Tasks / Product Craft

Customer research call guide

Can the model write a guide that uncovers behaviour rather than opinions?

Task type v1.0 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

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

From 18 graded outputs by 9 models. 33% were usable with at most a quick edit.

Reliably right

  1. Asks what people did, not what they think96% pass
    Core questions ask for specific recent actions and events ('What happened next?', 'Who did you call?'), not opinions or predictions.
    Sonnet 5.5 · API · Why are warehouses leaving? Two execs, two theories
  2. Tests both theories fairly92% pass
    Both theories get questions that could disprove them (e.g., no action on robotics, happiness after slow go-live), and open-ended timeline questions leave room for a third cause.
    Sonnet 5.5 · API · Why are warehouses leaving? Two execs, two theories
  3. Different questions for user and signer92% pass
    The guide has distinct tracks: daily user questions focus on day-to-day experience and workarounds, signer questions on the decision process and alternatives.
    Sonnet 5.5 · API · Why are warehouses leaving? Two execs, two theories

Where it slips

  1. Marks what to cut if the call runs over28% pass
    The guide includes timed sections that add up to 45 minutes but does not say what to cut if time runs short, nor does it mark must-ask questions.
    GPT-6.1 Sol · API · Why are warehouses leaving? Two execs, two theories
  2. Doesn't trust the default reason47% pass
    The plan names the pre-selected default as a bias but does not use the behaviour columns to show that most 'too expensive' cancellers had stopped cooking; it only picks two contrasting individuals without the aggregate pattern.
    GPT-6 Astra · ChatGPT · Too expensive, by default
  3. Uses the supplied evidence correctly61% pass
    The claim 'Customers will say what helps their negotiation' is an unsupported behavioural statement not derived from the context.
    Haiku 5.5 · API · Why are warehouses leaving? Two execs, two theories

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. Designed to falsify

    For each learning goal, does the guide include a question whose honest answer could show the team's hypothesis is wrong?

    Passes when Every learning goal has at least one disconfirming, non-leading question, such as asking about the last time they had the problem and what they did instead.

  2. Asks what people did, not what they think

    Are the core questions about specific things the person actually did recently, rather than opinions, predictions or hypotheticals?

    Passes when The core of the guide asks for recent, specific instances (the last time, what happened, what they did) and follows up on them; opinions and predictions are absent or clearly secondary.

  3. Doesn't lead the witness

    Are the questions free of leading wording, of pitching the idea, and of hints at the answer the team hopes for?

    Passes when Questions are neutral and open, and the idea is not described (or only at the very end, after the problem questions).

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 · Marks what to cut if the call runs over · and 3 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then compare up to three outputs, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're a Staff PM at Crate. In four weeks, the exec team decides which of two bets gets our two squads next half, and both bets rest on a theory of why customers are leaving. We have three weeks for 12 customer calls of 45 minutes. Write the guide for them. It should include: the learning goals; the questions, with timings, for the two kinds of people we'll talk to; guidance for whoever runs the calls; any changes you'd make to the plan below; and what we'd need to hear to back each bet, or neither. Keep it under 1,500 words. The exec team will read it before the calls start.

What the model was given8 items: About Crate, The decision, CEO, Ana Ferreira, CPO, Marcus Hill, Exit survey (41 churned customers, last 12 months), Implementation and CRM data, The call plan, Notes from Customer Success and Legal
About CrateWarehouse management software for mid-size third-party logistics companies (3PLs). 340 customers, $22M ARR. Gross logo churn rose from 11% to 18% over the last year.
The decisionBet A: integrations with warehouse robotics (autonomous carts, pick-assist robots). Bet B: rebuild implementation so customers go live faster. Two squads for the next half go to one of them.
CEO, Ana Ferreira“We're losing warehouses because they're automating and we don't talk to their robots. Every lost customer I've spoken to mentions it.”
CPO, Marcus Hill“We lose them in the first year because implementation takes forever. They're gone before they ever see the value.”
Exit survey (41 churned customers, last 12 months)A single-choice question, 'Why are you leaving?'. Missing features or integrations: 46%. Too hard to implement: 22%. Price: 17%. Other: 15% (free-text box, left blank by all but two). 'Missing features or integrations' was the first option in the list.
Implementation and CRM dataMedian time from signing to go-live: 94 days; the contract promises 45. Customers who went live after 90 days churned at 2.4× the rate of the rest in their first year. 3 churned customers moved to a competitor with robotics integrations; robotics was cited in 9 lost new-logo deals. We don't know when in the year churned customers decided to leave.
The call plan12 calls: 6 with customers who churned in the last 9 months, 6 with at-risk customers (health score red). On each account we want to talk to the warehouse operations manager who used Crate every day and, separately, the VP of Operations or COO who signed the contract. Ana wants to run 4 of the calls with former customers she knows personally.
Notes from Customer Success and Legal5 of the 6 at-risk accounts are in renewal talks, and Customer Success is offering them discounts. Interviewers must not discuss pricing, discounts or what's on our roadmap. Every call must start by asking permission to record.
What a strong answer doesThe answer key the graders mark against

A guide that tests both theories fairly and leaves room for a third: the goals ask what actually drove each decision to leave or doubt, when it happened, and who made it, with questions that could disprove robotics (did they automate, did robots come up before they decided, what did they switch to and why) and implementation (how long go-live took, what happened in those months, whether value arrived) alike. It treats the exit survey as weak evidence (single choice, integrations listed first, 'Other' left blank) and the 2.4× figure as a correlation to probe, not a cause. The core is a timeline of the account: the story from signing to the moment they decided to leave or started doubting, with sequential probes. It has separate tracks for the daily user (what went wrong day to day) and the signer (how the decision was made, what alternatives they weighed), fits 45 minutes with timings, and opens with consent to record. It guards the calls: no pitching, pricing, discounts or roadmap promises, with a script for when an at-risk customer asks, and it addresses Ana's bias: she has a public view and personal relationships, so she should observe or take notes rather than lead, or at minimum use the same script and have her calls read separately. It ends with decision signals: what pattern across the 12 would back Bet A, what would back Bet B, and what would back neither (price, a business change, a missing capability that isn't robotics).

