ML System

Training controls, experiment tracking, feature dictionary, performance monitoring

Intelligence Suite Status

Models Trained

Oldest Model

Max Features

Suite Health

Metric glossary R² How much revenue variation the model explains (1.0 = perfect) AUC How well the model separates yes/no outcomes (1.0 = perfect) F1 Balance between catching positives and avoiding false alarms WMAPE Average prediction error, weighted by account size MAE Typical dollar error per prediction Silhouette How well-separated the customer groups are (1.0 = perfect) Lift How much better the model's top picks are vs random

Model Suite

What this is

Seven models that analyze every revenue-active sponsor account from different angles. Together they produce 15+ intelligence dimensions per account — forecasts, risk scores, behavioral segments, expansion signals, and market positioning — so the business can act on data instead of intuition.

How they're organized

The suite trains in a cascade. Segmentation (M3) runs first and assigns every account to a behavioral cluster per sales team. Those segment labels then flow as input features into M1, M2, M4, M7, and M8, grounding their predictions in the behavioral patterns M3 discovered. M6 is independent — it reads government procurement data directly.

M3 Segments → M1 Revenue M2 Retention M4 Expansion M7 Portfolio M8 Cross-Sell | M6 Markets

What each model does

M1 — Revenue Forecast: Predicts next-year revenue per account. Drives the Revenue Intelligence page, quarterly projections, and revenue-at-risk calculations.
M2 — Account Retention Risk: Scores the probability an active account churns to zero revenue. Combined with M1, produces dollar-weighted risk.
M3 — Account Segmentation: Clusters accounts into behavioral segments (per sales team) using 250 features. Foundation for all downstream models.
M4 — Category Expansion: Predicts single-category accounts that will adopt a second product category. Identifies upsell readiness.
M6 — Event Market Intelligence: Rules-based engine that scores state×category procurement momentum and aligns sponsors to market tailwinds. No ML — pure analytics.
M7 — Event Portfolio Expansion: Predicts which event sponsors will diversify from one event type to many. Growth signal for the events team.
M8 — Cross-Sell Graduation: Predicts single-line accounts that will buy across multiple product lines, and recommends the most likely next category.

Reading the cards

Each card below shows a model's current state. The headline is the key metric at a glance — R² for regression, AUC for classifiers, Silhouette for clustering. Metric chips (colored badges) break this out: green is strong, amber is acceptable, red needs attention. The narrative block explains what the model does, how it's performing, and what it reveals. Feature importances show which input signals the model relies on most (for M3 this shows per-team cluster quality; for M6, market momentum and alignment distributions). Use Configure to tune hyperparameters, History to see how performance changed across runs, Log for raw training output, and Debrief to get an LLM-generated analysis with actionable config recommendations.