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 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.

Usable with a quick edit11 of 13 checks passed

The memo is well-structured, evidence-based, and directly actionable with only minor wording tweaks needed.

Output

To: Maya Lind From: [Staff PM] Re: Retention diagnosis and where we're actually losing revenue

---

Bottom line

Retention did not collapse in March, and education is not in freefall. The March drop comes from a change in how we count active accounts. The education dips follow the school calendar. The data doesn't support Notion AI as the driver either: it launched five months after the March drop and shows up in 6% of exit responses.

We do have a real problem, and it's in mid-market. Since the 15 May price rise and seat audit, mid-market accounts have been removing seats faster than we add revenue. The segment went from growing about $10k MRR a month to shrinking slightly. It is now about $40k MRR (about $475k ARR) below where it was heading, and the excess seat removals haven't stopped.

I recommend we don't put two squads on an AI assistant yet. Put one on mid-market pricing and seat value, and one on SMB adoption.

---

1. What isn't happening

The March "collapse" is a metric redefinition. On 2 March the dashboard changed "active" from logged in within 30 days to created or edited a page within 30 days.

  • Active accounts fell from 2,697 to 2,176 (−19%, your "fifth") in exactly that month.
  • Paying accounts rose every single month, from 3,170 in January to 3,474 in September.
  • The active share dropped evenly in every segment at once, which is the signature of a definition change rather than customer behaviour:
SegmentActive share, FebActive share, MarActive share, Sep
Education86%68%69%
SMB82%65%65%
Mid-market90%73%73%

Since March the active share has been flat. The bar went up and nothing has deteriorated against it.

Education is seasonal, not in freefall. Education active accounts fell to 293 in June and 201 in July, then recovered to 461 in September. That September share (69%) matches March. Paying education accounts grew every month, from 610 to 669. Teachers don't edit pages in July.

August renewals did cost us something. Education churned MRR was $3,925 against a normal ~$900, so about $3k MRR (~4% of the segment) went at renewal. We have no August 2025 figure to compare against, so I can't yet say whether that's worse than usual. Education MRR still ended September at $72.2k, up 18% since January.

Notion AI isn't visible in the data. - It launched on 3 August, five months after the March drop. - "Switched to Notion" accounts for 5 of 79 exit responses (6%): 4 SMB and 1 mid-market. Education has none (2 cited Google Workspace). - Total company MRR grew from $543k in January to $640k in September, up 18%.

Notion is a competitor to watch, but it isn't what is costing us money today.

2. What is happening

Mid-market: price rise plus seat audit, both on 15 May

On 15 May we raised mid-market from $12 to $15 per seat (+25%). On the same day we gave admins a one-click way to remove seats with no activity in 60 days.

MonthExpansionContractionChurnEnding MRR
Jan–Apr avg$8.4k$2.4k$2.7k+$10.3k/mo growth
May$31.6k$10.2k$2.8k$334.9k
Jun$8.6k$22.4k$2.6k$325.4k
Jul$8.8k$16.5k$2.8k$321.6k
Aug$8.7k$12.5k$2.7k$322.0k
Sep$8.7k$13.2k$2.7k$321.9k

Three things stand out:

  • Customers aren't leaving. Churn is flat and paying accounts keep rising. Instead, customers are cutting seats.
  • Excess contraction since May totals ~$63k MRR, measured against the $2.4k baseline. The price rise added only a one-off ~$23k of extra expansion. The net loss is ~$40k MRR. This matches the gap between actual September MRR ($322k) and the pre-May trend (~$361k).
  • It isn't a one-time cleanup. September contraction is still 5.5× baseline. We're losing roughly $11k MRR a month above normal, which adds about $130k ARR of loss each month it continues.

The exit survey points the same way: 11 of 15 mid-market leavers cite "too expensive per seat after the price change."

My read is that the two changes reinforce each other. We made each seat 25% more expensive and, on the same day, handed admins a list of idle seats with a delete button. I can't yet separate the two effects. That's the first thing to measure.

SMB: stable, but a persistent leak we can fix

SMB is steady. MRR is growing about $3.6k a month and churn is flat at about $7.5k a month. Still, that's ~3.1% of SMB MRR lost every month, roughly a third a year, and it's our largest absolute churn.

Of 44 SMB exit responses:

ReasonResponses
Didn't get the team to adopt it17 (39%)
Company closed or downsized14 (32%), not addressable
Too expensive9 (20%)
Switched to Notion4 (9%)

Adoption is the biggest reason we can actually act on. It fits a broader signal: about a third of paying accounts don't create or edit anything in a given month.

