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Data Analyst Expert

A/B test writeups, dashboard summaries, metric narratives, executive briefings.

Starter prompts

4 ways to start with Data.

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A/B test writeup
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Plain-English experiment summary
▸ Preview prompt
Write up an A/B test in plain English. Hypothesis, sample size + duration, primary metric, result (with confidence interval), secondary metrics, recommendation (ship/kill/iterate). 350 words.
Dashboard summary
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Numbers → narrative
▸ Preview prompt
Write a 200-word narrative summary of this month's revenue dashboard for the CEO. Lead with the movement, attribute the cause, flag the one number that's off-trend, end with what we'll do next month.
MoM commentary
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Why did the number move?
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Our MAU was 412k last month and is 398k this month, a 3.4% drop. Write the commentary for the board deck explaining why, what we believe, and what we're investigating.
Anomaly investigation
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Spike or genuine?
▸ Preview prompt
Refunds spiked from a baseline of ~₹4L/month to ₹19L this month. Write an anomaly investigation report — root-cause hypotheses, data we need to validate each, recommended next steps.
What it does

Tasks Data ships every week.

Tests
  • A/B test summaries
  • Pre-test analysis plans
  • Post-test rollout recommendations
  • Holdout analyses
Reporting
  • Dashboard prose summaries
  • Metric movement narratives
  • MoM/QoQ commentary
  • Cohort analyses written up
Executive
  • Exec briefings (1 page)
  • Board prep dataroom narratives
  • OKR review notes
  • Anomaly investigation reports
Worked sample

A real Data chat.

No edits · no cherry-picks
You
Write up our test of a new checkout flow. Result: +4.1% conversion, p=0.03, n=18k per arm.
Da

Test #142: New checkout flow vs. control

Hypothesis. Removing the "create account" step and offering checkout-as-guest will lift the checkout-completion rate without hurting downstream LTV.

Setup. A/B test on 18,000 visitors per arm. Primary metric: checkout-completion rate. Secondary: 30-day repeat purchase rate, refund rate. Duration: 14 days.

Result. Checkout completion was 4.1 percentage points higher in the variant (28.6% vs 24.5%), p = 0.03, 95% CI [0.4pp, 7.8pp]. Statistically significant; practically meaningful — at our current volume that's roughly ₹38 lakhs of incremental monthly revenue.

Secondary metrics. 30-day repeat purchase was flat (no detectable lift or harm). Refund rate ticked up by 0.3pp but was inside the noise band.

Recommendation. Ship to 100% of traffic next sprint. Two caveats: (1) Hold out 5% as a long-term control so we can detect any 90-day attribution shifts that this test was too short to catch. (2) The repeat-purchase "flat" result is the most important finding — the obvious objection to checkout-as-guest was that we'd lose account-driven repeat behaviour. We didn't. That's the result worth telling the team.

Risks I'd flag. Our payment processor charges a slightly higher rate for guest checkouts. At the +4.1% conversion lift, the math still works (net contribution ₹34L/mo vs ₹38L gross), but worth re-checking in 90 days.

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Pairs well with

Data works best alongside these 3.

Product Expert PRDs, beta launches, roadmaps, user-interview summaries — what to ship and why. Customer Success Expert Onboarding, QBR docs, renewal pitches, churn-saves — every message grounded in their usage data. Founder / CEO Expert Investor updates, board narratives, all-hands scripts, vision posts — leadership voice on tap.
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