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 edit12 of 13 checks passed

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

Output

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

Bottom line

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

1. What isn't happening

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

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

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

2. What is happening

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

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

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

3. Size of the problem

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 92 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly25%
  • passAddresses the actual decision94%
  • passRespects explicit constraints58%
  • passIdentifies material uncertainty97%
  • passAvoids unsupported claims30%
  • passProduces the required deliverable86%
  • passSeparates what isn't churn30%
  • passFinds and sizes the real loss100%
  • passAnswers the AI plan with evidence100%
  • passUnderstands before it justifies100%
  • failGets the base of every number right18%
  • passSplits growth into new, lost and returning94%
  • passReads the shape of the curves100%
Run
Run
#1
API response time
65 s
Submitted
8 Oct 2026

Needs more than a quick edit8 of 13 checks passed

The memo contains a miscalculation of SMB churn average and an unsupported claim about Notion revenue, requiring re-checking before use.

Output

MEMORANDUM

TO: Maya Lind, CEO FROM: Staff Product Manager DATE: October 2, 2026 SUBJECT: Diagnostic: 2026 Retention Dynamics, Revenue Bleed, and Product Priorities

---

Executive Summary

Our customer and revenue retention did not collapse in March, Education is not in freefall, and Notion AI is not causing our churn. Allocating two squads to an AI assistant next quarter misdiagnoses our core retention drivers and will not stem our real revenue losses.

1. The March "Collapse" is a Telemetry Artifact: On March 2, we updated the definition of an “active account” on the main dashboard from any login within 30 days to creating/editing a page within 30 days. Paying accounts and monthly recurring revenue (MRR) actually grew in March. 2. Education is Cyclical, Not Collapsing: The summer dip in active accounts was normal academic seasonality. Active accounts rebounded by +129% in September as schools resumed, and paying Education accounts reached an all-time high (669). 3. Notion AI is Not Displacing Us: Notion launched AI in August—five months after the perceived March drop. Only 6% of churned accounts cited Notion in exit surveys. 4. Where We Are Actually Bleeding ($75k+ MRR Impact): * Mid-Market Contraction: The May 15 price hike ($12 to $15/seat) combined with the newly launched "Seat Audit" tool prompted enterprise admins to aggressively purge inactive seats. Contraction MRR quintupled from ~$2.4k/month to an average of ~$15k/month, erasing $62.8k in cumulative MRR since May and flatlining Mid-Market growth. * SMB Onboarding Failure: 39% of SMB churn is driven by failed team adoption, draining ~$7.5k in MRR every month.

---

Part 1: What Is NOT Happening (The False Alarms)

#### 1. Retention Did Not Collapse in March * What the dashboard showed: Total active accounts dropped 19.3% from February (2,697) to March (2,176). * The reality: On March 2, the data team modified the “active account” definition from passive logins to active page creation/editing. * The evidence: Paying accounts grew across every segment in March (total paying accounts rose from 3,208 to 3,246). Total MRR grew from $573.3k to $588.5k (+2.6% MoM). Real account and revenue retention remained entirely stable.

#### 2. Education is Following Predictable Academic Seasonality * What the dashboard showed: Active accounts fell from 443 in May to 201 in July (-55%). * The reality: K-12 and university campuses close for summer break. As classes resumed in September, active Education accounts surged back to 461. * The evidence: Paying accounts grew every single month without interruption (from 610 in January to 669 in September). The one-time churn spike in August ($3,925 churned MRR vs. ~$900 monthly baseline) is the annual renewal window for school districts, where 60% of exiting accounts cited "end of academic year review" and 30% cited "budget cuts"—not product dissatisfaction.

#### 3. Notion AI is a Phantom Culprit * Notion launched its AI assistant on August 3, 2026. It cannot explain a trend that supposedly started in March. * Across all 79 exit survey responses in 2026, only 5 customers (6.3%) cited switching to Notion (4 SMBs, 1 Mid-Market, 0 Education). In contrast, 0 Education accounts left for Notion (2 left for Google Workspace).

