Tasks / Metrics & Experimentation

Pick a North Star metric

Can the model pick the one metric that measures the value a product creates, and break it into inputs teams can move?

Task type v1.1 · 2 tasksLast changed 5 Oct 2026 · ChangelogDifficulty

What AI gets right here, and what you’ll still have to catch

From 16 graded outputs by 8 models. 75% were usable with at most a quick edit.

Reliably right

  1. Says how it could be gamed100% pass
    Identifies gaming vectors (tapping through, notification overuse, short-term spikes, revenue manipulation, MAU drift) and sets specific guardrails (quality completion, push caps, blocking metrics, cohort retention checks, subscriber churn tracking).
    Sonnet 5.5 · API · Revenue, actives, or something else?
  2. Sees the growth is narrow100% pass
    It shows the split (chains up, independents' filled shifts and fill rate down, more leaving) and tracks independent venues via segment reporting and guardrails.
    GPT-6 Luna · API · Growing, on paper
  3. Checks the North Star can be measured100% pass
    It flags the 64% confirmation gap and proposes instrumenting check-in/out, reconciliation, and auditing to fix it before the board readout.
    GPT-6 Luna · API · Growing, on paper

Where it slips

  1. Identifies material uncertainty55% pass
    It identifies the confirmation gap as an unknown but does not say how resolving it could change the North Star decision.
    GPT-6.1 Sol · API · Growing, on paper
  2. Addresses the actual decision58% pass
    The output commits to a North Star but does not state what result or condition would change that recommendation.
    GPT-6 Luna · API · Growing, on paper
  3. Avoids unsupported claims67% pass
    It presents 'easy to tap through' and 'we don't track today' as facts without support, and does not label them as assumptions.
    Opus 5.5 · Claude · Revenue, actives, or something else?

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. Measures value created, not captured

    Does the chosen North Star measure the value customers get from the product, rather than revenue or a count of people who merely showed up?

    Passes when Picks a metric of value delivered to customers (a core action that predicts retention), and keeps revenue as a separate business measure.

  2. Breaks it into inputs teams can move

    Does the output break the North Star into a few input metrics, each one a team could own and move week to week, and pair it with a separate business measure of value captured?

    Passes when Gives a small tree of input metrics, each with an owner and a link to the North Star, plus the revenue or business measure it's paired with.

  3. Says how it could be gamed

    Does the output pressure-test the North Star for how teams could raise it without delivering more value, and set guardrail metrics against that?

    Passes when Names a realistic way the metric could be gamed in this product and the guardrails that would catch it.

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 4 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 Crewmate. Every team has been steering by its own number, and the exec team wants to pick one North Star at next week's offsite, so the CEO can report it to the board three weeks later. Write the memo they'll read beforehand, in no more than 1,200 words: the North Star you recommend and why, its metric tree with an owner for each input, the business measure it's paired with, how it could be gamed and the guardrails against that, and what each team's current metric becomes. The numbers below are as the data team sent them. Not all of it matters equally.

