# Relationship model answers

Synthetic descriptions only. Personal responses are in the separate blank worksheet.

## N01

The default view contains 70 fictional rows. week_start=2026-05-04, series=Export recorded, accounts=2; week_start=2026-05-11, series=Confirmed quality use, accounts=1

Spoken answer: I would use confirmed quality-use accounts as the proposed North Star and keep exports as a diagnostic. Exporting a video does not tell us whether an approved version reached its audience. I would first test confirmation coverage before interpreting the gap as poor value.

Interpretation and guardrail: Weekly distinct accounts, anchored to each milestone time. Full UTC weeks only. Ready/export alternatives do not redefine DB01 or prove audience value. Missing confirmations undercount.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## N02

The default view contains 4 fictional rows. driver=Render completion / all tasks, fraction=0.80154; driver=Selected value / all tasks, fraction=0.17

Spoken answer: In this fictional snapshot, render completion is about 80 percent while confirmed quality use is 17 percent. I would investigate the post-render handoff, but the gap also contains missing confirmation and review. I would inspect representative task records before choosing a redesign.

Interpretation and guardrail: Task fractions use stated denominators. These are candidate drivers, not additive contributions to distinct-account North Star value. Missing reviews are unknown.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## N03

The default view contains 5 fictional rows. link=Arithmetic identity, relationship=Eligible account-weeks × confirmed quality-use account share, boundary=Only within exactly the same eligible account-week population; not task conversion multiplication; link=Selected alternative, relationship=quality, boundary=Changes reach definition only; DB01 remains quality use

Spoken answer: I would keep the value chain visible without pretending the arrows are proven. Within one account-week population, eligible accounts times the quality-use share gives quality-use accounts. Recovery leading to renewal is a hypothesis that needs a separate intervention test.

Interpretation and guardrail: Links are labeled identities, descriptive associations or hypotheses. Arrows are not causal weights; selected definition is a practice alternative.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## J01

The default view contains 14 fictional rows. source=Confirmed use, target=Meets selected definition, tasks=221; source=Created, target=Last recorded checkpoint, tasks=258

Spoken answer: I would follow the recorded path to find where evidence stops, rather than force review and export into one funnel. A direct link may mean a skipped event. I would inspect the task evidence and operator workflow before calling that point abandonment.

Interpretation and guardrail: Widths are tasks. Events ordered by recorded time; review and export are parallel, not forced into a funnel. Missing events are skipped. Terminal state is not proven abandonment. Before-creation timestamps excluded in J02.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## J02

The default view contains 1 fictional rows. tasks=1300, chronology_excluded=0, ready_review_unknown=425, exports_without_preview=0, tasks_with_failed_attempt=193, recovered_to_quality_value=6

Spoken answer: The fictional audit has 425 ready outputs without a review and 193 tasks with a failed attempt; only six of those tasks later meet quality use. I would separate missing reviews from recovery friction before proposing a retry change. Recent tasks also have less time to recover.

Interpretation and guardrail: Inspect audit counts before interpreting flow. Failed attempts can recover within one task. Unreviewed ready output is unknown quality, not rejected output.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## G01

The default view contains 9 fictional rows. source=Mature signups, target=Early selected value, users=12; source=Mature signups, target=Early task; no selected value, users=78

Spoken answer: I would compare later quality return for all mature signups, including users with no first-week task. The workflow control changes early exposure, not the denominator. Any difference suggests an investigation; it does not prove activation caused retention.

Interpretation and guardrail: All mature signups including zero activity. Workflow filters first-week task exposure only; later quality return covers any workflow. Quality early exposure requires review and use before day seven. Association is not causal.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## G02

The default view contains 10 fictional rows. cohort_month=2026-05-01, signal=Early selected value, return_fraction=0.4, eligible_users=5; cohort_month=2026-05-01, signal=No early selected value, return_fraction=0.06383, eligible_users=47

Spoken answer: I would compare early-value groups within the same mature signup month, then check the counts. Mixing months can hide acquisition changes. I would avoid treating the partially mature latest signup month as a complete cohort.

