Predictive Analytics Analytics Checklist

Agree on what you're predicting (churn, revenue, demand), the business impact, and acceptable accuracy thresholds (e.g., MAPE ≤ 10%) before building.

Use your key metrics to scope the problem and prevent over-engineering. Aligns team on success criteria upfront.

Calculate naive forecast accuracy first. This simple baseline reveals whether your ML model actually adds value over statistical methods.

Document baseline metrics in your model registry so future team members understand why this model was chosen.

Extract features from raw data (lag features, rolling averages, domain indicators). Use scikit-learn or XGBoost feature importance to keep only predictive features.

Drop the weakest 30% of features; this speeds up retraining and reduces model complexity without sacrificing accuracy.

Use time-series splits (not random) to prevent data leakage. Reserve recent data for validation to simulate production conditions accurately.

Use Databricks or SageMaker to automate validation pipelines; catch issues before deployment and avoid surprises in production.

Start with gradient boosting (XGBoost, LightGBM) for structured data. Benchmark against simpler models and ensemble approaches before committing.

Use H2O.ai or DataRobot for rapid prototyping across algorithms; saves weeks compared to manual scikit-learn tuning.

Package your model (scikit-learn, XGBoost binary) with exact Python versions and library requirements. Deploy via Docker for consistency across environments.

Use Databricks Model Registry or Amazon SageMaker Model Registry to version models and track lineage automatically.

Schedule model retraining weekly or monthly using fresh data. Automate testing so new versions only go live if they beat current accuracy thresholds.

Model retraining frequency is a leading KPI; teams that retrain monthly outperform those that retrain quarterly.

Expose predictions via REST API (for real-time) or batch jobs (for daily/weekly). Stakeholders access predictions through dashboards, not notebooks.

Use Google Vertex AI or Amazon SageMaker endpoints for auto-scaling; eliminates infrastructure headaches and latency issues.

Track prediction response time and compute cost. Alert when latency exceeds SLAs so you catch performance regressions before users experience impact.

Set latency budgets (e.g., <100ms for real-time) and test with production data volumes before deployment.

Record which model version runs in production, what changed, and how to revert. Create runbooks for rapid rollback if predictions drift unexpectedly.

Automate rollback if accuracy drops below baseline; prevents bad predictions from eroding stakeholder trust.

Continuously measure prediction accuracy in the real world. Compare actual outcomes to forecasts and alert when accuracy degrades below thresholds.

Segment metrics by cohort (high-value vs low-value customers); segmentation reveals where models fail and need improvement.

Show stakeholders why predictions were made. Use SHAP values or feature importance plots to explain which inputs drove each forecast.

Use Databricks or Julius AI to generate SHAP explanations automatically; stakeholders trust transparent, interpretable predictions.

Monitor whether model inputs or distributions shift over time. Drift is a leading indicator that retraining is needed before accuracy degrades.

Alert on input drift before output drift appears; catch problems early and avoid surprising stakeholders with sudden inaccuracy.

Track the range of actual outcomes and compare to prediction intervals. Wide intervals suggest uncertainty; narrow intervals may indicate overconfidence.

Use prediction intervals (quantile regression or bootstrapping) to communicate confidence; helps stakeholders decide whether to act on forecasts.

Build dashboards showing MAPE, accuracy trends, and explainability for each prediction. Update weekly so stakeholders see consistent performance.

Highlight high-confidence predictions separately; stakeholders are more likely to act on forecasts they understand.

Identify exactly where and how stakeholders will use forecasts (e.g., churn predictions trigger retention campaigns). Document the workflow before deployment.

Work backwards from business goals; a perfect model is worthless if stakeholders don't know what to do with it.

Track what percentage of predictions result in actual business actions (e.g., how many churn predictions lead to retention offers). Target 30%+.

Low conversion (< 20%) signals unclear workflows or lack of trust; investigate with stakeholders before blaming the model.

Send alerts to decision-makers when predictions hit critical values (high-risk churn, demand spike). Automation ensures no predictions are missed.

Use Alteryx or workflow automation to route alerts to the right team; delays between prediction and action cost revenue.

Measure dollars saved (churn prevented, fraud detected) or earned (upsell conversions) by predictions. Link to business outcomes, not just model metrics.

Quantify business impact monthly; demonstrates ROI to leadership and justifies continued investment in model development.

Collect feedback from decision-makers on prediction usefulness, frequency, and accuracy. Use this to inform model improvements and retraining priorities.

Schedule quarterly business reviews with stakeholders; align predictions to evolving needs and prevent model drift.