Why this matters
As a Product Analyst, you often have strong findings but limited time to influence a decision. A clear narrative turns numbers into action. You will use this skill to:
- Explain why a KPI moved and what to do about it.
- Present A/B test outcomes and recommend rollout or iteration.
- Align cross-functional teams on root causes and next steps.
- Write crisp memos executives can skim in under two minutes.
What stakeholders really want
They want: the decision, the reason, the risk, and the next step—fast. Evidence matters, but only after the main point is obvious.
Concept explained simply
An analytical narrative is a structured path from data to decision. It answers four questions in order:
- What decision is on the table?
- What is the key insight that supports it?
- What evidence proves the insight?
- What is the recommended next step and risk to watch?
Mental model
SCQA (Situation–Complication–Question–Answer)
Use this to create tension and purpose: Situation (stable context), Complication (what changed), Question (what must we decide), Answer (your recommendation with reason).
Pyramid Principle
Lead with the main message (top of the pyramid), then support with 2–4 grouped arguments, each backed by data. Readers can stop at any layer and still get the point.
ABT (And–But–Therefore)
State what we know AND why it matters, BUT what problem appeared, THEREFORE what we should do. Great for short verbal stories.
A reliable structure (Decision → Insight → Evidence → Action)
- Decision: One sentence. Example: "Roll out variant B to 100% of new users."
- Insight: The reason that makes the decision obvious. Example: "B reduces onboarding time by 23% with no retention loss."
- Evidence: 2–4 bullets or visuals that prove the insight. Prioritize effect size, confidence, and impact.
- Action: What to do next and what risk to monitor.
- Lead with the decision (headline).
- Support with 2–4 grouped points (not a data dump).
- Show only the charts that prove the point.
- Close with action and risk.
Worked examples
1) KPI drop investigation
Decision: Prioritize fixing Android signup crash before launching new referral flow.
Insight: 68% of MAU dip is explained by a new Android crash, not by referral performance.
Evidence:
- Crash rate on Android 13 spiked from 0.6% to 5.4% post v7.2; 82% during signup.
- MAU down 7.8% WoW; 5.3 pts from Android 13 cohort alone.
- Referral traffic flat (+1.2% WoW), conversion stable.
Action: Hotfix within 24h; freeze feature launches; monitor crash rate and MAU recovery for 3 days.
Why this works
It isolates the cause, quantifies impact, and proposes a time-bound fix.
2) A/B test recommendation
Decision: Roll out Variant B to 100% with a 2-week guardrail watch.
Insight: B increases day-7 activation by +3.1 pp (p=0.01) with no meaningful CAC or churn change.
Evidence:
- D7 activation: A 41.2%, B 44.3% (diff +3.1 pp).
- ARPU unchanged (+0.2%); churn within noise (+0.1 pp).
- Guardrails: Page speed no worse; NPS within ±0.2.
Action: Rollout to 100%; monitor activation, churn, performance for 14 days; prepare rollback plan.
3) Retention cohort story
Decision: Invest in habit-forming cues for new creators, not discounts.
Insight: Retention gap is explained by first-week creation cadence, not price sensitivity.
Evidence:
- Creators who publish ≥2 items in week 1 retain 2.4x at day 30.
- Discount exposure correlates weakly with retention (r=0.06).
- Qual research: "Publishing streak" cited as main motivator.
Action: Ship streak nudges and template suggestions; A/B test versus increased discounts.
Templates you can reuse
Executive one-pager
- Headline (Decision): [one sentence]
- Why (Insight): [one sentence]
- Proof (Evidence): [3 bullets + 1 chart max]
- Action & Risk: [what, when, how to monitor]
Meeting opener (60 seconds)
Situation, Complication, Question, Answer. Then stop and ask for objections before diving into details.
Exercises
Complete the exercises below. The quick test is available to everyone; only logged-in users get saved progress.
- Exercise 1: Structure a narrative for a metric drop (see details in the Exercises section below).
- Exercise 2: Rewrite a messy slide into SCQA + Pyramid.
- I stated the decision in one sentence.
- I grouped evidence into 2–4 themes.
- I limited visuals to those that prove the point.
- I ended with action and risk.
Common mistakes and how to self-check
- Data dump instead of a story. Self-check: Can someone read your headline and know the decision?
- Burying the lead. Self-check: Is the decision in the first 1–2 sentences?
- Too many charts. Self-check: Remove any chart that doesn’t change the decision.
- Unclear causality. Self-check: Label what is correlation vs. causal evidence.
- No risks or guardrails. Self-check: Name at least one risk and how you will monitor it.
Practical projects
- Turn last month’s KPI review into a one-page executive memo using SCQA.
- Repackage an A/B test readout using the Pyramid Principle; limit to 3 supporting points.
- Create a "decision-first" slide template your team can reuse.
Who this is for
- Product Analysts and Data Analysts presenting insights to PMs, designers, and engineers.
- PMs who want tighter, data-backed recommendations.
Prerequisites
- Basic SQL or analytics familiarity (you can pull metrics and understand test outputs).
- Comfort with charts (line, bar, retention curves).
Learning path
- Start with structuring narratives (this page).
- Then sharpen visual storytelling (choose the right chart; declutter).
- Practice decision-first writing (memos, executive summaries).
- Rehearse delivery (60-second openers, Q&A handling).
Next steps
- Do Exercises 1–2 and compare with the provided solutions.
- Take the Quick Test to check your understanding.
- Apply the structure in your next team update.
Mini challenge
In 3 sentences, pitch a decision to an executive about a recent A/B test using ABT: one AND, one BUT, one THEREFORE. Keep it under 60 seconds.