What the model was given9 items: About Crewmate, What each exec wants, Shifts by quarter (monthly averages), GMV (monthly average), Venues, Workers, Same-day shifts, Completion data, From the COO
About CrewmateA marketplace for hospitality shifts: venues post shifts, and vetted workers pick them up. Crewmate takes 18% of each shift's value. Two kinds of venue: four enterprise hotel and restaurant chains (310 venues between them, on annual contracts) and 2,790 independent venues (bars, restaurants, cafes) who pay as they go.
What each exec wantsCEO: GMV, 'because it's what the board looks at'. Head of Demand: shifts posted. Head of Supply: active workers. COO: fill rate.
Shifts by quarter (monthly averages)Enterprise chains: Q2 posted 21,000, filled 19,500. Q3 posted 34,000, filled 31,600. Independent venues: Q2 posted 56,000, filled 44,200. Q3 posted 58,000, filled 39,400.
GMV (monthly average)Q2 $8.5M, Q3 $9.8M. Enterprise chains were 34% of GMV in Q2 and 46% in Q3. The chains raised hourly rates by 11% for the holiday season from August.
VenuesVenues whose shifts fill 90% of the time or more: 84% still posting six months later. Under 70%: 41%. In Q3, 9% of independent venues stopped posting altogether (5% in Q2). No chain venue stopped.
WorkersRegistrations: 6,100 in Q2, 7,900 in Q3, after Supply introduced a $25 sign-up bonus in July. 2,370 of the Q3 registrants have worked a shift. Workers who work three or more shifts in their first month: 66% still working after three months; everyone else: 12%.
Same-day shiftsShifts posted less than 24 hours before they start fill 58% of the time. They are 31% of independents' posts and 8% of the chains'.
Completion dataVenues confirm in the app that a shift was worked for 64% of filled shifts; the rest are assumed worked unless the venue raises a dispute. Of confirmed shifts, 6% were no-shows or disputed.
From the COO"Since September, Ops pays workers a $15 rescue bonus to pick up same-day shifts that would otherwise go unfilled. 6,200 rescue fills last month, $93,000. Rescue-filled shifts have a 13% no-show rate, against 4% for the rest. It's kept our fill rate from falling further."
What a strong answer doesThe answer key the graders mark against

Works out from the raw numbers what the pack doesn't say: all of the growth is the four chains (filled shifts up from 19,500 to 31,600 a month, plus an 11% rate rise), while independent venues filled fewer shifts (44,200 to 39,400, so their fill rate fell from 79% to 68%) and are leaving faster (9% stopped posting, against 5%); GMV rose mostly on the chains' volume and prices. Picks a North Star of value delivered to both sides, such as shifts worked, but designs it so the decline can't hide: measured for independents separately, or as reliably staffed venues, or with an explicit independent-venue guardrail. Notices that 'worked' is only confirmed for 64% of shifts, so the North Star can't be trusted yet, and makes fixing confirmation an early input or uses a measurable proxy until then. Treats the rescue bonus as a way the fill rate (and any fills-based North Star) is being bought: 13% no-shows against 4%, so it needs guardrails such as cost per worked shift and no-show rate. Shows how shifts posted, registrations (2,370 of 7,900 worked) and GMV each rose without more value. Builds a metric tree with owners, pairs it with revenue or GMV as the business measure, and redefines each team's metric as an input.

Critical failures (cap the score at 40)
  • Recommends GMV or shifts posted as the North Star
Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

Usable with a quick edit13 of 14 checks passed

The memo is well-structured, uses evidence correctly, and covers all required elements. A PM could use it with minor wording edits, without re-checking data or reasoning.

Output

To: Executive team Subject: Choose successful completed shifts as Crewmate’s North Star

Recommendation

Adopt monthly successful completed shifts as Crewmate’s North Star: the number of unique posted shifts that are filled, actually worked, and have no unresolved dispute. Count a shift once, after its scheduled end; exclude cancellations, no-shows, and disputed shifts until resolved.

This measures the outcome venues hire us for and workers show up to deliver—not activity on either side of the marketplace. It also directs attention to the reliability that supports repeat use. Venues with fill rates of at least 90% are much more likely to still be posting six months later than venues below 70% (84% vs. 41%).

The latest figures show why the distinction matters. Monthly posts rose from 77,000 in Q2 to 92,000 in Q3, while fills rose from 63,700 to 71,000. But overall fill rate fell from 82.7% to 77.2%; independent-venue fill rate fell from 78.9% to 67.9%, and independent venues stopping posts rose from 5% to 9%. Meanwhile, GMV grew 15%, but chains’ share of GMV rose from 34% to 46% and they raised rates 11% for the holiday season. GMV is important, but this mix and price change make it a poor standalone signal of marketplace health.

Nor should we equate registrations with supply: only 2,370 of 7,900 Q3 registrants worked a shift, after the sign-up bonus began. Workers who complete three or more shifts in their first month are far more likely to remain active. We should reward dependable activity, not sign-ups.

