Every seat tomorrow is
already accounted for.
Score watches every reservation click, review mention, and loyalty swipe across your portfolio โ then ranks every guest by lifetime spending potential before the host glances at the floor plan.
No form on this page โ one click, one field.
Three signals.
One ranked list.
Hover each card to see how every signal is weighted in the model. No black box โ every point is explainable.
Recency
When did they last show up?
Guests who visited in the last 30 days score 3ร higher than those who visited 90+ days ago. Recency decay is logarithmic โ a guest who came last Tuesday is 8 points ahead of one who came last month.
Hover to see signal breakdown โ
Signal Weighting
R-SIGNALFrequency
How often do they choose you?
Visit frequency over a rolling 12-month window, normalized against your location's average cover count. A guest at 2.4ร your median frequency is a loyalty anchor worth protecting at cost.
Hover to see signal breakdown โ
Signal Weighting
F-SIGNALMonetary
What's their actual check average?
Lifetime spend normalized to per-visit average, weighted by upsell behavior (wine selection, add-ons, dessert attach rate). The guest who orders a $12 cocktail every visit outscores the one who splurges once.
Hover to see signal breakdown โ
Signal Weighting
M-SIGNAL47 signals.
One unified score.
Click any card to see how each platform connects into the Score engine.
Point of Sale
Toast, Square, Lightspeed, Revel โ Score ingests check-level transaction data in real time. Every line item, every modifier, every comped dessert.
Reservations
OpenTable, Resy, Tock, SevenRooms โ booking history, cancellation rate, party size trends, and special-occasion flags all feed the recency and frequency models.
Reviews & Loyalty
Yelp, Google, Tripadvisor, and your own loyalty app. Sentiment scoring and review velocity feed the monetary model โ a guest who writes reviews is worth 1.4ร a silent spender.
Real numbers.
Real restaurants.
Toggle the time window on each card to see how Score's impact compounds over 30, 60, and 90 days.
Aria Restaurant Group
8-location fine dining ยท Chicago, IL
Aria's host team was seating high-frequency regulars at bar tables and giving prime four-tops to one-time Yelp visitors. Score flipped the seating logic โ VIP-ranked guests now receive preferred placement before service starts.
Harbor House Hospitality
14-location casual dining ยท Miami, FL
Harbor House had 2,300 guests who visited once in Q4 and never returned. Score's at-risk alerts triggered targeted re-engagement โ 847 guests reactivated within 45 days, averaging 2.1 visits each in the following 60 days.
Nakamura Restaurant Co.
5-location Japanese ยท New York, NY
Nakamura was comping desserts based on server intuition โ 38% of comps went to guests who never returned. Score's monetary model identified which guests respond to comps with increased visit frequency. Comp waste dropped 62% in 8 weeks.
See the model logic.
For the data-skeptical operator who needs to see the math before handing over a login. Every assumption is documented. Every weight is explainable.
The composite Score is a weighted sum of three normalized sub-scores, each ranging 0โ100: Score = (0.35 ร R_norm) + (0.30 ร F_norm) + (0.35 ร M_norm) Where: R_norm = 100 ร e^(โ0.023t) // t = days since last visit F_norm = 100 ร log(n) / log(ฮผ) // n = annual visits, ฮผ = location median M_norm = 100 ร ฮฃ(spend_i ร w_i) / (n ร ฮผ_spend) Normalization is performed per-location to account for cuisine type and market differences. A Score of 85+ qualifies as VIP tier. Scores are recalculated nightly at 3:00 AM local time.
Recency uses an exponential decay model rather than a linear drop-off. This reflects real guest psychology โ a guest who visited yesterday is not merely "one day better" than a guest who visited last week; the recency signal compounds. ฮป = 0.023 (tuned on 14M guest visits across 200+ locations) Half-life: 30.1 days (a guest's recency score halves every ~30 days of absence) Decay is paused during documented closures (holidays, renovations) and adjusted for seasonal patterns specific to each location's historical data.
Guest tiers are defined by composite Score percentile within each location's active guest base: VIP Score โฅ 85 Top ~17% of active guests Regular Score 65โ84 Next ~25% Occasional Score 40โ64 Middle ~28% At-Risk Score 20โ39 Next ~18% Ghost Score < 20 Bottom ~12% "Active" is defined as any guest with at least one visit in the trailing 365 days. Guests with zero visits in 365 days are moved to a separate "Churned" segment and excluded from tier calculations.
Score processes data under a data processing agreement (DPA) with each restaurant operator. All PII is pseudonymized at ingestion โ Score never stores full names or contact information; guest identities are represented by a deterministic hash of (email_normalized + phone_normalized). Score is SOC 2 Type II compliant. Data is encrypted at rest (AES-256) and in transit (TLS 1.3). Restaurant operators retain full data ownership. Score does not cross-sell or share guest data between unaffiliated operators.
You've seen the math. Now see your actual guest list.