CDP-Overlaid Feedback
CDP-Overlaid Feedback is survey and review data matched to each guest’s profile, so you can see a guest’s value tier and churn status next to their feedback instead of reading a flat average. A single NPS of +42 becomes Super Guests +72, Regulars +56, New Guests +38, and At-Risk −14, revealing which guests are unhappy and attaching a one-click recovery action. A standalone survey tool cannot produce this because it has no customer data platform underneath.
CDP-overlaid feedback
CDP-overlaid feedback is survey and review data matched to each guest’s profile, so you can see a guest’s value tier and churn status next to their feedback instead of reading a flat average, revealing which guest segments are unhappy and attaching one-click recovery actions. It answers a question a raw rating can’t: not just “how happy are guests?” but “how happy are the guests who matter most?”
Why the average hides the truth
A 4.2 average rating can conceal that your highest-value regulars are quietly dissatisfied while one-time visitors are delighted, a dangerous blind spot, since losing a super guest costs far more than losing a first-timer. Overlaying feedback on CDP value tiers exposes that, so you can see whose sentiment is slipping and act on it directly, rather than chasing an average that flatters and misleads.
Tying recovery actions to the feedback closes the gap between insight and response: the moment a high-value guest signals dissatisfaction, the win-back is one click away.
Frequently asked questions
How is CDP-overlaid feedback different from regular survey results?
Regular results give you a flat average across all guests. CDP-overlaid feedback matches that sentiment to each guest’s profile, so you can see whether your most valuable regulars are happy, not just whether the overall number looks fine.
Why does matching feedback to guest profiles matter?
Because not all dissatisfaction costs the same. An unhappy high-value regular is a far bigger threat than an unhappy one-time visitor, but a flat average treats them identically. Matching feedback to each guest’s profile reveals where sentiment problems actually endanger revenue.
Bloom turns guest data into recovered revenue: an average of $53,000+ per location in the first year.
See your restaurant's data