Tasks / Growth

Activation & onboarding review

Can the model find the friction that matters most and prioritise the fixes?

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. 69% were usable with at most a quick edit.

Reliably right

  1. Respects explicit constraints100% pass
    The output is a review addressed to Omar, well under 800 words, and respects the requested form.
    GPT-6 Luna · API · The checklist isn't the moment
  2. Produces the required deliverable100% pass
    The review covers activation definition, evidence, changes, design proposal, and goal, is addressed to Omar, within length, and is actionable.
    GPT-6 Luna · API · The checklist isn't the moment
  3. Activation defined by what predicts retention100% pass
    Activation is defined by the behaviour that predicts retention (bank+cat 10+), and fixes are judged by their effect on that event.
    GPT-6 Luna · API · The checklist isn't the moment

Where it slips

  1. Uses the supplied evidence correctly44% pass
    The output states 'we have no step-level data for screens 2–8', 'a blank builder is hard for non-analysts', and 'people invite colleagues to something useful, not to a setup screen' as facts, none of which appear in the brief or follow from arithmetic.
    Opus 5.5 · Claude · Analytics tool losing users at setup
  2. Avoids unsupported claims55% pass
    It presents 'a blank builder is hard for non-analysts' and 'people invite colleagues to something useful, not to a setup screen' as established facts without labelling them as hypotheses.
    Opus 5.5 · Claude · Analytics tool losing users at setup
  3. Addresses the actual decision56% pass
    The output commits to a ranked order but does not state what result or condition would change that order, as required.
    GPT-6 Astra · ChatGPT · Analytics tool losing users at setup

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. Activation defined by what predicts retention

    Does the review define (or question) the activation event by whether it predicts retention, and judge fixes by their effect on that event rather than on step completion?

    Passes when Names the activation behaviour and its link to retention (from the supplied data, or as an explicit assumption to verify), and ranks fixes by their effect on it.

  2. Gets people to value sooner

    Do the recommended fixes shorten the path to the first moment of value, by removing or deferring steps before it or starting people with templates or sample data, rather than adding tours, tooltips or extra steps?

    Passes when The main fixes cut or defer steps before first value, or give a warm start, and say which steps go.

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 1 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

Review our onboarding flow and funnel below. Write a memo to the onboarding squad saying where we are losing people and what to fix, in the order you would fix it, with the reason for each. Keep it under 500 words.

What the model was given4 items: Scenario, Funnel (last 90 days, 12,400 signups), Onboarding flow, screen by screen, Support
ScenarioChartwise is a self-serve product-analytics tool. Most people who sign up are product managers or marketers, not data engineers. Pricing starts at $49 a month after a 14-day free trial.
Funnel (last 90 days, 12,400 signups)Signup 100% → Connect data source 41% → First chart 33% → Invite teammate 9%. Week-2 retention: 62% for users who reach a first chart, 11% otherwise.
Onboarding flow, screen by screen1. Sign up: email, password, company name. 2. Verify email: the flow pauses until the link is clicked. 3. “What's your role?” (seven options). 4. “How big is your team?” (five options). 5. Connect your data: choose Postgres, BigQuery, Snowflake or CSV upload. There is no sample data and no skip. 6. Connection form: host, port, database, username, password and SSL mode. The help link opens a 14-page setup guide. 7. Choose tables: a list of every table in the database. 8. Syncing: a progress bar that can take up to 20 minutes; leaving the page cancels the sync. 9. Pick a dashboard template (twelve options). 10. First chart: a chart builder with an empty canvas. 11. Invite teammates: email fields, with a “Skip for now” link.
SupportThe most common onboarding ticket (41% of them) is some version of “I don't have the database credentials.” The next is “my sync failed” (18%).
What a strong answer doesThe answer key the graders mark against

The data-source connection step (screens 5–8) is the activation bottleneck; prioritise a sample-data or CSV-first path to a first chart, then non-admin credential handoff and a sync that survives leaving the page, ahead of cosmetic fixes.

Task

Mid-level · v1.12

The outputs

8 setups answered this brief.

Usable with a quick edit7 of 10 checks passed

The invite placement error is a minor factual slip that can be corrected quickly; the rest is structured and actionable.

Output

MEMO

To: Onboarding squad Re: Where we lose people, and what to fix first

Where we lose people

  • Signup to connect (100% to 41%): The largest loss. Most of our users are PMs and marketers who don't have database credentials, and the flow gives them no way forward without them.
  • Connect to first chart (41% to 33%): About a fifth of people who connect never build a chart. Sync and the empty canvas are the likely causes.
  • First chart to invite (33% to 9%): Expected, since invites come after value. Lower priority for now.