---

Part 2: What IS Happening (The Real Revenue Leaks)

``` Mid-Market Contraction MRR (2026) Jan - Apr Baseline: ~$2,400/mo May: ██████████ $10,239 <-- May 15: Price hike to $15 + Seat Audit tool Jun: ██████████████████████ $22,351 Jul: ████████████████ $16,514 Aug: ████████████ $12,486 Sep: █████████████ $13,212 ```

#### Leak #1: Mid-Market Seat Slashing (The Primary Financial Threat) On May 15, we paired a 25% price increase ($12 to $15/seat) with an admin "Seat Audit" tool that surfaces inactive 60-day seats with a one-click removal button.

  • The Reaction: While May expansion MRR spiked to $31.6k as existing seats re-billed at $15, customers responded by aggressively cutting seats to offset the price hike.
  • The Impact:
  • Mid-Market contraction MRR jumped from a baseline of $2,397/month (Jan–Apr average) to $22,351 in June.
  • Contraction has remained elevated through September ($13,212/month).
  • In total, Mid-Market experienced $74,802 in contraction MRR between May and September—$62,817 above historical baseline.
  • Mid-Market ending MRR peaked in May at $334.9k and has since declined to $321.9k.
  • Exit survey validation: 11 out of 15 cancelled Mid-Market accounts (73%) explicitly cited: "Too expensive per seat after the price change."

#### Leak #2: SMB Team Adoption Failure * SMB churn is relentless, averaging $7,466 in churned MRR each month (~$90k annual run-rate). * Exit survey validation: The #1 reason for SMB churn was "Didn't get the team to adopt it" (17/44 responses, or 38.6%), outranking company failure/downsizing (31.8%). * The March metric change exposed that ~35% of paying SMB accounts lack active editors. When a tool is not operationalized across the team, small businesses churn at the first sign of cost scrutiny.

---

Part 3: Sizing the Revenue Impact

Leak SourceMechanismMonthly Revenue DragCumulative 2026 Impact
:---:---:---:---
Mid-Market Excess ContractionPrice increase shock + 1-click seat audit tool~$10,800/mo above baseline$62,817 MRR lost since May (~$754k ARR run-rate)
SMB Adoption ChurnPoor onboarding / team activation failure~$7,500/mo churned MRR$67,192 MRR lost YTD
Education August Lapsed RenewalsUnmanaged annual renewal cliff & budget cutsOne-time ~$3,000 above baseline$3,000 MRR recurring annual hit

Note on Notion AI: Revenue lost to Notion across all exit survey respondents totals less than $1,200 MRR. Chasing an AI assistant does not address our $750k+ ARR seat-contraction problem.

---

Part 4: Recommendations & Squad Allocation

Rather than committing two squads to build an AI assistant to fight a competitor who is not taking our customers, we should realign our product and go-to-market efforts to protect existing contract values and solve team activation.

#### 1. Reallocate Squads to High-ROI Retention Drivers

  • Squad 1: Mid-Market Packaging & Admin Value (Immediate Priority)
  • Rethink Seat Pruning: Modify the Seat Audit tool. Instead of a direct "remove seat" button that prompts immediate seat downgrades, guide admins to reassign unused licenses to other departments or switch inactive users to free "Viewer/Commenter" roles without reducing tier commitments.
  • Volume & Tier Structuring: Implement tiered seat thresholds (e.g., bundles of 25/50/100 seats) so mid-market accounts are not incentivized to trim single individual seats every billing cycle.
  • Success Target: Reduce Mid-Market contraction from ~$13k/month back toward the $5k/month range, preserving ~$95k in ARR each quarter.
  • Squad 2: SMB Activation & Collaborative Onboarding
  • Target Adoption Failure: Build guided team-onboarding templates, Slack/Google Workspace identity integrations, and automated "first shared document" workflows.
  • Early Warning Telemetry: Flag accounts where <30% of paid seats have edited a doc within 14 days of purchase, triggering targeted in-app adoption guides or CS intervention.
  • Success Target: Decrease SMB "failure to adopt" churn by 25%, saving ~$22k in annual churned MRR.

#### 2. Commercial & Operational Interventions

  • Customer Success Pricing Outreach (Mid-Market): Have Account Managers proactively reach out to Mid-Market accounts experiencing contraction with annual lock-in discounts (e.g., offer $12/seat on annual prepaid agreements instead of $15/seat monthly).
  • Education Annual Renewal Playbook: Education accounts cannot be left unattended until August. Account Managers must run an "Academic Review" campaign in April/May to lock in fall renewals before university administrative teams depart for the summer.
  • Dashboard Governance: Establish data governance protocols. Major metric definition shifts must be annotated on executive dashboards to prevent false alarms and reactive product strategy shifts.

Claims the judge couldn’t find in the brief

  • The May 15 price hike combined with the newly launched 'Seat Audit' tool prompted enterprise admins to aggressively purge inactive seats.
