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

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
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
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

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
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
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.

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.
Bangaly KabaLenny’s Podcast Lauryn IsfordLenny’s Podcast
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.
Hila QuLenny’s Podcast

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · Gets the base of every number right · and 2 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 the PM for onboarding at Tidepool. Omar, our Head of Product, has asked for a review of onboarding (his note is below). Write it in no more than 800 words: what activation should mean for us, the evidence, what to change in onboarding, and what to make of the design team's proposal and next quarter's goal. The files are attached.

What the model was given4 items: About Tidepool, omar_note.md, onboarding_today.md, first_week_behaviour.csv (August sign-ups: how many who did each thing in their first week were still active at day 60)

About TidepoolA personal finance app: connect your bank, see where your money goes, and budget.

omar_note.md1 lines · Download

From Omar (Head of Product): "Activation is flat at 61%. Review our onboarding and tell me what to fix. The design team has proposed a celebration screen and a progress bar for the checklist."

onboarding_today.md8 lines · Download

# Onboarding today (for 4,000 sign-ups in August)

1. Sign up (100%)
2. Setup checklist: name your account, pick a theme, set a savings goal, enable notifications (61% complete it)
3. Connect a bank (50%)
4. Categorise transactions (34% reach 10)

…

first_week_behaviour.csv (August sign-ups: how many who did each thing in their first week were still active at day 60)first_week_behaviour,retained_at_day_60,churned_by_day_60 All sign-ups,1120,2880 Completed the setup checklist,742,1696 Connected a bank account,964,1036 Categorised 10+ transactions,891,489 Connected a bank AND categorised 10+ transactions,842,238 Created a budget,410,820 Opened the app 4+ days,870,1310

What a strong answer doesThe answer key the graders mark against

Works out retention for each first-week behaviour from the file: all sign-ups 28% (1,120 of 4,000); completed the checklist about 30% (barely better than average); connected a bank about 48%; categorised 10+ transactions about 65%; connected a bank and categorised 10+ about 78%, and that group is about three-quarters of everyone retained (842 of 1,120). So the checklist isn't activation and raising its completion to 75% would do little; activation should be connecting a bank and categorising ten transactions in week one. Recommends cutting or deferring checklist steps that sit before that (theme, notifications, savings goal) and getting people to connect a bank and categorise sooner (for example suggested categories to confirm), and resets the goal to the new activation rate (about 27% today, 1,080 of 4,000). Treats the celebration screen and progress bar as polishing the wrong step. Notes that the data shows correlation and proposes a test to confirm that driving the behaviour improves retention.

Critical failures (cap the score at 40)

Recommends raising checklist completion as the main goal

Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

GPT-6 LunaAPI100Usable with a quick edit GPT-6 AstraChatGPT92Usable with a quick edit GPT-6.1 SolAPI92Usable with a quick edit Sonnet 5.5API92Usable with a quick edit Haiku 5.5API88Usable with a quick edit Opus 5.5Claude75Needs more work Gemini 3.8 FlashAPI60Needs more work Gemini 3.5 Flash-LiteGemini40Needs more work

GPT-6 LunaAPI

Usable with a quick edit11 of 11 checks passed

The output is clear, evidence-based, and actionable; a PM could use it with minor edits without rechecking evidence or redoing reasoning.

Output

To Omar — onboarding review

What activation should mean

Activation should mean a new user has reached Tidepool’s core value: connected a bank account and categorized at least 10 transactions within their first seven days. Completing setup tasks is not the same as understanding where your money goes.

What the evidence says

Of 4,000 August sign-ups, 61% completed the checklist, 50% connected a bank, and 34% categorized 10+ transactions. The behavioral data points to which events are most associated with retention at day 60:

All sign-ups: 28% retained (1,120/4,000)
Completed the checklist: 30% (742/2,438)
Connected a bank: 48% (964/2,000)
Categorized 10+ transactions: 65% (891/1,380)
Connected a bank and categorized 10+: 78% (842/1,080)

The combined behavior is the strongest signal and represents 27% of all sign-ups today. By contrast, checklist completion is barely above the overall retention rate. Creating a budget is associated with 33% retention, so it is not a better activation milestone on this evidence.

These are associations, not proof that onboarding changes will cause retention to rise: users who connect accounts or categorize transactions may already be more motivated. We should treat the numbers as a strong direction for what value to help users reach, then test changes and track outcomes.

