Tasks / Design

Activation & onboarding review

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

Measures the modelTask v1.1 · 2 casesDifficulty

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

From 14 graded outputs by 7 models. 50% were usable with at most a quick edit.

Reliably right

  1. Activation defined by what predicts retention100% pass
    Defines activation as three workouts linked to day-60 retention, and evaluates fixes by effect on that event.
    GPT-6 Astra · ChatGPT · Fitness app first week
  2. Prioritises by impact98% pass
    Ranks changes by likely impact on achieving three workouts, aligned with the identified activation threshold.
    GPT-6 Astra · ChatGPT · Fitness app first week
  3. Produces the required deliverable93% pass
    Provides a complete, actionable memo that a growth PM could implement without major gaps.
    GPT-6 Astra · ChatGPT · Fitness app first week

Where it slips

  1. Avoids unsupported claims54% pass
    It presents causal or evaluative claims such as 'successful connection is the gateway' and 'a required technical connection is an especially poor first step' as established fact rather than hypothesis.
    GPT-6 Luna · API · Analytics tool losing users at setup
  2. Uses the supplied evidence correctly54% pass
    It includes an unsupported motivation claim and a miscomputed '4% of trial starters' figure, so not every current-situation statement is supported by the brief or arithmetic.
    Sonnet 5.5 · API · Fitness app first week
  3. Identifies material uncertainty59% pass
    Identifies the lack of step-level data but does not state what result would change the call or how it would affect the ranking.
    Opus 5.5 · Claude · Analytics tool losing users at setup

Case viewer

Read the brief, then put up to three outputs side by side, 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.

Case

Mid-level · v1.8 · anonymised real · B2B SaaS, data

The outputs

7 setups answered this brief. Pick up to three to read side by side.

Needs more than a quick edit4 of 9 checks passed

The output misinterprets the support ticket data as a user proportion and presents unsupported causal claims, so a PM would need to re-check the evidence before using it.

Output

To: Onboarding Squad Subject: Fixing Our Activation Funnel: Priorities & Action Plan

Where We Are Losing People

Our biggest drop-off occurs at Step 5–8 (Connect Data): 59% of signups leave without connecting a data source.

We are asking PMs and marketers for database ports, SSL modes, and passwords they do not possess. 41% of our support tickets are “I don't have credentials,” followed by 18% for failed syncs. Furthermore, requiring a 20-minute, open-tab sync kills momentum.

Because Week-2 retention jumps from 11% to 62% once a user builds their first chart, our sole objective is getting non-technical users to that "aha!" moment in under two minutes.

---

What to Fix (In Priority Order)

#### 1. Add “Explore with Sample Data” & a Clear “Skip” Button (Step 5) * What: Let users bypass database connection entirely using a fully populated e-commerce or SaaS demo dataset. * Why: This is the highest-leverage fix. It immediately unblocks the 59% who drop off at Step 5 and the 41% who lack DB credentials, giving them an instant path to create a first chart and experience the product’s value on Day 1.

#### 2. Introduce “Invite a Data Teammate” at Connection (Step 6) * What: Add a 1-click option: “Don't have credentials? Send an invite link to your data lead to connect your warehouse.” * Why: Replaces a dead end with collaboration. The PM stays in the app using sample data while the engineer handles credentials asynchronously.

#### 3. Make Syncing Asynchronous (Step 8) * What: Run syncing in the background. Notify users via email/in-app alert when ready instead of forcing them to keep a 20-minute tab open. * Why: Directly addresses our second-biggest support issue (18% sync failures/cancellations) and eliminates the drop-off caused by tab abandonment.

#### 4. Pre-Populate the Dashboard; Kill the Blank Canvas (Steps 9 & 10) * What: When a user selects a template, generate it automatically with 3–4 standard charts instead of dropping them onto an empty canvas. Prompt them to tweak one existing chart. * Why: Bridges the 8% drop-off between connecting data (41%) and creating a first chart (33%). Editing an existing chart is far lower friction than building from scratch.

