B.E.S.T. Score Methodology

How the B.E.S.T. Score works

A weighted 100-point AI-visibility health check for independent restaurant and bar venues. Four signals, transparent weights, calculated by src/lib/best-score/calculator.ts.

The formula

B.E.S.T. is the weighted sum of four dimensions. The letters used to stand for "Business visibility / Employee excellence / Service quality / Traction" (V2). The current V4 calculator computes:

B
Build
15
of 100
E
Employee Excellence
30
of 100
S
Service Reputation
35
of 100
T
Traction
20
of 100

Weights are fixed. The overall score is rounded to one decimal.

The four dimensions

B

Build

15 of 100 points

Does the owner know their venue well enough to defend it under scrutiny? Build measures the owner-knowledge inputs that feed every other dimension.

Components

5 pts
Engagement
Questions answered out of 29 (asymptotic curve)
4 pts
Streak
Current onboarding streak capped at 90 days (linear)
3 pts
Knowledge
Owner-knowledge confidence average on a 0–5 scale
3 pts
Milestones
Milestones completed out of 15

Data sources

  • Owner-input intelligence questions
  • Onboarding milestone tracker
E

Employee Excellence

30 of 100 points

Are the people behind the venue visible to customers AND to the AI engines reading reviews? Employee Excellence is the highest-leverage dimension because customer-visible team is what AI engines actually quote.

Components

8 pts
Populated zones
Workzones with team data (0/2/4/6/8 for 0–4 zones)
10 pts
Stability
Long-tenure team ratio (asymptotic, pinned so 0.75 = 6 pts)
6 pts
Profile completion
Average team-member profile completion %
5 pts
Customer-visible team
V4: 0.6 × workzone-coverage + 0.4 × aspect-breadth from dow.team_mention_aggregates
3 pts
Distinct talents
Talents mapped from our proprietary hospitality talent taxonomy

Data sources

  • dow.team_mention_aggregates (review-mentioned staff aggregates)
  • Team Composition entries
  • Hospitality talent taxonomy (booteek proprietary)
S

Service Reputation

35 of 100 points

What does the public review record say about service quality, and is the trend moving up or down? Service Reputation is the largest dimension because it carries the most independent third-party signal.

Components

12 pts
Weighted rating
Weighted-average rating delta vs 3.5 baseline (asymptotic)
5 pts
Review velocity
Reviews per month (asymptotic curve)
6 pts
Response rate
Response rate over the last 90 days (linear)
5 pts
Response quality
Derived from response rate when not separately measured
7 pts
Sentiment trend
V4 hybrid: 0.6 × 30d–90d rating delta + 0.4 × food/service aspect delta. V3 fallback: max(0, rating − 3.8).

Data sources

  • Google Business Profile reviews
  • TripAdvisor reviews
  • dow.venue_snapshots (rolling rating + aspect averages)
  • dow.reviews.food_rating + service_rating (aspect-based sentiment)
T

Traction

20 of 100 points

Is the venue maintained, visible across platforms, and discoverable by AI assistants? Traction reads the maintenance and AI-visibility signals that keep a venue from drifting off the map.

Components

6 pts
GBP completeness
GBP fields completed out of 20 (linear)
5 pts
Platform breadth
Verified platforms out of 6 target platforms (linear)
4 pts
Content freshness
Days since last content (30-day decay window)
5 pts
AI visibility
Cross-engine AI visibility probe score on a 0–100 scale

Data sources

  • Google Business Profile field completion
  • Multi-platform verification status (Google, TripAdvisor, OpenTable, TheFork, Facebook, Instagram)
  • AI visibility probes (ChatGPT, Perplexity, Gemini)

Certification tiers

The overall 0–100 score maps to a five-tier certification:

Master
≥ 90
Elite
≥ 80
Excellence
≥ 65
Verified
≥ 45
Member
below 45

External entities the methodology grounds in

B.E.S.T. references external public concepts so the methodology can be cross-checked, not taken on faith. Each is also exposed as an entity in the page's structured data.

Google Business Profile
Primary platform for venue identity, review aggregation, GBP-field completeness signal
Aspect-based sentiment analysis
Method underlying the V4 Sentiment Trend hybrid component on Service Reputation
Schema.org structured data
Venue identity that AI engines read when answering "what is this restaurant?" — feeds into the AI visibility signal on Traction
Generative Engine Optimization
The discipline B.E.S.T. operationalises — making venues discoverable by generative AI search

Authored by

Anthony Robinson, Founder & CEO, booteek AI Limited. Identity: Wikidata Q139609210. First published 2026-05-19. Last reviewed 2026-05-19.

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