Tasks / Growth

Diagnose a retention problem

Can the model work out from raw data what's really happening to retention, separating churn from measurement and seasonality, and find the cause?

Task type v1.1 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

What AI gets right here, and what you’ll still have to catch

From 16 graded outputs by 8 models. 81% were usable with at most a quick edit.

Reliably right

  1. Produces the required deliverable100% pass
    The diagnosis is complete, actionable, and in the right format for Leo; a PM could act on it with light edits.
    GPT-6 Astra · ChatGPT · Flat, while sign-ups grow
  2. Understands before it justifies100% pass
    It tests the summer explanation against the data, finds the break aligns with the onboarding change not the paid spend increase, and reaches its own diagnosis from the evidence.
    GPT-6 Astra · ChatGPT · Flat, while sign-ups grow
  3. Splits growth into new, lost and returning100% pass
    It splits growth into new, retained, churned, and returning users, and uses the net growth equation to show which flow changed.
    GPT-6 Astra · ChatGPT · Flat, while sign-ups grow

Where it slips

  1. Gets the base of every number right61% pass
    The SMB adoption churn calculation is incorrect; 39% of ~$7.4k/month churn is ~$2.9k/month, not $34k MRR, and the annualized figure is ~$34.8k ARR, not $0.4M.
    Sonnet 5.5 · API · The collapse that wasn't
  2. Avoids unsupported claims67% pass
    The output presents causal interpretations (e.g., onboarding changes broke retention, low-intent users) as established fact without labelling them as hypotheses.
    Gemini 3.5 Flash-Lite · Gemini · Flat, while sign-ups grow
  3. Identifies material uncertainty77% pass
    The output does not name specific unknowns that could change the decision or say how they would be resolved.
    Gemini 3.5 Flash-Lite · Gemini · Flat, while sign-ups grow

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

  1. Understands before it justifies

    Does the diagnosis start from what the data shows (which users, when, at what point in their life with the product) before accepting or proposing an explanation, including the one it was handed?

    Passes when Tests the explanation it was given against the data, and reaches its own diagnosis from the evidence.

  2. Splits growth into new, lost and returning

    Does the diagnosis separate new users or revenue from what was lost and what came back, rather than reading a single total?

    Passes when Uses growth accounting (new, retained, churned, returning, or new, expansion, contraction, churn) to show which flow changed.

  3. Reads the shape of the curves

    Does the diagnosis read how retention curves behave over time (flattening, still falling, or dipping and recovering) and what each shape means?

    Passes when Describes the curve shapes in the data and draws the right conclusion from each, such as a dip that recovers being seasonal.

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · Gets the base of every number right · and 2 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then compare up to three outputs, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're the PM for activation at Stride. Leo, our Head of Growth, thinks weekly actives are flat because it's summer, and wants to double paid acquisition (his note is below). Before he does, write him a diagnosis of no more than 800 words: what's actually happening to retention, the evidence, and what we should do instead of (or before) spending more on acquisition. The files are attached.