Critical failures (cap the score at 40)
  • Includes questions that promise roadmap items or discuss pricing or discounts with customers in renewal talks
  • Asks customers to choose between robotics and implementation as the reason they left
Task

Staff-level · v1.3

The outputs

9 setups answered this brief.

Needs more than a quick edit11 of 13 checks passed

Must-ask questions aren't marked and there's no guidance on what to drop if a 45-minute call runs over. A PM would need to add priority markers and cut instructions before running the calls.

Output

TO: Crate Executive Team FROM: Staff PM DATE: October 24, 2023 SUBJECT: Research Guide: H1 Bet Allocation & Churn Investigation

---

1. Learning Goals

Our existing data is noisy. The exit survey suffers from primacy bias (“Missing features/integrations” is the first option and bundles robotics with basic APIs/ERPs). Meanwhile, our CRM shows a strong correlation between >90-day implementations and churn, and robotics appears more often in lost new-logo deals (9) than confirmed churn (3).

Over the next three weeks, these calls must uncover: 1. The "Point of No Return": When in the lifecycle did the customer mentally commit to leaving, and what specific operational event triggered it? 2. Implementation Reality: What caused the gap between the 45-day promise and the 94-day reality? Is slow implementation a root cause of churn, or a symptom of a deeper product mismatch? 3. The "Missing Integration" Truth: For those who cited integrations, what specific systems failed to connect? How many actually bought or deployed warehouse robotics? 4. Post-Churn Ground Truth: What did churned customers switch to, how long did the replacement take to go live, and are they using robotics today?

---

2. Recommended Changes to the Call Plan

To get unbiased data within 12 calls, we must adjust the proposed plan:

  • Split the persona tracks across 8–10 accounts (Don't double up on 12): If we interview both the VP and the Ops Manager separately on each account, 12 calls only covers 6 accounts (3 churned, 3 at-risk)—too small a sample. Instead, target 8 churned accounts and 4 at-risk accounts, interviewing the VP on 6 and the Ops Manager on 6 (overlapping on 2–3 accounts to cross-verify stories).
  • Drop the 5 at-risk accounts in active renewal negotiations: Interviewing customers while CS is actively negotiating discounts with them is high-risk. Economic buyers will use the call as leverage to complain about price or demand roadmap commitments, skewing the data. Replace them with 1st-year customers who took >90 days to go live (regardless of health score) or churned customers from months 9–12.
  • Ana should not lead calls with personal contacts: Ana, your relationships are invaluable for securing these interviews, so we’d love your warm intros. However, former customers who like you personally will try to spare your feelings (blaming an external trend like "we needed robotics" rather than saying "your onboarding team failed us"). A neutral PM should lead all calls; execs can join as silent observers or listen to the recordings.
  • Sample across implementation times: Ensure the 8 churned accounts include both <60-day and >90-day implementations so we don't pre-validate Bet B.

---

3. Interview Guides (45 Minutes)

#### Track A: Economic Buyer (VP of Ops / COO) — 6 Calls Focus: ROI expectations, contract promises, strategic tech stack, and the switching decision.

  • 0–3m | Intro & Legal: Thank them for their time. Emphasize that this is a product research call, not a sales/renewal call. Ask permission to record.
  • 3–10m | The Buying Thesis:
  • "Take me back to when you signed with Crate. What was broken in your warehouse that you needed us to fix?"
  • "What were your expectations for how quickly Crate would be up and running?"
  • 10–22m | The Implementation Experience:
  • "Our contract promises a 45-day go-live. Walk me through what actually happened during your rollout."
  • If delayed: "What was the single biggest bottleneck? How did that delay impact your business and your team’s trust in Crate?"
  • 22–35m | The Turning Point & Tech Stack:
  • For Churned: "When did you first realize Crate wasn't going to work out long-term? What happened that week?"
  • For At-Risk: "As you look at your operations today, where is Crate falling short of what you signed up for?"
  • "What other software or hardware did you need Crate to talk to?" (Do not mention robotics first; see if they bring it up).
  • 35–42m | The Aftermath & Automation (Probing Bet A):
  • For Churned: "What system did you move to, and how was their implementation by comparison?"
  • "Where does warehouse automation or robotics—like autonomous carts or pick-assist—fit into your current operations or 12-month plan?" (If they have them: "Did Crate's lack of integration factor into your decision to leave?")
  • 42–45m | Wrap-up: "If you were in our shoes and could fix only one thing for mid-size 3PLs over the next six months, what would it be?"

#### Track B: Daily User (Warehouse Operations Manager) — 6 Calls Focus: Day-to-day friction, rollout reality on the floor, and manual workarounds.

