Common marketing uses
Scoring leads by likelihood to close, predicting which customers may churn, estimating lifetime value, forecasting demand and choosing who should get which offer.
Built-in tools
You don't always need a data science team. GA4 offers predictive metrics like purchase probability for sites with enough data, and many CRMs and email platforms include predictive scores.
Limits
Predictions are probabilities, not certainties, and they rely on the past looking like the future. Models need enough clean data and should be checked against real results regularly.
Why Predictive Analytics matters
Knowing who is likely to act lets you spend effort and budget where it matters most, like retention offers for customers at risk or faster follow-up for leads likely to buy.
Saving at-risk subscribers
A meal kit company used order history to flag subscribers likely to cancel soon. Sending them a menu preview and a flexible pause option kept more of them subscribed than its old blanket discount emails.
Common mistakes
Too little data
Small datasets give unreliable predictions. Start with simple rules if you lack history.
Trusting scores blindly
Check predictions against what actually happens and adjust.
Predicting without acting
A churn score only helps if someone does something with it. Plan the action first.
Where we work on this