What to change

Move bank connection earlier. Defer account naming, theme choice, savings goals, and notifications until after users see value. Explain why connection helps, address security concerns, and make the flow as clear and low-friction as possible.
Guide users to their first insight. After connection, help them review and categorize transactions with sensible suggestions. Measure progress toward 10 categorized transactions, not just completion of onboarding screens.
Offer budgeting in context. Prompt users to create a budget after they have seen their spending, rather than treating it as a prerequisite.

Design proposal and next-quarter goal

A progress bar could help, but only if it tracks the path to value—connect a bank, review transactions, categorize 10—not the existing checklist. A celebration screen after checklist completion would celebrate the wrong milestone; consider celebrating the first meaningful spending insight instead. Test both against a control, measuring bank connection, 10+ categorization, and retention—not checklist completion alone.

Replace the goal of raising checklist completion from 61% to 75% with a provisional target: raise seven-day activation from 27% to 35% of sign-ups next quarter. Track day-60 retention as a lagging outcome and guardrail. This keeps the goal tied to Tidepool’s promise while making clear that improved activation is a hypothesis to validate, not a guaranteed retention lift.

Grades and run details

Decision model 100 · LLM judge 100

Decision model checks

passUses the supplied evidence correctly53%
passAddresses the actual decision86%
passRespects explicit constraints78%
passIdentifies material uncertainty39%
passAvoids unsupported claims68%
passProduces the required deliverable93%
passComputes retention by behaviour82%
passTests before trusting the correlation88%
passActivation defined by what predicts retention100%
passGets the base of every number right44%
passGets people to value sooner79%

Run

Run: #1
API response time: 20 s
Submitted: 6 Oct 2026

GPT-6.1 SolAPI

Usable with a quick edit9 of 11 checks passed

The review is data-driven, clearly structured, and makes a strong, actionable recommendation. A PM could use it with minor wording tweaks and no need to re-verify evidence or reasoning.

Output

Onboarding review for Omar

Recommendation

We are measuring setup, not activation. Completing a checklist of preferences does not establish that someone has experienced Tidepool’s value: seeing where their money goes and taking control of it.

Redesign onboarding around that first useful financial insight. Treat the celebration screen and progress bar as supporting elements—not the main fix—and replace next quarter’s checklist-completion goal.

What activation should mean

Use “connected a bank and categorised at least 10 transactions within the first week” as our provisional activation definition.

This combines access to real financial data with an action that helps users understand their spending. It is a stronger approximation of delivered value than naming an account or picking a theme.

The threshold of 10 is a starting hypothesis, not a proven magic number. We should validate whether users actually gain an insight at this point and whether a different threshold or milestone—such as reviewing a spending summary—better predicts durable use. Creating a budget may become a subsequent milestone, but the current evidence does not support making it mandatory for initial activation.

What the evidence says

Among August’s 4,000 sign-ups, overall day-60 retention was 28%.

First-week behaviour	Users	Day-60 retention
---	---:	---:
Completed setup checklist	2,438	30.4%
Connected a bank	2,000	48.2%
Categorised 10+ transactions	1,380	64.6%
Connected a bank and categorised 10+	1,080	78.0%
Created a budget	1,230	33.3%
Opened the app on 4+ days	2,180	39.9%

The strongest signal is the combined bank-and-categorisation milestone: 27% of sign-ups reached it, and those users accounted for 842 of our 1,120 retained users. Retention among everyone else was approximately 9.5%.

Checklist completion, by contrast, is associated with retention only slightly above the cohort average. Raising it could improve the reported activation rate without improving users’ outcomes.

These are observational, overlapping groups. Motivated users may both complete meaningful actions and remain active; the data does not prove that pushing everyone through those actions will cause retention. Nor can we infer exact step-by-step drop-off from these aggregates. The CSV also differs slightly from the funnel summary on checklist and categorisation counts; reconcile reporting before setting the baseline.

What to change in onboarding

Lead with the value proposition, then bank connection. Explain what users will get from connecting, how their data is handled, and what happens next. Instrument connection starts, failures, abandonment and successful imports; address reliability and trust barriers.

Replace the upfront preference checklist with a guided first-value journey. Move theme selection and account naming out of the critical path. Ask for notification permission when there is a clear benefit. Keep savings goals optional or introduce them after users understand their finances.