  • 0–3m | Intro & Legal: Set a candid, blame-free tone. Ask permission to record.
  • 3–12m | Go-Live on the Floor:
  • "Were you involved in setting up Crate? Walk me through what it took to get your first warehouse workflow live."
  • "At what point did you feel your floor staff actually trusted the system?"
  • 12–27m | Daily Friction & Workarounds:
  • "Walk me through a typical peak day using Crate. Where did the software slow your team down?"
  • "What tasks were you forced to do outside of Crate—in spreadsheets or on paper—because the software couldn't handle it?"
  • 27–38m | Equipment & Integrations:
  • "What other tools, scanners, ERPs, or floor equipment did you use alongside Crate?"
  • "Did you have to manually move data between Crate and other systems? Which ones?"
  • "Are you using any autonomous carts or robotics on the floor today, or testing them?"
  • 38–45m | The Verdict:
  • For Churned: "When leadership decided to replace Crate, was your team relieved or frustrated? Why?"
  • For At-Risk: "If you could wave a magic wand and fix one bottleneck in how Crate runs your floor tomorrow, what would it be?"

---

4. Guidance for Interviewers

1. Legal & CS Guardrails (Strict): * Always get explicit verbal consent to record at the start. * Never discuss pricing, discounts, or commercial terms. If an at-risk customer brings up their renewal, say: "I’m completely firewalled from commercial discussions so I can focus purely on product feedback. I’ll make sure your CS manager follows up on the renewal side." * Never mention our roadmap or hint that we are debating Robotics vs. Implementation. 2. Don't Lead the Witness: Do not use the words "robotics" or "94-day implementation" until the final 10 minutes of the call. Let them name the pain first. If they say "integrations," ask "Which specific tools?" before assuming they mean hardware. 3. Hunt for Past Behavior, Not Future Wishes: Discount hypothetical statements like "We’d love to look at robotics next year." Focus on concrete actions: "Did you sign a vendor contract for robots?" or "How many hours a week did your team spend fixing data imports during onboarding?" 4. Two People Per Call: One interviewer drives; one silent note-taker logs verbatim quotes and timestamps in our research repository immediately after the call.

---

5. Decision Framework: How We Will Score the Evidence

After the 12 calls, we will map the findings against this rubric to allocate the two squads:

#### What we need to hear to back Bet A (Robotics Integrations): * Active Deployment: At least 4 of the 12 accounts either actively use warehouse robotics today, are in a paid pilot, or migrated to a competitor specifically and demonstrably to connect to robotics hardware. * Retention Blocker: At-risk customers state that robotics integration is a hard requirement for their upcoming 12-month operations, not just a "nice to have" for 3 years from now. * Implementation Was Survivable: Customers who took >90 days to go live tell us that while onboarding was painful, they eventually stabilized and only left because they outgrew our automation capabilities.

#### What we need to hear to back Bet B (Rebuild Implementation): * Early "Point of No Return": Churned customers report that executive trust was broken during a drawn-out onboarding (months 2–4), and they began shopping for replacements before month 6—meaning they never reached full adoption. * Software-Fixable Bottlenecks: The reasons for the 94-day median go-live are things engineering squads can actually solve (e.g., broken data migration tools, rigid warehouse configuration, self-serve setup gaps) rather than pure services/staffing issues. * Misattributed Survey Data: When we unpack "Missing features/integrations" from the exit survey, we find customers were actually frustrated that initial setup of standard integrations took months during onboarding.

#### What we need to hear to back NEITHER (Pivot the Roadmap): * Software vs. Services on Bet B: Implementation takes 94 days because customers have messy data or our Implementation Managers are understaffed—problems solved by hiring or process changes, not two engineering squads. * A Hidden Third Driver: "Missing features/integrations" turns out to be a core software gap unrelated to robotics (e.g., 3PL billing, client portals, or basic ERP/WMS syncs like NetSuite or Shopify), while robotics is exposed as a shiny object mainly affecting a small subset of new-logo sales.

Claims the judge couldn’t find in the brief

  • The exit survey option 'Missing features/integrations' bundles robotics with basic APIs/ERPs

Grades and run details

Decision model 85 · LLM judge 79
Decision model checks
  • partialUses the supplied evidence correctly11%
  • passAddresses the actual decision70%
  • passRespects explicit constraints9%
  • passIdentifies material uncertainty98%
  • partialAvoids unsupported claims40%
  • passProduces the required deliverable83%
  • passTests both theories fairly92%
  • passDifferent questions for user and signer100%
  • passProtects the calls and the accounts98%
  • passDesigned to falsify70%
  • passAsks what people did, not what they think92%
  • partialDoesn't lead the witness25%
  • partialMarks what to cut if the call runs over53%
Run
Run
#1
API response time
27 s
Submitted
13 Oct 2026
Critical failure: Includes questions that promise roadmap items or discuss pricing or discounts with customers in renewal talks. Score capped at 40.

Needs more than a quick edit3 of 13 checks passed

A PM would need to fix the prohibited either/or question, add a pricing/roadmap handling script, remove unsupported claims, and clarify the revised call coverage before using it. The reasoning and evidence also need rechecking.

Output

Customer Research Guide: H2 Strategic Bets (Robotics vs. Implementation)

To: Executive Team From: Staff Product Manager, Crate Date: Today Subject: 3-Week Customer Research Plan for H2 Resource Allocation (12 Calls)

---

Executive Summary & Plan Adjustments

In four weeks, the executive team will decide where to allocate both engineering squads for the next half. Ana believes we are losing customers because we lack warehouse robotics integrations (Bet A). Marcus believes we are losing them because our implementation cycle is broken and slow (Bet B).

