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

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
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
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

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
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
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.

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.
Bangaly KabaLenny’s Podcast
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.
Naomi GleitLenny’s Podcast
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.
Product and growth leadersOn Lenny’s Podcast

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 3 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 a Staff PM at Tandem. Our CEO, Maya Lind, believes retention collapsed in March and wants two squads on an AI assistant next quarter (her note is below). She's asked you to confirm the diagnosis and find where else we're losing customers. Write her a memo of no more than 1,200 words: what is and isn't happening to retention, the evidence for each, the size of the real problem in revenue, and what to do about it. The files are attached, as exported.

What the model was given6 items: About Tandem, ceo_note.md, active_accounts_by_month.csv (from the main dashboard), mrr_movements_by_month.csv, exit_survey_2026.csv (accounts that cancelled), product_and_pricing_changes.md

About TandemA shared workspace for teams: docs, wikis and project pages. Sold per seat per month. Three segments: education (schools and universities, mostly annual contracts renewing in August), SMB, and mid-market.

ceo_note.md1 lines · Download

From Maya (CEO): "Retention collapsed in March and hasn't recovered: active accounts are down by a fifth and education is in freefall. Notion AI is eating us. I want two squads on an AI assistant next quarter. Can you confirm the diagnosis and tell me where else we're bleeding?"

active_accounts_by_month.csv (from the main dashboard)month,segment,paying_accounts,active_accounts 2026-01,Education,610,525 2026-01,SMB,2140,1758 2026-01,Mid-market,420,378 2026-02,Education,617,531 2026-02,SMB,2166,1785 2026-02,Mid-market,425,381 2026-03,Education,625,428 2026-03,SMB,2191,1434 2026-03,Mid-market,430,314 2026-04,Education,632,439 2026-04,SMB,2217,1447 2026-04,Mid-market,435,317 2026-05,Education,639,443 2026-05,SMB,2243,1448 2026-05,Mid-market,440,322 2026-06,Education,647,293 2026-06,SMB,2268,1476 2026-06,Mid-market,445,325 2026-07,Education,654,201 2026-07,SMB,2294,1497 2026-07,Mid-market,450,327 2026-08,Education,661,296 2026-08,SMB,2320,1514 2026-08,Mid-market,455,330 2026-09,Education,669,461 2026-09,SMB,2345,1524 2026-09,Mid-market,460,335

mrr_movements_by_month.csv27 rows · Download

month,segment,starting_mrr_usd,new_mrr_usd,expansion_mrr_usd,contraction_mrr_usd,churned_mrr_usd,ending_mrr_usd
2026-01,Education,61000,2237,603,311,874,62655
2026-01,SMB,214000,9047,3152,1616,7154,217429
2026-01,Mid-market,268000,7253,8246,2374,2613,278512
2026-02,Education,62655,2092,584,300,934,64097
2026-02,SMB,217429,9117,3227,1592,7555,220626
2026-02,Mid-market,278512,6797,8384,2324,2769,288600
…