Metric tree and accountability

The core identity is:

Successful completed shifts = valid posted shifts × fill rate × successful-completion rate

InputDefinitionAccountable owner
Valid posted shiftsUnique, legitimate shifts with a real venue, role, time, and pay; split by enterprise/independent and lead timeHead of Demand
Fill rateFilled shifts ÷ valid posted shiftsCOO
Successful-completion rateShifts actually worked without a no-show or unresolved dispute ÷ filled shiftsCOO; Product/Data owns reliable status capture
Qualified, available worker coverageRepeat, activated workers available for the shifts and locations demanded; leading driver of fill and completionHead of Supply
Repeat venue demandActive venues returning to post, with retention and posting frequency by venue segmentHead of Demand

The first three inputs calculate the North Star; worker coverage and repeat venue demand are leading drivers and diagnostic cuts, not extra terms to multiply into it. Review all results by enterprise vs. independent, and scheduled vs. same-day.

Paired business measure

Pair the North Star with monthly contribution margin dollars: Crewmate’s 18% take-rate revenue on completed-shift GMV, less variable fulfillment costs, including rescue bonuses and other worker incentives. This checks that completed shifts create sustainable value rather than being purchased at any cost. Continue reporting GMV to the board as a scale measure, but not as our North Star.

Measurement, gaming, and guardrails

Completion data is not yet strong enough for an unqualified claim: venues confirm only 64% of filled shifts, and the rest are assumed worked unless disputed. Among confirmed shifts, 6% were no-shows or disputed; rescue-filled shifts have a 13% no-show rate versus 4% for other fills. For the first board readout, label the metric provisional, disclose the confirmation rate, and show the calculation and coverage. Then instrument worker and venue check-in/out, reconcile mismatches, and audit a sample. Do not treat missing confirmation as proof of success indefinitely.

Key gaming risks and controls:

  • Post inflated, duplicate, or low-quality shifts: count only valid shifts; monitor cancellations, disputes, and fill rate.
  • Favor easy or high-priced chain shifts over independent or same-day demand: report North Star and fill/completion rates by segment; track independent-venue retention and same-day performance as guardrails.
  • Buy apparent success with rescue bonuses: monitor rescue share, no-show rate, and contribution margin alongside completed shifts. Rescue fills are not successes unless the shift is actually worked.
  • Misreport completion or suppress disputes: require auditable status capture and monitor confirmation coverage and dispute rates.

What current metrics become

  • CEO — GMV: remains a board/business measure, paired with contribution margin; it is not the North Star.
  • Demand — shifts posted: becomes a leading input, with valid posts and repeat independent-venue demand emphasized over raw volume.
  • Supply — active workers: shifts to qualified, repeat workers and first-month activation (especially the three-shift threshold); registrations are a funnel diagnostic.
  • COO — fill rate: remains a critical driver and guardrail, but success is completed shifts—not fills that fail to show up.

At the offsite, agree on the definition, owners, and guardrails. Before the board update, establish the provisional baseline and be explicit about its measurement limits.

Grades and run details

Decision model 100 · LLM judge 93
Decision model checks
  • passUses the supplied evidence correctly60%
  • passAddresses the actual decision75%
  • passRespects explicit constraints56%
  • passIdentifies material uncertainty85%
  • passAvoids unsupported claims24%
  • passProduces the required deliverable82%
  • passSees the growth is narrow98%
  • passChecks the North Star can be measured100%
  • passSees the fill rate being bought98%
  • passOne North Star for both sides99%
  • passMeasures value created, not captured100%
  • passGets the base of every number right45%
  • passBreaks it into inputs teams can move100%
  • passSays how it could be gamed100%
Run
Run
#1
API response time
37 s
Submitted
5 Oct 2026

Check by check

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

Mixed 1

Addresses the actual decisionMixed

The output commits to a North Star but does not state what result or condition would change that recommendation.

Got right 13

Uses the supplied evidence correctlyRight

All claims about the current situation are taken directly from the brief or derived by correct arithmetic.

