[Steve Side Hustler]

I manage a product and have routine usage logs (events like page views, clicks, feature use, and timestamps). I’m not a data scientist but I’d like to use AI to surface clear, testable hypotheses about user behavior and product improvements.

What I’m hoping to learn:

1. What simple workflow should a non-technical person follow to get useful hypotheses from logs?
2. What minimal summaries or anonymized inputs does AI need (for example: event counts, funnels, session lengths)?
3. Which beginner-friendly tools or prompt examples work well for this task?
4. How can I validate and turn AI suggestions into small experiments or tests?

I’d appreciate short, practical steps, example prompts or templates, and any common pitfalls to avoid (especially around privacy and noisy data). If you need a tiny anonymized example of my log structure to give a concrete prompt, tell me what format to share.

Thanks — looking forward to simple, actionable ideas!

[aaron]

Quick note: Good, practical question — you want automation that turns raw product logs into testable hypotheses, not vague ideas. I’ll give a direct, actionable pathway you can run in a week.

The problemProduct usage logs are dense and noisy. You can’t manually scan thousands of events and confidently know what to test next.

Why this mattersTurning logs into prioritized, measurable hypotheses reduces wasted engineering time and accelerates learning. It gets you from data to decisions.

Core lesson from working with non-technical teamsStart small, standardize the inputs, use the AI to surface hypotheses, then apply simple prioritization and sample-size checks before engineering work begins.

What you’ll need

• Export or query of product events (CSV with user_id, timestamp, event_name, properties).
• Short data dictionary describing key events and user attributes.
• Access to an LLM (ChatGPT or similar) or AI assistant you can paste prompts into.
• Spreadsheet or simple analytics tool to run basic aggregations.

Step-by-step process

1. Prepare a 1–2 page summary: top 10 events, definitions, high-level goals (acquisition, activation, retention, revenue).
2. Export a 2–4 week sample of anonymized events (CSV) and create 5–10 aggregated metrics: DAU, key funnel drop-offs, feature use rates, churn rate.
3. Feed those aggregates and the event list into the AI with a clear prompt (example below). Ask for hypotheses phrased as testable statements with causal rationale and suggested metrics/test types.
4. Prioritize hypotheses using ICE (Impact, Confidence, Ease) and choose top 2–3 to validate.
5. For each chosen hypothesis, create a test plan: variant details, primary metric, required sample size, duration, QA checklist, and rollout criteria.
6. Instrument events, run the experiment, and evaluate with pre-defined metrics and significance thresholds.

AI prompt (copy-paste)

I have these anonymized event aggregates and a short event list: 1) events.csv summary: funnel_top=signup_rate 15%, activation=first_task_completed 8%, weekly_retention=18%, feature_X_use=12%. 2) Event list: signup, first_task_completed, feature_X_use, upgrade, session_start. Company goal: increase 28-day retention. Using this information, generate 8 testable hypotheses. For each hypothesis provide: a short statement (If we X then Y because Z), causal rationale, primary and secondary metrics, suggested experimental design (A/B or cohort), estimated direction and rough sample size needed for detecting a 5% lift in the primary metric, and one simple QA checklist item.

Metrics to track

• Number of hypotheses generated and prioritized.
• Predicted vs observed lift on primary metric (conversion/retention).
• Experiment duration and sample size achieved.
• Time from hypothesis to experiment launch.

Common mistakes & fixes

• Relying on raw events without definitions — fix: create a short data dictionary first.
• Too many low-quality hypotheses — fix: force prioritization with ICE and limit to top 3.
• No instrumentation to validate metrics — fix: QA checklist and smoke tests before launch.

One-week action plan

1. Day 1: Export events, write data dictionary, compute 5 aggregates.
2. Day 2: Run the AI prompt to generate hypotheses; get 8 candidates.
3. Day 3: Score with ICE and pick top 3; draft test plans.
4. Day 4: Compute sample sizes; finalize instrumentation tasks.
5. Day 5: QA instrumentation and dry-run analytics.
6. Day 6: Launch first experiment(s).
7. Day 7: Monitor early signals, ensure data quality, and prepare interim report.