#### 5. Defer Email Verification and Profiling Questions (Steps 2, 3, & 4) * What: Move verification to a non-blocking banner at the top of the app, and cut or defer the role/team-size questions until after the user creates their first chart. * Why: Every barrier before core value reduces trial-to-paid conversion. Let users reach the product before demanding administrative tasks.

---

Next Step

Let’s implement Priority 1 (Sample Data) this sprint. It requires zero infrastructure changes to our warehouse sync logic and directly targets our biggest activation leak.

Claims the judge couldn’t find in the brief

  • Requiring a 20-minute, open-tab sync kills momentum.
  • The 41% who lack DB credentials (as a proportion of users).
  • The drop-off caused by tab abandonment.

Grades and run details

Decision model 72 · LLM judge 4 of 10 checks
Decision model checks
  • failUses the supplied evidence correctly22%
  • passAddresses the actual decision93%
  • passRespects explicit constraints40%
  • failIdentifies material uncertainty99%
  • partialAvoids unsupported claims39%
  • passProduces the required deliverable96%
  • passPrioritises by impact98%
  • passActivation defined by what predicts retention100%
  • passGets the base of every number right44%
Run
Run
#1
API response time
21 s
Submitted
1 Oct 2026

Usable with a quick edit9 of 9 checks passed

The memo is prioritised, evidence-based, and actionable as-is. At most one could add an explicit reprioritization trigger, but that would be a light wording edit.

Output

To: Onboarding squad Subject: Prioritize getting users to a useful first chart

Our biggest leak is before data connection: 59% of signups—7,316 people—never connect a source. Another 8 percentage points disappear before the first chart. Only 9% invite a teammate, but collaboration should come after users see value.

First-chart users have 62% week-2 retention versus 11% for everyone else. That makes time-to-first-chart our priority, although this correlation does not prove that creating a chart causes retention.

Fixes, in priority order

1. Remove the database-credentials gate. Offer a prominent sample-data path and a clearly explained CSV path. Let users request access from a data owner without abandoning onboarding. For database connections, replace the 14-page guide with source-specific instructions, inline examples, and actionable error messages.

Why first: Our audience is mostly PMs and marketers, yet onboarding assumes database expertise and access. “I don’t have the credentials” accounts for 41% of onboarding tickets. Sample data can demonstrate value immediately, but we should separately measure whether those users eventually connect their own data.

2. Make syncing resilient and asynchronous. Run syncs server-side so leaving the page does not cancel them. Show status, provide completion notifications, and support retries with clear failure explanations. Where technically feasible, sync a useful subset first rather than requiring a full load.

Why second: A wait of up to 20 minutes is already costly; requiring an open tab makes it fragile. Sync failures account for another 18% of tickets and block users who have overcome the access hurdle.

3. Deliver a chart, not another blank canvas. Recommend relevant tables rather than listing everything. Reduce twelve templates to a few guided choices, then populate a starter chart that users can edit.

Why third: Choosing a template and then seeing an empty builder breaks the promise of guidance. About 20% of connected users fail to reach a first chart. Focus on a useful result, not merely recording a chart-creation event.

4. Shorten the route to value. Move role and team-size questions until after the first chart, unless they directly personalize onboarding. Allow users to explore before email verification where security requirements permit.

Why fourth: These steps add friction before any payoff, but we lack screen-level evidence that they explain the largest losses.

5. Make invitations contextual. Prompt users to share a useful chart after success; keep invitations optional. Distinguish “ask a data owner for access” from “invite a collaborator.”

Why fifth: Low invitation rates may reflect weak activation, not a broken invitation screen.

Instrument every screen, connection method, and sync failure. Evaluate changes on useful first-chart completion, time to value, own-data activation, and week-2 retention—not just step clicks.

Grades and run details

Decision model 100 · LLM judge 10 of 10 checks
Decision model checks
  • passUses the supplied evidence correctly28%
  • passAddresses the actual decision96%
  • passRespects explicit constraints31%
  • passIdentifies material uncertainty34%
  • passAvoids unsupported claims48%
  • passProduces the required deliverable95%
  • passPrioritises by impact98%
  • passActivation defined by what predicts retention99%
  • passGets the base of every number right55%
Run
Run
#1
API response time
17 s
Submitted
29 Sept 2026

Needs more than a quick edit3 of 9 checks passed

It exceeds the word limit and contains an unsupported user-capability claim. A PM would need to recheck evidence and trim before use.