Respects explicit constraintsRight

The output is a memo under 1,200 words, addressed to the exec team, and includes all requested elements.

Identifies material uncertaintyRight

It names the confirmation gap as a key unknown, bounds it with the 64% figure, and says how to resolve it (instrument check-in/out, reconcile, audit).

Avoids unsupported claimsRight

Interpretations are presented as conclusions from the data, not as established facts, and no unsupported causal claims are made.

Produces the required deliverableRight

The memo is complete, within word count, and usable by the exec team with all required components.

Sees the growth is narrowRight

It shows the split (chains up, independents' filled shifts and fill rate down, more leaving) and tracks independent venues via segment reporting and guardrails.

Checks the North Star can be measuredRight

It flags the 64% confirmation gap and proposes instrumenting check-in/out, reconciliation, and auditing to fix it before the board readout.

Sees the fill rate being boughtRight

It uses the 13% no-show rate and cost to show rescue fills inflate the number, and sets guardrails like no-show rate and contribution margin.

One North Star for both sidesRight

The North Star (successful completed shifts) only rises when a venue and a worker both get value, and the memo explains why that matters in a marketplace.

Measures value created, not capturedRight

The North Star measures completed shifts, a core action that predicts retention, and keeps revenue as a separate business measure.

Gets the base of every number rightRight

All derived percentages and rates are computed correctly from the supplied data, and bases are stated where needed.

Breaks it into inputs teams can moveRight

It breaks the North Star into valid posted shifts, fill rate, and successful-completion rate with owners, and pairs it with contribution margin.

Says how it could be gamedRight

It names realistic gaming risks (inflated posts, favoring chain shifts, buying fills, misreporting) and sets specific guardrails against each.

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
1Sonnet 5.5withAPI95.996.72None
2GPT-6.1 SolwithAPI97.793.32None
3GPT-6 LunawithAPI95.588.32None
4GPT-6 AstrawithChatGPT94.288.32None
5Opus 5.5withClaude94.680.82None
6Haiku 5.5withAPI88.881.72None
7Gemini 3.8 FlashwithAPI78.965.82None
8Gemini 3.5 Flash-LitewithGemini54.438.321 capped

What you get for the cost

GPT-6 Luna · API scores within 4 points of Sonnet 5.5 · API at about a twentieth of the cost.

Task score against relative cost for each setup. The table below has the same figures.304050607080901001×3×10×30×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreSonnet 5.5 · API: task score 96, 22× the cheapestGPT-6.1 Sol · API: task score 96, 9.6× the cheapestGPT-6 Luna · API: task score 92, the cheapestGPT-6 Astra · ChatGPT: task score 91, 52× the cheapestOpus 5.5 · Claude: task score 86, 36× the cheapestHaiku 5.5 · API: task score 86, the cheapestGemini 3.8 Flash · API: task score 71, 7.5× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 44, 1.8× the cheapest, 1 critical failureSonnet 5.5 · APIGPT-6.1 Sol · APIGPT-6 Luna · APIGPT-6 Astra · ChatGPTOpus 5.5 · ClaudeHaiku 5.5 · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and costAt least one critical failure
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API921.0×
Haiku 5.5 · API861.1×
Gemini 3.5 Flash-Lite · Gemini441.8×
Gemini 3.8 Flash · API717.5×
GPT-6.1 Sol · API969.6×
Sonnet 5.5 · API9622×
Opus 5.5 · Claude8636×
GPT-6 Astra · ChatGPT9152×

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

Choosing the metric the whole company steers by.

Why it matters

Pick revenue and you steer by the rear-view mirror. Pick a vanity metric and teams will happily grow it while the product gets worse.

What good looks like

  • Measures value delivered to customers, not value captured
  • Grounded in what predicts retention in the data
  • Broken into input metrics a team can own
  • Says how it could be gamed, with guardrails

Deliberately not measured

  • Dashboard design
  • Naming
Capability tested

Metric design

The failure we’re looking for

Revenue, or a vanity metric, as the North Star

Grading

Decision model and LLM judge, calibrated against a blind PM review