Your move.

[Jeff Bullas]

Hook

You’re on the right track — AI can convert messy product logs into clear, testable experiments. One quick clarification: “detecting a 5% lift” is ambiguous. Do you mean a 5-percentage-point (absolute) lift or a 5% relative lift? That difference changes your sample size hugely. I’ll show both and give a practical path to get started this week.

What you’ll need

• CSV export: user_id, timestamp, event_name, properties (anonymized).
• Short data dictionary (5–10 lines) defining key events and user attributes.
• Aggregates: baseline rates for the metrics you care about (e.g., 28-day retention, activation).
• Access to an LLM or AI assistant and a spreadsheet or simple analytics tool.

Do / Don’t checklist

• Do standardize event names and define key metrics before asking the AI.
• Do specify whether lifts are absolute (percentage points) or relative (%) when estimating sample size.
• Do prioritize hypotheses with ICE (Impact, Confidence, Ease).
• Don’t ask the AI to produce experiments without baseline numbers — it will guess and mislead.
• Don’t launch without a QA checklist for instrumentation.

Step-by-step (fast, 1 week)

1. Day 1: Export 2–4 weeks of events and write a 1-page data dictionary.
2. Day 2: Compute 5 aggregates (DAU, funnel rates, feature use, 28-day retention).
3. Day 3: Run the AI with the prompt below to generate 6–8 hypotheses.
4. Day 4: Score with ICE and pick top 2–3. Draft test plans (variant, primary metric, QA).
5. Day 5: Instrument, smoke-test events, and finalize sample-size calc.
6. Day 6–7: Launch and monitor early signals; hold to pre-defined stopping rules.

Quick worked example

Data: signup_rate=15%, activation (first_task)=8%, weekly_retention=18%, feature_X_use=12%. Goal: increase 28-day retention.

Hypothesis (example): If we show an in-app new-user checklist during signup, then 28-day retention will increase because checklists guide users to their first meaningful task.

• Primary metric: 28-day retention.
• Design: A/B test, equal allocation.
• Estimated sample size (important correction):
• • Detecting a 5-percentage-point (absolute) lift from 18% → 23%: ~926 users per arm (~1,852 total).
• • Detecting a 5% relative lift (18% → 18.9%): ~28,580 users per arm (~57k total) — much larger.
• QA checklist: Verify the new-user event and retention event fire for 100 test users across both variants.

Common mistakes & fixes

• Mixing up absolute vs relative lift — fix: state which you mean and compute sample size accordingly.
• Generating too many hypotheses — fix: force ICE scoring and test only top 2–3.
• No smoke tests — fix: run a QA script that verifies event counts and variant assignment for a sample.

Copy-paste AI prompt (use as-is)

I have these anonymized aggregates and an event list: signup_rate=15%, activation_first_task=8%, weekly_retention=18%, feature_X_use=12%. Events: signup, first_task_completed, feature_X_use, upgrade, session_start. Company goal: increase 28-day retention. Generate 6 testable hypotheses. For each: one-line hypothesis (If we X then Y because Z), causal rationale, primary and secondary metrics, suggested experiment design (A/B or cohort), estimated direction and rough sample size needed to detect a 5-percentage-point lift (state assumptions), and one QA checklist item.

Action plan — first 48 hours

1. Export events + build data dictionary (2 hours).
2. Compute 5 aggregates in a spreadsheet (2–3 hours).
3. Run AI prompt, get hypotheses, and score with ICE (3–4 hours).

Reminder: Start with one small, instrumented test. Quick wins build momentum and make larger experiments possible.

[Becky Budgeter]

Quick win: In under 5 minutes open your CSV and calculate one simple baseline — the current 28-day retention rate (count users seen at day 28 ÷ users in cohort). That single number will immediately make AI suggestions a lot more useful.

Nice point in your write-up about absolute vs relative lift — that choice really changes sample sizes and what’s realistic for a first test. I’d add a practical filter: for early experiments use absolute (percentage-point) targets so you can pick changes you can actually detect with modest traffic.

