Translating Business Needs Into Research Questions

Why this matters

Applied Scientists often get goals like “reduce churn” or “improve relevance.” You succeed by translating these into precise, testable research questions that guide data collection, modeling, and experiments. This avoids wasted effort, sets clear success metrics, and keeps science tied to decisions that matter.

• Real task: Turn “grow MAUs” into measurable objectives, guardrails, and a research plan.
• Real task: Choose the right formulation (prediction, uplift, causal, ranking, generation, optimization).
• Real task: Align with stakeholders on decisions, metrics, timelines, and risk.

Concept explained simply

Translating business needs is the process of moving from a vague outcome to a concrete question you can answer with data and methods. You define the decision to be made, the metric that guides it, the data you need, and the method that fits.

Mental model: ODM-FIVE

• Outcome: What business outcome matters? (e.g., retention +5%)
• Decision: What decision will this research inform? (e.g., who to target, which ranking to show)
• Metric: Primary success metric and guardrails (e.g., churn rate, NPS as guardrail)
• Feasibility: Data, constraints, timeline, privacy/ethics
• Inquiry type: Predictive, causal, uplift, ranking, optimization, generative
• Validation: Offline proxies, online tests, error bars, decision thresholds
• Experiment plan: How you’ll test and iterate

Use this quick template

• Business need: …
• Decision to inform: …
• Primary metric + guardrails: …
• Inquiry type: …
• Data needed + gaps: …
• Research question: “To what extent/under what conditions …?”
• Validation plan: Offline …, Online …

Worked examples

Example 1: Reduce churn in a subscription app

• Business need: Reduce monthly churn by 2% over next quarter.
• Decision: Who to target with retention offers and when.
• Metric: Churn rate (primary), Gross margin and inbox spam rate (guardrails).
• Inquiry type: Uplift/causal prediction (who benefits from an offer), time-to-event survival analysis.
• Research question: “Which users are most likely to have reduced churn if offered incentive X this week, and what is the expected incremental effect?”
• Validation: Offline uplift metrics (Qini, AUUC), then online A/B with treatment holdouts.

Example 2: Increase marketplace GMV without hurting trust

• Business need: Increase GMV by 5% while keeping fraud rate below baseline.
• Decision: Ranking policy for search results.
• Metric: GMV per session (primary), Fraud rate and dispute rate (guardrails).
• Inquiry type: Multi-objective ranking/optimization (revenue vs. risk).
• Research question: “Does a risk-aware ranking score that penalizes fraud propensity increase GMV while keeping fraud at or below baseline?”
• Validation: Offline counterfactual ranking metrics; online A/B with guardrail monitors.

Example 3: Cut warehouse pick time

• Business need: Reduce average pick time by 10% within 2 months.
• Decision: Route policy to deploy across sites.
• Metric: Avg pick time per order (primary), On-time SLAs and safety incidents (guardrails).
• Inquiry type: Operations optimization with simulation, contextual bandits for policy testing.
• Research question: “Do learned routing policies (vs. current heuristic) reduce pick time in simulation and in phased rollout?”
• Validation: Digital twin simulation, shadow experiments, phased rollout with CUPED.

How to do it: step-by-step

1. Clarify the outcome: Ask “How will we know this worked?” Write the metric and guardrails.
2. Pin the decision: “What decision changes because of this research?” If none, re-scope.
3. Choose inquiry type: Map decision to predictive/causal/optimization/ranking/generative.
4. Draft the research question: Make it testable, time-bound, and decision-linked.
5. Set validation plan: Offline metric(s), online test design, success thresholds.
6. List data/constraints: Availability, bias risks, privacy, compute/time limits.
7. Pressure test with stakeholders: Rephrase in plain language; confirm alignment.

Starter phrasings you can copy

• “To what extent does policy A vs. B improve [metric] for [segment] within [timeframe] under [constraints]?”
• “Which users/items are likely to have the highest incremental gain in [metric] if we apply [intervention] now vs. later?”
• “What offline proxy best predicts online lift in [metric], and what threshold yields a meaningful effect size?”

Exercises

Note: The quick test is available to everyone; only logged-in users have their progress saved.

Exercise 1 — Rewrite a vague need into research questions

Business need: “Grow monthly active users by 10% in 2 quarters.”

