Humanity’s Last PRD
Latest drop: Gemini 4 Argon · 13 Oct
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 18 graded outputs by 9 models. 78% were usable with at most a quick edit.

Reliably right

  1. Respects explicit constraints100% pass
    The output is a diagnosis under 800 words, addressed to Leo, and proposes actions that would enforce the constraint of holding acquisition spend.
    GPT-6.1 Sol · API · Flat, while sign-ups grow
  2. Produces the required deliverable100% pass
    Provides a complete diagnosis with evidence and recommendations, in memo form for Leo, within 800 words, and is actionable as-is.
    GPT-6.1 Sol · API · Flat, while sign-ups grow
  3. Understands before it justifies100% pass
    Tests the summer explanation against the data, finds it insufficient, and reaches its own diagnosis from the cohort and accounting evidence.
    GPT-6.1 Sol · API · Flat, while sign-ups grow

Where it slips

  1. Uses the supplied evidence correctly67% 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 claims72% pass
    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 3.8 Flash · API · Flat, while sign-ups grow
  3. Gets the base of every number right75% 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

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.

    Bangaly KabaLenny’s Podcast

  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.

    Naomi GleitLenny’s Podcast

  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.

    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 · v1.1

The outputs

9 setups answered this brief.

GPT-6 AstraChatGPT100Usable with a quick editHaiku 5.5API100Usable with a quick editOpus 5.5Claude96Usable with a quick editGPT-6 LunaAPI96Usable with a quick editGPT-6.1 SolAPI93Usable with a quick editSonnet 5.5API79Needs more workGemini 3.5 Flash-LiteGemini70Needs more workGemini 4 ArgonAPI61Needs more workGemini 3.8 FlashAPI59Needs more work
Gemini 4 ArgonAPI

Needs more than a quick edit8 of 13 checks passed

The memo contains a couple of minor arithmetic mistakes (SMB active ratio 65.4% vs. 65.5%, education churn baseline ~$906) that would require a double-check; otherwise it is well-structured and actionable.

Output

MEMORANDUM

TO: Maya Lind, CEO FROM: Staff Product Manager DATE: October 2026 SUBJECT: Retention Diagnosis, Revenue Impact, and Q4 Squad Allocation

Executive Summary

Retention did not collapse in March, Education is not in freefall, and Notion AI is not driving customer losses.

The apparent ~20% drop in active accounts in March was a reporting artifact caused by a stricter analytics definition launched on March 2. Furthermore, the summer dip in Education usage is normal academic seasonality, which fully rebounded in September. We should not allocate two squads to an AI assistant next quarter.

However, we are bleeding significant revenue in two other areas: 1. Mid-Market Contraction ($62,800/mo in excess contraction since May): On May 15, we simultaneously raised Mid-market prices by 25% ($12 to $15/seat) and launched a "one-click remove seat" audit tool for admins. This prompted admins to aggressively purge read-only seats, wiping out the gains from the price hike and stalling Mid-market growth. 2. SMB Gross Churn (~$7,400/mo in lost MRR): SMB is our largest source of full account cancellations, driven primarily by poor team onboarding and macro business closures.

Instead of building an AI assistant, we should redirect those two squads toward Mid-market pricing/packaging (introducing free "Viewer" seats) and SMB onboarding and activation.

---

1. What Isn’t Happening (Evaluating the Initial Diagnosis)

The March "Collapse" Was a Metric Definition Change * The Evidence: On March 2, we changed the dashboard definition of an "active account" from anyone logging in within 30 days to anyone creating or editing a page within 30 days. Immediately, reported active-to-paying ratios dropped by roughly 20% across all segments simultaneously: * Education: 86.1% (Feb) $\rightarrow$ 68.5% (Mar) * SMB: 82.4% (Feb) $\rightarrow$ 65.4% (Mar) * Mid-market: 89.6% (Feb) $\rightarrow$ 73.0% (Mar) * The Reality: Underlying retention remained completely stable. Paying accounts grew steadily in March (from 3,208 to 3,246 total), and total Churned MRR was flat ($11,258 in Feb vs. $11,403 in Mar). Under the new definition, engagement rates have remained completely stable from March through September (~65% in SMB, ~73% in Mid-market).

