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.

Needs more than a quick edit9 of 13 checks passed

The memo contains factual errors (mid-market exit reasons and education churn drivers) that would require re-checking the evidence before use. A PM would need to correct those claims before acting on it.

Output

MEMORANDUM

TO: Maya Lind, CEO FROM: Staff Product Manager DATE: October 1, 2026 SUBJECT: Retention Analysis & Strategic Realignment

*

Maya,

You asked me to investigate your hypothesis that retention collapsed in March due to Notion AI, and to find where else we are bleeding customers.

The short answer: Your diagnosis of a retention collapse in March is incorrect, and Notion AI is not the primary culprit. What happened on March 2nd was a definition change in our analytics dashboard.

However, we do have a severe, bleeding problem in our Mid-market segment that started quietly in May. It has nothing to do with AI competitors and everything to do with a self-inflicted wound in our pricing and seat-management mechanics.

Below is the breakdown of what is and isn't happening, the true revenue impact, and where we should actually deploy our engineering squads next quarter.

---

1. What Is NOT Happening (The March "Collapse")

The Claim: Active accounts dropped sharply in March across all segments, indicating a market-wide retention failure. * Education active accounts: 531 $\rightarrow$ 428 * SMB active accounts: 1,785 $\rightarrow$ 1,434 * Mid-market active accounts: 381 $\rightarrow$ 314

The Reality: This was an artifact of measurement, not a behavioral collapse.

On March 2nd, engineering updated the dashboard metric for an "active account" from “anyone logged in within 30 days” to “anyone created or edited a page within 30 days.” Passive logins (users opening the app and reading without contributing) were stripped out overnight.

The Evidence: * MRR Movements tell the truth: If active accounts had "collapsed by a fifth" in March, revenue would have followed. Yet March MRR grew healthily across the board (SMB ending MRR grew from \$220k to \$223k; Mid-market from \$288k to \$298k). Churning MRR in March (SMB: \$7,728; Mid-market: \$2,779) was virtually identical to February. * The Notion AI Red Herring: Notion launched its AI assistant on August 3rd. March predates this launch by five months. While exit surveys show a tiny trickle of churn to Notion (4 SMB, 1 Mid-market accounts total across the year), it is not a systemic threat driving our numbers down.

---

2. What IS Happening (The Mid-Market Bleed)

While March was a reporting illusion, May 15th brought a real, severe revenue contraction driven by two concurrent product updates: 1. The Mid-market plan price increased from \$12 to \$15 per seat. 2. A "Seat Audit" tool launched for admins, surfacing seats with no activity in 60 days with a one-click "remove seat" button.

The Evidence: * Massive Contraction Spikes: Look at Mid-market contraction MRR (voluntary license shedding/downgrades): * April: \$2,501 * May: \$10,239 (a 309% jump the month of the price hike/audit tool) * June: \$22,351 * July: \$16,514 * August: \$12,486 * September: \$13,212 * Exit Surveys Confirm Price Sensitivity: Every single Mid-market cancellation in the exit log cites “Too expensive per seat after the price change” (combined with tool consolidation). * The Education Annual Cliff: Education accounts show a predictable annual churn spike in August (churned MRR jumped from \$935 in July to \$3,925 in August), entirely driven by the end of the academic cycle (“End of academic year, will review”), not competitive displacement.

---

3. The Size of the Real Problem in Revenue

Our core business is stable, but we are actively amputating our Mid-market expansion engine.

  • The Mid-Market Leak: Prior to May, Mid-market expansion MRR outpaced contraction by roughly \$6,000/month (e.g., Feb expansion was \$8,384 vs contraction of \$2,324).
  • The Reversal: In June, following the price hike and seat audit tool, Mid-market contraction (\$22,351) completely crushed expansion (\$8,565). We hemorrhaged net expansion capability in our highest-value tier.
  • The Net Result: While Mid-market paying accounts continue to creep up slowly (435 in April $\rightarrow$ 460 in September), the revenue per account is being aggressively optimized downward by our own customers using the admin seat audit tool to purge dormant seats they were previously paying for.

---

4. Strategic Recommendations for Next Quarter

Maya, your instinct to mobilize two squads around AI is understandable given market noise, but building an AI assistant will not solve a pricing-induced contraction problem or an academic seasonality cycle.

Instead, I recommend the following adjustment to next quarter’s roadmap:

1. Scrap the Notion AI panic response. Do not put two squads on an AI assistant. Notion’s bundle play works for them because of their architecture, but copying them reactively without product-market validation in our core workspace loops is a trap. 2. Deploy Squad 1 to Mid-Market Value Realization & Packaging. * The \$12 $\rightarrow$ \$15 price jump triggered a violent contraction because enterprise buyers immediately audited and purged unproven seats. * Instead of raw seat-pricing friction, we need tiered packaging (e.g., introducing advanced security, admin controls, or compliance features at the \$15 tier) rather than a naked price hike on the base tier that incentivizes seat-cutting. 3. Deploy Squad 2 to Education Onboarding & Workspace Activation. * Education is entering its post-August renewal trough. Squad 2 should focus on automated workspace templates and cross-semester continuity features that reduce the friction of the annual academic reset, preventing schools from hitting the "pause" button every summer.