Education: winnable lapses

Of 20 education exits, 12 said "end of academic year, will review." These are deferrals, not rejections, and we should win them back before term starts. Another 6 cited budget cuts.

3. Recommendations

1. Squad 1: mid-market pricing and seat value (urgent). - Within two weeks, split contraction by source: seat-audit removals versus other seat reductions. - Interview 10–15 contracting accounts. - Options to test: - Make the audit default to "reassign" or "nudge user" rather than "remove". - Offer annual or volume pricing to soften the per-seat increase. - Introduce a cheaper light or viewer seat. - Proactively offer at-risk accounts a temporary rate. - Target: bring contraction back toward ~$3k a month.

2. Squad 2: SMB activation. - Focus on first-30-day team adoption: invites, templates, a first shared project. - Measure success as the active share in month two and SMB churn.

3. AI assistant: discovery, not two squads. - Run a small spike or customer research. - Track Notion mentions in exit surveys from August onward. - Revisit next quarter if "switched to Notion" or AI-related reasons start rising.

4. Education win-back. - Contact the 12 "will review" accounts now. - Pull August 2025 renewal churn to judge whether this year's ~$3k was unusual.

5. Fix the dashboard. - Restate January–February on the new active definition, or annotate the break, so nobody reads March as a collapse again. - Show education with a year-on-year view.

4. Caveats

  • The exit survey is undated, single-choice and small (79 responses). We can't see whether Notion mentions rose after August. Responses should carry a date going forward.
  • The recorded price uplift (~$23k) is smaller than a full 25% on ~$309k of MRR would imply (~$77k). Finance should confirm how the increase was booked. If some of it was netted into contraction, the mid-market loss could look somewhat different.
  • Education accounts rose in August even as churned MRR quadrupled. The account and MRR files may count things differently, and they need reconciling.

None of these caveats change the conclusion: no March collapse, no education freefall, no evidence yet of a Notion problem, and a real, ongoing mid-market seat loss we caused ourselves and can fix.

Grades and run details

Decision model 81 · LLM judge 100
Decision model checks
  • failUses the supplied evidence correctly9%
  • passAddresses the actual decision93%
  • passRespects explicit constraints43%
  • passIdentifies material uncertainty96%
  • partialAvoids unsupported claims49%
  • passProduces the required deliverable81%
  • passSeparates what isn't churn100%
  • passFinds and sizes the real loss99%
  • passAnswers the AI plan with evidence100%
  • passUnderstands before it justifies100%
  • failGets the base of every number right19%
  • passSplits growth into new, lost and returning98%
  • passReads the shape of the curves100%
Run
Run
#1
Time to output
67 s
Submitted
5 Oct 2026

Needs more than a quick edit11 of 13 checks passed

The SMB adoption churn calculation is incorrect, so a PM would need to redo that analysis before using the memo.

Output

To: Maya Lind From: Staff PM Re: What is happening to retention, and what we should do about it

Bottom line

Retention did not collapse in March, and education is not in freefall. The "fifth" drop in active accounts comes from a metric redefinition. The real leak is in mid-market, where our May price increase and seat audit launched together and revenue has stalled since. I recommend we don't staff two squads on an AI assistant yet. Put one squad on mid-market pricing and seats and one on SMB activation.

1. March was a metric change, not churn

On 2 March the dashboard changed "active account" from "logged in within 30 days" to "created or edited a page within 30 days." Active accounts fell from 2,697 in February to 2,176 in March (−19%), which is the drop you saw. Everything that measures money or contracts says nothing happened:

  • Paying accounts rose in every segment in March (total 3,208 → 3,246).
  • Total MRR rose 2.6% in March ($573k → $588k), and every segment grew.
  • Churned MRR in March was in line with January and February in every segment.

The new definition simply counts fewer accounts as active. The active-to-paying ratio stepped down once and then held: education 86% → 69%, SMB 82% → 65%, mid-market 90% → 73%. The January–February numbers need restating on the new definition. Until then, no one should compare across March.

2. Education is seasonal, with one modest renewal bump

Education "active" fell to 293 in June and 201 in July, then recovered to 296 in August and 461 in September (69% of paying, against 68–70% in March–May). That's a school-year pattern. We have no prior-year data to confirm it, so I'm inferring it from the academic calendar. Education paying accounts and MRR never fell off a cliff.

The one real event is August, the renewal month. Churned MRR was $3,925 against a normal ~$910, so about $3,000 MRR (~$36k ARR) of excess churn. September MRR ($72.2k) is already above July's. Exit reasons are end of academic year (12 of 20), budget cuts (6), and Google Workspace (2). None cite Notion.

