Making Actionable Recommendations

Why this matters

As a Data Scientist, your analysis only creates value when someone can act on it. Actionable recommendations bridge the gap between insight and impact.

• Prioritize features that move a KPI, not just what looks interesting.
• Guide A/B tests with clear hypotheses, owners, and timelines.
• Help non-technical stakeholders decide quickly with quantified trade-offs.
• Prevent analysis paralysis by proposing the next best step.

Concept explained simply

An actionable recommendation is a specific, realistic next step linked to a metric, an owner, and a time window. It tells a busy team exactly what to do, why, and how to know it worked.

Mental model: PIER-DO

1. Problem: The business problem in one sentence.
2. Insight: The key finding that matters to the problem.
3. Evidence: The minimal data/analysis supporting the insight.
4. Recommendation: One clear action (verb-first).
5. Decision owner: Who is responsible.
6. Outcome metric: How success will be measured and when.

Worked examples

Example 1: Checkout drop-off

Problem: Checkout completion rate fell from 52% to 45% week-over-week.

Insight: 63% of drop-offs occur on the address step; mobile users are 2.1x more likely to abandon.

Evidence: Funnel analysis + session replay tags show repeated errors on “Apartment/Suite” field; 18% of sessions show keyboard overlap on smaller screens.

Recommendation: Enable guest checkout and collapse optional address fields on mobile for 50% of traffic for 2 weeks; Owner: Product/Engineering.

Outcome metric: +3–5 pp checkout completion; measure via A/B test; guardrail: refund rate unchanged.

Example 2: Email engagement

Problem: Email-driven activations are below target.

Insight: Users who receive personalized subject lines have 1.4x higher open rate in historical campaigns, but only 20% of sends use personalization.

Recommendation: Run an A/B test adding first-name personalization and benefit framing in subject lines across the next two weekly campaigns; Owner: Lifecycle Marketing.

Outcome metric: +10–15% opens; downstream activation rate tracked as secondary metric.

Example 3: Fraud threshold tuning

Problem: Support tickets up due to false-positive fraud blocks.

Insight: New users from low-risk geos have 4x higher false-positive rate at the global threshold.

Recommendation: Implement segment-specific thresholds (by geo risk tier) and add manual review for borderline cases; Owner: Risk Engineering.

Outcome metric: Reduce false positives by 30% while keeping fraud loss within 0.2% of GMV.

How to quantify impact quickly

1. Baseline: State current level (e.g., 45% conversion).
2. Delta: Estimate conservative lift (e.g., +2–3 pp).
3. Value: Translate to business terms (orders, revenue, time saved).
4. Confidence: Label as low/medium/high and plan to test.

Exercises

Do these to build the skill. Then check against the solutions. Everyone can take the quick test; only logged-in users get saved progress.

Exercise 1: Turn an insight into an Action Card

Scenario: A food-delivery app sees weekend delivery times increase by 12%, and churn among new users rises from 8% to 11% on weekends. 34% of delays are due to courier shortages 6–9pm; 22% due to restaurant prep variability; 44% due to traffic spikes in two downtown zones.

• Write a one-sentence recommendation (verb-first).
• Assign a decision owner and 2-week timeline.
• Define the outcome metric and a guardrail.
• Outline a minimal test or rollout plan.

Exercise 2: Prioritize with impact/effort

Scenario: A subscription news site wants more trial-to-paid conversions. Options:

• A) Extend trial from 7 to 14 days. Effort: Low. Expected lift: +1–2 pp.
• B) Add resume-reading notification to re-engage trials. Effort: Medium. Expected lift: +2–3 pp.
• C) Reduce paywall friction with one-click sign-in. Effort: High. Expected lift: +4–6 pp.

Rank the options and write a single prioritized recommendation with owner, metric, and measurement plan.

Self-check checklist

• The recommendation starts with a clear action verb.
• There is exactly one decision owner named.
• The outcome metric and timeframe are explicit.
• There is a simple test/rollout plan.
• Risks/assumptions are acknowledged.

Common mistakes and how to self-check

• Vague actions: “Improve onboarding.” Fix: “Reduce steps from 7 to 5 for new Android users.”
• No owner: If anyone could own it, no one will. Name a role or team.
• Overfitting: Recommending for an outlier cohort. Add a guardrail and validate with a holdout.
• Metric mismatch: Using clicks when the goal is retention. Tie to the primary KPI with guardrails.
• Boil-the-ocean: Proposing multi-quarter rebuilds. Offer a minimum viable experiment first.

Practical projects

• Build a library of three Action Cards from your past analyses. Share with a colleague and revise based on feedback.
• Run a small A/B test end-to-end (design, power, launch, readout) and ship a one-page recommendation memo.
• Create a dashboard view that directly reflects your recommendation’s success metric and guardrails.

Learning path

1. Clarify the business problem and KPI tree.
2. Derive insights tied to the KPI.
3. Formulate testable recommendations (this lesson).
4. Design experiments and measurement.
5. Communicate results and iterate.

Who this is for

• Data Scientists and ML practitioners who present findings to product, marketing, or operations teams.
• Analysts transitioning to product-facing roles.

Prerequisites

• Comfort with basic metrics (conversion, retention, CTR) and A/B testing concepts.
• Ability to summarize insights from data clearly.

Next steps

• Turn one of your active analyses into an Action Card and review it with the decision owner.
• Schedule a 15-minute readout focused on a single recommendation.

Mini challenge

You find that 28% of search queries on mobile return zero results due to strict exact-match. In one sentence, write an actionable recommendation with owner and metric.

Quick Test

Anyone can take this test for free. Only logged-in users will see saved progress and history.