Output

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

Summary: Reaching a first chart is what predicts retention (62% at week 2 versus 11% otherwise). Only 33% of signups get there, and the biggest loss, 59% of signups, happens before they connect data. Most of our users are PMs and marketers who can't complete a database connection alone.

Fixes, in priority order

1. Add sample data and a skip option on the "Connect your data" screen. The most common ticket (41%) is "I don't have the database credentials." We ask non-engineers for host, port and SSL mode before they've seen any value. Sample data lets them reach a chart in minutes, and they can connect real data later. Caveat: sample-data users may retain worse than the 62% figure, which likely reflects real data. Track them as a separate cohort and prompt them to connect real data after their first chart.

2. Make it easy to get credentials from someone else. Add a "Send this to your engineer" option: a shareable link or prefilled email containing the connection form and a short setup checklist. Replace the 14-page guide with inline, per-field help. Promote CSV upload as the no-engineer path, since it's currently one of four equal options.

3. Fix the sync experience and its failures. Syncs run up to 20 minutes and cancel if the user leaves. Run them in the background, send an email when they finish, and let users start exploring once the first tables land. Then look at the 18% of tickets that say "my sync failed" and surface specific error messages. In the table-selection step, preselect a sensible default instead of listing every table.

4. Get people to a first chart without an empty canvas. We show twelve templates, then a blank chart builder. Cut the choice to two or three based on role, and auto-generate a first chart from the synced data (or sample data) that the user can edit. This targets the 41% → 33% drop and shortens time to value.

5. Remove friction at the front. Let users into the product before email verification, and enforce it later (e.g., before inviting teammates). Move the role question ahead of template selection, where it drives the default, and drop or defer team size unless something uses it. These are cheap fixes, but I'm ranking them below the others because I can't yet show they're a big loss.

6. Move the invite prompt to after the first chart. The 9% invite rate is 27% of users who reach a chart, so it's mostly a downstream symptom. Fix activation first. Then trigger the invite prompt right after a first chart is created, with a message like "Share this chart," rather than as a generic final step.

Measure first The funnel jumps from signup straight to data connection, so we can't see how many people drop at email verification, role, team size, or the connection form. Add per-screen events this sprint, before changing anything, so we can confirm the ordering above and measure each fix.

Success metrics: signup → first chart (33% today), share of users who connect real data within 7 days, sync failure rate, and week-2 retention split by sample-data versus real-data users.

Claims the judge couldn’t find in the brief

  • Most users are PMs and marketers who can't complete a database connection alone.

Grades and run details

Decision model 83 · LLM judge 3 of 10 checks
Decision model checks
  • failUses the supplied evidence correctly7%
  • passAddresses the actual decision99%
  • passRespects explicit constraints24%
  • passIdentifies material uncertainty90%
  • partialAvoids unsupported claims43%
  • passProduces the required deliverable93%
  • passPrioritises by impact95%
  • passActivation defined by what predicts retention100%
  • passGets the base of every number right42%
Run
Run
#1
API response time
15 s
Submitted
29 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 7

Uses the supplied evidence correctlyWrongRightWrong
Gemini 3.8 Flash · API

The output presents 'the 41% who lack DB credentials' as a user proportion, which is not supported by the ticket share data, and states 'kills momentum' and 'drop-off caused by tab abandonment' as facts without evidence.

GPT-6.1 Sol · API

All current-state facts and figures in the output are drawn or derived correctly from the supplied funnel, scenario, support data, and onboarding flow.

Sonnet 5.5 · API

It presents as fact that PMs and marketers cannot complete a database connection alone, which the supplied context does not establish.

Addresses the actual decisionMixedRightMixed
Gemini 3.8 Flash · API

The output does not state what result or condition would change the prioritised order, failing the requirement to say what would change the answer.