Retention is the reason to care about the first chart: 62% of users who reach one are active in week 2, versus 11% of those who don't. This is correlational, since engaged users may be the ones who build charts, but it is the strongest signal we have.

What to fix, in order

  1. Add sample data and a skip at step 5. Users without credentials currently hit a dead end. Sample data gets them to a chart in minutes, which is the milestone that predicts retention. This also targets the top support ticket (41%). Reason: highest expected impact on the number that matters.
  1. Let people in before email verification, and drop the role and team screens from the critical path. Step 2 blocks all progress, and steps 3–4 ask questions before the user has seen any value. Move role and team to after the first chart, where they can personalize templates. Reason: removes the earliest friction, when drop-off is highest.
  1. Rebuild the credentials path. Replace the 14-page guide with short, per-warehouse steps for Postgres, BigQuery and Snowflake, and add a "send these instructions to your data engineer" link that delivers a connect-only invite. The person who has the credentials is often not the person signing up. Reason: addresses the remaining credential tickets and the step 6 drop-off.
  1. Fix sync. Let leaving the page not cancel the sync. Show partial results as tables finish. Pre-select likely tables and add search to the step 7 list. Make CSV upload the fastest route to a first chart. Reason: sync failures are the second-largest ticket category (18%), and cancellation on exit loses users mid-setup.
  1. Make the first chart appear without effort. Templates (step 9) should render a live chart immediately. Replace the empty canvas at step 10 with a suggested chart the user can edit. Reason: a blank canvas is the last hurdle before the retention milestone.
  1. Move team invites after the first chart, and prompt for them there. Keep the skip link. Reason: invites are useful but come after value; test this once the upstream fixes land.

Measurement

Track signup-to-first-chart as the primary metric, with week-2 retention as the outcome. Run fixes 1 and 2 as separate experiments so we can see which one moves the funnel.

Claims the judge couldn’t find in the brief

  • Most of our users are PMs and marketers who don't have database credentials.
  • Sync and the empty canvas are the likely causes (of drop-off from connect to first chart).
  • The person who has the credentials is often not the person signing up.
  • There is a 'step 6 drop-off' that the credentials path fix addresses.
  • Invite teammates is currently before the first chart (implied by 'Move team invites after the first chart').

Grades and run details

Decision model 70 · LLM judge 82
Decision model checks
  • failUses the supplied evidence correctly33%
  • failAddresses the actual decision46%
  • passRespects explicit constraints38%
  • partialIdentifies material uncertainty30%
  • partialAvoids unsupported claims38%
  • passProduces the required deliverable97%
  • passPrioritises by impact73%
  • passActivation defined by what predicts retention100%
  • passGets the base of every number right45%
  • passGets people to value sooner100%
Run
Run
#1
API response time
11 s
Submitted
8 Oct 2026

Usable with a quick edit9 of 10 checks passed

The memo is clear, evidence-based, and actionable; a PM could add a condition for changing priorities with a quick edit without re-checking evidence or redoing reasoning.

Output

To: Onboarding squad Subject: Fix access and sync before optimising chart creation

Our biggest measured loss is before data connection: 59% of signups, roughly 7,300 people, never connect a source. We ask PMs and marketers to complete a database setup that many cannot do themselves, then make a lengthy sync depend on keeping a page open.

Another 8 percentage points disappear between connection and first chart: about 20% of those who connect. Teammate invitations fall to 9% of signups, but we should establish whether collaboration is necessary for value before treating that as our next bottleneck.

First-chart users have much higher week-2 retention, 62% versus 11%. That makes chart creation a promising activation milestone, but does not prove that pushing everyone through a chart will cause better retention.

Fix in this order

1. Give users a route forward without database credentials. Offer a clearly labelled sample-data experience and make CSV upload a prominent alternative.1 Add a setup hand-off that lets users ask a colleague to connect the source, then resume when it is ready. Replace the generic setup guide with short, source-specific instructions.

This addresses the largest measured loss and the strongest support signal: missing credentials account for 41% of onboarding tickets. That is a share of tickets, not signups, so we cannot yet quantify how much drop-off it explains. Sample data should demonstrate value while preserving a clear path to the user’s own data.

2. Make syncing durable and recoverable. Run syncs in the background so closing the page does not cancel them. Show meaningful status, explain failures and allow retries without repeating setup. Notify users when their data is ready.

A 20-minute foreground wait is a fragile requirement. Sync failures generate 18% of onboarding tickets and can waste the effort users have already invested.

3. Turn connected data into a useful first chart. Help users identify relevant tables instead of presenting every table equally. Use their chosen template to produce an editable chart from compatible data, with guided choices where mapping is needed. Do not finish template selection with an empty canvas.

