Requirements Gathering and Business Analysis Questions

Situation/Goal: Reduce customer churn by 5% next quarter via a predictive model + targeted interventions.

1. Milestones (12 weeks)

• Week 0–1: Kickoff, define churn definition (e.g., no purchase in X days or subscription cancel), success metrics, stakeholders, data access.
• Week 2–3: Data ingestion & profiling (CRM, transactions, web logs), privacy review, baseline analysis.
• Week 4–5: Feature engineering (recency/frequency/monetary, engagement, support tickets, campaign exposure), exploratory analysis.
• Week 6: Model prototypes & shortlist.
• Week 7: Validation & uplift modeling for treatment strategy.
• Week 8: Pilot deployment (2–4 weeks) to a holdout test cohort.
• Week 10–11: Analyze pilot, iterate.
• Week 12: Full rollout or rollback decision and handoff.

2. Data requirements

• CRM: demographics, segment, lifecycle dates, support interactions.
• Transactions: purchase history, returns, LTV, frequency, recency.
• Web logs: sessions, pageviews, product views, churn-related behavior (cart abandonment).
• Labels: churn window, treatment history. Ensure joins by customer_id, timestamp alignment.

3. Model candidates

• Classification: Gradient Boosted Trees (XGBoost/LightGBM) for baseline.
• Neural net: simple feedforward or embedding layers if high-cardinality categorical features.
• Temporal: RNN/Transformer or survival analysis (Cox) for time-to-churn.
• Causal/Uplift: Two-model or meta-learners to predict incremental impact of retention offers.

4. Validation plan

• Use time-based train/validation/test splits to avoid leakage.
• Metrics: Precision@k, ROC-AUC, PR-AUC, calibration, and business KPIs (expected churn reduction, uplift).
• Backtest: simulate interventions historically.
• Robustness: cohort/subgroup performance, feature importance, SHAP explanations.

5. Deployment strategy

• Pilot: Score a random holdout sample (e.g., 5–10% of population). Run treatment (offers/emails) vs control A/B test for 2–4 weeks.
• Rollout: Phased increase (25%, 50%, 100%) with monitoring dashboards for model drift, conversion, costs.

6. Rollback criteria

• No statistically significant uplift in retention vs control after pilot period.
• Increase in cost per retained customer above threshold (e.g., >2x expected).
• Adverse business signals (customer complaints spike, KPI regressions).

7. Measure business impact

• Primary: Absolute churn rate change vs control (target −5%).
• Secondary: CLTV uplift, cost per retained customer, response rate, ROI.
• Statistical significance testing, confidence intervals, and lift by segment.
• Post-rollout: monitor monthly churn, recalc model refresh cadence (e.g., retrain monthly).

Final notes: Instrument tracking to attribute impact, maintain explainability for business ops, and schedule retraining and data-quality checks.

Situation: Marketing asks for a 10% conversion uplift from a predictive model; Legal mandates model explainability and forbids using demographic features. My goal: reconcile business impact with compliance and deliver an actionable roadmap.

Decomposition

• Technical requirements
  • Target metric: +10% conversion (baseline, measurement window)
  • Data: event-level conversion labels, behavioral and product features, A/B test instrumentation
  • Models: interpretable baseline models (logistic regression, GAMs), and higher-capacity models (tree ensembles, neural nets) with post-hoc explainability
  • Evaluation: holdout/test AUC, calibration, business uplift (incremental conversions), offline causal proxies and online A/B testing
  • Instrumentation: logging, feature lineage, model monitoring
• Policy / compliance requirements
  • No demographic features in training or inference pipelines
  • Explainability standard: feature-level attributions, human-readable rationale, documentation for audits
  • Data governance: provenance, retention, access controls, privacy checks

Negotiation questions to stakeholders

• To Marketing: How is the 10% measured (absolute vs relative)? What is baseline and timeline? Are there acceptable interim goals?
• To Legal: Define acceptable explainability standards (local vs global, fidelity thresholds). Are proxy features or derived embeddings acceptable if not correlated with demographics? What audit artifacts satisfy compliance?
• To both: Are controlled experiments (A/B tests) acceptable to validate uplift? What risk tolerance exists for false positives/negatives?