Guide users through real transactions. Provide sensible category suggestions, easy corrections and a short review flow. Then show a useful spending summary—not merely confirmation that ten items were completed.

Offer budgeting as the next step. Help users turn their spending insight into a realistic budget, without making budget creation another prerequisite that delays first value.

Measure the full journey and test changes against the existing experience, including first-week milestone attainment and day-60 retention.

Design proposal and next quarter’s goal

A progress bar is worth testing if it tracks meaningful value steps, not cosmetic setup tasks. A celebration screen belongs after the first useful insight, should be brief, and should not interrupt it. Neither proposal addresses the core problem on its own.

Replace “raise checklist completion from 61% to 75%” with “increase first-week bank-plus-10-transaction activation.” A provisional target of 27% to 35% is reasonable to plan against, subject to baseline reconciliation and experiment sizing—not a forecast supported by this dataset.

Success must also include improved day-60 retention, with bank-connection reliability, privacy complaints and abandonment monitored as guardrails. Keep checklist completion as a diagnostic metric, not the team’s definition of success.

Grades and run details

Decision model 82 · LLM judge 100

Decision model checks

failUses the supplied evidence correctly86%
passAddresses the actual decision79%
passRespects explicit constraints38%
passIdentifies material uncertainty80%
passAvoids unsupported claims47%
passProduces the required deliverable43%
passComputes retention by behaviour26%
passTests before trusting the correlation73%
passActivation defined by what predicts retention100%
failGets the base of every number right68%
passGets people to value sooner75%

Run

Run: #1
API response time: 26 s
Submitted: 6 Oct 2026

Gemini 3.5 Flash-LiteGemini

Needs more than a quick edit4 of 11 checks passed

The output misstates the baseline activation rate (34% instead of 27%) and lacks a causation test, so a PM would need to re-check the numbers and add a test plan.

Output

To: Omar, Head of Product From: PM, Onboarding Date: September 2024 Subject: Onboarding Review & Activation Strategy

---

1. What Activation Should Mean for Us

Currently, our definition of activation—completing a checklist of cosmetic preferences (name, theme, savings goal, notifications)—is flawed. It measures vanity completion rather than value realization.

As a personal finance app, Tidepool delivers core value when users connect their financial reality and engage with it. Looking at our August cohort (4,000 sign-ups), the data tells a clear story about Day 60 retention:

Checklist completers retain at 30.4% (742/2,438).
Bank connectors retain at 48.2% (964/2,000).
Users who categorise 10+ transactions retain at 64.5% (891/1,380).
Users who do BOTH (Connect + Categorise) retain at 78.0% (842/1,080).

Recommendation: We must redefine "Activation" from “completed the setup checklist” to “Connected a bank AND categorised at least 10 transactions within their first week.” This anchors our success metric to actual product utility, shifting our focus from cosmetic setup to establishing long-term financial habits.

---

2. The Evidence: Where We Are Losing Users

Our current funnel drops off steeply: 1. Sign up: 4,000 (100%) 2. Setup checklist: 2,440 (61%) 3. Connect a bank: 2,000 (50%) 4. Categorise 10 transactions: 1,360 (34%)

The biggest leak is bank connection (dropping 11 percentage points right after the checklist) and transaction categorisation (dropping another 16 points).

Interestingly, the checklist itself acts as a barrier. Users are spending cognitive load on naming accounts and picking themes before experiencing the core product. Meanwhile, power features like "Created a budget" (33% retention) and opening the app 4+ days are strong secondary indicators, but bank linking and categorisation are the undisputed gateways to retention.

---

3. What to Change in Onboarding

To drive our new definition of activation, we need to restructure the first-time user experience (FTUX):

Kill the upfront setup checklist: Strip out non-essential steps (naming accounts, picking themes, notifications) from the critical path. Move them to profile settings or prompt for them after value has been proven.
Accelerate time-to-bank: Move bank connection immediately following sign-up. Users downloaded Tidepool to see their money; let them see it instantly via Plaid/Aggregator integration.
Gamify the "aha" moment: Guide users smoothly from bank connection directly into categorising their first 10 transactions. Use smart auto-categorisation with a clear, frictionless bulk-approval or swipe UI to make reaching the 10-transaction milestone effortless.