Interpretation and guardrail: Same mature signup-month denominator and fixed day28–41 outcome. Inspect G03 counts. Latest partially mature signup month is not equivalent to a full-month cohort; prediction excludes it.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## G03

The default view contains 10 fictional rows. cohort_month=2026-05-01, signal=Early selected value, eligible_users=5, returned_users=2, return_fraction=0.4, approximate_standard_error=0.21909; cohort_month=2026-05-01, signal=No early selected value, eligible_users=47, returned_users=3, return_fraction=0.06383, approximate_standard_error=0.03566

Spoken answer: I would lead with the eligible and returned user counts before the percentage. Sparse groups and shared team accounts make the uncertainty larger than a simple chart suggests. I would collect another mature cohort before making a rollout decision.

Interpretation and guardrail: Counts precede rates. Standard error is a descriptive binomial approximation; small groups and clustered team accounts weaken independence. No treatment effect or company baseline.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## G04

The default view contains 4 fictional rows. task_band=0, mean_early_value_tasks=0.0, return_fraction=0.12687, eligible_users=134; task_band=1, mean_early_value_tasks=0.06897, return_fraction=0.18966, eligible_users=58

Spoken answer: These points are activity bands, not individual users. A band with more early value and more later return could reflect stronger initial intent. I would inspect user rows and persona mix before proposing an account-level randomized intervention.

Interpretation and guardrail: Each point is an activity band, not a person. Aggregation can conceal user differences. Zero-task signups remain. Use counts and persona slices before proposing a test.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## G05

The default view contains 20 fictional rows. signup_week=2026-04-27, early_value_fraction=0.0, return_fraction=0.0, eligible_users=4; signup_week=2026-05-04, early_value_fraction=0.06667, return_fraction=0.0, eligible_users=15

Spoken answer: The two lines describe the same mature signup-week cohorts, so they are useful for checking whether the pattern repeats. Their moving together does not establish causality. I would inspect cohort counts and acquisition mix before choosing a test.

Interpretation and guardrail: Both lines use the same mature signup-week users. Shared trends can reflect acquisition mix or seasonality; they do not prove leading-signal causality. Edge signup weeks can be partial.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## G06

The default view contains 2 fictional rows. signal=Early selected value, heldout_users=1, training_users=10, predicted_return=0.3, actual_return=0.0; signal=No early selected value, heldout_users=44, training_users=149, predicted_return=0.15436, actual_return=0.13636

Spoken answer: I would not trust this calibration result yet: the early-value held-out segment contains one user. Its actual outcome can move the rate from zero to one. I would retain the base-rate forecast and collect a larger future cohort before using the signal operationally.

Interpretation and guardrail: Earlier complete mature signup months train group probabilities; latest complete mature month is held out. Missing train group produces null, not zero. Compare actual held-out outcomes; predictions alone prove no improvement.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## G07

The default view contains 1 fictional rows. heldout_users=45, scored_users=45, signal_brier=0.11747, baseline_brier=0.11647, brier_improvement=-0.001

Spoken answer: The first-week signal does not beat the training base-rate prediction in this fictional held-out cohort. Its Brier error is about 0.1175 versus 0.1165, where lower is better. I would not claim predictive lift or causal improvement; I would test a better-defined signal on new evidence.

Interpretation and guardrail: Brier error uses actual held-out outcomes. Lower is better. Baseline and signal score exactly the same covered users; positive difference favors the signal. Synthetic exercise, no deployment or causal improvement claim.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.

## G08

The default view contains 224 fictional rows. user_id=1, account_id=1, persona=training, primary_workflow=document_avatar, cohort_month=2026-07-01, early_tasks=0, early_value=0, later_quality_return=0; user_id=2, account_id=1, persona=developer, primary_workflow=api_template, cohort_month=2026-07-01, early_tasks=2, early_value=0, later_quality_return=0

Spoken answer: I would inspect one mature user row to explain the denominator and exposure window. Zero early tasks in the selected workflow is different from no activity anywhere. That distinction matters before I recommend onboarding or customer-success outreach.

Interpretation and guardrail: One row per mature signup; joins retain zero early activity. Workflow filters early exposure, not user eligibility or later any-workflow return. Inspect row evidence before claiming activation predicts renewal.

Counterargument: selection, instrumentation coverage or workload mix may explain the difference. Next evidence: inspect source rows and prospectively record missing events before any randomized product test. Deliberately omit a broad company narrative: this question needs a population, decision and evidence limit.