bar = W1 retention of users who touched the feature in their first 7 days. The white line is the 16.6% base. Mono tag = cohort size · adoption.

Robust sample (n≥150) Moderate (n 70–149) Exploratory (n<70) 16.6% base
★ Best scalable lever

Rezzy AI Chat

50.7% W1 · +34.1pp lift · n=278 · only 9.5% adoption. Large, distinctive lift on a robust sample with real headroom — the highest-leverage "aha" to promote in session one.

⚙ Activation backbone

Daily Plan

39.7% W1 · +23.1pp · n=599 · 20.5% adoption — the most-adopted habit anchor and the most scalable surface to build a daily-streak loop around.

⚗ High-upside, thin

Voice · Flashcards · Smart Notes

Lifts of +66 / +49 / +41pp but on n=46–96 and 1.6–3.3% adoption. Treat as A/B-validated bets, not headline claims.

Self-selection caveat: engaged users both adopt more features and retain, so lift overstates causal impact. Daily Review (n=15, 73%) and VideoFlix (n=12, 83%) were excluded for n<30.

06 — What they actually do

Reach vs intensity

Two different questions: how many people touch a surface (reach), and how hard the engaged ones lean in (events per user). The gap between them exposes the funnel.

Top by reach · % of 58,908 active persons

Top-of-funnel signup events dominate. Discrete product use sits far lower — the acquisition→activation leak in one chart.

Top by intensity · events / user

Among engaged users (n≥150). The intensity leaders map onto the retained behaviors: deep Exercise & Daily Plan use.

~21,700 open the welcome modal. ~4,900 finish onboarding. The funnel is leaking before users ever reach a feature.

Only 11.9% of monthly-active persons ever fire Application Opened. Onboarding is also bloated — 24.8 pages & 23.98 button-clicks per user.

⚠ Red flag in the activity set

Billing Error Occurred

Fires 3.97× per user across 1,978 users — a monetization-and-trust leak that directly threatens the high-retention paid segment (40.3% W1). Likely driver of involuntary churn at the month-3 peak.

07 — Where it splits

The segment gaps

Two cuts dwarf everything else: platform and monetization. Both point to concrete, ownable programs.

W1 retention by platform

iOS retains 1.82× better than Android — and Android is the largest cohort, so its low rate is the single biggest absolute drag and the biggest opportunity.

Paid / trial vs free

The strongest signal in the whole dataset. Causality runs both ways — but getting users to a trial state early is itself a retention lever.

40.3%
TRIAL / PAID · n=494
11.7%
FREE · n=2,423

A 3.4× retention gap.

08 — The roadmap

What to do about it

Prioritised by leverage. The cliff is the P0 — everything downstream is starved of volume until activation improves. Then build the daily loop and protect the paid core.

09 — On the watchlist

Risks

10 — Receipts

Method & sources

How this was measured

Signal: Application Opened (cold launches) as the activity event; first occurrence defines a new-user cohort.

Curves: eligibility-adjusted — each day/week only counts cohorts old enough to have realized it, removing time-truncation bias. "Day-N classic" = active on exactly day N.

Feature lift: cohort of 2,917 users whose first open was 14–70 days ago; feature "used" within first 7 days; retained = re-opened in days 7–14.

Tooling: PostHog HogQL, project 61503, timezone Asia/Kolkata, as of 26 Jun 2026.

Read these before quoting a number
  • Application Opened is effectively newly-instrumented (ramp from ~22 May); stickiness ratios are directional.
  • HogQL queries are not test/QA-account filtered — volumes may include staff traffic.
  • Late-curve points are tiny-sample noise (D28 n=15, D30 n=5, W4 n=16) and are excluded from reads.
  • Feature lifts are correlational with strong self-selection bias.
  • Benchmarks mix methodologies (OneSignal engaged-app ceiling vs AppsFlyer install floor) — comparisons are directional.
References