---

4. Verdict on the Design Proposal and Next Quarter's Goal

The Design Team’s Proposal (Celebration screen + Progress bar): * Verdict: Reject in its current form. Adding a progress bar and celebration screen for the existing checklist optimizes the wrong metric. It puts lipstick on a superficial activation loop. * Pivot: If we redesign onboarding to focus on connecting a bank and categorising transactions, then we can use a celebration screen and progress bar—not to celebrate picking a theme, but to celebrate linking an account and hitting that crucial 10-transaction milestone.

Next Quarter's Goal (Raise checklist completion from 61% to 75%): * Verdict: Abandon this goal. Optimizing checklist completion is a local maximum that misaligns product and user success. Chasing 75% checklist completion will likely result in higher drop-offs further down the funnel and flat-to-declining Day 60 retention. * New Goal: We should replace this with a target to raise our true activation rate (Bank Connected + 10 Transactions Categorised) from the current baseline (~34%) to 50% next quarter. This directly correlates with our actual business health and user retention.

Claims the judge couldn’t find in the brief

The checklist itself acts as a barrier.
Users are spending cognitive load on naming accounts and picking themes before experiencing the core product.
Bank linking and categorisation are the undisputed gateways to retention.

Grades and run details

Decision model 50 · LLM judge 33

Decision model checks

failUses the supplied evidence correctly41%
passAddresses the actual decision53%
passRespects explicit constraints33%
failIdentifies material uncertainty87%
failAvoids unsupported claims33%
passProduces the required deliverable48%
partialComputes retention by behaviour25%
failTests before trusting the correlation98%
passActivation defined by what predicts retention99%
failGets the base of every number right55%
passGets people to value sooner93%

Run

Run: #1
Time to output: 5 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 7

Uses the supplied evidence correctlyRightMixedWrong

GPT-6 Luna · API

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

GPT-6.1 Sol · API

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

Gemini 3.5 Flash-Lite · Gemini

The output states 'the checklist itself acts as a barrier' and similar causal claims as facts, which are not in the supplied context and do not follow by arithmetic.

Addresses the actual decisionRightRightMixed

GPT-6 Luna · API

The output commits unambiguously to a new activation definition, specific onboarding changes, rejecting the design proposal for the checklist, and a new goal, and says to test and track retention to validate.

GPT-6.1 Sol · API

Commits early to a new activation definition and onboarding changes, framed for Omar, and states that validation of the threshold and a test would change the call.

Gemini 3.5 Flash-Lite · Gemini

The output does not state what result or condition would change its recommendation, so it fails to commit with a clear change condition.

Identifies material uncertaintyRightRightWrong

GPT-6 Luna · API

It explicitly names the correlation-vs-causation risk and proposes a controlled test measuring retention to resolve it.

GPT-6.1 Sol · API

Identifies the unproven threshold, the correlation-causation risk, and data discrepancies, and says how to resolve each (validate with users, run a test, reconcile reporting).

Gemini 3.5 Flash-Lite · Gemini

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

Avoids unsupported claimsRightRightWrong

GPT-6 Luna · API

Interpretations are clearly labelled as associations and hypotheses, not established facts.

GPT-6.1 Sol · API

Hypotheses and limitations are clearly labelled; no interpretation is presented as established fact without support.

Gemini 3.5 Flash-Lite · Gemini

Interpretations like 'the checklist itself acts as a barrier' and 'undisputed gateways' are presented as established fact without being labelled as hypotheses.

Computes retention by behaviourRightRightWrong

GPT-6 Luna · API

It computes day-60 retention for each first-week behaviour (checklist ~30%, bank+cat ~78%, average 28%) and compares them.

GPT-6.1 Sol · API

Day-60 retention rates are computed for each first-week behaviour (30.4%, 48.2%, 64.6%, 78.0%, etc.) and compared with the 28% average.

Gemini 3.5 Flash-Lite · Gemini

The output does not compute the overall average retention (28%) and does not compare the subgroup rates to it.

Tests before trusting the correlationRightRightWrong

GPT-6 Luna · API

It notes the correlation risk and proposes a specific test (control group, measuring bank connection, 10+ categorization, and retention).

GPT-6.1 Sol · API

Explicitly notes the observational nature and correlation risk, and proposes testing onboarding changes with first-week milestone attainment and day-60 retention as measures.

Gemini 3.5 Flash-Lite · Gemini

The output does not note that the retention differences are correlations and does not propose a test to confirm causation.