What you’ll need

• CSV of anonymized events with user_id, timestamp, event_name, and one user attribute (e.g., signup date).
• Short data dictionary (5–10 lines) that defines your key events (e.g., signup, first_task_completed, retention_event).
• A spreadsheet or analytics tool where you can compute simple aggregates (cohorts, rates).
• An AI assistant or LLM you can describe these aggregates to (you don’t need to paste raw data).

Step-by-step (what to do, how to do it, what to expect)

1. Prepare baselines (30–90 minutes): in your spreadsheet compute 3 numbers — current signup rate, activation (first meaningful action) rate, and 28-day retention. Expect a single row per metric.
2. Summarize context (10–20 minutes): write one short paragraph with company goal (e.g., increase 28-day retention), your baselines, and a one-line description of key events. This is what you’ll feed the AI — not the raw CSV.
3. Ask the AI for hypotheses (15 minutes): give the AI your short summary and ask for 6 testable hypotheses. Tell it to return each as a one-line If-Then statement, a short rationale, the primary metric, suggested design (A/B or cohort), and a rough sample-size order (assume a 5–percentage-point absolute lift for early tests). Expect clear, ranked ideas you can review in one sitting.
4. Prioritize and pick one (30–60 minutes): score top ideas with ICE (Impact, Confidence, Ease) and choose one small experiment. Expect to keep the test scoped to one change and a primary metric you can instrument quickly.
5. Instrument & QA (1–2 days): add the minimum events, run smoke tests with ~50 test users, and confirm event counts. Expect to catch naming/duplication issues here — fix those before launch.

What to expect

• Quick hypotheses from AI but not perfect — you’ll need to sanity-check assumptions.
• Early tests aimed at absolute lifts give realistic sample sizes; big traffic sites can aim for smaller relative lifts.
• One useful QA item: verify variant assignment and that the retention event fires for 100 test users in each variant before you consider results reliable.

Small tip: when the AI suggests a sample size, ask it to show the baseline, the assumed lift (absolute), and the per-arm count — that makes the numbers easy to scan. Quick question: for your work, are you targeting a 5-percentage-point (absolute) lift or a 5% relative lift?

[Jeff Bullas]

Spot on about the quick baseline and using absolute (percentage-point) lifts for early tests. That single choice keeps experiments realistic and shaves weeks off your timeline. Let’s turn that momentum into a lightweight “hypothesis factory” you can run every sprint.

High-value twist

Don’t just ask AI for ideas. Feed it contrasts. Give it tiny, targeted slices (retained vs churned, fast activators vs slow) and short sequences (what happens in the first 10 minutes). Contrast drives sharper, more testable hypotheses.

What you’ll need

• 2–4 weeks of anonymized events (user_id, timestamp, event_name, properties).
• Mini data dictionary (10 lines): define signup, activation, retention_event, key feature events.
• Five aggregates: signup %, activation %, 7-day retention %, 28-day retention %, top funnel drop-off.
• Two contrasts: retained vs churned cohort counts; fast activators (first_task < 24h) vs slow (> 24h).

Step-by-step: from logs to testable hypotheses (in one week)

1. Compute baselines (30–60 min): signup %, activation %, 28-day retention %. Stick with absolute lifts for targets (e.g., +5 percentage points).
2. Create contrasts (30–60 min):
   • Retained28 = users with retention_event on day 28; Churned28 = without.
   • Fast vs slow activators (time to first_task). Add simple counts and rates.
3. Sequence snapshot (30 min): list top 5 event sequences in first session for Retained28 vs Churned28 (e.g., session_start → tutorial_view → first_task_completed).
4. Ask the AI (15–20 min): paste the prompt below with your numbers. Expect 6–10 crisp hypotheses with metrics, design, and rough sample sizes.
5. Prioritize (30–45 min): score with ICE (Impact, Confidence, Ease). Pick top 2–3. Favor changes you can ship in a week.
6. Plan & QA (1–2 days): define variant, primary metric, guardrails, sample-size target per arm, and a short QA checklist (event names, SRM check, retention event firing).
7. Launch & monitor: hold to your stopping rules. Review data quality at 24–48 hours before reading results.

