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

Needs more than a quick edit11 of 13 checks passed

The guide lacks separate question tracks for the daily user and the decision-maker, and it doesn't prioritize questions for time management. A PM would need to add role-specific questions and mark must-asks, which is more than a quick edit.

Output

Crate customer research guide: why are warehouses leaving?

Purpose: In three weeks, learn whether churn is more plausibly driven by missing robotics integrations (Bet A), slow implementation (Bet B), or something else. These calls are for understanding customers’ experiences—not selling, negotiating renewals, or validating either executive’s theory.

Learning goals

For each account, establish:

  • What happened, and when? Separate implementation, go-live, first value, automation needs, and the point when leaving became likely or decided.
  • What mattered most? Distinguish a decisive problem from a feature request, contributing frustration, or reason offered after the fact.
  • What evidence supports the explanation? Look for actual workflow impact, workarounds, alternatives evaluated, and actions taken—not just opinions or hypotheticals.
  • What might change the outcome? Explore credible counterfactuals without promising anything.
  • What else explains churn? Surface causes beyond A and B.

Recommended plan changes

The proposed 12 calls cannot cover both the daily user and contract signer on all 12 accounts: that would require 24 interviews. Keep the 12-call cap, but interview two roles separately at each of six accounts: three churned accounts and three red-health accounts, with one warehouse operations manager and one VP of Operations/COO per account. This gives paired perspectives and preserves both customer situations, but is a small, directional sample—not a prevalence estimate. Select accounts for varied implementation times and automation situations where possible; don’t choose only accounts with known robotics issues.

Do not have Ana conduct four calls with former customers she knows personally. Her involvement risks courtesy bias and leading the conversation. Use an independent interviewer; ideally don’t include personal contacts in this small core sample. If one is included, disclose the relationship, and have Ana neither attend nor receive attributable notes.

Because five at-risk accounts are in renewal talks and receiving discount offers, use an interviewer outside the account/renewal team. Tell participants their answers won’t affect service or renewal discussions. Don’t share interview content with the account team in a way that could be used in negotiation. Follow Legal’s recording requirement below.

45-minute guide: churned customers

TimeQuestions
0–3Start by asking: “May I record this conversation?” If no, take notes instead. Explain the purpose, that there are no sales or renewal implications, and that we won’t discuss pricing, discounts, or roadmap.
3–7“What was your role in choosing and using Crate?” “What did your warehouse need the system to help you do?”
7–18“Take me through your experience with Crate, from signing through the decision to leave.” “When did you first consider switching? What happened just before that?” “Who was involved, and when was the decision effectively made?”
18–27“How did implementation and go-live go?” “Where, if anywhere, did it take longer or require more effort than expected?” “What was the impact on your operation?” “When did you first get value from Crate?”
27–35“Did automation or robotics play a role in your warehouse during this period?” If yes: “What equipment or workflow?” “How did it work with Crate?” “What did you do when it didn’t?” “When did this become a problem relative to the decision to leave?”
35–41“What alternatives did you consider, and what drove the choice?” “What, if anything, could realistically have changed your decision?” “What was the most important reason—not just one frustration?”
41–45“What haven’t I asked that matters?” “Is there anything we should correct about our understanding?” Thank them.

45-minute guide: red-health customers

Use the same opening and close. Do not imply that Crate can fix an issue or influence renewal terms.

TimeQuestions
0–3Start by asking: “May I record this conversation?” If no, take notes. Explain that this is research, not a sales or renewal conversation; participation won’t affect service or renewal discussions.
3–7“What is your role in using or overseeing Crate?” “What does a successful warehouse operation look like for you?”
7–17“How has your experience with Crate unfolded so far?” “When have things gone better or worse than expected?” “What is working well today?”
17–27“Where are you in implementation and go-live?” “What has taken the most effort or time?” “What value, if any, have you realized so far?” “What is the operational impact of anything that remains difficult?”
27–35“How, if at all, does automation or robotics fit into your operation?” If relevant: “What equipment or workflow?” “How does it work with Crate today?” “What workarounds do you use?” “When did this become important?”
35–41“Have you considered alternatives to Crate? What prompted that, if so?” “What would need to be true for Crate to work well for your operation?” “What are the most important unresolved issues?”
41–45“What haven’t I asked that matters?” “Is there anything we should correct?” Thank them.

Guidance for interviewers

  • Ask the questions as written, but follow the participant’s timeline. Use neutral probes: “What happened next?” “Can you give me an example?” “How do you know?”
  • Don’t offer Ana’s or Marcus’s theories, mention the exit-survey results, or ask leading questions such as “Would robots have saved you?” Ask about events before asking for interpretations.
  • Separate when an issue occurred from when the participant says it mattered. Probe for specific actions and evidence: evaluations, workarounds, escalations, or a switch decision.
  • Don’t discuss pricing, discounts, renewal negotiations, or roadmap. If asked about roadmap, say you can’t speak to future plans. Don’t make promises or relay an interview answer as a renewal signal.
  • Interview roles separately. Record role and account context in notes, but distinguish direct experience from second-hand views. Afterward, write a short timeline, leading cause(s), evidence, and uncertainties for each account.
  • Treat recordings and notes as confidential; use only with consent and Legal-approved handling. Don’t present anecdotes as representative of all customers.