What the model was given6 items: About Stride, leo_note.md, weekly_growth_accounting.csv (weeks run Monday to Sunday), weekly_signup_cohorts.csv (share of each week's sign-ups active in each following week), release_and_campaign_log.md, winback_campaign_results.md
About StrideA fitness habit app: short guided workouts and a weekly plan. Free, with Premium at $9.99 a month.
leo_note.md1 lines · Download
From Leo (Head of Growth): "Weekly actives have been flat for three months even though sign-ups are up 30%. It's summer: people are outdoors and on holiday. I want to double paid acquisition to push through it."
weekly_growth_accounting.csv (weeks run Monday to Sunday)week,week_starting,new_users,returning_users,retained_users,churned_users,weekly_active_users 1,2026-07-06,6034,973,44884,7116,51891 2,2026-07-13,6142,989,44529,7362,51660 3,2026-07-20,6184,1040,45074,6586,52298 4,2026-07-27,6385,991,44843,7455,52219 5,2026-08-03,6597,975,44085,8134,51657 6,2026-08-10,6689,2541,42516,9141,51746 7,2026-08-17,6779,2666,42506,9240,51951 8,2026-08-24,6955,2552,42723,9228,52230 9,2026-08-31,7156,2618,42134,10096,51908 10,2026-09-07,7230,2675,42393,9515,52298 11,2026-09-14,7564,2586,41984,10314,52134 12,2026-09-21,7550,2557,41808,10326,51915 13,2026-09-28,7847,2635,41636,10279,52118
weekly_signup_cohorts.csv (share of each week's sign-ups active in each following week)signup_week,signups,week_1_retained_pct,week_2_retained_pct,week_3_retained_pct,week_4_retained_pct,week_5_retained_pct,week_6_retained_pct,week_7_retained_pct,week_8_retained_pct 1,6000,55.4,43.2,36.4,34.2,31.9,30.9,29.8,30.1 2,6150,55.3,43.2,37.0,34.2,32.4,30.5,29.9,30.2 3,6300,55.2,43.0,37.7,33.4,32.1,30.6,29.8,30.0 4,6450,54.3,42.3,37.0,33.4,32.4,31.4,30.0,29.5 5,6600,58.4,39.4,29.6,24.5,22.1,21.1,19.8,19.9 6,6750,58.6,39.9,30.1,24.4,21.8,21.2,19.9, 7,6900,58.4,40.5,29.4,24.9,22.2,21.2,, 8,7050,58.1,40.1,29.9,25.0,21.3,,, 9,7200,57.5,40.5,29.7,25.0,,,, 10,7350,58.3,40.5,29.4,,,,, 11,7500,57.4,40.6,,,,,, 12,7650,57.5,,,,,,,
release_and_campaign_log.md6 lines · Download
# Release and campaign log

- Week 3: New workout library (40 new routines).
- Week 5: Shorter sign-up. 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 rose from 61% to 74%.
- Week 6: Win-back emails start: anyone inactive for 21 days gets "We miss you: here's a free week of Premium".
- Week 9: Paid social budget up 25%.
winback_campaign_results.md3 lines · Download
# Win-back campaign, weeks 6 to 13 (from the lifecycle team)

About 1,600 extra returning users a week compared with before the campaign. Of users who came back through a win-back email, 12% were still active two weeks later. Of users who came back on their own before the campaign, 41% were.
What a strong answer doesThe answer key the graders mark against

Shows that flat weekly actives aren't the summer: new users rose about 30% (about 6,000 to 7,800 a week) but churned users rose faster (about 7,100 to 10,300), and returning users jumped from about 1,000 to 2,600 a week only because of the win-back emails from week 6, of whom 12% stay two weeks against 41% of organic returners, so actives are being propped up. The cohort table shows why churn rose: cohorts from week 5, when the goal-setting step was removed from sign-up, retain far worse (week 4 about 25% against 34%, week 8 about 20% against 30%) even though more people finish sign-up. So more paid acquisition would pour more users into a leaky first month. Recommends restoring goal-setting (or testing it back against the short sign-up, judged on week-4 retention, not sign-up completion), measuring returning users separately so the campaign doesn't hide churn, and holding paid acquisition until new-user retention recovers.

Critical failures (cap the score at 40)
  • Recommends doubling paid acquisition now
Task

Mid-level · v1.0

The outputs

8 setups answered this brief.

Usable with a quick edit12 of 12 checks passed

The diagnosis is complete, uses the supplied evidence correctly, and gives a concrete, actionable plan with no missing reasoning; a PM could use it with only superficial edits.

Output

To: Leo | From: Activation PM | Re: Why WAU is flat, and why I'd hold off on doubling paid

Bottom line: This isn't a summer dip. Retention has been deteriorating since early August, and new sign-ups and win-back emails are hiding it. Doubling paid now would mostly buy users who leave faster.

1. What's happening

WAU = new + returning + retained users, which reconciles exactly every week. Across weeks 1–13, WAU moved only +227 (51,891 → 52,118). That flatness is three movements netting out:

  • New users: +1,813 (up 30%, as you said)
  • Returning users: +1,662, nearly all from win-back
  • Retained users (active last week and this week): −3,248

Retained users held near 44.5–45k through week 4, then stepped down from week 5 and kept sliding to 41.6k. Churn (last week's actives who didn't return) rose from about 14% of prior-week actives in weeks 2–4 to about 20% in weeks 11–13.