Copy-paste prompt (general hypothesis generator)

Context: Our goal is to improve 28-day retention (absolute lift target: +5 percentage points). Baselines: signup_rate=X%, activation_rate=Y%, 28d_retention=Z%. Key events: signup, first_task_completed, feature_X_used, upgrade, session_start, retention_event. Contrasts: Retained28 cohort size vs Churned28, and Fast activators (<24h) vs Slow (>24h). Top first-session sequences for Retained28 and Churned28 are listed below.

Based on this, generate 8 testable product hypotheses. For each, provide: 1) one-line If-Then-Because statement, 2) causal rationale tied to the contrasts or sequences, 3) primary and secondary metrics, 4) suggested test design (A/B or cohort), 5) rough sample size per arm to detect a +5-percentage-point absolute lift from Z% (state assumptions), 6) minimal instrumentation checklist (event names), 7) one QA step (include SRM check and retention_event validation).

Data to use: [paste your five aggregates], [two contrasts], [top 5 first-session sequences for Retained28], [top 5 for Churned28]. Only propose changes we can build in <1 week.

Variant prompts (use when you want sharper ideas)

• Contrastive slice: “Using Fast vs Slow activators, propose 5 hypotheses that reduce time-to-first_task by 20%. Tie each to a specific UI step and suggest one in-product nudge or default change. Include the expected direction on activation rate and a simple QA step.”
• Friction map: “Given this funnel with drop-off percentages at each step, propose 5 micro-copy or layout changes. For each, state the behavioral friction you’re addressing, the primary metric (step conversion), and a 7-day holdout plan.”
• Sequence repair: “Compare these top sequences for Retained28 vs Churned28. Identify 5 missing or misplaced steps for the churned cohort and propose one-step interventions (tooltips, defaults, auto-advance). Include a metric and a +5pp sample-size estimate.”

Worked example (what good output looks like)

• If we show a 3-step checklist after signup, then 28d retention increases because retained users almost always complete first_task in session one.
• Metrics: primary=28d_retention; secondary=activation_rate, time_to_first_task.
• Design: A/B, equal split. Sample-size order: if baseline Z=18%, +5pp target → roughly ~900–1,100 users per arm. Assumptions: 95% confidence, 80% power.
• Instrumentation: checklist_viewed, checklist_completed, first_task_completed, retention_event.
• QA: verify event names and firing order on 100 test users; SRM within ±2% of 50/50 at 24h.

• If we auto-open feature_X in the first session, then activation increases because fast activators engage with feature_X early.
• Metrics: primary=activation_rate; secondary=first_session_duration; guardrail=error_rate.
• Design: A/B. Sample-size: compute for +5pp on activation baseline.
• QA: confirm feature_X_used fires once per session; compare average event counts by variant.

Insider checks that save you

• Absolute vs relative lifts: default to absolute for early tests; relative lifts demand far larger samples.
• SRM check: after 24 hours, ensure variant allocation is near your split (e.g., 50/50 ±2%). If off, pause and fix assignment.
• One change per test: avoid bundles. You want clean reads, fast learnings.
• Sequencing matters: prioritize changes that bring first_task into session one; it usually pays off on retention.

Common mistakes & fixes

• Vague events (e.g., “task_done”). Fix: rename to “first_task_completed” and document it.
• Dumping raw logs into the AI. Fix: pre-aggregate. Give it baselines, contrasts, and top sequences.
• Over-optimistic targets. Fix: cap early goals at +3–5pp and move quickly.
• No guardrails. Fix: add error_rate and support_tickets as basic safety metrics.

48-hour action plan

1. Compute 3 baselines + 2 contrasts + top 5 first-session sequences.
2. Run the general prompt and the contrastive slice prompt; shortlist 8 ideas → pick top 2 with ICE.
3. Write one-page test cards: variant, metrics, +5pp sample-size target, QA checklist.
4. Instrument, smoke test with 50–100 users, confirm SRM and event firing, then launch.

Expectation reset: The AI won’t replace your judgment; it will amplify it. Start with absolute lifts, run one clean change, and let your contrasts point to the next best test.

[Author]

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