Engagement Score

Engagement scores solve the problem of information overload: instead of monitoring dozens of individual metrics per user, you distill them into one number that summarizes account health. A typical engagement score combines login frequency, feature usage breadth and depth, content creation volume, collaboration activity, and recency of last interaction, with weights reflecting each signal's importance.

Building an effective engagement score requires choosing the right inputs (signals that actually predict retention and revenue), assigning appropriate weights (ideally learned from data rather than assumed), and calibrating the scale (making the score intuitively interpretable). ML-based scoring models that predict retention probability are often more accurate than manually weighted scores because they discover non-obvious signal combinations.

Engagement scores power operational workflows across the business. Customer success teams prioritize accounts with declining scores. Sales teams focus expansion efforts on accounts with rising scores. Product teams monitor how feature changes affect scores across segments. Marketing teams target re-engagement campaigns to specific score ranges. The score becomes a shared language for customer health that aligns cross-functional teams around a common understanding.