Predictive Guest Scoring
Predictive guest scoring is the use of AI and historical guest data — visit frequency, spend, recency, and sentiment — to forecast each guest's likelihood to return, risk of churning, and future lifetime value. Restaurants use these scores to act on the right guests before revenue is lost, not after. Bloom Intelligence assigns them automatically across every guest profile.
Predictive guest scoring for restaurants
Predictive guest scoring uses AI and a guest’s history to forecast three things that drive revenue: how likely they are to return, their risk of churning, and their expected lifetime value. Instead of treating every guest the same, it ranks them by what they’re likely to do next — so the restaurant can act before revenue is lost.
From hindsight to foresight
A return-likelihood or churn-risk score turns the guest list into a priority list. The regular whose visits are quietly tapering off surfaces as a high churn risk while there’s still time for a win-back; the new guest showing high-value patterns gets nurtured early. The scores are only as trustworthy as the data underneath them, which is why identity-resolved guest data across WiFi, POS, and ordering is the prerequisite — better inputs, more reliable forecasts.
The payoff is focus: limited marketing attention goes to the guests where it will move the most revenue, rather than being spread evenly across everyone.
Frequently asked questions
What does predictive guest scoring actually predict?
Typically three things: a guest's likelihood to return, their churn risk, and their projected lifetime value. Those scores let a restaurant prioritize — winning back at-risk regulars and nurturing guests who show high-value behavior — before revenue slips away.
What data does predictive guest scoring need?
Identity-resolved guest history — visits, frequency, recency, and spend pulled together across WiFi, POS, and online ordering. The quality of the scores depends on the quality and unity of that data; fragmented records produce unreliable predictions.
Bloom turns guest data into recovered revenue — an average of $53,000+ per location a year.
See your restaurant's data