Gets the base of every number rightRightMixedWrong

GPT-6 Luna · API

All percentages are computed from the correct denominators, and bases are stated where needed (e.g., 1,120/4,000, 842/1,080).

GPT-6.1 Sol · API

All percentages use the correct denominators (e.g., 1080/4000 for 27%, 842/1080 for 78.0%, 278/2920 for 9.5%) and the base is clear from context.

Gemini 3.5 Flash-Lite · Gemini

The baseline for the new activation rate is given as ~34%, but the correct rate for 'connected bank AND categorised 10+' is 1,080/4,000 = 27%.

All got right 4

Respects explicit constraintsRightRightRight

GPT-6 Luna · API

The output is a review addressed to Omar, well under 800 words, and respects the requested form.

GPT-6.1 Sol · API

The output is a review under 800 words, addresses all requested parts, and respects the reader and the brief's form.

Gemini 3.5 Flash-Lite · Gemini

The output is a memo to Omar, under 800 words, and addresses the requested topics.

Produces the required deliverableRightRightRight

GPT-6 Luna · API

The review covers activation definition, evidence, changes, design proposal, and goal, is addressed to Omar, within length, and is actionable.

GPT-6.1 Sol · API

The review is complete, in the right form for Omar, within the word limit, and actionable with light edits.

Gemini 3.5 Flash-Lite · Gemini

The review is complete, in memo form, within the word limit, and a PM could act on it with light edits.

Activation defined by what predicts retentionRightRightRight

GPT-6 Luna · API

Activation is defined by the behaviour that predicts retention (bank+cat 10+), and fixes are judged by their effect on that event.

GPT-6.1 Sol · API

Defines activation as bank-and-categorise based on its strong retention signal, and judges fixes by their ability to drive that event.

Gemini 3.5 Flash-Lite · Gemini

Activation is redefined based on retention prediction (bank + categorise), and fixes are judged by their effect on that event.

Gets people to value soonerRightRightRight

GPT-6 Luna · API

The main fixes cut or defer steps before first value (defer theme, savings goal, notifications; move bank connection earlier) and guide users to categorize quickly.

GPT-6.1 Sol · API

Recommends cutting or deferring theme, account naming, notifications, and savings goals, and guiding users through transactions with suggestions, shortening the path to first value.

Gemini 3.5 Flash-Lite · Gemini

The main fixes cut or defer checklist steps before value and move bank connection and categorisation earlier.

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 · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6 AstrawithChatGPT	85.9	95.5	2	None
2	Sonnet 5.5withAPI	78.4	100.0	2	None
3	GPT-6.1 SolwithAPI	85.9	90.9	2	None
4	GPT-6 LunawithAPI	90.0	81.8	2	None
5	Haiku 5.5withAPI	75.9	86.7	2	None
6	Opus 5.5withClaude	88.4	78.4	2	None
7	Gemini 3.8 FlashwithAPI	66.6	51.9	2	None
8	Gemini 3.5 Flash-LitewithGemini	52.5	39.4	2	None

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.

Best valueNobody beats these on both score and costCost partly estimated (app runs with no usage meter)

Task score and relative cost by setup
Setup	Task score	Relative cost (cheapest = 1)
GPT-6 Luna · API	86	1.0×
Haiku 5.5 · API	82	1.8×
Gemini 3.5 Flash-Lite · Gemini	44	3.4× (partly estimated)
Gemini 3.8 Flash · API	56	13×
GPT-6.1 Sol · API	89	15×
Sonnet 5.5 · API	90	32×
Opus 5.5 · Claude	82	101× (partly estimated)
GPT-6 Astra · ChatGPT	91	122× (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

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

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

Reliably right

Where it slips

How it’s graded

Activation defined by what predicts retention

Gets people to value sooner

Plus our standard checks

The tasks

The outputs

Output

What activation should mean

What the evidence says

What to change

Design proposal and next-quarter goal

Grades and run details

Decision model checks

Run

Output

Onboarding review for Omar

Recommendation

What activation should mean

What the evidence says

What to change in onboarding

Design proposal and next quarter’s goal

Grades and run details

Decision model checks

Run

Output

1. What Activation Should Mean for Us

2. The Evidence: Where We Are Losing Users

3. What to Change in Onboarding

4. Verdict on the Design Proposal and Next Quarter's Goal

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Run

Check by check

Where they differ 7

All got right 4

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured