ML Steps & High-Value Use Cases

Product/analyst signal • 6-8 min read • 1-2 weeks for a pilot

TL;DR: Apply ML to problems where prediction or pattern discovery adds measurable value: classification, forecasting, recommendations, and anomaly detection.

Map pipeline steps to business outcomes

• Problem definition → ROI focus (what to measure)
• Data collection → feasibility (is there signal?)
• Modeling → accuracy vs. explainability trade-offs
• Deployment → integration and observability

High-value use cases

1. Classification (spam, fraud, churn)
2. Regression/forecasting (sales forecasting, demand)
3. Recommendation (personalized content, products)
4. Anomaly detection (ops, security)
5. Clustering for segmentation and exploration

Prioritization checklist

• Is the outcome measurable? (conversion uplift, time saved)
• Can you collect labels or infer them reliably?
• Is model explainability required for the use case?
• What is the cost of false positives vs. false negatives?

Example: Churn prediction

• Metric: AUC and lift at top-decile
• Feature sources: usage logs, billing history, support tickets
• Action: Targeted retention campaign for top-risk users
• Evaluation: A/B test the campaign vs. control

Example: Demand forecasting

• Metric: Mean Absolute Percentage Error (MAPE)
• Data: historical demand, promotions, seasonality signals
• Deployment: daily model with rolling retrain

Quick win patterns

• Heuristic → Baseline ML: Automate rule-based scoring with a trained model
• Feature store: Centralize and reuse features across models
• Shadow mode: Run model predictions to compare before full rollout

Next steps

Choose one high-impact use case from this list. The next post dives into supervised learning fundamentals and how to pick the right algorithms.

Visual: use-case matrix

Replace with a small 2x3 matrix SVG linking problem → metric → impact.