exit_survey_2026.csv (accounts that cancelled)response_id,segment,main_reason X501,Mid-market,Too expensive per seat after the price change X502,Mid-market,Too expensive per seat after the price change X503,Mid-market,Too expensive per seat after the price change X504,Mid-market,Too expensive per seat after the price change X505,Mid-market,Too expensive per seat after the price change X506,Mid-market,Too expensive per seat after the price change X507,Mid-market,Too expensive per seat after the price change X508,Mid-market,Too expensive per seat after the price change X509,Mid-market,Too expensive per seat after the price change X510,Mid-market,Too expensive per seat after the price change X511,Mid-market,Too expensive per seat after the price change X512,Mid-market,Consolidating tools X513,Mid-market,Consolidating tools X514,Mid-market,Consolidating tools X515,Mid-market,Switched to Notion X516,SMB,Company closed or downsized X517,SMB,Company closed or downsized X518,SMB,Company closed or downsized X519,SMB,Company closed or downsized X520,SMB,Company closed or downsized X521,SMB,Company closed or downsized X522,SMB,Company closed or downsized X523,SMB,Company closed or downsized X524,SMB,Company closed or downsized X525,SMB,Company closed or downsized X526,SMB,Company closed or downsized X527,SMB,Company closed or downsized X528,SMB,Company closed or downsized X529,SMB,Company closed or downsized X530,SMB,Too expensive X531,SMB,Too expensive X532,SMB,Too expensive X533,SMB,Too expensive X534,SMB,Too expensive X535,SMB,Too expensive X536,SMB,Too expensive X537,SMB,Too expensive X538,SMB,Too expensive X539,SMB,Switched to Notion X540,SMB,Switched to Notion X541,SMB,Switched to Notion X542,SMB,Switched to Notion X543,SMB,Didn't get the team to adopt it X544,SMB,Didn't get the team to adopt it X545,SMB,Didn't get the team to adopt it X546,SMB,Didn't get the team to adopt it X547,SMB,Didn't get the team to adopt it X548,SMB,Didn't get the team to adopt it X549,SMB,Didn't get the team to adopt it X550,SMB,Didn't get the team to adopt it X551,SMB,Didn't get the team to adopt it X552,SMB,Didn't get the team to adopt it X553,SMB,Didn't get the team to adopt it X554,SMB,Didn't get the team to adopt it X555,SMB,Didn't get the team to adopt it X556,SMB,Didn't get the team to adopt it X557,SMB,Didn't get the team to adopt it X558,SMB,Didn't get the team to adopt it X559,SMB,Didn't get the team to adopt it X560,Education,"End of academic year, will review" X561,Education,"End of academic year, will review" X562,Education,"End of academic year, will review" X563,Education,"End of academic year, will review" X564,Education,"End of academic year, will review" X565,Education,"End of academic year, will review" X566,Education,"End of academic year, will review" X567,Education,"End of academic year, will review" X568,Education,"End of academic year, will review" X569,Education,"End of academic year, will review" X570,Education,"End of academic year, will review" X571,Education,"End of academic year, will review" X572,Education,Budget cut X573,Education,Budget cut X574,Education,Budget cut X575,Education,Budget cut X576,Education,Budget cut X577,Education,Budget cut X578,Education,Switched to Google Workspace X579,Education,Switched to Google Workspace

product_and_pricing_changes.md6 lines · Download

# Product and pricing changes, 2026

- 2 March: Dashboard metric "active account" changed from "anyone logged in within 30 days" to "anyone created or edited a page within 30 days", to measure real use.
- 15 May: Mid-market plan from $12 to $15 per seat per month, at each account's next monthly bill.
- 15 May: Seat audit launched for admins: a page listing seats with no activity in 60 days, with a one-click "remove seat".
- 3 August: Notion launched its AI assistant on every paid plan.

What a strong answer doesThe answer key the graders mark against

Separates what isn't churn from what is. (1) March: active accounts fall about 17 points in every segment at once while paying accounts keep growing, because on 2 March 'active' changed from logging in to editing: a definition change, not a collapse. (2) Education: active accounts dip June to August and recover in September, the summer break; education MRR grows, with a modest churn bump at August renewals. (3) Logo churn is roughly flat throughout, and Notion is the main reason in only 5 of 79 exit responses, and Notion's AI launched in August, months after the 'collapse'. The real loss is mid-market contraction: after the 15 May rise from $12 to $15 per seat, shipped the same day as the seat audit, contraction jumps from about $2,400 a month to $12,000 to $22,000 a month (about $75,000 in May to September against about $9,600 in January to April), and mid-market MRR stops growing (flat at about $322,000 from June after rising about $10,000 a month), while 11 of 15 mid-market exits cite price per seat. A secondary issue is SMB adoption ('didn't get the team to adopt it' is the top SMB reason). Recommends fixing the mid-market seat economics (such as pricing active seats or a seat-band discount, and rethinking how the seat audit is presented), restating the dashboard on a consistent definition, and not putting two squads on an AI assistant as a retention fix without evidence it would help.

Critical failures (cap the score at 40)

Confirms that retention collapsed in March

Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

GPT-6 AstraChatGPT96Usable with a quick edit GPT-6 LunaAPI96Usable with a quick edit GPT-6.1 SolAPI96Usable with a quick edit Haiku 5.5API96Usable with a quick edit Opus 5.5Claude91Usable with a quick edit Sonnet 5.5API80Needs more work Gemini 3.5 Flash-LiteGemini66Needs more work Gemini 3.8 FlashAPI59Needs more work

Haiku 5.5API

Usable with a quick edit12 of 13 checks passed

The memo correctly diagnoses the problems using the available data, gives a clear recommendation, and is structured for immediate use with only light polish.

Output

To: Maya Lind, CEO From: Staff PM Re: Retention diagnosis and where we're losing revenue

Bottom line

The March drop is mostly a metric change, not customers leaving. Paying accounts rose every month. The real leaks are mid-market seat contraction after the May price change and seat audit, a steady SMB churn problem, and an August education renewal spike. Notion AI doesn't show up in the numbers. I don't think two squads on an AI assistant next quarter is the right bet. I'd fund one squad on SMB activation and fix the mid-market problem now.