One thing to reconcile: education paying accounts rose in August even as churned MRR quadrupled. Either the churn was seat reductions rather than lost accounts, or the account count lags.

3. Notion AI: no evidence yet

Notion launched its assistant on 3 August, five months after the "collapse," so it can't explain March. Since then:

  • SMB and mid-market churned MRR in August and September is flat against the prior months.
  • Only 5 of 79 exit responses (6%) say "Switched to Notion": 4 SMB, 1 mid-market, no education.

Two months of data is early, and the survey is undated, so I'd watch this rather than rule it out. But it doesn't support a two-squad bet today.

4. The real problem: mid-market, since 15 May

Two changes shipped on 15 May. The plan price rose from $12 to $15 per seat (+25%), and admins got a seat-audit page with one-click removal of seats inactive for 60 days.

Mid-marketJan–Apr avgMayJunJulAugSep
Contraction MRR$2.4k$10.2k$22.4k$16.5k$12.5k$13.2k
Net MRR change+$10.3k+$25.8k−$9.5k−$3.8k+$0.5k−$0.1k

May's net figure is inflated by a $31.6k expansion spike, which is the price increase hitting bills. After that, contraction ran 5–9× normal for five straight months. Churned MRR did not move (~$2.7k a month throughout), and mid-market paying accounts kept growing (435 → 460). So we're not losing logos. We're losing seats within accounts that stay. That is my inference from the contraction pattern, and the account-level data should confirm it.

Mid-market grew about $10.3k MRR a month before May. It has been flat since. Against that trend, September MRR should have been ~$360.6k and was $321.9k:

  • Gap: ~$38.6k MRR, or ~$0.46M ARR (about 6% of our $640k MRR).
  • It grows by roughly $10k MRR every month the pattern continues.

The exit survey agrees: 11 of 15 mid-market cancellations cite "too expensive per seat after the price change." The survey only covers cancelled accounts, so it misses the seat removals, which are the larger leak.

I suspect customers were paying for shelfware. The price rise made them look, and the audit tool made trimming a single click. Some of that contraction is a correction we'd have faced eventually. But we shipped both changes on the same day, so we can't separate the two effects today.

Two open questions: (a) the price increase booked only ~$23k of excess expansion, far less than 25% of a ~$309k base, so I want to confirm whether all accounts have been repriced or some are grandfathered; and (b) how much of the contraction is seats with zero activity versus active seats cut for cost.

5. Second leak: SMB activation

SMB loses the most revenue in absolute terms. Churned MRR runs ~$7.4k a month, about 3.2% of segment MRR monthly, or roughly $1.07M ARR a year. It's masked because new business (~$9–10k a month) covers it. Exit reasons (44 responses):

  • Didn't get the team to adopt it: 17 (39%)
  • Company closed or downsized: 14 (32%, not addressable)
  • Too expensive: 9 (20%)
  • Switched to Notion: 4 (9%)

SMB also has the lowest active ratio (65%). Applying the survey mix to churned dollars (a rough assumption from a small sample), adoption failures cost about $34k MRR, or ~$0.4M ARR, a year. That's roughly the same size as the mid-market gap, and it has nothing to do with AI.

Size of the problem

IssueRevenue at stake
March "collapse"$0 (metric artifact)
Education August bump~$36k ARR, largely recovered
Mid-market seats/pricing~$0.46M ARR gap so far, growing ~$120k ARR per month of delay
SMB adoption churn~$0.4M ARR per year
Notion switching (survey)Small, unproven

Recommendations

  1. Fix the metric this week. Restate January–February on the new definition, show active rate (active ÷ paying) alongside the count, and stop reporting the March figure as churn. Owner: Data/Analytics.

2. Put one squad on mid-market pricing and seats. In two weeks, pull account-level data to split contraction into zero-activity seats versus active seats, by account size. Then run a controlled test across renewal cohorts. Options include: - a lower price (such as $13.50), or grandfathering existing accounts for 12 months in exchange for annual commitments; - a seat-band discount for larger accounts; - a "reactivate seat" flow in the audit page.

I'd keep the audit. Hiding unused seats would cost us trust, and the fix is to make the value of each seat clearer. I wouldn't revert the price blindly until we know whether the lost seats were active.

  1. Put one squad on SMB activation. Focus on first-30-day adoption: invite flows, starter templates, and admin nudges when fewer than a set share of seats are active. A target of cutting adoption churn by a third is worth ~$0.13M ARR a year, and we'd see leading indicators in weeks.
  1. Education: no product response. Customer Success should confirm the August cancellations and offer budget-constrained schools a renewal option. Also reconcile the account counts.
  1. Hold the AI assistant decision until November. Add cancellation date, competitor, and free-text fields to the exit survey. If Notion-attributed churn or win/loss data shows a meaningful rise after August, we revisit with real evidence. A small discovery effort is fine, but not two squads.