The exit survey heavily favors Ana’s view (46% cite missing features/integrations), but UX research 101 teaches us that exit surveys are structurally flawed. Listing "missing features" first creates primacy bias, and the survey failed to capture temporal dynamics—specifically, when the decision to leave was made. Meanwhile, our internal data tells a damning story: our median time to go-live is 94 days (vs. 45 promised), and customers crossing 90 days churn at $2.4\times$ the rate.

We have 3 weeks to conduct 12 interviews (45 minutes each) to uncover the truth.

Critical Plan Adjustments (Read Before We Start)

  1. Split the Interviewees by Persona, Not Account: The original plan called for interviewing both the Ops Manager and the VP/COO on the same accounts. In 45 minutes, splitting time between a daily user and a high-level signer dilutes the insight. Instead, we will interview Warehouse Ops Managers to understand day-to-day friction (Implementation, UI, operational bottlenecks) and VPs of Ops / COOs to understand strategic drivers (ROI, contract expectations, robotics, and when the churn decision was actually made).
  2. Revise Ana’s Participation: Ana wants to run 4 calls with former customers she knows personally. We must advise against this. Founder-led interviews with churned customers carry massive politeness bias; customers will pull punches about implementation failures or invent reasons to let Ana down gently. Ana should observe these calls anonymously or listen to the recordings, but an unbiased PM or User Researcher must conduct them.
  3. Guardrails for At-Risk Accounts: Five of our six at-risk accounts are in active renewal talks with CS discounts on the table. Interviewers are strictly prohibited from discussing pricing, commercial concessions, or our product roadmap. We are strictly here to diagnose reality.

---

Learning Goals

  1. The Churn Timeline: Determine the exact month the customer mentally decided to leave. Did they decide during a painful 90+ day onboarding (supporting Bet B), or later when evaluating modernizing warehouse automation (supporting Bet A)?
  2. The "Missing Features" Audit: Deconstruct the 46% exit survey metric. When respondents checked "missing features/integrations," were they missing critical WMS functionality (e.g., wave planning, inventory accuracy) or specifically robotics middleware?
  3. Implementation Reality Check: Measure the operational impact of our 94-day median go-live time. Did the delay create cash-flow/trust erosion that doomed the account before day one, regardless of subsequent features?
  4. Robotics Urgency: For customers who mention robotics, are they actively deploying autonomous carts today, or is it a 3-year roadmap item they use as a stick to beat us with during negotiations?

---

Interview Guide: Two Personas

Persona 1: The Daily User (Warehouse Operations Manager)

Focus: Implementation friction, daily utility, operational roadblocks.

  • 00:00–03:00 | Intro & Permission:
  • "Thanks for taking the time. I’m [Name] from Crate. We’re doing research to figure out where we failed our customers and how we can improve. I am strictly here to learn about your experience—we won't be talking about pricing, renewals, or selling you anything today. Do I have your permission to record this call for internal notes?"
  • 03:00–12:00 | Day-to-Day Workflow & Reality:
  • "Take me back to your first 30 days using Crate. What did the day-to-day onboarding look like?"
  • "How long did it actually take for your floor team to feel comfortable using Crate without holding their breath?"
  • "What was the most painful workaround your team had to invent during your first three months?"
  • 12:00–25:00 | The Implementation Experience (Bet B Probe):
  • "Our data shows it takes about 90+ days for most warehouses to go live on Crate. What was your experience? How did those delays affect your team's stress levels and your relationship with our implementation team?"
  • "Looking back, did the slow start permanently damage your trust in the software, or did you bounce back once things were running?"
  • 25:00–35:00 | Equipment & Floor Integration (Bet A Probe):
  • "What automated hardware or mobile carts are running on your floor today? How do they talk to Crate, or do they?"
  • "When a pick-assist cart or AGV moves through your warehouse, where does the process break down between the robot and Crate?"
  • 35:00–42:00 | The Breaking Point:
  • "Think about the moment you realized you wanted to leave Crate (or started looking at alternatives). What happened on the warehouse floor that week?"
  • 42:00–45:00 | Wrap-Up:
  • "If you could wave a magic wand and change one thing about your onboarding or your day-one experience with Crate, what would it be?"

---

Persona 2: The Decision Maker (VP of Operations / COO)

Focus: Strategic expectations, ROI timelines, contract promises, and robotics strategy.

  • 00:00–03:00 | Intro & Permission:
  • "Thanks for joining. I’m [Name] from Crate. We’re reviewing why customers leave us to make radical changes to our roadmap and processes. I'm not here to pitch you or talk commercial terms—just to understand your true experience. May I record this call for note-taking purposes?"
  • 03:00–10:00 | The Purchase & Promise:
  • "When you signed with Crate, what was the primary business problem you needed solved in your first 6 months?"
  • "What timeline were you given for go-live during the sales cycle, and how did reality compare?"
  • 10:00–22:00 | Implementation & Time-to-Value (Bet B Probe):
  • "For a mid-size 3PL, cash flow and client onboarding speed are everything. How did Crate's onboarding timeline impact your ability to onboard your own end-clients?"
  • "At what point in your first year did you officially consider the implementation 'failed' or deeply troubled?"
  • 22:00–34:00 | The Robotics Mandate (Bet A Probe):
  • "Let’s talk about warehouse automation. How central are autonomous carts or robotics to your strategy today versus three years from now? Are you actively running them?"
  • "When you evaluated staying vs. leaving Crate, how heavy was the absence of robotics integrations in that final calculus? Was it the straw that broke the camel's back, or a nice-to-have?"
  • 34:00–42:00 | Uncovering the Timeline:
  • "Looking back at the lifecycle of your account, when did you mentally check out and decide you wouldn't renew? Was it during implementation, or months later?"
  • 42:00–45:00 | Wrap-Up:
  • "If Crate could fix only one thing—either our onboarding speed or our hardware/robotics connectivity—which one would have saved your account?"

