Predictive Health Scoring

A traditional health score tells you where the account is right now. A predictive health score tells you where the account will be in 90 days. That distinction is the difference between reacting to churn and preventing it. CX health scoring gives you the four pillars — engagement, satisfaction, adoption, outcomes — and each pillar produces a current-state number. BI health modeling adds velocity and trajectory to each pillar, transforming static readings into forward-looking predictions.

The predictive model ingests both internal behavioral data and external intelligence. Internally: meeting attendance trends, response time velocity, stakeholder breadth changes, support ticket patterns, adoption metrics. Externally — and this is where BEACON's customer intelligence becomes critical — leadership changes at the client, competitor wins in their market, budget cycle timing, organizational restructuring signals. A client whose internal engagement is stable but whose industry is being disrupted has a different risk profile than the engagement data alone would suggest. The model must account for both dimensions.

1. Build the Internal Signal Layer Instrument every client touchpoint to feed the health model. Email response latency, meeting attendance ratio, stakeholder count trend, support ticket frequency and severity, deliverable feedback depth. Each signal updates in real time. The model weights signals by their historical correlation with churn — response latency has a 0.7 correlation in our data, stakeholder breadth decline has 0.8. Weight accordingly.
2. Add the External Intelligence Layer BEACON's continuous monitoring feeds external signals into the model: client leadership changes, funding events, competitive losses, market shifts, regulatory changes. Each external signal has a risk modifier — leadership change at the champion level multiplies churn risk by 2.5x regardless of internal engagement scores. The model that ignores external context is blind to half the risk landscape.
3. Generate 90-Day Trajectory Projections The model produces a trajectory curve for each account: current health, projected health at 30/60/90 days, and confidence interval. Accounts whose trajectory crosses the risk threshold within 90 days enter the intervention queue automatically. You do not wait for the score to drop. You act on the trajectory before the drop materializes. This is what predictive means — seeing the future in the present data.