Goal: produce a simple, interpretable propensity-to-buy model to rank inbound leads by purchase likelihood across segments so sales/marketing can prioritize outreach and personalize nurture.
Candidate input features:
- Lead-level: job title, industry (SIC/NAICS), company size, annual revenue, geography, channel (organic/paid/partner), lead age, pages visited, time on site, product pages viewed, demo request flag, email engagement (opens/clicks), last activity timestamp.
- Account-level enrichment: technographic stack, existing customers in same account, funding stage.
- Behavioral recency/frequency features: sessions_last_7d, events_last_30d, time_since_last_activity.
- Derived features: lead_score_history, segment interaction (e.g., SMB vs Enterprise).
Modeling approach:
- Start with logistic regression with L1/L2 regularization for interpretability and fast iteration. Use categorical encoding (target/one-hot) and scaling.
- If nonlinearity or interactions matter, compare with gradient-boosted trees (XGBoost/LightGBM). Keep a simple decision-tree-based model only if rules will be surfaced to sales.
Evaluation metrics:
- Primary: AUC-ROC for ranking; Precision@K and lift (top decile) for business usefulness.
- Calibration: Brier score and reliability plots so predicted probabilities map to real conversion rates.
- Operational: coverage (percent of leads with score), false positive rate at chosen threshold, and expected conversion lift vs baseline.
Expected data needs:
- 6–12 months of labeled history with conversion outcome and timestamps; minimum thousands of leads (more for many features).
- Data quality: canonicalized company names, deduped leads, synced CRM events.
- Privacy/compliance checks for PII and enrichment sources.
Deployment & monitoring:
- Deployment: wrap model as a microservice (REST) or integrate into ETL to append score into CRM/marketing platform daily or in near-real-time for high-value events.
- Retraining: scheduled monthly retrain with rolling window; trigger ad-hoc retrain on performance drift.
- Monitoring: track model metrics in prod—AUC on recent holdout, calibration drift, feature distribution (population drift via PSI), score distribution shifts, and business KPIs (lead-to-opportunity rate by score bin, revenue per scored cohort).
- Alerting: automated alerts when PSI > threshold or conversion rates in top decile drop >X%.
- Governance: maintain model card documenting features, data lineage, evaluation, intended use, and fairness checks (ensure no discriminatory segments).
- Feedback loop: capture outcomes from CRM and A/B test score-driven workflows to measure lift and continually improve.
This balances speed, interpretability for stakeholders, and a clear operational plan to ensure the model delivers business impact.