1. What isn't happening

The March "collapse" is a definition change. On 2 March, "active account" changed from "logged in within 30 days" to "created or edited a page within 30 days." The dashboard shows the step in March and doesn't restate January and February. Paying accounts rose from 3,208 in February to 3,246 in March and reached 3,474 in September. Active accounts as a share of paying accounts were 84% in February. They were 67% in March and have stayed at about 67% since. The series is flat after the change, so it hasn't "failed to recover." It rebased.

Education isn't in freefall. Active education accounts dip in June–August (293, 201, 296) and recover to 461 in September, above March's 428. Paying education accounts grew from 625 to 669, and education MRR grew from $64k to $72k. We only have 2026 data, so I can't confirm the summer dip is seasonal, but the September recovery is clear.

Notion AI isn't visibly eating us. The AI assistant launched on 3 August, five months after the March step, so it can't explain March. Of 79 exit-survey responses, 5 cite Notion (4 SMB, 1 mid-market). Mid-market churned MRR was $2.7k in both August and September, in line with January–July. SMB churned MRR was $7.5k in August and September, also in line. The survey has no dates, so I can't rule out a Notion effect in a given month, but nothing in the MRR series changes after 3 August.

2. What is happening

A. Mid-market: the price change is costing more in seats than it's adding. - On 15 May the plan went from $12 to $15 per seat (+25%). The seat audit launched the same day, with a one-click "remove seat" action. - Logo churn hasn't moved. Churned MRR is $2.6–2.8k a month before and after. Eleven of 15 mid-market exits cite price. - Contraction jumped from about $2.4k a month to $10–22k: $10.2k in May, $22.4k in June, $16.5k in July, $12.5k in August, $13.2k in September. That's about $63k of excess contraction since May. - May expansion rose by about $23k, which is probably the price uplift. That's well short of the roughly $75k a full 25% increase on a ~$309k base would produce. Billing needs to confirm how the increase was applied. - Mid-market MRR peaked at $335k in May and has been flat at about $322k since July. Before the changes it was growing about $10k a month. Against that trend, it's about $37k MRR behind, roughly $0.45M ARR. - The export can't separate seat-audit removals from price-driven downgrades. We need to pull remove-seat events and plan changes separately.

B. SMB: a chronic leak, not a new one. - SMB churned MRR has been flat at $7.1–7.7k a month all year. In September it was 3.1% of starting MRR, or about $90k of ARR a year. - Of 44 SMB exits: 17 cite "didn't get the team to adopt it," 14 say the company closed or downsized, 9 say too expensive, and 4 switched to Notion. - Paying-but-inactive SMB accounts grew from 757 in March to 821 in September. That's the pool adoption work would target. - SMB is still growing: MRR is $246k, up 15% since January, because new MRR of $9.5–9.9k a month exceeds churn and contraction.

C. Education: one renewal cliff in August. - Churned education MRR was $3.9k in August against a typical $0.9k. That's about $3k of excess, roughly $36k ARR, in a single month. - Of 20 education exits: 12 say "end of academic year, will review," 6 cite budget cuts, and 2 switched to Google Workspace. - The 12 "will review" accounts are the live risk. Their decision is still open.

3. Size of the problem

Issue	Revenue impact	Status
Mid-market gap to trend	~$37k MRR (~$0.45M ARR)	Ongoing since May; flat since July
SMB churn	~$7.5k MRR/month (~$90k ARR/year)	Chronic, not rising
Education August renewals	~$3k excess MRR (~$36k ARR)	Renewal-timed; 12 accounts still open
Notion AI	Not visible in MRR	5 of 79 exits
March "active" drop	$0	Metric change

Total MRR is up 18% since January, to $640k (about $7.7M ARR). The mid-market gap is an estimate, based on extrapolating pre-May growth, so treat it as a range rather than a precise figure.

4. What to do

Restate the active-account series before it goes anywhere else. Recompute January and February on the new definition, or show both. Drop the "retention collapsed in March" claim. (Data team, two weeks.)

Don't fund two AI-assistant squads yet. Fund one squad on SMB activation. Adoption is the largest controllable SMB exit reason, and the inactive-paying pool is growing. If the AI assistant can help new teams reach their first shared page, test it inside that squad's scope for six weeks against an activation metric. Fund a second squad only if that test moves activation.