1. State the decision to inform (be specific).
2. Choose inquiry type(s) and justify.
3. Write 2–3 concrete research questions with metrics and guardrails.

Show solution

Decision: Which re-engagement policy (message content, timing, channel) to deploy per segment.

Inquiry types: Uplift modeling for messaging; sequential policy optimization for cadence.

• RQ1: “Which inactive users will show the largest incremental MAU increase if sent message type A this week?”
• RQ2: “What send-time policy maximizes MAU uplift without increasing opt-out rate above baseline?”
• RQ3: “Which offline proxy (open/click) best predicts online MAU lift?”

Primary metric: MAU uplift. Guardrails: Unsubscribes, complaint rate.

Exercise 2 — Define metrics and guardrails

Scenario: A recommendation team wants “more relevant results.”

1. Propose a primary metric and two guardrails.
2. Propose an offline proxy and explain why it correlates with the online metric.

Show solution

Primary: Conversion rate per session.

Guardrails: Dwell-time dropouts; complaint/abuse flags.

Offline proxy: NDCG@K on labeled relevance and/or implicit signals (saves, long dwell). Correlates with purchase/conversion in historical experiments; supports fast iteration.

Exercise 3 — Map data and feasibility

Scenario: You need to evaluate whether a free-shipping threshold increases AOV without hurting margin.

1. List data needed and likely gaps.
2. Draft a feasible validation plan (offline + online).
3. Write one precise research question.

Show solution

Data: Orders, item costs, shipping costs, historical thresholds, user segments; gaps: hidden confounders (seasonality, promos).

Validation: Offline diff-in-diff on past threshold changes; simulate baskets; then geo-split A/B with CUPED.

RQ: “Does setting threshold T increase AOV by ≥x% while keeping gross margin ≥y% over 4 weeks across segments S?”

Self-check checklist

• Outcome and decision are explicitly written and agreed by stakeholders.
• Primary metric has clear guardrails and success thresholds.
• Inquiry type matches the decision (predict vs. cause vs. rank vs. optimize).
• Data availability, bias/ethics, and constraints are acknowledged.
• Offline and online validation plans are defined.
• Research question is testable and time-bounded.

Common mistakes and how to self-check

• Vague goals: If your question lacks a metric/timeframe, rewrite it.
• Metric mismatch: CTR optimized when profit matters. Ask: “Will this proxy move the business metric?”
• No decision owner: Name who will act on results. If none, pause.
• Method mismatch: Using A/B when you need uplift or causal inference. Map method to decision.
• Ignoring guardrails: Add safety/trust/quality metrics to avoid regressions.
• Unvalidated proxy: Correlate offline proxy with historical online wins.

Who this is for

• Applied Scientists and Data Scientists aiming to make research directly drive product or operations decisions.
• ML Engineers who need crisp problem definitions and metrics before building.
• Product-minded researchers who influence roadmaps.

Prerequisites

• Comfort with metrics (classification/regression metrics, ranking metrics).
• Basic experimentation concepts (A/B tests, guardrails).
• Understanding of common ML/causal approaches (prediction, uplift, diff-in-diff).

Learning path

1. Practice ODM-FIVE on 3 past projects you know; rewrite each as a research question.
2. Master metric design: pick a primary, choose guardrails, and define success thresholds.
3. Choose inquiry type by decision: build a small map from decision → method.
4. Validate proxies: show offline-online correlation from prior launches.
5. Run a mini pilot: ship a low-risk change with clear RQ and review outcomes.

Practical projects

• Retention pilot: Design an uplift-based outreach experiment for a sandbox dataset; report RQ, metrics, and expected lift.
• Ranking tune-up: Create a multi-objective scoring function with a risk penalty; evaluate with offline metrics and write the RQ.
• Policy test: Draft a phased rollout plan for an operations policy with guardrails and a precise causal RQ.

Next steps

• Use the template with your product partner; get sign-off on outcome, decision, and metrics.
• Run the quick test below to cement the habit of precise phrasing.
• Pick one project and execute a small, time-boxed pilot.

Mini challenge

Take a current OKR in your team. In 5 sentences or less, write: the decision to inform, primary metric, two guardrails, inquiry type, and one research question. Share it with a stakeholder and ask, “If I answer this question, will you be able to make a decision?” If not, refine it.