What would support each bet?

Support Bet A (robotics integrations): In multiple independent accounts, a specific robotics workflow or integration gap caused meaningful operational friction, and that issue arose before the decision to leave or seriously consider leaving. Stronger evidence: an actual robot deployment or concrete plan, a costly workaround, and a competitor selected or evaluated for this capability. A feature mention alone—or robotics cited in a new-logo deal—doesn’t establish that it drove existing-customer churn.

Support Bet B (faster implementation): In multiple accounts, implementation delays or effort materially postponed go-live or first value, caused operational consequences, and preceded the decision to leave or consider leaving. Stronger evidence: specific preventable blockers, an unmet expectation, and a credible account that earlier go-live would have changed the outcome. The 94-day median, 45-day promise, and 2.4× association make this worth testing, but do not prove causation.

Support neither / remain uncertain: The call evidence points more strongly to another cause; A or B appears only after the decision; accounts describe issues without meaningful consequences; or evidence is mixed and no cause consistently precedes churn. Don’t force a winner from 12 interviews. Compare patterns across accounts, not individual interview counts, and triangulate with CRM timelines and other churn evidence. The exit survey’s single-choice format, first-listed feature option, and sparse free text limit what it can establish.

Grades and run details

Decision model 92 · LLM judge 79
Decision model checks
  • passUses the supplied evidence correctly76%
  • passAddresses the actual decision73%
  • passRespects explicit constraints43%
  • passIdentifies material uncertainty95%
  • passAvoids unsupported claims45%
  • passProduces the required deliverable72%
  • passTests both theories fairly100%
  • partialDifferent questions for user and signer74%
  • passProtects the calls and the accounts76%
  • passDesigned to falsify79%
  • passAsks what people did, not what they think97%
  • passDoesn't lead the witness64%
  • partialMarks what to cut if the call runs over52%
Run
Run
#1
API response time
29 s
Submitted
30 Sept 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 4

Uses the supplied evidence correctlyWrongRightMixed
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'.

GPT-6 Luna · API

Every statement about the current situation is taken directly from the supplied context, with no invented facts.

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.

GPT-6 Luna · API

The guide clearly states what evidence from the calls would back Bet A, Bet B, or neither, which is the decision framework the exec team needs.

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 constraintsRightRightMixed
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.

GPT-6 Luna · API

The guide respects all constraints: it includes learning goals, timed questions for both customer types, interviewer guidance, plan changes, and decision signals; it stays under 1,500 words; it enforces no pricing/roadmap discussion and recording consent.

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.

Different questions for user and signerRightWrongRight
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.

GPT-6 Luna · API

The guide does not provide distinct question tracks for the daily warehouse operations manager and the executive who signed; it only differentiates churned vs at-risk customers, with the same questions for both roles within each track.

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.

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.

GPT-6 Luna · API

Although timed sections add up to 45 minutes, the guide does not mark must-ask questions or say what to cut if time runs short, so an interviewer could run out of time without covering the most critical probes.

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 8

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.

GPT-6 Luna · API

It names key unknowns (exit survey weakness, correlation vs causation, timing of churn decision) and says how the calls will resolve them through patterns and timelines.

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.

Avoids unsupported claimsRightRightRight
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.

GPT-6 Luna · API

Interpretations like the exit survey's limitations and the 2.4× association are clearly labeled as not proving causation, not presented as established fact.

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 deliverableRightRightRight
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.

GPT-6 Luna · API

The output is a complete call guide with all requested sections, written for the exec team, and well under 1,500 words.

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 fairlyRightRightRight
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.

GPT-6 Luna · API

Both the robotics and implementation theories get questions that could disprove them, open timeline questions come first, and the guide explicitly leaves room for a third cause.

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 accountsRightRightRight
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.

GPT-6 Luna · API

It gives a clear rule and script for pricing/roadmap questions, starts with recording consent, and addresses Ana's bias by recommending she not lead calls and, if a contact is included, that she not attend or receive attributable notes.

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 falsifyRightRightRight
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.

GPT-6 Luna · API

Every learning goal has questions whose honest answers could disprove the hypothesis, such as asking when automation became a problem relative to the decision to leave.

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 thinkRightRightRight
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.

GPT-6 Luna · API

Core questions ask for specific past events and actions (e.g., 'When did you first consider switching? What happened just before that?'), with opinions and predictions clearly secondary.

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 witnessRightRightRight
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.

GPT-6 Luna · API

Questions are neutral and open; the guide avoids mentioning either executive's theory until after the participant's own timeline, and prohibits leading probes.

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.

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