2. Why it isn't summer

Seasonality would hit all cohorts alike. It doesn't. Cohorts 1–4 (signed up July to late July) had week-4 retention of 33–34%. Cohorts 5 onward (from the week of 3 August, the first week of the shorter sign-up) drop to 24–25% and stay there, even though cohorts 2–4 went through the same weather. The break lines up with the week-5 release, which removed the goal-setting step.

The funnel math shows the change is a net loss. Per 100 visitors:

CompletionWeek-4 activesWeek-8 actives
Before (61%, cohorts 1–4)6161 × 33.4% ≈ 20.461 × 30.1% ≈ 18.4
After (74%, cohorts 5+)7474 × 24.5% ≈ 18.174 × 19.9% ≈ 14.7

We're converting more visitors into accounts, but by week 8 each signup is about 20% less likely to still be active. We're getting more users who don't build the habit.

3. Win-back is flattering the numbers

Win-back adds about 1,600 returning users a week. Of users who came back through a win-back email, 12% were still active two weeks later. Of users who came back on their own, 41% were. At 1,600 a week, that's roughly 190 durable returners instead of about 650 if they'd come back organically. We're also giving away a free week of Premium. "Returning users" now looks like a health signal, but most of it is low-quality.

The week-9 paid social increase (+25%) isn't visible as a break in the new-user trend, so I can't yet say what paid is adding.

What I'd do instead

  1. Hold the paid increase. Keep current spend. Revisit when cohort week-4 retention is back above 30%.
  1. Restore the goal-setting step, or move it into the app after the first workout. Run it as a holdout (50/50, old flow vs new), and judge on week-4 and week-8 retention per signup, not completion. Using the cohort tables, bringing cohort retention back to about 30% at week 4 is worth roughly 3–4k weekly actives at steady state. That's about the size of the whole retained-user decline. This is my rough estimate and needs confirming with the holdout.
  1. Find the churn in tenured users. Most of the 41k retained base isn't new. Segment weeks 4–13 churn by tenure, plan usage, workout type, and platform. Also check whether win-back returners churn quickly the following week, since they add to churn in the same weeks.
  1. Rework win-back. Test a smaller, conditional offer, or a guided re-entry workout instead of a free Premium week. Measure two-week retention against a holdout. Keep it only if the incremental retained users justify the cost.
  1. Change what we report. Show the WAU decomposition (new, returning, retained) and cohort retention by signup week every week. Stop counting returners as growth.

On the summer theory: it's testable. If we have last year's data for the same weeks, compare churn rates. If summer were the cause, we'd expect last year's churn to rise over these weeks too, and we'd expect older cohorts to show the same pattern as new ones. The data so far points to the week-5 change.

Ask: Can we set up the sign-up holdout this week and pull the tenure-level churn cut before any paid budget decision?

Grades and run details

Decision model 92 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly35%
  • passAddresses the actual decision100%
  • passRespects explicit constraints41%
  • passIdentifies material uncertainty97%
  • partialAvoids unsupported claims31%
  • passProduces the required deliverable92%
  • passFinds the onboarding change100%
  • passSees the win-back masking churn100%
  • passUnderstands before it justifies100%
  • partialGets the base of every number right1%
  • passSplits growth into new, lost and returning100%
  • passReads the shape of the curves85%
Run
Run
#1
API response time
37 s
Submitted
8 Oct 2026

Usable with a quick edit10 of 12 checks passed

The output is well-structured, evidence-based, and actionable. A PM could soften the causal claim and add a caveat about uncertainty with a quick edit, without redoing the analysis.

Output

To: Leo, Head of Growth From: PM for Activation Subject: Diagnosis: Why WAU is flat and why we shouldn’t double paid acquisition yet

*

Executive Summary

WAU is not flat because of summer seasonality. It is flat because of a structural collapse in early habit retention starting in Week 5, which is currently masked by two temporary factors: the Week 6 win-back email campaign and higher sign-up volume.

Our core retained user base has dropped by over 3,200 users, and weekly churn has surged by 44% (from ~7,100 to over 10,200). Doubling paid acquisition today would pour money into an onboarding funnel that currently retains a third fewer users long-term than it did two months ago.

Here is what the data shows and what we should do instead.

