Live Guest Intelligence

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.

Score Estimator v2.4 โ€” Live
OPERATIONAL
1,200
2008,000
Guest Value DistributionOpenTable ยท Fine Dining

No form on this page โ€” one click, one field.

+$31K
Avg revenue lift
per location / month
14M+
Guests ranked
across portfolio clients
47
Signal sources
integrated data streams
// RFM Scoring Model

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.

R
MODEL WEIGHT
35%

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.

0%100%

Hover to see signal breakdown โ†’

Signal Weighting

R-SIGNAL
Last visit date
14pts
Reservation cadence
11pts
Last review timestamp
6pts
Loyalty app last open
4pts
Formula
R = 100 ร— e^(โˆ’ฮปt)
ฮป = 0.023, t = days since last visit
F
MODEL WEIGHT
30%

Frequency

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.

0%100%

Hover to see signal breakdown โ†’

Signal Weighting

F-SIGNAL
12-month visit count
12pts
Party size consistency
8pts
Day-part loyalty
6pts
Location spread
4pts
Formula
F = log(n) / log(ฮผ_location)
n = visits, ฮผ = location median
M
MODEL WEIGHT
35%

Monetary

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.

0%100%

Hover to see signal breakdown โ†’

Signal Weighting

M-SIGNAL
Per-visit avg spend
15pts
Upsell attach rate
10pts
Lifetime value trend
7pts
Comp acceptance rate
3pts
Formula
M = ฮฃ(spend_i ร— w_i) / n
w = upsell weight coefficient
ToastOpenTableResySquareYelpGoogle ReviewsLightspeedTockSevenRoomsTripadvisorRevelLoyalty+ToastOpenTableResySquareYelpGoogle ReviewsLightspeedTockSevenRoomsTripadvisorRevelLoyalty+
// Data Sources

47 signals.
One unified score.

Click any card to see how each platform connects into the Score engine.

POS Systems

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.

Check averageItem-level spendUpsell attachComp historyServer assignmentTable turn time
18signals ingested
View diagram
Connection Diagram
Toast POS
Square
Lightspeed
โ†’
Score Engine
โ†’
Guest Rank
Host Dashboard
Reservation Platforms

Reservations

OpenTable, Resy, Tock, SevenRooms โ€” booking history, cancellation rate, party size trends, and special-occasion flags all feed the recency and frequency models.

Booking lead timeNo-show rateParty sizeSpecial occasionsPreferred timesCancellation history
14signals ingested
View diagram
Connection Diagram
OpenTable
Resy
Tock
โ†’
Score Engine
โ†’
Recency Score
Frequency Score
Review & Loyalty

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.

Review sentimentStar rating trendReview frequencyLoyalty pointsRedemption rateSocial reach
15signals ingested
View diagram
Connection Diagram
Yelp
Google Reviews
Loyalty App
โ†’
Score Engine
โ†’
Monetary Score
Advocacy Index
// Case Studies

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

+$23.40
Revenue per cover

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.

Revenue Window
Before Score
After Score
Verified result
30-day window

Harbor House Hospitality

14-location casual dining ยท Miami, FL

+$41K/mo
At-risk guest recovery

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.

Revenue Window
Before Score
After Score
Verified result
30-day window

Nakamura Restaurant Co.

5-location Japanese ยท New York, NY

โˆ’62% waste
Comp efficiency

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.

Revenue Window
Before Score
After Score
Verified result
30-day window
// Technical Appendix

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 0โ€“100:

Score = (0.35 ร— R_norm) + (0.30 ร— F_norm) + (0.35 ร— M_norm)

Where:
  R_norm = 100 ร— e^(โˆ’0.023t)          // 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 โ‰ฅ 85        Top ~17% of active guests
  Regular     Score 65โ€“84       Next ~25%
  Occasional  Score 40โ€“64       Middle ~28%
  At-Risk     Score 20โ€“39       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.