  • SMB churn is relentless, averaging $7,466 in churned MRR each month.
  • Revenue lost to Notion across all exit survey respondents totals less than $1,200 MRR.

Grades and run details

Decision model 65 · LLM judge 57
Decision model checks
  • failUses the supplied evidence correctly62%
  • passAddresses the actual decision61%
  • partialRespects explicit constraints22%
  • failIdentifies material uncertainty89%
  • failAvoids unsupported claims30%
  • passProduces the required deliverable75%
  • passSeparates what isn't churn99%
  • passFinds and sizes the real loss98%
  • passAnswers the AI plan with evidence100%
  • passUnderstands before it justifies100%
  • failGets the base of every number right59%
  • passSplits growth into new, lost and returning86%
  • passReads the shape of the curves99%
Run
Run
#1
API response time
43 s
Submitted
6 Oct 2026

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

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 correctlyRightWrongMixed
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 3.8 Flash · API

Claims 7, 12, and 15 are not fully supported: the causal link for seat audit is interpretation, SMB churn average is miscalculated, and Notion revenue is invented.

Opus 5.5 · Claude

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

Addresses the actual decisionRightMixedRight
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 3.8 Flash · API

The memo commits to a clear answer but does not state what result or condition would change the recommendation.

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).

Identifies material uncertaintyRightWrongRight
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 3.8 Flash · API

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

Opus 5.5 · Claude

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

Avoids unsupported claimsRightWrongRight
Haiku 5.5 · API

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

Gemini 3.8 Flash · API

It presents the causal claim about the seat audit prompting seat purges as fact and invents a Notion revenue figure without support.

Opus 5.5 · Claude

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

Gets the base of every number rightMixedWrongMixed
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 3.8 Flash · API

The SMB churn average of $7,466 is miscalculated; the correct average from the data is $7,444.

Opus 5.5 · Claude

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

All got right 8

Respects explicit constraintsRightRightRight
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 3.8 Flash · API

The memo is within 1,200 words, addressed to the CEO, and in the requested form.

Opus 5.5 · Claude

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

Produces the required deliverableRightRightRight
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 3.8 Flash · API

The memo is complete, in the right form, and the CEO could act on it with light edits.

Opus 5.5 · Claude

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

Separates what isn't churnRightRightRight
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 3.8 Flash · API

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

Opus 5.5 · Claude

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

Finds and sizes the real lossRightRightRight
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 3.8 Flash · API

It identifies the mid-market contraction jump after May 15, links it to the price rise and seat audit, and sizes it at $62,817 above baseline.

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.

Answers the AI plan with evidenceRightRightRight
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 3.8 Flash · API

It uses exit data and the August Notion launch to show few competitor exits and advises against the AI assistant as a retention fix.

Opus 5.5 · Claude

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

Understands before it justifiesRightRightRight
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 3.8 Flash · API

The diagnosis tests the CEO's explanation against the data and reaches its own conclusions from the evidence.

Opus 5.5 · Claude

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

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

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

Gemini 3.8 Flash · API

It separates new, expansion, contraction, and churn MRR to show which flow changed.

Opus 5.5 · Claude

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

Reads the shape of the curvesRightRightRight
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 3.8 Flash · API

It describes the education dip as seasonal and recovering, and the mid-market contraction spike.

Opus 5.5 · Claude

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

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