Education is Experiencing Normal Seasonality, Not Freefall * The Evidence: Education active accounts dropped to 293 in June and 201 in July, before rebounding to 461 in September (68.9% of paying accounts, matching March's 68.5% rate). Meanwhile, paying Education accounts grew every single month from January (610) to September (669). * The Reality: Schools and universities go on break over the summer. Because most Education contracts are annual and renew in August, we saw a predictable annual churn spike in August ($3,925 vs. a ~$906/mo baseline), with exit surveys citing "End of academic year, will review" (12/20 responses) and "Budget cut" (6/20). By September, Education MRR hit an all-time high of $72,211.

Notion AI is Not Eating Our Customer Base * The Evidence: Notion launched its AI assistant on August 3. However, our total Churned MRR across SMB and Mid-market actually decreased slightly after the launch ($10,386 in July $\rightarrow$ $10,189 in Aug $\rightarrow$ $10,173 in Sep). In our 2026 exit surveys (79 total responses), 0 customers mentioned AI, and only 5 (6.3%) switched to Notion (4 SMB, 1 Mid-market).

---

2. Where We Are Actually Bleeding (and Size of the Problem)

While logo churn is stable, revenue growth has stalled due to two distinct problems in Mid-market and SMB.

#### Problem A: Mid-Market Seat Contraction (The May 15 Self-Inflicted Wound) On May 15, we increased Mid-market pricing from $12 to $15/seat (+25%) and simultaneously launched the Admin Seat Audit page, which flags seats with no activity in 60 days and offers a one-click "remove seat" button.

  • The Dynamics: The price hike generated a one-time $31,585 expansion spike in May (~$23,200 above our ~$8,360/mo baseline). However, because we don't differentiate between "Editors" and "Viewers," the price increase gave admins sticker shock, and the audit tool gave them an immediate mechanism to cut costs by purging passive readers.
  • The Evidence: Mid-market Contraction MRR averaged $2,397/mo from Jan–Apr. Following the May 15 changes, it jumped to $10,239 in May, peaked at $22,351 in June, and remains elevated at $13,212 in September (over 5x the pre-May baseline). In exit surveys, 11 of 15 (73%) Mid-market cancellations cited "Too expensive per seat after the price change."
  • Revenue Impact:
  • From May to September (5 months), total Mid-market contraction was $74,802—representing ~$62,816 in excess contraction above our pre-May baseline.
  • This completely wiped out the ~$23.2k May price-hike expansion, resulting in a net revenue loss of ~$39,600 from the May 15 initiatives.
  • Mid-market MRR is now actively shrinking (falling from $334,910 in May to $321,932 in September), and we are still bleeding ~$10,800/mo in excess contraction ($129,600/yr annualized run-rate).

#### Problem B: SMB Churn & The Activation Gap SMB is our highest-churning segment in both percentage and absolute dollar terms.

  • The Evidence: SMB Churned MRR averages $7,444/mo (~$89,300/yr in lost ARR, or ~3.3% monthly gross MRR churn), accounting for 65% of all company Churned MRR.
  • The Drivers: Out of 44 SMB exit survey responses:
  • 39% (17/44) left because they "Didn't get the team to adopt it."
  • 32% (14/44) left because the "Company closed or downsized" (uncontrollable macro).
  • 20% (9/44) cited "Too expensive."
  • Further, under our new active metric, 35% of paying SMB accounts (~821 accounts in September) did not create or edit a single page in the last 30 days. These dormant accounts are prime candidates for future churn.