---

Decision Criteria: What We Need to Hear

To make our final recommendation to Ana and Marcus, we will evaluate the 12 calls against the following thresholds:

Backing Bet A (Robotics Integrations)

We back Bet A only if: 1. VPs/COOs explicitly state that the lack of robotics was the primary, active catalyst for churning (not just a grievance listed in a post-hoc survey). 2. Churned customers are actively utilizing or piloting autonomous hardware today that could not function with Crate, resulting in operational deadlocks. 3. The exit survey's 46% "missing features" bucket is proven via interviews to be overwhelmingly about hardware/robotics connectivity rather than core WMS gaps (e.g., billing, inventory tracking).

Backing Bet B (Implementation Rebuild)

We back Bet B only if: 1. Interviews reveal that the 90+ day onboarding process created an insurmountable trust deficit; customers felt abandoned and never recovered faith in the product, rendering subsequent features irrelevant. 2. Daily users report that the complexity of setup and configuration was the primary driver of operational frustration, overshadowing missing integrations. 3. Churned customers admit they mentally checked out during or immediately after the botched implementation window, long before robotics ever entered the conversation.

Backing Neither (The Pivot Option)

We back Neither if: * The qualitative data shows a split narrative where both bets are symptoms of a deeper root cause—such as core WMS unreliability, pricing/value misalignment (17% in exit survey), or poor account management—meaning that building robots or speeding up a broken implementation will simply churn customers faster down a different path.

Claims the judge couldn’t find in the brief

  • For a mid-size 3PL, cash flow and client onboarding speed are everything.

Grades and run details

Decision model 65 · LLM judge 21
Decision model checks
  • partialUses the supplied evidence correctly28%
  • passAddresses the actual decision67%
  • partialRespects explicit constraints9%
  • passIdentifies material uncertainty92%
  • partialAvoids unsupported claims52%
  • passProduces the required deliverable22%
  • partialTests both theories fairly41%
  • passDifferent questions for user and signer97%
  • partialProtects the calls and the accounts88%
  • partialDesigned to falsify49%
  • passAsks what people did, not what they think59%
  • failDoesn't lead the witness83%
  • partialMarks what to cut if the call runs over45%
Run
Run
#1
Time to output
11 s
Submitted
1 Oct 2026

Needs more than a quick edit9 of 13 checks passed

A PM could use most of it after light edits, but the unsupported data-source claim and missing must-ask/cut guidance require re-checking and revision before running the calls.

Output

Crate churn research: decision guide

Decision: In four weeks, allocate two squads next half to robotics integrations (A), implementation redesign (B), or conclude that neither retention thesis is sufficiently supported.

Research window: Three weeks; twelve 45-minute interviews. These calls will test explanations and reconstruct decisions—not estimate how common each churn cause is across 340 customers.

1. Learning goals

  1. Reconstruct the departure sequence: What happened between signing, implementation, first value, operational problems, searching for alternatives, and deciding to leave?
  2. Identify the decisive mechanism: Was robotics incompatibility or implementation friction necessary to the departure, merely contributory, or a justification offered afterward?
  3. Find actionable scope: Which integrations or implementation changes could plausibly have prevented the problem? Could two squads address them within a half?
  4. Look for competing explanations and counterexamples: Other missing capabilities, service failures, customer-side constraints, or business changes may explain both delays and churn.
  5. Separate operational pain from buying decisions: Did daily users and economic buyers experience the same problem and agree on why the relationship failed?

Starting evidence is suggestive, not conclusive. The survey’s broad, first-listed integration option does not establish robotics demand. The 2.4× churn association does not establish that delays caused churn. Competitor destinations do not establish purchase motives; nine lost prospects are acquisition evidence, not retention evidence.

2. Changes to the call plan

Resolve the interview arithmetic. Twelve accounts with two separate interviews each would require 24 calls. Within the limit, recruit six accounts, interviewing both roles separately: twelve calls total.

Use four churned accounts and two at-risk accounts. This prioritizes actual decisions and reduces exposure to active renewal negotiations while retaining prospective evidence.

Select accounts purposively from the eligible pool, not through executive relationships:

  • Include late and on-time go-lives, plus a failed/never-live implementation if available.
  • Include known robotics signals and accounts without them.
  • Include first-year and longer-tenure departures.
  • Seek counterexamples: late implementation without abandonment; robotics discussion without an actual deployment or switch.

Do not attempt a fully balanced matrix with six accounts. Document selection, refusals, substitutions, and gaps. Check whether the nine-month churn window excludes relevant cases from the annual churn rise; expand to twelve months if needed.

For at-risk accounts, prioritize the one outside renewal talks. CS must confirm that participation—or refusal—will not affect commercial treatment. If a renewal makes independent research impractical, substitute another eligible account.