---

1. The Evidence: The Week 5 Onboarding Change Broke Habit Retention

If summer outdoor seasonality were the culprit, we would expect a gradual, platform-wide decay across all cohorts. Instead, the cohort data reveals an immediate step-change break between Cohort 4 and Cohort 5:

  • Cohorts 1–4 (Before Week 5): Week 4 retention was steady at 33.4%–34.2%, flattening to a healthy 30% at Week 8.
  • Cohorts 5+ (After Week 5): Week 4 retention plummeted to 24.5%–25.0%, and Week 8 dropped to 19.9%.

This represents a ~33% relative drop in long-term cohort retention.

Why did this happen? In Week 5, 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 minute. While sign-up completion jumped from 61% to 74% and bumped Week 1 retention slightly (55% to 58%), we stripped away the user’s commitment and personal investment.

Stride is a habit product. Without clear goals and a tailored weekly schedule, users drop off rapidly after Week 1 because they lack direction and accountability.

---

2. The Illusion: Why Topline WAU Looks Flat

If retention collapsed by a third, why hasn't WAU cratered? Two factors created an artificial floor:

  1. Top-of-Funnel Inflow: Sign-ups grew ~30% (boosted by higher sign-up completion and the Week 9 paid budget increase), adding ~1,800 gross new sign-ups per week.
  2. The Week 6 Win-Back "Sugar Rush": The win-back campaign added ~1,600 returning users per week (jumping from ~1,000 to ~2,600 in growth accounting).

However, this resurrected volume is extremely low-quality: * Historically, 41% of organic returning users were still active two weeks later. * For win-back users, only 12% remain active after two weeks.

We are cycling low-intent, discount-incentivized users who churn almost immediately. Meanwhile, underlying retained users dropped from 45,074 (Week 3) to 41,636 (Week 13), while churn surged from 7,116 to 10,279 per week.

---

3. What Happens If We Double Paid Acquisition Now?

Doubling paid acquisition right now will accelerate cash burn without growing the business:

  • Impaired Unit Economics: With Week 8 retention down from 30% to 20%, LTV has dropped sharply. Premium conversion will suffer because fewer users build the 3-week habit required to see the value of paid features.
  • Higher CAC for Diminishing Returns: Scaling paid spend when the conversion engine is degraded will cause CAC to rise while payback periods stretch unsustainably.
  • A Leaky Bucket: In Week 13, we added 7,847 new users and churned 10,279. Adding more sign-ups does not fix a machine that is churning users faster than it can onboard them.

---

4. Recommended Action Plan

Before increasing ad spend, we need to plug the leak and fix activation.

1. Reinstate and Optimize Goal-Setting (Immediate): * Re-introduce the goal-setting and schedule-commitment step in onboarding, framed as building the user's "Personalized Weekly Plan." * Test progressive onboarding: let users quickly select goals without friction, but ensure their first workout calendar is anchored before Day 1. * Target: Return Week 4 retention to ≥33%. 2. Re-evaluate Win-Back & Lifecycle Spend: * Stop relying on the 21-day "free Premium week" email to prop up WAU. Shift CRM focus to Days 1–7 activation, reinforcing the weekly plan set during onboarding. 3. Hold Paid Social at Current Levels: * Keep paid spend flat (or pull back the Week 9 25% increase) until Cohort Week 3/4 retention recovers. 4. Scale Acquisition Once Activation Is Fixed: * Once Cohort Week 4 retention crosses 32% for two consecutive cohorts, we double paid acquisition. At that point, every dollar spent will generate 50% more long-term active users than it would today.

Grades and run details

Decision model 79 · LLM judge 85
Decision model checks
  • partialUses the supplied evidence correctly10%
  • passAddresses the actual decision100%
  • passRespects explicit constraints32%
  • failIdentifies material uncertainty6%
  • failAvoids unsupported claims47%
  • passProduces the required deliverable95%
  • passFinds the onboarding change100%
  • passSees the win-back masking churn100%
  • passUnderstands before it justifies100%
  • passGets the base of every number right13%
  • passSplits growth into new, lost and returning100%
  • passReads the shape of the curves78%
Run
Run
#1
API response time
29 s
Submitted
6 Oct 2026

Usable with a quick edit9 of 12 checks passed

The reasoning and evidence are solid; a PM could add a condition for changing the call and soften causal language with a quick edit, without redoing any analysis.

Output

To: Leo, Head of Growth From: PM, Activation Subject: Diagnosis: Why WAU is flat (and why doubling paid acquisition right now will burn cash)

Leo, you suggested doubling paid acquisition to push through flat summer WAU. I’ve dug into the numbers, and the data tells a different story.