Run a 30-day review of the mid-market price change and seat audit. Split contraction into seat removals and plan downgrades. Check whether the audit is removing seats customers still use. Options include making the audit recommend rather than remove, offering right-sizing at renewal, and protecting accounts that contract. Judge the price change on net MRR, not the headline uplift. Ask billing why the May step was about $23k rather than about $75k.

Work the education accounts now. Contact the 12 "will review" and 6 budget-cut accounts this month with a renewal offer, such as term or seat flexibility. Set up a renewal review about 60 days before each August cohort, so we're not finding out in the renewal window.

Fix the reporting. We need monthly logo churn by segment and renewal cohort, contraction reasons, and an exit survey with dates and a response rate. Right now 79 exit responses can't be matched to MRR movements, and the Notion share can't be measured by month.

Grades and run details

Decision model 92 · LLM judge 100

Decision model checks

passUses the supplied evidence correctly25%
passAddresses the actual decision94%
passRespects explicit constraints58%
passIdentifies material uncertainty97%
passAvoids unsupported claims30%
passProduces the required deliverable86%
passSeparates what isn't churn30%
passFinds and sizes the real loss100%
passAnswers the AI plan with evidence100%
passUnderstands before it justifies100%
failGets the base of every number right18%
passSplits growth into new, lost and returning94%
passReads the shape of the curves100%

Run

Run: #1
API response time: 65 s
Submitted: 8 Oct 2026

Check by check

Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Mixed 1

Gets the base of every number rightMixed

Derived percentages (84%/67% active, 3.1% SMB churn, 18% MRR growth) are computed correctly, and bases are stated or clear from context.

Got right 12

Uses the supplied evidence correctlyRight

All factual claims are drawn directly from the supplied data or derived by correct arithmetic, with no invented facts about the current situation.

Addresses the actual decisionRight

The memo commits early to a clear call (one squad on SMB activation, fix mid-market, not two AI squads) and states what would change it (a successful activation test).

Respects explicit constraintsRight

The memo is a properly formatted note to the CEO, stays well under 1,200 words, and addresses the required topics.

Identifies material uncertaintyRight

Identifies key unknowns (summer seasonality, Notion month effect, billing application, seat-audit vs downgrade split) and says how to resolve them or what condition changes the recommendation.

Avoids unsupported claimsRight

Causal interpretations are hedged ('probably', 'not visibly'), estimates are labelled, and conclusions stick to what the evidence directly supports.

Produces the required deliverableRight

Produces a complete, actionable memo for Maya that diagnoses retention, sizes the revenue impact, and lays out specific next steps.

Separates what isn't churnRight

Clearly links the March drop to the 2 March active-account definition change and shows education's summer dip recovering in September.

Finds and sizes the real lossRight

Identifies the mid-market contraction spike after the 15 May price rise and seat audit, quantifies it in MRR, and contrasts it with flat logo churn.

Answers the AI plan with evidenceRight

Uses exit data (5 of 79 mention Notion) and the August launch date to show Notion AI doesn't explain the March 'collapse', and advises against two AI squads as a retention fix.

Understands before it justifiesRight

Tests the CEO's collapse claim against paying accounts and the definition change before offering explanations, then builds its own diagnosis from the data.

Splits growth into new, lost and returningRight

Breaks MRR changes into new, expansion, contraction and churn across segments to isolate which flow changed.

Reads the shape of the curvesRight

Describes the active-account series as a rebased flat line and the education curve as a dip that recovers, drawing the correct conclusions (definition change, seasonality).

Results

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

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6 AstrawithChatGPT	96.2	100.0	2	None
2	GPT-6 LunawithAPI	96.2	100.0	2	None
3	GPT-6.1 SolwithAPI	94.1	100.0	2	None
4	Haiku 5.5withAPI	92.0	100.0	2	None
5	Opus 5.5withClaude	82.1	100.0	2	None
6	Sonnet 5.5withAPI	90.1	89.3	2	None
7	Gemini 3.5 Flash-LitewithGemini	76.4	74.2	2	None
8	Gemini 3.8 FlashwithAPI	72.3	70.9	2	None

What you get for the cost

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

Best value

Task score and relative cost by setup
Setup	Task score	Relative cost (cheapest = 1)
GPT-6 Luna · API	98	1.0×
Gemini 3.5 Flash-Lite · Gemini	74	3.0×
Haiku 5.5 · API	96	3.6×
Gemini 3.8 Flash · API	71	13×
GPT-6.1 Sol · API	97	15×
Sonnet 5.5 · API	88	50×
Opus 5.5 · Claude	92	77×
GPT-6 Astra · ChatGPT	98	79×

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

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