Prioritized implementation plan (phases + timelines)

1. Week 0–2: Align & define success
   • Confirm baseline conversion, metric, acceptable timelines, and Legal’s explicit explainability requirements.
2. Weeks 2–6: Data & guardrails
   • Build feature catalog; implement policy checks to block demographic fields and flag correlated proxies. Create logging & lineage.
3. Weeks 4–10: Interpretable baseline + monitoring
   • Train interpretable models (logistic regression, GAM) with business features; produce feature importance, SHAP-lite explanations; run offline evaluation against uplift proxy.
4. Weeks 8–16: High-capacity models with constrained features
   • Train XGBoost/NN with explainability pipeline (SHAP, LIME, counterfactuals) and fairness checks. Compare to baseline.
5. Weeks 12–20: Experimentation & deployment
   • Run A/B tests measuring incremental conversions. Only deploy models meeting Legal explainability criteria and passing fairness checks.
6. Ongoing: Monitoring & audits
   • Drift, performance, and periodic re-evaluation; maintain audit docs for Legal.

Trade-offs

• Speed vs rigor: Interpretable models give faster compliance but may yield smaller uplift; complex models can boost performance but require stronger explainability and validation.
• Feature utility vs policy: Blocking demographics may reduce predictive power; mitigate via richer behavioral/product features and causal signals.
• Explainability fidelity vs model accuracy: Post-hoc explanations may not perfectly reflect complex models—Legal may prefer inherently interpretable models.

Expected timeline: measurable uplift signal in A/B within 3–5 months; full production with monitoring and audit trail by ~5 months. If Legal requires only interpretable models, expect smaller immediate uplift but faster deployment (6–8 weeks). If complex models are approved with robust explainability, potential to reach 10% within 4–6 months.

I would start by aligning stakeholders on definitions and acceptable trade-offs, deliver an interpretable baseline quickly to build trust, and iterate toward higher-performing models under Legal’s documented explainability guardrails.

Data Scientist perspective — simple RACI by artifact with brief justification:

Data ingestion

• Responsible: Software Engineer (build ETL pipelines)
• Accountable: Data Scientist (owns data quality requirements for modeling)
• Consulted: SRE (scalability/ops), Product Manager (data sources/priorities)
• Informed: Legal (privacy considerations)
  Justification: Engineers implement; I ensure data meets modeling needs and sign off.

Model (training & validation)

• Responsible: Data Scientist
• Accountable: Data Scientist
• Consulted: Product Manager (business goals), Legal (bias/privacy risks)
• Informed: Software Engineer, SRE
  Justification: I develop and validate models; stakeholders provide constraints and are looped in.

API (serving model)

• Responsible: Software Engineer
• Accountable: Product Manager
• Consulted: Data Scientist (model integration), SRE (deployment)
• Informed: Legal
  Justification: Eng builds/owns delivery; PM prioritizes product behavior.

Monitoring (performance, drift, infra)

• Responsible: SRE (runtime monitoring) & Data Scientist (model performance)
• Accountable: SRE
• Consulted: Software Engineer, Product Manager
• Informed: Legal
  Justification: Ops own alerts; I own metrics for model validity.

Compliance docs

• Responsible: Legal
• Accountable: Legal
• Consulted: Data Scientist, Product Manager
• Informed: SRE, Software Engineer
  Justification: Legal ensures regulatory compliance; I provide technical details and impact.

1. How do you define "retention"? (e.g., 7/30/90-day active return, subscription renewals, daily/weekly active users) — Clarifies the target metric and measurement window so we can compute baseline and track change.

2. Which user cohorts should we target? (new users, paying users, high-risk churners, geography, channel) — Different cohorts have different behaviors and interventions; prioritization affects analysis and sample size.

3. What’s the current baseline retention and the timeframe for the 10% lift? — Needed to calculate required absolute improvement, statistical power, and realistic goals.

4. What success metric(s) beyond retention do you care about? (LTV, revenue, engagement, CAC) — Ensures we don’t optimize retention at the expense of revenue or other KPIs.