Ana should not lead interviews with former customers she knows. Use a neutral researcher or PM who owns neither bet. Ana can help recruit through a standard invitation and review consented recordings afterward. Avoid executive attendance that could suppress criticism.

Before calls, prepare a factual account timeline from CRM, implementation logs, support records, and available usage data. Keep interpretations separate. Examine cohort definitions and potential confounders behind the 2.4× figure.

3. Interview guides

Both guides total 45 minutes. Ask open questions first; introduce the two hypotheses only after the participant’s unaided account.

Guide A: Warehouse operations manager

TimeQuestions
0–4 minFirst words, before recording: “May we record this conversation for internal research? Saying no is completely fine; we can take notes instead.” Wait for permission before starting recording. Explain that this is research, not a sales or renewal conversation; participation is optional. “What did you personally own, and during which period?”
4–10 min“What was happening in the warehouse when you chose Crate? What job did you expect it to improve? How would you have recognized success?”
10–20 min“Walk me through signing to the first real production use.” Probe planned versus actual milestones, dependencies, workarounds, ownership, and first useful outcome. “Tell me about a specific day when progress stalled. What happened next?” If never live, trace the last completed milestone and stopping point.
20–30 minChurned: “When did you first think Crate might not work for you? Describe the incident. What happened between that and leaving?” At-risk: “How is Crate working today? Describe the most recent serious problem. Has anyone discussed changing systems? What has actually happened so far?” For both: “What did this cost operationally—time, throughput, errors, or customer commitments? Who saw it?”
30–39 min“What changes in equipment or workflow occurred during this period?” Then probe robotics neutrally: “Were robots evaluated or deployed? Which systems, for what workflow, and when? What specifically could Crate not do? What workaround did you try?” Separately: “Once implementation ended—or stalled—what problems remained?” Do not assume either issue existed.
39–45 min“Which issue mattered most, and what makes you say that? If only that issue had been resolved then, what would still have made Crate unsuitable?” Ask for optional, redacted supporting artifacts and names of decision participants. Summarize the timeline and invite corrections: “What important explanation have I missed?”

Guide B: VP Operations or COO

TimeQuestions
0–4 minUse the same recording-permission opening and research boundaries. “What was your role in selection, implementation oversight, and the decision to stay or leave?”
4–10 min“What business outcome justified choosing Crate? What deadline or event made that outcome important? What expectations were set?”
10–22 minChurned: “Walk me from the first concern to the decision to leave. When was that decision effectively made, rather than formally communicated? Who influenced it? What alternatives did you evaluate?” At-risk: “How are you evaluating whether Crate is working? Has a change been proposed or authorized? What evidence and next steps are involved?” Separate firsthand knowledge from reports by others.
22–31 min“What specific event most changed your confidence? What did you do afterward? What attempts were made to recover the relationship?” Churned: “What did you choose instead, and what requirement made it preferable? Is it operating successfully yet?” At-risk: “What requirements would any alternative have to meet?”
31–39 minTest both explanations, varying their order between interviews. “What role, if any, did implementation timing play? When did it affect your judgment?” “What role, if any, did warehouse automation play? Was there an approved project, named equipment, deployment date, or demonstrated integration requirement?” “Which problems would have remained if each had been solved?”
39–45 min“What would Crate have needed to change, by what date, to alter your decision?” Treat this counterfactual as weaker evidence than actions already taken. “What evidence might contradict your explanation?” Request optional artifacts, recap, and invite corrections.

4. Interviewer operating rules

  • Be curious, not persuasive. Do not quote Ana, Marcus, survey percentages, or the churn correlation.
  • Probe concrete events: “When?” “Who?” “What happened next?” “How do you know?” Distinguish recollection, inference, and observed action.
  • Do not discuss pricing, discounts, or roadmap commitments. If raised: “I can’t discuss commercial terms or future plans here; your account team can handle those separately.” Do not promise escalation outcomes.
  • Interview roles separately; do not reveal the other person’s answers. Disagreement is evidence to investigate, not average away.
  • Use a neutral note-taker. After each call, record chronology, spontaneous versus prompted reasons, supporting evidence, contradictions, and confidence.

5. Evidence required for the decision

Back A if multiple independent accounts—including actual departures—show committed robotics adoption, a specific blocking Crate incompatibility before the departure decision, failed workarounds, and credible evidence that resolving it would have preserved the relationship. Shared, feasible integration needs must emerge. Generic “missing features,” aspirational automation, or competitor capabilities alone do not qualify.

Back B if multiple accounts—including actual departures—show preventable implementation failures preceding loss of confidence, concrete operational consequences, and a credible path from faster successful activation to retention. Identify repeatable Crate-owned bottlenecks. Delays driven mainly by customer readiness, complexity, or already-failing relationships weaken this case.

Back neither if accounts remain contradictory, both theories depend on hypothetical claims, another mechanism dominates, or neither intervention fits the capacity and time available. If both qualify, compare affected retention exposure, intervention feasibility, and remaining uncertainty—not interview vote totals.

Run two pilot calls in week one, finish most interviews in week two, and reserve week three for remaining calls and synthesis. Produce one evidence sheet per account; paired interviews count as one account, not two confirmations. The final readout should combine those cases with cohort data, explicitly separate causality from correlation, and state what remains unproven before recommending either allocation.

Claims the judge couldn’t find in the brief

  • Support records and usage data exist and can be used to prepare account timelines.