GPT-6.1 Sol · API

It commits early to prioritising time-to-first-chart, gives a ranked fix order for the onboarding squad, and flags retention and own-data activation as conditions that would change the approach.

Sonnet 5.5 · API

It commits to a ranked order but does not state a result or condition that would change the answer, only that per-screen events would confirm it.

Respects explicit constraintsRightRightMixed
Gemini 3.8 Flash · API

The memo is under 500 words, addressed to the onboarding squad, and provides a ranked list with reasons.

GPT-6.1 Sol · API

The output is a memo addressed to the onboarding squad, stays under 500 words, and provides the requested ordered fixes.

Sonnet 5.5 · API

The memo is approximately 541 words, exceeding the explicit 500-word limit.

Identifies material uncertaintyWrongRightMixed
Gemini 3.8 Flash · API

No unknowns are named, and the output does not discuss what could change the decision or how to resolve uncertainty.

GPT-6.1 Sol · API

It names the causal uncertainty around first-chart retention, the risk that sample-data users may not connect their own data, and the lack of screen-level evidence, with ways to resolve them.

Sonnet 5.5 · API

It names sample-data retention and missing funnel events but does not specify what data result would change the prioritisation.

Avoids unsupported claimsWrongRightWrong
Gemini 3.8 Flash · API

Interpretations like 'kills momentum', 'the 41% who lack DB credentials' as a user share, and 'drop-off caused by tab abandonment' are presented as established facts without labelling them as hypotheses.

GPT-6.1 Sol · API

Interpretive statements are either supported by the supplied flow or explicitly hedged as possibilities or caveats rather than established fact.

Sonnet 5.5 · API

The user-capability claim and 'mostly downstream symptom' are stated as established fact rather than labelled interpretations.

Produces the required deliverableRightRightMixed
Gemini 3.8 Flash · API

The output is a complete memo with the requested structure, reader, and length, and a PM could act on it with light edits.

GPT-6.1 Sol · API

The requested memo is present, complete, actionable, and usable by the onboarding squad with little or no editing.

Sonnet 5.5 · API

It is a usable memo in form and audience, but it does not meet the required length constraint.

Gets the base of every number rightMixedRightRight
Gemini 3.8 Flash · API

The output misstates the 41% support-ticket share as a proportion of users lacking credentials, getting the base wrong.

GPT-6.1 Sol · API

Percentages and differences use the correct denominators, step-to-step drops are computed from the right stages, and ambiguous bases are clarified where needed.

Sonnet 5.5 · API

Derived percentages use the correct denominators, including 59% before data, 27% invite of first-chart users, and the 41% to 33% step.

All got right 2

Prioritises by impactRightRightRight
Gemini 3.8 Flash · API

Fixes are ranked by likely impact on activation, starting with the biggest drop-off and linking to the retention-driving first chart.

GPT-6.1 Sol · API

Fixes are ranked by likely impact on activation, starting with the data-connection bottleneck and moving to later-stage friction.

Sonnet 5.5 · API

Fixes are explicitly ranked by likely impact on reaching first chart, with reasons for each.

Activation defined by what predicts retentionRightRightRight
Gemini 3.8 Flash · API

The output defines activation as building a first chart, cites the retention difference (11% vs 62%), and ranks fixes by their effect on reaching that event.

GPT-6.1 Sol · API

It defines the activation event as reaching a useful first chart, ties it to retention, and ranks fixes by effect on that outcome rather than step completion.

Sonnet 5.5 · API

It defines first chart as the activation behaviour using the retention split and ranks fixes by effect on that event.

Results

Every setup we’ve tested on this task, across all cases and repeats, graded on the current checklist. Calibrated: the graders match our PM on 82% of checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6.1 SolwithAPI100.0100.02None
2GPT-6 AstrawithChatGPT94.4100.02None
3GPT-6 LunawithAPI94.485.02None
4Opus 5.5withClaude80.665.02None
5Gemini 3.5 Flash-LitewithGemini69.455.02None
6Sonnet 5.5withAPI77.845.02None
7Gemini 3.8 FlashwithAPI69.450.02None

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