We do not have a top-of-funnel retention collapse. We have a self-inflicted Mid-market pricing shock. Let’s fix the bucket before we build new ways to pour water into it.

Claims the judge couldn’t find in the brief

  • Every single Mid-market cancellation in the exit log cites 'Too expensive per seat after the price change' (combined with tool consolidation).
  • Education accounts show a predictable annual churn spike in August (churned MRR jumped from $935 in July to $3,925 in August), entirely driven by the end of the academic cycle.
  • The revenue per account is being aggressively optimized downward by our own customers using the admin seat audit tool to purge dormant seats they were previously paying for.

Grades and run details

Decision model 65 · LLM judge 71
Decision model checks
  • failUses the supplied evidence correctly27%
  • passAddresses the actual decision43%
  • passRespects explicit constraints37%
  • failIdentifies material uncertainty85%
  • failAvoids unsupported claims33%
  • passProduces the required deliverable45%
  • partialSeparates what isn't churn25%
  • passFinds and sizes the real loss93%
  • passAnswers the AI plan with evidence99%
  • passUnderstands before it justifies99%
  • failGets the base of every number right33%
  • passSplits growth into new, lost and returning86%
  • passReads the shape of the curves61%
Run
Run
#1
Time to output
7 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 4

Uses the supplied evidence correctlyWrongMixed
Gemini 3.5 Flash-Lite · Gemini

Two claims are factually wrong: 'Every single Mid-market cancellation' cites price (only 11 of 15 do), and education churn is 'entirely driven by the end of the academic cycle' (budget cuts and Google Workspace switches also appear).

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.

Identifies material uncertaintyWrongRight
Gemini 3.5 Flash-Lite · Gemini

The memo does not name specific unknowns that could change the decision, nor does it say what would change the call or how to resolve them.

Sonnet 5.5 · API

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

Avoids unsupported claimsWrongRight
Gemini 3.5 Flash-Lite · Gemini

It presents 'Every single Mid-market cancellation' and 'entirely driven by the end of the academic cycle' as established facts, and states the seat audit tool is the cause of contraction without labeling it as a hypothesis.

Sonnet 5.5 · API

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

Gets the base of every number rightMixedWrong
Gemini 3.5 Flash-Lite · Gemini

Derived figures like the 309% jump and the $6,000/month expansion-contraction gap are computed correctly from the supplied data.

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 9

Addresses the actual decisionRightRight
Gemini 3.5 Flash-Lite · Gemini

The memo commits early and unambiguously that the March collapse is incorrect and Notion AI is not the culprit, and recommends specific squad deployments instead of the AI plan.

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
Gemini 3.5 Flash-Lite · Gemini

The output is a memo addressed to the CEO, under 1,200 words, and respects the requested form and length.

Sonnet 5.5 · API

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

Produces the required deliverableRightRight
Gemini 3.5 Flash-Lite · Gemini

The memo is complete, in the correct form for the CEO, within the word limit, and actionable with clear recommendations.

Sonnet 5.5 · API

Memo is complete, actionable, and within length.

Separates what isn't churnRightRight
Gemini 3.5 Flash-Lite · Gemini

It clearly links the March drop to the definition change on 2 March and shows education's summer dip recovering in September, using the dashboard change log and active account data.

Sonnet 5.5 · API

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

Finds and sizes the real lossRightRight
Gemini 3.5 Flash-Lite · Gemini

It identifies the mid-market contraction spike after 15 May, links it to the price rise and seat audit, and sizes it with monthly contraction MRR figures.

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
Gemini 3.5 Flash-Lite · Gemini

It uses the exit data (few Notion switchers) and the August launch timeline to argue against the AI assistant as a retention fix, and advises against two squads on it.

Sonnet 5.5 · API

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

Understands before it justifiesRightRight
Gemini 3.5 Flash-Lite · Gemini

The memo tests the CEO's hypothesis against the data, rejects the March collapse narrative, and reaches its own diagnosis from the evidence.

Sonnet 5.5 · API

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

Splits growth into new, lost and returningRightRight
Gemini 3.5 Flash-Lite · Gemini

It uses MRR components (new, expansion, contraction, churn) to separate contraction from churn and show which flow changed.

Sonnet 5.5 · API

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

Reads the shape of the curvesRightRight
Gemini 3.5 Flash-Lite · Gemini

It reads the education dip and September recovery as seasonal, and the mid-market contraction as a spike after May, drawing correct conclusions from the curve shapes.

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