PE-201b · Module 3

Anomaly Detection

3 min read

Validation rules catch known problems. Anomaly detection catches unknown ones. An anomaly is a data point that deviates significantly from the expected pattern — a deal amount that is 10x the average for its segment, a conversion rate that drops 20 points in a week, or a rep whose pipeline value doubled overnight. Anomalies are not necessarily wrong, but they are worth investigating.

  1. Statistical Baselines Calculate baselines for key metrics: average deal size by segment, average time in each stage, average activities per deal per stage. Any deal that deviates by more than two standard deviations from the baseline is an anomaly. The baseline updates quarterly as your data evolves.
  2. Trend Break Detection Monitor stage conversion rates weekly. A conversion rate that changes by more than 10 percentage points in a single week is a trend break — something changed. It could be a data quality issue (bulk imports, stage reassignments) or a genuine change in the business. Either way, it demands investigation.
  3. Velocity Outliers Deals that move through stages dramatically faster or slower than average warrant attention. A deal that went from Lead to Negotiation in 3 days when the average is 45 days might be a fast-tracked enterprise deal — or a rep who bulk-advanced stages to inflate their pipeline for a forecast call.

Do This

  • Build anomaly detection as alerts, not blocks — anomalies need investigation, not automatic rejection
  • Review anomaly alerts in the weekly pipeline meeting as a standing agenda item
  • Use anomalies as feedback to create new validation rules for recurring patterns

Avoid This

  • Ignore outliers because they are edge cases — edge cases corrupt averages
  • Investigate every anomaly with equal urgency — prioritize by pipeline impact
  • Treat anomaly detection as a one-time analysis instead of a continuous monitoring system