This targets the roughly one in five connected users who never reach a chart. Measure whether they produce something useful, not merely whether a chart renders.

4. Remove avoidable preliminary work. Defer role and team-size questions unless their answers immediately improve setup. Review whether email verification must block exploration, retaining verification where required for account security.

These steps add friction before value, but we lack evidence that they drive the main loss.

Measure alongside delivery: instrument each screen, connection method, error and return visit. Test changes against first charts using customers’ own data, week-2 retention and trial-to-paid conversion. Track sample-data charts separately. Revisit invitations once we understand whether users need collaboration to succeed. To: Onboarding squad Subject: Fix access and sync before optimising chart creation

Our biggest measured loss is before data connection: 59% of signups, roughly 7,300 people, never connect a source. We ask PMs and marketers to complete a database setup that many cannot do themselves, then make a lengthy sync depend on keeping a page open.

Another 8 percentage points disappear between connection and first chart: about 20% of those who connect. Teammate invitations fall to 9% of signups, but we should establish whether collaboration is necessary for value before treating that as our next bottleneck.

First-chart users have much higher week-2 retention, 62% versus 11%. That makes chart creation a promising activation milestone, but does not prove that pushing everyone through a chart will cause better retention.

Fix in this order

1. Give users a route forward without database credentials. Offer a clearly labelled sample-data experience and make CSV upload a prominent alternative. Add a setup hand-off that lets users ask a colleague to connect the source, then resume when it is ready. Replace the generic setup guide with short, source-specific instructions.

This addresses the largest measured loss and the strongest support signal: missing credentials account for 41% of onboarding tickets. That is a share of tickets, not signups, so we cannot yet quantify how much drop-off it explains. Sample data should demonstrate value while preserving a clear path to the user’s own data.

2. Make syncing durable and recoverable. Run syncs in the background so closing the page does not cancel them. Show meaningful status, explain failures and allow retries without repeating setup. Notify users when their data is ready.

A 20-minute foreground wait is a fragile requirement. Sync failures generate 18% of onboarding tickets and can waste the effort users have already invested.

3. Turn connected data into a useful first chart. Help users identify relevant tables instead of presenting every table equally. Use their chosen template to produce an editable chart from compatible data, with guided choices where mapping is needed. Do not finish template selection with an empty canvas.

This targets the roughly one in five connected users who never reach a chart. Measure whether they produce something useful, not merely whether a chart renders.

4. Remove avoidable preliminary work. Defer role and team-size questions unless their answers immediately improve setup. Review whether email verification must block exploration, retaining verification where required for account security.

These steps add friction before value, but we lack evidence that they drive the main loss.

Measure alongside delivery: instrument each screen, connection method, error and return visit. Test changes against first charts using customers’ own data, week-2 retention and trial-to-paid conversion. Track sample-data charts separately. Revisit invitations once we understand whether users need collaboration to succeed.

What a PM had to fix

  1. 1Decision deferredMake the callQuick edit

    What we’d changeRank the four parts of fix 1 so the squad knows what ships first, and make the calls fix 4 leaves open.

Grades and run details

Decision model 90 · LLM judge 91
Decision model checks
  • passUses the supplied evidence correctly22%
  • failAddresses the actual decision42%
  • passRespects explicit constraints18%
  • passIdentifies material uncertainty88%
  • passAvoids unsupported claims28%
  • passProduces the required deliverable63%
  • passPrioritises by impact98%
  • passActivation defined by what predicts retention95%
  • passGets the base of every number right73%
  • passGets people to value sooner93%
Run
Run
#1
Time to output
40 s
Submitted
25 Sept 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 3

Uses the supplied evidence correctlyWrongRight
Haiku 5.5 · API

Several statements about the current situation are unsupported or contradicted, such as inviting teammates being before the first chart and the existence of a 'step 6 drop-off' with no data.

GPT-6 Astra · ChatGPT

All factual claims about the current situation are directly from the supplied context or derived by correct arithmetic.

Addresses the actual decisionMixedWrong
Haiku 5.5 · API

Commits unambiguously to a ranked list of fixes in order and says what to measure.

GPT-6 Astra · ChatGPT

The output commits to a ranked order but does not state what result or condition would change that order, as required.

Avoids unsupported claimsWrongRight
Haiku 5.5 · API

Presents several causal claims and inferences (e.g., who holds credentials, step 6 drop-off) as facts without labelling them as hypotheses.

GPT-6 Astra · ChatGPT

Interpretations and forecasts are labelled as such (e.g., 'does not prove that pushing everyone through a chart will cause better retention').