---

3. Recommendations: What to Do Next Quarter

Instead of putting two squads on an AI assistant, we should deploy them against the two problems costing us nearly $220,000/yr in combined annualized MRR losses:

#### Squad 1: Mid-Market Packaging, Pricing & Admin Flows Goal: Stop the $10.8k/mo excess contraction bleed by aligning our pricing model with how teams actually use docs and wikis. 1. Introduce Free "Viewer / Commenter" Roles: Wikis and project pages require broad read access across a company, even if only a core group edits them. Charging $15/mo for passive readers forces admins to remove them, which shrinks our footprint inside the account. Make read/comment-only seats free, and charge $15/mo strictly for Editors/Creators. 2. Redesign the Admin Seat Audit Flow: Replace the destructive "one-click remove seat" button with a "Downgrade to Free Viewer" default action. Additionally, ensure the 60-day inactivity check tracks page views rather than just edits, so admins don't accidentally purge executives and cross-functional stakeholders who rely on Tandem to read documentation. 3. Win-Back & Annual Lock-In: Have Customer Success reach out to Mid-market accounts that contracted heavily since May, offering to restore removed employees as free Viewers and offering a $12/Editor rate in exchange for an annual contract commitment.

#### Squad 2: SMB Onboarding, Activation & Health Intervention Goal: Reduce the $7.4k/mo SMB churn rate by solving the #1 controllable churn driver ("Didn't get the team to adopt it") and activating the 35% of paying SMBs that are currently read-only/dormant. 1. Template-Driven Multiplayer Onboarding: New SMB workspaces fail when the admin faces a blank page. Build guided setup flows with pre-built templates (e.g., Company Wiki, Weekly Sprint Tracker) that prompt the admin to invite teammates and assign them specific sections to edit during the first 7 days. 2. Automated Zero-Edit Playbooks: Trigger automated lifecycle nudges and in-app guidance for paying SMB accounts that hit 14 days without creating or editing a page, helping admins migrate existing docs from Google Docs/Notion before renewal. 3. Pause/Downsize Flows: For the 32% of SMBs churning due to downsizing or closures, introduce a self-serve "Pause Subscription" or "Startup Relief" discount tier in the cancellation flow to retain logos through tough macro periods.

Claims the judge couldn’t find in the brief

  • Active-to-paying ratios dropped: Education 86.1% → 68.5%, SMB 82.4% → 65.4%, Mid-market 89.6% → 73.0%.
  • Education contracts are annual and renew in August; saw an annual churn spike in August of $3,925 vs. a ~$906/mo baseline.

Grades and run details

Decision model 62 · LLM judge 64
Decision model checks
  • failUses the supplied evidence correctly31%
  • failAddresses the actual decision89%
  • passRespects explicit constraints43%
  • failIdentifies material uncertainty95%
  • failAvoids unsupported claims22%
  • passProduces the required deliverable86%
  • passSeparates what isn't churn100%
  • passFinds and sizes the real loss98%
  • passAnswers the AI plan with evidence100%
  • passUnderstands before it justifies100%
  • failGets the base of every number right22%
  • passSplits growth into new, lost and returning88%
  • passReads the shape of the curves100%
Run
Run
#1
API response time
22 s
Submitted
13 Oct 2026
Haiku 5.5API

Usable with a quick edit13 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

IssueRevenue impactStatus
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 AINot visible in MRR5 of 79 exits
March "active" drop$0Metric 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

  1. 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.)
  1. 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.
  1. 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.
  1. 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.
  1. 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 100 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly13%
  • passAddresses the actual decision91%
  • passRespects explicit constraints69%
  • passIdentifies material uncertainty97%
  • passAvoids unsupported claims28%
  • passProduces the required deliverable85%
  • passSeparates what isn't churn41%
  • passFinds and sizes the real loss100%
  • passAnswers the AI plan with evidence100%
  • passUnderstands before it justifies100%
  • passGets the base of every number right2%
  • passSplits growth into new, lost and returning92%
  • passReads the shape of the curves100%
Run
Run
#1
API response time
65 s
Submitted
8 Oct 2026
Gemini 3.5 Flash-LiteGemini

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 73 · LLM judge 71
Decision model checks
  • failUses the supplied evidence correctly25%
  • failAddresses the actual decision85%
  • passRespects explicit constraints58%
  • failIdentifies material uncertainty86%
  • partialAvoids unsupported claims38%
  • passProduces the required deliverable46%
  • passSeparates what isn't churn25%
  • passFinds and sizes the real loss94%
  • passAnswers the AI plan with evidence98%
  • passUnderstands before it justifies98%
  • passGets the base of every number right10%
  • passSplits growth into new, lost and returning85%
  • passReads the shape of the curves67%
Run
Run
#1
Time to output
7 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 5

Uses the supplied evidence correctlyMixedRightWrong
Gemini 4 Argon · API

All key facts are taken correctly from the supplied context; minor arithmetic rounding issues do not invent new facts.

