Why this matters
Applied Scientists often produce strong analyses that stall because stakeholders don’t see a clear path to action. Decision-ready recommendations bridge that gap: they translate evidence into a specific, safe-to-act choice with quantified impact and a clear ask.
- Product: Prioritize features based on expected value, time-to-impact, and risk.
- Risk/ML Ops: Choose thresholds, guardrails, and roll-out plans that meet constraints.
- Go-to-market: Set pricing/discount caps balancing revenue vs. churn risk.
- Operations: Recommend staffing or inventory levels with scenario trade-offs.
Quick note: The Quick Test is available to everyone; saving progress requires login.
Concept explained simply
A decision-ready recommendation is a one-slide story: what we should do, why, expected impact, risks, and the specific decision needed now.
Mental model: The "CEO at 5pm" test
Imagine a busy exec with 60 seconds at 5pm. If your first sentence and graphic let them approve or reject confidently, you have a decision-ready recommendation.
What must be in the first 30 seconds?
- One-sentence recommendation starting with an action verb.
- Expected impact with order-of-magnitude numbers and uncertainty.
- Key risk/guardrail in plain language.
- Explicit ask: approve, pilot, or allocate resources.
Framework: From analysis to action
- Start with the decision. Write the question as a choice (e.g., "Ramp feature to 50% vs. hold at 10%?").
- List viable options. 2–4 realistic alternatives, including a "do nothing" baseline.
- Quantify impact and uncertainty. Show ranges and expected values; avoid false precision.
- State constraints. SLAs, regulatory, fairness, costs, latency, staffing.
- Choose and justify. Pick the option with the best expected value that respects constraints; explain trade-offs.
- Package the message. One-slide headline, numbers, risks, and the ask. Put details in backup.
- Pre-mortem. Name top 1–2 failure modes and add guardrails/owners.
One-slide template you can copy
Headline: Recommend [Option] to achieve ~[Impact] with [Guardrail]. Decision: Approve [Scope + Timing]. Why: - Evidence 1 (data) - Evidence 2 (experiment/benchmark) - Constraint fit (e.g., latency, budget) Impact (range): Best [X], Likely [Y], Worst [Z] Risks + Guardrails: [Top risk] → [Mitigation/owner] Next step & Owner: [Action], [Date], [Owner]
Worked examples
Example 1: Product A/B test rollout
Context: New recommendation widget increased add-to-cart rate by +2.1% (p=0.06). Baseline revenue $10M/mo.
Recommendation: Ramp to 50% traffic for 2 weeks with guardrails; expected +$150k–$300k/mo. Decision: Approve ramp + monitoring today.
Why: Even with borderline significance, the expected value is positive and risk-limited by a ramp + automated rollback if add-to-cart dips >0.5% vs. control.
Rationale
- Options: Hold at 10% (status quo), Ramp 50% with guardrails, Full 100% rollout.
- Impact: 2.1% of $10M ≈ $210k/mo; with uncertainty → range $150k–$300k.
- Constraints: Page latency budget +40ms; widget adds 12ms (OK).
- Risks: Cannibalization; mitigated by metric guardrails and rollback.
Example 2: Credit risk threshold
Context: Model AUC 0.82. Current accept rate 60%. Threshold shift could reduce default rate by 0.7% while lowering accept rate 3%.
Recommendation: Increase threshold by +0.05 for new applicants; expected +$180k/mo net profit; within fairness and SLA. Decision: Approve change, go live Monday.
Rationale
- Options: Keep threshold; +0.03; +0.05.
- Impact: Profit curve shows +$90k, +$180k respectively; fairness deltas <0.2pp across groups (meets 1pp limit).
- Guardrail: Real-time drift alert; rollback if default rate > baseline +0.3% over 7 days.
Example 3: Inventory forecasting safety stock
Context: Lead-time variance increased. Stockouts cost ~$50k/day; holding cost ~$6k/day.
Recommendation: Raise safety stock by +12% for SKUs A–D for 6 weeks; expected net savings ~$320k. Decision: Approve temporary policy + review date.
Rationale
- Options: No change; +8%; +12% for top SKUs only.
- Impact: +12% targeted balances stockout vs. holding best in scenarios.
- Guardrail: Weekly review; revert if lead-time variance normalizes.
Exercises
Do these to build the habit. The Quick Test at the end checks your mastery.
Exercise 1: From finding to decision
Prompt: You ran an email subject test: Variant B lifts open rate from 24% to 26% (p=0.09). CTR unchanged. List size 2M; average revenue per open $0.06. Draft a one-sentence decision-ready recommendation plus a 3-bullet justification.
Hints
- Include the action, expected impact (range), and a guardrail.
- Acknowledge uncertainty; use a limited ramp if needed.
- End with a clear decision ask and timing.
Sample shape (don’t copy exact numbers blindly)
Recommend [Action] to achieve ~[Impact]; [Guardrail]. Decision: [Ask]. - Evidence - Constraint fit - Risk + Mitigation
Exercise 2: Quantify impact with uncertainty
Prompt: A feature is expected to increase conversion from 5.0% to 5.6% (95% CI: +0.3 to +0.9 pp). Monthly sessions: 1,200,000. Average order value: $45. Compute likely monthly revenue uplift and provide a conservative range. Then state a decision with guardrails.
Hints
- New conversions = sessions × conversion rate.
- Incremental conversions = sessions × delta.
- Convert to revenue using AOV; use CI bounds for a range.
Common mistakes and self-check
- Burying the ask. Self-check: Is the first sentence an action with a decision verb?
- Fake certainty. Self-check: Did you include a range and the key risk?
- No alternatives. Self-check: Did you list at least one viable option besides your pick?
- Ignoring constraints. Self-check: Did you mention latency, budget, fairness, or policy constraints?
- Ownerless next step. Self-check: Is there a named owner and date?
Practical projects
- Take a past analysis and produce a one-slide decision brief with two options, impact range, and a guardrail.
- Run a pre-mortem: List top 3 failure modes for a model change and propose mitigations and owners.
- Create a decision playbook template your team can reuse (headline, options, impact table, risks, ask).
Next steps
- Practice the one-sentence recommendation daily on small choices.
- Shadow a PM/ops review and rewrite one agenda item into a decision-ready brief.
- Take the Quick Test to validate understanding; revisit exercises if needed.
Reminder: The Quick Test is available to everyone; log in to save your progress.
Who this is for
- Applied Scientists, Data Scientists, and ML Engineers presenting to decision-makers.
- PMs and Analysts who want clearer, action-oriented recommendations.
Prerequisites
- Basic understanding of experimental/scenario analysis and uncertainty.
- Comfort summarizing results at a high level.
Learning path
- Start here: decision-first framing and one-sentence recommendations.
- Next: quantifying impact under uncertainty and guardrails.
- Then: packaging for executives and running a pre-mortem.
Mini challenge
Scenario: A latency optimization reduces p95 from 480ms to 360ms, improving session length by 3–5%. Revenue impact expected +$90k–$180k/mo. Risk: potential cache staleness causing 0.2% pricing mismatch incidents; mitigation: 5-min TTL and alerting.
Write a one-sentence recommendation and a 3-bullet justification with an explicit decision ask and owner.
Example answer
Recommend enabling the latency optimization for 100% traffic with a 5-min TTL to capture ~$120k/mo (range $90k–$180k); Decision: Approve full rollout today, SRE owns alerting.
- Evidence: p95 down 25%, sessions +3–5% in canary.
- Constraint fit: Infra cost +$6k/mo within budget.
- Risk & guardrail: Pricing mismatch alert >0.3% triggers rollback; SRE on-call.