All got right 7

Respects explicit constraintsRightRight
Haiku 5.5 · API

Delivers a memo to the onboarding squad, under 500 words.

GPT-6 Astra · ChatGPT

The output is a memo to the onboarding squad, under 500 words, and respects the requested form and length.

Identifies material uncertaintyRightRight
Haiku 5.5 · API

Acknowledges the retention correlation may not be causal and proposes experiments to resolve which fixes move the funnel.

GPT-6 Astra · ChatGPT

It identifies key unknowns (causal effect of chart creation on retention, drop-off explained by ticket share, necessity of collaboration) and says how to resolve them through measurement.

Produces the required deliverableRightRight
Haiku 5.5 · API

The memo is a complete, actionable set of recommendations that can be used with light editing.

GPT-6 Astra · ChatGPT

The memo is complete, in the right form, for the named reader, and a PM could act on it without filling major gaps.

Prioritises by impactRightRight
Haiku 5.5 · API

Fixes are ranked by expected impact on reaching the first chart and retention.

GPT-6 Astra · ChatGPT

Fixes are ranked by likely impact on activation, starting with the largest drop-off and strongest support signal.

Activation defined by what predicts retentionRightRight
Haiku 5.5 · API

Defines activation by the first chart and its link to retention, and judges fixes by their effect on that milestone.

GPT-6 Astra · ChatGPT

It defines activation by the first-chart milestone linked to retention (62% vs 11%) and ranks fixes by their effect on reaching that milestone.

Gets the base of every number rightRightRight
Haiku 5.5 · API

Percentages are computed from the correct steps, and ticket shares are correctly sourced.

GPT-6 Astra · ChatGPT

All percentages and derived figures use the correct denominators, and ambiguous bases (ticket share vs. signup share) are explicitly stated.

Gets people to value soonerRightRight
Haiku 5.5 · API

Main fixes cut or defer steps before first value (sample data, skip, deferring role/team) and provide a warm start (live templates).

GPT-6 Astra · ChatGPT

The main fixes cut or defer steps before first value (sample data, background sync, template-driven chart, deferring role/team-size questions) and say which steps go.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated. Where our PM checked the graders by hand, they agreed on 79% of 53 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT85.995.52None
2Sonnet 5.5withAPI78.4100.02None
3GPT-6.1 SolwithAPI85.990.92None
4GPT-6 LunawithAPI90.081.82None
5Haiku 5.5withAPI75.986.72None
6Opus 5.5withClaude88.478.42None
7Gemini 3.8 FlashwithAPI66.651.92None
8Gemini 3.5 Flash-LitewithGemini52.539.42None

What you get for the cost

GPT-6 Luna · API scores within 5 points of GPT-6 Astra · ChatGPT at about a hundredth of the cost.

Task score against relative cost for each setup. The table below has the same figures.304050607080901001×3×10×30×100×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Astra · ChatGPT: task score 91, 122× the cheapest (1 of 2 outputs estimated)Sonnet 5.5 · API: task score 90, 32× the cheapestGPT-6.1 Sol · API: task score 89, 15× the cheapestGPT-6 Luna · API: task score 86, the cheapestHaiku 5.5 · API: task score 82, 1.8× the cheapestOpus 5.5 · Claude: task score 82, 101× the cheapest (1 of 2 outputs estimated)Gemini 3.8 Flash · API: task score 56, 13× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 44, 3.4× the cheapest (1 of 2 outputs estimated)GPT-6 Astra · ChatGPTSonnet 5.5 · APIGPT-6.1 Sol · APIGPT-6 Luna · APIHaiku 5.5 · APIOpus 5.5 · ClaudeGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and costCost partly estimated (app runs with no usage meter)
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API861.0×
Haiku 5.5 · API821.8×
Gemini 3.5 Flash-Lite · Gemini443.4× (partly estimated)
Gemini 3.8 Flash · API5613×
GPT-6.1 Sol · API8915×
Sonnet 5.5 · API9032×
Opus 5.5 · Claude82101× (partly estimated)
GPT-6 Astra · ChatGPT91122× (partly estimated)

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

Reviewing a signup and onboarding flow that is losing users.

Why it matters

Anyone can list fifty UX nits. The job is finding the two that explain the drop-off, backed by the funnel data supplied.

What good looks like

  • Ties each issue to the funnel data
  • Prioritises by likely impact
  • Defines activation as the behaviour that predicts retention, not finishing onboarding

Deliberately not measured

  • Accessibility audit completeness
  • Visual redesign
Capability tested

Consequential critique

The failure we’re looking for

A generic UX checklist

Grading

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