Haiku 5.5 · API

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

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).

Addresses the actual decisionWrongRightMixed
Gemini 4 Argon · API

The output commits to a clear call (no AI squads, redirect) but does not state what result or condition would change that recommendation.

Haiku 5.5 · API

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).

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.

Identifies material uncertaintyWrongRightWrong
Gemini 4 Argon · API

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

Haiku 5.5 · API

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.

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.

Avoids unsupported claimsWrongRightWrong
Gemini 4 Argon · API

The causal claim that the seat audit 'prompted admins to aggressively purge read-only seats' is presented as established fact without direct evidence beyond timing.

Haiku 5.5 · API

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

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.

Gets the base of every number rightWrongRightRight
Gemini 4 Argon · API

The SMB active-to-paying ratio for March is given as 65.4% instead of the correct 65.5%; the Education churn baseline is ~$906 not the computed ~$912, making figures slightly off.

Haiku 5.5 · API

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

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.

All got right 8

Respects explicit constraintsRightRightRight
Gemini 4 Argon · API

The memo is well under 1,200 words, addressed to CEO Maya Lind, and covers the required topics.

Haiku 5.5 · API

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

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.

Produces the required deliverableRightRightRight
Gemini 4 Argon · API

The output is a complete, actionable memo that the CEO could use with light edits.

Haiku 5.5 · API

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

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.

Separates what isn't churnRightRightRight
Gemini 4 Argon · API

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

Haiku 5.5 · API

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

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.

Finds and sizes the real lossRightRightRight
Gemini 4 Argon · API

Identifies the mid-market contraction spike after the 15 May price rise and seat audit, and sizes it in MRR with specific numbers.

Haiku 5.5 · API

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.

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.

Answers the AI plan with evidenceRightRightRight
Gemini 4 Argon · API

Uses exit surveys and the August Notion launch timeline to show AI is not behind the losses and advises against the AI squad plan.

Haiku 5.5 · API

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.

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.

Understands before it justifiesRightRightRight
Gemini 4 Argon · API

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

Haiku 5.5 · API

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

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.

Splits growth into new, lost and returningRightRightRight
Gemini 4 Argon · API

Uses new, expansion, contraction and churn MRR to show which flows changed (e.g., expansion spike, contraction jump).

Haiku 5.5 · API

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

Gemini 3.5 Flash-Lite · Gemini

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

Reads the shape of the curvesRightRightRight
Gemini 4 Argon · API

Describes the dip-and-recover shape of education and the stable ratios after the definition change.

Haiku 5.5 · API

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).

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.

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 AstraChatGPT100.0100.02None
2Haiku 5.5API100.0100.02None
3GPT-6 LunaAPI96.2100.02None
4GPT-6.1 SolAPI92.3100.02None
5Opus 5.5Claude85.7100.02None
6Sonnet 5.5API92.389.32None
7Gemini 3.5 Flash-LiteGemini80.374.22None
8Gemini 4 ArgonAPI70.482.12None
9Gemini 3.8 FlashAPI76.470.92None

What you get for the cost

GPT-6 Luna · API scores within 2 points of 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 100, 79× the cheapestHaiku 5.5 · API: task score 100, 3.6× the cheapestGPT-6 Luna · API: task score 98, the cheapestGPT-6.1 Sol · API: task score 96, 15× the cheapestOpus 5.5 · Claude: task score 93, 77× the cheapestSonnet 5.5 · API: task score 89, 50× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 76, 3.0× the cheapestGemini 4 Argon · API: task score 76, 17× the cheapestGemini 3.8 Flash · API: task score 73, 13× the cheapestGPT-6 Astra · ChatGPTHaiku 5.5 · APIGPT-6 Luna · APIGPT-6.1 Sol · APIOpus 5.5 · ClaudeSonnet 5.5 · APIGemini 3.5 Flash-Lite · GeminiGemini 4 Argon · APIGemini 3.8 Flash · API
Best valueNobody beats these on both score and cost

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