Claims the judge couldn’t find in the brief

  • Adoption failures cost about $34k MRR, or ~$0.4M ARR, a year (applying survey mix to churned dollars).

Grades and run details

Decision model 88 · LLM judge 79
Decision model checks
  • passUses the supplied evidence correctly13%
  • passAddresses the actual decision99%
  • passRespects explicit constraints42%
  • passIdentifies material uncertainty99%
  • partialAvoids unsupported claims28%
  • passProduces the required deliverable86%
  • passSeparates what isn't churn98%
  • passFinds and sizes the real loss100%
  • passAnswers the AI plan with evidence100%
  • passUnderstands before it justifies100%
  • failGets the base of every number right39%
  • passSplits growth into new, lost and returning99%
  • passReads the shape of the curves100%
Run
Run
#1
API response time
65 s
Submitted
6 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 1

Gets the base of every number rightMixedWrong
Opus 5.5 · Claude

All percentages and ratios (active share, 6%, 39%, 5.5×) are computed correctly with clear denominators.

Sonnet 5.5 · API

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.

All mixed 1

Uses the supplied evidence correctlyMixedMixed
Opus 5.5 · Claude

All facts and figures are taken correctly from the supplied data, with no invented information.

Sonnet 5.5 · API

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.

All got right 11

Addresses the actual decisionRightRight
Opus 5.5 · Claude

Commits to a clear answer (no March collapse, real problem is mid-market contraction, don't put two squads on AI) and states what would change the call (rising Notion mentions).

Sonnet 5.5 · API

Commits to not staffing two AI squads, recommends mid-market and SMB squads, and says what would change the AI decision.

Respects explicit constraintsRightRight
Opus 5.5 · Claude

Memo is under 1,200 words, addressed to Maya Lind, and respects the requested form.

Sonnet 5.5 · API

Memo is within 1,200 words, addressed to the CEO, and respects all explicit constraints.

Identifies material uncertaintyRightRight
Opus 5.5 · Claude

Caveats section names unknowns (exit survey dating, price uplift booking, education account reconciliation) and how to resolve them.

Sonnet 5.5 · API

Identifies unknowns about repricing and seat activity, and says how to resolve them.

Avoids unsupported claimsRightRight
Opus 5.5 · Claude

Interpretations are labelled as such (e.g., 'My read is...'), and confident claims are supported by evidence.

Sonnet 5.5 · API

Hypotheses are labelled, and the SMB adoption figure is presented as a rough assumption, not established fact.

Produces the required deliverableRightRight
Opus 5.5 · Claude

The memo is complete, actionable, and within the word limit for the CEO.

Sonnet 5.5 · API

Memo is complete, actionable, and within length.

Separates what isn't churnRightRight
Opus 5.5 · Claude

Shows March drop is the 2 March definition change and education summer dip recovers in September.

Sonnet 5.5 · API

Shows March drop is definition change and education dip is seasonal with September recovery.

Finds and sizes the real lossRightRight
Opus 5.5 · Claude

Identifies mid-market contraction jump after 15 May, links to price rise and seat audit, and sizes it at ~$40k MRR net loss.

Sonnet 5.5 · API

Identifies mid-market contraction jump after 15 May, links to price rise and seat audit, and sizes gap at ~$38.6k MRR.

Answers the AI plan with evidenceRightRight
Opus 5.5 · Claude

Uses exit data (5/79) and timeline (August launch) to advise against AI assistant as a retention fix.

Sonnet 5.5 · API

Uses exit data and Notion launch timeline to advise against AI assistant as retention fix.

Understands before it justifiesRightRight
Opus 5.5 · Claude

Tests the CEO's collapse diagnosis against the data and reaches its own conclusion from the evidence.

Sonnet 5.5 · API

Tests CEO's diagnosis against data and reaches own diagnosis.

Splits growth into new, lost and returningRightRight
Opus 5.5 · Claude

Splits MRR into new, expansion, contraction, churn, and shows contraction, not churn, is the problem.

Sonnet 5.5 · API

Splits MRR into new, expansion, contraction, churn to identify the leak.

Reads the shape of the curvesRightRight
Opus 5.5 · Claude

Describes education dip as seasonal recovery and mid-market contraction as ongoing, not a one-time cleanup.

Sonnet 5.5 · API

Describes education dip and recovery as seasonal, and mid-market contraction as a step change.

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