Grades and run details

Decision model 96 · LLM judge 64
Decision model checks
  • passUses the supplied evidence correctly41%
  • passAddresses the actual decision64%
  • passRespects explicit constraints18%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims64%
  • passProduces the required deliverable83%
  • passTests both theories fairly100%
  • passDifferent questions for user and signer100%
  • passProtects the calls and the accounts100%
  • passDesigned to falsify80%
  • passAsks what people did, not what they think100%
  • passDoesn't lead the witness59%
  • partialMarks what to cut if the call runs over55%
Run
Run
#1
Time to output
60 s
Submitted
1 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 10

Uses the supplied evidence correctlyWrongWrongMixed
Gemini 4 Argon · API

Claims that the exit survey option 'bundles robotics with basic APIs/ERPs' is interpretation presented as fact; the context only gives the label 'Missing features or integrations'.

Gemini 3.5 Flash-Lite · Gemini

It uses most supplied numbers correctly, but invents or overstates current-situation claims such as “cash flow and client onboarding speed are everything” and “90+ days for most warehouses” when the context only gives a 94-day median.

GPT-6 Astra · ChatGPT

It invents current data sources by saying to prepare timelines from support records and usage data, which are not supplied.

Addresses the actual decisionRightRightMixed
Gemini 4 Argon · API

Provides a clear framework with specific evidence thresholds that would back Bet A, Bet B, or neither, which is what the brief asked for in a research guide.

Gemini 3.5 Flash-Lite · Gemini

It does not choose a bet now, but the supplied evidence cannot support a final call; it clearly says the research will settle the decision and gives explicit thresholds for Bet A, Bet B, or neither.

GPT-6 Astra · ChatGPT

It does not commit to one answer early; it only states the decision frame and later gives evidence thresholds, leaving the exec team without a clear recommendation.

Respects explicit constraintsRightWrongMixed
Gemini 4 Argon · API

Includes all requested sections, stays under 1,500 words, addresses no-pricing/roadmap rule, recording consent, Ana's calls, and is written for the exec team.

Gemini 3.5 Flash-Lite · Gemini

It is under 1,500 words and includes the required sections, but it does not enforce the no-pricing/no-roadmap rule in practice because it lacks a script for when customers raise those topics and even mentions “roadmap” in the executive intro.

GPT-6 Astra · ChatGPT

It respects the no-pricing/roadmap and consent constraints, but it does not mark must-ask questions or say what to cut if time runs short, and it introduces unsupported data sources.

Avoids unsupported claimsRightWrongRight
Gemini 4 Argon · API

Most interpretations are framed as hypotheses or things to probe; the primacy-bias point is a reasonable reading of the evidence even if stated confidently.

Gemini 3.5 Flash-Lite · Gemini

It presents several interpretations as established facts, including that the exit survey “heavily favors” Ana’s view, that founder-led interviews carry “massive” politeness bias, and that cash flow/onboarding speed are “everything” for mid-size 3PLs.

GPT-6 Astra · ChatGPT

It labels exec theories and survey/correlation evidence as suggestive rather than established, though it still has a minor unsupported data-source claim.

Produces the required deliverableRightMixedRight
Gemini 4 Argon · API

A complete research guide memo with learning goals, two timed question tracks, interviewer guidance, plan changes, and decision framework, usable by the exec team.

Gemini 3.5 Flash-Lite · Gemini

The guide is mostly present, but it is not fully usable because it changes the plan to split personas without specifying how the 12 calls and 6 accounts will be covered, and it omits a required handling script for pricing/roadmap questions.

GPT-6 Astra · ChatGPT

It provides learning goals, timed two-role questions, interviewer guidance, plan changes, and decision signals within the length, and the exec team could act on it with light edits.

Tests both theories fairlyRightWrongRight
Gemini 4 Argon · API

Each theory gets questions that could disprove it—robotics via actual deployment status, implementation via early trust-break timing—and open questions about the leaving decision come before either theory is raised.

Gemini 3.5 Flash-Lite · Gemini

It gives both theories probes, but the daily-user track is weighted toward implementation, the signer track asks about robotics only after implementation, and the wrap-up asks which bet would have saved the account, so it does not test both theories fairly.

GPT-6 Astra · ChatGPT

It asks open timeline questions before introducing either theory and gives both robotics and implementation questions that could disprove each, while leaving room for third causes.

Protects the calls and the accountsRightWrongRight
Gemini 4 Argon · API

Script for deflecting pricing/roadmap questions, recording consent at start, and concrete plan for Ana to do warm intros only while a neutral PM leads.

Gemini 3.5 Flash-Lite · Gemini

It includes consent to record and a no-pricing/no-roadmap rule, but it provides no script for handling pricing, discount, or roadmap questions and the executive intro mentions roadmap changes.

GPT-6 Astra · ChatGPT

It starts with recording consent, bans pricing/discounts/roadmap, gives a script if raised, and concretely removes Ana from leading calls with people she knows.

Designed to falsifyRightWrongRight
Gemini 4 Argon · API

Each learning goal has a disconfirming path: e.g. 'Did Crate's lack of integration factor into your decision?' could yield 'no,' and probing actual robot deployment could show it wasn't relevant.

Gemini 3.5 Flash-Lite · Gemini

Not every learning goal has a neutral disconfirming question; for example, the implementation goal asks whether delays “permanently damaged trust,” and the robotics goal asks whether robotics was “the straw that broke the camel's back,” rather than first eliciting the decision story.