Weekly actives are flat (averaging ~52,000 for 13 weeks), but sign-ups are up 30% (from ~6,000 to ~7,800). The issue isn't seasonal churn—it's that our onboarding changes broke cohort retention, and our win-back campaigns are masking the leak.

Doubling down on paid acquisition right now will just pour more water into a bucket with a widening hole. Here is the diagnosis, the evidence, and what we should do instead.

---

1. What’s actually happening?

#### A. Onboarding optimization created low-intent users (Week 5) In Week 5, we removed the goal-setting step to cut sign-up time from 3 minutes to 1. Completion jumped from 61% to 74%, and sign-ups grew.

However, look at the cohort data (`weeklysignupcohorts.csv`). For Cohorts 1–4 (pre-change), Week 1 retention hovered around 55%, and Week 4 retention was ~34%. From Cohort 5 onward, Week 1 retention jumped to ~58% (thanks to frictionless sign-up), but Week 4 retention cratered from ~34% down to ~25%.

By removing goal-setting, we optimized for top-of-funnel conversion at the expense of user intent. We are acquiring users who don't know why they are here or what plan fits them, leading them to drop off heavily by week 4.

#### B. Core retention is quietly declining Look at the growth accounting data (`weeklygrowthaccounting.csv`). Total `retained_users` (users active week-over-week) dropped steadily from ~44,800 in Week 1 to ~41,600 in Week 13, a loss of over 3,200 loyal weekly actives.

#### C. Win-backs are masking the bleeding So why is WAU flat if retained users dropped by 3,200? Look at `returning_users`. From Weeks 1–5, returning users averaged ~980 per week. Starting in Week 6 (when the win-back email launched), returning users spiked to ~2,600 per week.

The win-back campaign is successfully dragging churned users back, propping up our headline WAU. But it’s a leaky bucket: lifecycle data shows that only 12% of win-back users are active two weeks later (compared to 41% of organic returning users). They churn right back out.

---

2. Why "Summer" isn't to blame

Your theory about summer (outdoors, holidays) would manifest as broad seasonal churn across all cohorts. But our data shows: 1. Older cohorts (1–4) maintained stable long-term retention. 2. The drop in retention is strictly tied to Cohort 5 onwards—the exact week we shipped the shorter sign-up flow.

It is product-led churn, not seasonal.

---

3. What we should do instead of doubling paid acquisition

Before we scale ad spend, we need to fix activation and stop relying on expensive band-aids. Let’s execute a 3-step pivot:

  1. Re-introduce intent-matching to onboarding (without hurting conversion): We need to qualify users so they receive the right weekly plan. Instead of a heavy 3-minute form, let’s test a lightweight, single-tap goal selector or defer personalization to the end of the first workout. We must restore Week 4 retention from 25% back toward 34%.
  2. Refine the win-back targeting: Pumping win-back emails to all churned users after 21 days is bringing back low-intent users who churn in 14 days. Let’s restrict win-backs to users who reached a milestone or completed at least 3 workouts before churning, matching the profile of our 41% organic returners.
  3. Optimize the Week 3–4 activation loop: We shipped 40 new routines in Week 3, but post-onboarding drop-off is neutralizing it. We need to focus activation efforts on guiding new users to these routines before they hit the Week 3 cliff.

Bottom line: Let's hold off on doubling paid acquisition for 4 weeks. If we fix the onboarding leak and improve post-signup intent, organic growth + recovered retention will naturally push us past our WAU plateau without wasting ad dollars on leaky traffic. Let's sync on this today.

Grades and run details

Decision model 88 · LLM judge 77
Decision model checks
  • passUses the supplied evidence correctly41%
  • passAddresses the actual decision91%
  • passRespects explicit constraints47%
  • partialIdentifies material uncertainty39%
  • failAvoids unsupported claims30%
  • passProduces the required deliverable93%
  • passFinds the onboarding change100%
  • passSees the win-back masking churn100%
  • passUnderstands before it justifies100%
  • passGets the base of every number right27%
  • passSplits growth into new, lost and returning99%
  • passReads the shape of the curves49%
Run
Run
#1
Time to output
6 s
Submitted
5 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 3

Addresses the actual decisionRightRightMixed
Haiku 5.5 · API

Output commits early to 'hold off on doubling paid', gives clear condition (cohort week-4 retention above 30%) to revisit.

Gemini 3.8 Flash · API

The output clearly commits to not doubling paid acquisition now and states the condition (Week 4 retention >32% for two cohorts) that would change the call.

Gemini 3.5 Flash-Lite · Gemini

The output commits to holding off on paid acquisition but does not state what result or condition would change that call, as required.

Identifies material uncertaintyRightWrongWrong
Haiku 5.5 · API

Names unknowns (paid social effect not visible, win-back durability, exact size of benefit from restoring goal-setting, and that summer theory could be tested with last year's data) and says how each would be resolved (holdout, tenure analysis, comparison).

Gemini 3.8 Flash · API

The output does not name specific unknowns that could change the decision; it presents the causal link as certain and only gives a success target, not unresolved uncertainties.

Gemini 3.5 Flash-Lite · Gemini

The output does not name specific unknowns that could change the decision or say how they would be resolved.

Avoids unsupported claimsRightWrongWrong
Haiku 5.5 · API

Hypotheses and forecasts (e.g., 'most of retained base isn't new' is a premise for investigation, and the 3-4k projected gain is labelled as 'rough estimate') are presented as assumptions or estimates, not as established fact without evidence.

Gemini 3.8 Flash · API

The output states as fact that removing goal-setting 'stripped away the user’s commitment and personal investment' and caused the drop, without labelling this as a hypothesis.

Gemini 3.5 Flash-Lite · Gemini

The output presents causal interpretations (e.g., onboarding changes broke retention, low-intent users) as established fact without labelling them as hypotheses.

All got right 9

Uses the supplied evidence correctlyRightRightRight
Haiku 5.5 · API

All listed statements about the current situation are taken directly from the supplied context or follow by straightforward arithmetic; no invented facts.

Gemini 3.8 Flash · API

All factual claims about the current situation are directly from the supplied context or derived by correct arithmetic.

Gemini 3.5 Flash-Lite · Gemini

All factual claims about the current situation are taken directly from the supplied context or derived correctly by arithmetic.

Respects explicit constraintsRightRightRight
Haiku 5.5 · API

Diagnosis within 800 words, addressed to Leo as a memo, respects all requested form and length constraints.

Gemini 3.8 Flash · API

The output is a diagnosis memo to Leo under 800 words, respecting the requested form, reader, and length.

Gemini 3.5 Flash-Lite · Gemini

The output is a memo to Leo, well under 800 words, and respects the requested form and reader.

Produces the required deliverableRightRightRight
Haiku 5.5 · API

A complete diagnosis memo to Leo exists, under 800 words, with evidence and actionable recommendations; a PM could act on it with minor edits.

Gemini 3.8 Flash · API

The diagnosis is complete, in memo form, under 800 words, and a PM could act on it with light edits.

Gemini 3.5 Flash-Lite · Gemini

The diagnosis is complete, in memo form, within the word limit, and usable by Leo with light edits.

Finds the onboarding changeRightRightRight
Haiku 5.5 · API

Links new-user retention drop to week-5 removal of goal-setting using cohort table (24-25% week4 retention vs 33-34% before), noting higher sign-up completion hides the loss.

Gemini 3.8 Flash · API

The diagnosis links the retention drop to the Week 5 goal-setting removal using cohort numbers and notes the sign-up completion increase hides it.

Gemini 3.5 Flash-Lite · Gemini

It links the retention drop to the Week 5 sign-up change using cohort numbers (Week 4 retention ~25% vs ~34%) and notes higher completion hides it.

Sees the win-back masking churnRightRightRight
Haiku 5.5 · API

Uses the jump in returning users from week 6 and the 12% vs 41% two-week retention to show the campaign inflates returning users and masks churn.

Gemini 3.8 Flash · API

The diagnosis uses the returning-user jump and the 12% vs 41% two-week retention to show the win-back campaign masks rising churn.

Gemini 3.5 Flash-Lite · Gemini

It identifies the jump in returning users from Week 6 and uses the 12% vs 41% retention to show the win-back campaign masks churn.

Understands before it justifiesRightRightRight
Haiku 5.5 · API

Tests the summer hypothesis against cohort data, finds it inconsistent, and arrives at own onboarding-cause diagnosis from the evidence.

Gemini 3.8 Flash · API

The output tests the summer explanation against the data, finds a step-change rather than gradual decay, and reaches its own diagnosis.

Gemini 3.5 Flash-Lite · Gemini

It tests the summer explanation against the data (stable older cohorts, drop tied to cohort 5) and reaches its own product-led diagnosis.

Gets the base of every number rightRightRightRight
Haiku 5.5 · API

All percentages and differences (churn as share of prior-week actives, funnel math using sign-up completion and cohort retention) are computed with correct denominators and steps.

Gemini 3.8 Flash · API

All derived percentages and differences (44% churn increase, 33% relative retention drop, 1,800 new users) are computed correctly from the supplied data.

Gemini 3.5 Flash-Lite · Gemini

All percentages and differences are computed correctly from the supplied data, with no ambiguous bases.

Splits growth into new, lost and returningRightRightRight
Haiku 5.5 · API

Separates WAU into new, returning, retained, and churned, and shows how the movements net out to flat total.

Gemini 3.8 Flash · API

The diagnosis separates new, retained, churned, and returning users to show which flows changed.

Gemini 3.5 Flash-Lite · Gemini

The diagnosis separates new, retained, churned, and returning users to show which flows changed.

Reads the shape of the curvesRightRightRight
Haiku 5.5 · API

Describes cohort retention curves (early cohorts high and stable, post-week5 cohorts drop and stay low; win-back returners have low two-week retention), drawing the right conclusion about habit formation and low-quality returners.

Gemini 3.8 Flash · API

The output describes the cohort retention curves (flattening at 30% vs dropping to 20%) and correctly concludes a structural break, not seasonality.

Gemini 3.5 Flash-Lite · Gemini

It describes the retention curve change (Week 1 up, Week 4 down) and the low persistence of win-back returns, drawing correct conclusions.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT96.2100.02None
2GPT-6 LunawithAPI96.2100.02None
3GPT-6.1 SolwithAPI94.1100.02None
4Haiku 5.5withAPI92.0100.02None
5Opus 5.5withClaude82.1100.02None
6Sonnet 5.5withAPI90.189.32None
7Gemini 3.5 Flash-LitewithGemini76.474.22None
8Gemini 3.8 FlashwithAPI72.370.92None

What you get for the cost

GPT-6 Luna · API matches GPT-6 Astra · ChatGPT at about a fiftieth of the cost.

Task score against relative cost for each setup. The table below has the same figures.607080901001×3×10×30×100×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Astra · ChatGPT: task score 98, 79× the cheapestGPT-6 Luna · API: task score 98, the cheapestGPT-6.1 Sol · API: task score 97, 15× the cheapestHaiku 5.5 · API: task score 96, 3.6× the cheapestOpus 5.5 · Claude: task score 92, 77× the cheapestSonnet 5.5 · API: task score 88, 50× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 74, 3.0× the cheapestGemini 3.8 Flash · API: task score 71, 13× the cheapestGPT-6 Astra · ChatGPTGPT-6 Luna · APIGPT-6.1 Sol · APIHaiku 5.5 · APIOpus 5.5 · ClaudeSonnet 5.5 · APIGemini 3.5 Flash-Lite · GeminiGemini 3.8 Flash · API
Best value
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API981.0×
Gemini 3.5 Flash-Lite · Gemini743.0×
Haiku 5.5 · API963.6×
Gemini 3.8 Flash · API7113×
GPT-6.1 Sol · API9715×
Sonnet 5.5 · API8850×
Opus 5.5 · Claude9277×
GPT-6 Astra · ChatGPT9879×

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

About the task

The PM job

Working out why customers leave, and what to fix first.

Why it matters

Teams pick a story (the season, a competitor) and then pull data to justify it. Half the time the 'drop' is a definition change, and the real leak is somewhere else.

What good looks like

  • Starts from what the data shows, not a story
  • Splits growth into new, lost and returning users
  • Separates measurement and seasonality from churn
  • Sizes the real problem and fixes new-user retention first

Deliberately not measured

  • Dashboard design
Capability tested

Retention diagnosis

The failure we’re looking for

Confirming the story everyone already believes

Grading

Decision model and LLM judge, calibrated against a blind PM review