5. What data sources are available and their freshness? (event logs, CRM, billing, product telemetry) — Determines feasibility, feature engineering, and integration effort.

6. What constraints exist? (budget, team bandwidth, time-to-impact, privacy/GDPR, data retention policies) — Impacts choice of methods, A/B testing scope, and tooling.

7. What interventions are acceptable or planned? (email nudges, in-app prompts, price changes, UX experiments) — Defines features to model/predict and operational requirements for rollout.

8. How do you want rollout and validation handled? (A/B testing, ramp strategy, minimum detectable effect, rollback rules) — Ensures rigorous causal inference and risk control.

9. Any regulatory or business rules to follow? (opt-outs, communication frequency limits, contract terms) — Prevents legal violations and user backlash.

10. What timeline and reporting cadence do you expect? (weekly dashboards, monthly reviews, executive checkpoints) — Aligns deliverables and monitoring.

These answers let me produce a measurable plan: define cohorts, compute sample sizes, build predictive models/experiment designs, and recommend prioritized interventions with monitoring and compliance safeguards.

Situation: A feature believed highly predictive is 50% missing in production. Immediate goal: decide whether to wait/collect, engineer proxies, impute, change model, or release without it — balancing accuracy, cost, time, and regulatory risk.

Decomposition of actions (what, why, how):

1. Collect more data

   • What: Instrument systems / capture the missing source at origin.
   • How: Engineering ticket, logging, API changes, maybe A/B rollout.
   • Pros: Restores ground truth, best long-term model fidelity.
   • Cons: High engineering cost, weeks–months, possible privacy/regulatory gating.

2. Create proxy features

   • What: Derive correlated signals (user behavior, timestamps, related sensors).
   • How: Feature engineering, feature crosses, representation learning.
   • Pros: Fast, low regulatory risk, interpretable proxies.
   • Cons: Lower ceiling than original feature, risk of leakage.

3. Imputation strategies

   • What: Missing-value flags + impute (mean, KNN, MICE, model-based).
   • How: Train imputer on observed subset; include indicator features.
   • Pros: Simple to implement, preserves sample size.
   • Cons: Can bias estimates if data not missing at random (MNAR).

4. Model selection tolerant to missingness

   • What: Use tree ensembles (XGBoost with missing handling), models that accept masks, or deep nets with learned embeddings + mask.
   • How: Train with missing patterns, include mask as feature.
   • Pros: Robust to missingness patterns, can exploit mask signal.
   • Cons: May still lose predictive power vs true value.

5. Business decision (delay vs release)

   • What: Release with degraded model or delay until better data.
   • How: Cost–benefit and regulatory assessment; pilot releases.
   • Pros/Cons depend on downstream impact and risk appetite.

Decision matrix (estimate bands: High/Med/Low):

Option | Expected accuracy gain | Collection cost | Time-to-implement | Regulatory risk
Collect more data | High | High | Long (weeks–months) | Medium–High (depends on source/privacy)
Proxy features | Medium | Medium | Short–Medium (days–weeks) | Low
Imputation (with flag) | Low–Medium | Low | Short (days) | Low
Models tolerant to missingness | Medium | Low–Medium | Short–Medium | Low
Release/delay business choice | Variable | Variable | Immediate or Long | High if wrong decision

Recommended course (practical plan):

1. Short-term (days): implement imputation + missingness flag and train a model tolerant to masks; evaluate lift on holdout and monitor calibration.
2. Short–medium (weeks): engineer proxy features; run incremental experiments (A/B or shadow routing).
3. Parallel long-term: scope data collection engineering work and regulatory review; quantify expected uplift by training on full-record subset to estimate upper-bound.
4. Decision rule: if short-term hybrid (impute+proxy+robust model) reaches >80–90% of upper-bound accuracy and business risk is low, release with monitoring; otherwise delay and invest in collection.

Key risk checks:

• Test for MNAR: compare observed vs missing distributions.
• Use uplift between models trained with/without feature to estimate value.
• Add safety gates: rollback, human review, and flagging if model relies heavily on imputed values.