GPT-6 Astra · ChatGPT

Each learning goal has disconfirming probes, including counterexamples, timing of decisions, failed workarounds, and questions about what would still have caused churn.

Asks what people did, not what they thinkRightMixedRight
Gemini 4 Argon · API

Core questions ask about specific past events—'Walk me through what actually happened during your rollout,' 'When did you first realize,' 'What system did you move to'—not hypotheticals.

Gemini 3.5 Flash-Lite · Gemini

Some questions ask about recent actions, but the core includes many opinions, hypotheticals, and leading prompts such as “magic wand,” “how heavy was the absence of robotics,” and “which one would have saved your account.”

GPT-6 Astra · ChatGPT

The core questions ask for specific events, timelines, actions, alternatives evaluated, and what happened next, with hypotheticals clearly secondary.

Doesn't lead the witnessRightWrongRight
Gemini 4 Argon · API

Explicitly instructs interviewers not to mention robotics or '94-day implementation' until the final 10 minutes, and to let customers name the pain first; questions are open-ended.

Gemini 3.5 Flash-Lite · Gemini

Several questions are leading or biased, including “Our data shows it takes about 90+ days,” “did the slow start permanently damage your trust,” “cash flow and client onboarding speed are everything,” and the final either/or bet question.

GPT-6 Astra · ChatGPT

Questions are open and neutral, hypotheses are introduced only after unaided accounts, and the guide does not pitch either bet or ask customers to choose.

All got wrong 1

Marks what to cut if the call runs overWrongWrongWrong
Gemini 4 Argon · API

Sections are timed and sum to 45 minutes, but must-ask questions are not marked and there is no guidance on what to cut if the call runs over.

Gemini 3.5 Flash-Lite · Gemini

Timings add to 45 minutes, but the number of questions is too high for 45 minutes, must-ask questions are not marked, and there is no guidance on what to cut if time runs short.

GPT-6 Astra · ChatGPT

Timings add to 45 minutes, but the guide does not mark must-ask questions or specify what to cut if time runs short.

All got right 2

Identifies material uncertaintyRightRightRight
Gemini 4 Argon · API

Explicitly flags the exit survey as noisy, treats the 2.4× implementation-churn correlation as something to probe not assume causal, and names a third-driver possibility with concrete signals.

Gemini 3.5 Flash-Lite · Gemini

It names the key unknowns—when customers decided to leave, whether “missing features” means robotics or core WMS, and whether slow implementation caused trust loss—and says the calls and decision thresholds would resolve them.

GPT-6 Astra · ChatGPT

It names key unknowns such as survey bias, correlation versus causation, timing of churn decisions, confounders, and renewal exposure, and says how calls and data checks would resolve them.

Different questions for user and signerRightRightRight
Gemini 4 Argon · API

Track A targets the economic buyer with decision-process and ROI questions; Track B targets the daily user with floor-level friction and workaround questions, clearly differentiated.

Gemini 3.5 Flash-Lite · Gemini

It has distinct tracks: the daily user is asked about onboarding, workarounds, floor equipment, and the breaking point, while the signer is asked about purchase promises, time-to-value, robotics strategy, and renewal decision timing.

GPT-6 Astra · ChatGPT

It has separate timed tracks for the daily warehouse operations manager and the VP/COO signer, matched to operational experience versus decision process.

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 95% of 42 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Sonnet 5.5withAPI88.088.52None
2GPT-6.1 SolwithAPI83.784.92None
3Opus 5.5withClaude92.074.72None
4GPT-6 AstrawithChatGPT89.774.52None
5Haiku 5.5withAPI89.770.12None
6GPT-6 LunawithAPI83.773.92None
7Gemini 4 ArgonwithAPI75.670.12None
8Gemini 3.8 FlashwithAPI67.851.92None
9Gemini 3.5 Flash-LitewithGemini63.926.121 capped

What you get for the cost

GPT-6.1 Sol · API scores within 4 points of Sonnet 5.5 · API at a third 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 87, 43× the cheapestGPT-6.1 Sol · API: task score 83, 13× the cheapestOpus 5.5 · Claude: task score 82, 71× the cheapestGPT-6 Astra · ChatGPT: task score 81, 66× the cheapestHaiku 5.5 · API: task score 78, 2.3× the cheapestGPT-6 Luna · API: task score 78, the cheapestGemini 4 Argon · API: task score 70, 13× the cheapestGemini 3.8 Flash · API: task score 57, 8.3× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 42, 3.0× the cheapest, 1 critical failureSonnet 5.5 · APIGPT-6.1 Sol · APIOpus 5.5 · ClaudeGPT-6 Astra · ChatGPTHaiku 5.5 · APIGPT-6 Luna · APIGemini 4 Argon · 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 · API781.0×
Haiku 5.5 · API782.3×
Gemini 3.5 Flash-Lite · Gemini423.0×
Gemini 3.8 Flash · API578.3×
GPT-6.1 Sol · API8313×
Gemini 4 Argon · API7013×
Sonnet 5.5 · API8743×
GPT-6 Astra · ChatGPT8166×
Opus 5.5 · Claude8271×

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

Preparing for customer calls.

Why it matters

Leading questions produce the answers you wanted. A good guide is designed to prove you wrong.

What good looks like

  • Past-behaviour questions
  • No leading prompts
  • Clear learning goals
  • Could disprove the team's hypothesis

Deliberately not measured

    Capability tested

    Question design

    The failure we’re looking for

    Hypothetical, leading questions

    Grading

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