Four Worlds, One Language
Restaurant data lives in silos. Guest profiles sit in loyalty systems. Menus live in ordering platforms. Nutrition facts hide in spreadsheets. Transaction history scatters across POS systems. EveryBite connects these worlds—and the key that unlocks everything is food.
Guest
Who they are, what they want, how they behave
Food
Menus, recipes, ingredients, nutrition, allergens
Ordering
What they ordered, how they customized, when & where
Loyalty
Points, tiers, tags, segments, campaigns
The insight: You don’t truly know a guest until you understand their relationship with food. Loyalty points tell you they’re valuable. Order history tells you they visit Tuesdays. But food tells you they’re managing a wheat allergy, building muscle, feeding a family, or celebrating a birthday with something indulgent.
The Four Domains
1. Guest
Who is this person? The guest domain captures identity, preferences, and behavior:| Data | Source | Example |
|---|---|---|
| Identity | Loyalty ID, Passport, device fingerprint | ”This is Sarah, a returning customer” |
| Demographics | Inferred from behavior patterns | ”Young professional, health-conscious” |
| Preferences | Explicit settings or observed patterns | ”Avoids dairy, prefers high-protein” |
| Behavior | Session analytics, interaction patterns | ”Browses thoroughly, customizes often” |
2. Food
What can they eat? The food domain is the Ingredient Intelligence layer—a seven-level hierarchy from menu down to ingredient specification:| Level | What It Captures |
|---|---|
| Menu | What’s available today |
| Dish | What guests see and order |
| Recipe | How the dish is assembled |
| Prep Recipe | House-made components |
| Ingredient | Individual items with allergens |
| Ingredient Data | Nutrition facts, dietary classifications |
| Ingredient Specification | Exact products and brands |
3. Ordering
How do they buy? The ordering domain captures transactions and customization:| Data | Source | Example |
|---|---|---|
| Order History | POS, ordering platforms | ”24 orders, $18.50 average” |
| Frequent Items | Transaction analysis | ”Honey Garlic Stir-Fry, ordered 8 times” |
| Customizations | Modifier selections | ”Always adds extra protein, removes sauce” |
| Timing | Order timestamps | ”Tuesday lunch, Thursday dinner” |
4. Loyalty
What do their programs know? The loyalty domain pulls data from third-party systems:| Data | Source | Example |
|---|---|---|
| Tier & Points | Thanx, Punchh, Paytronix | ”Gold tier, 2,450 points” |
| Tags | Loyalty program segmentation | ”birthday_month”, “protein_lover” |
| Offers | Campaign targeting | ”Eligible for free dessert promo” |
| Preferences | Stated in loyalty profile | ”Prefers no onions” |
Food: The Connective Tissue
Here’s the key insight: Food is how you understand people. Without food intelligence, the other domains are disconnected:| Domain | Without Food | With Food |
|---|---|---|
| Guest | ”Sarah visits Tuesdays" | "Sarah avoids wheat, loves high-protein, customizes for her macros” |
| Ordering | ”She orders the stir-fry" | "She orders the stir-fry because it’s her highest-protein wheat-free option” |
| Loyalty | ”Gold tier, 2,450 points" | "High-value guest who needs wheat-free options—show her the new grain bowl” |
- Why does she order that dish? (It fits her dietary needs)
- What else would she like? (Other high-protein, wheat-free options)
- How should we personalize? (Highlight compatible dishes, warn about allergens)
- When is she at risk? (If we remove her favorite dish, she might churn)
“EveryBite” — The name isn’t accidental. Every bite tells you something about the person. What they choose, what they avoid, how they customize—these are signals that reveal who they are and what they want.
How the Domains Connect
Guest + Food = Personalization
When a guest opens the menu, we know their dietary restrictions before they search:Ordering + Food = Understanding
When a guest customizes, we understand why:Loyalty + Food = Actionable Segments
Loyalty tags become meaningful when combined with food:Guest + Ordering + Loyalty + Food = GuestIQ
When all four domains connect, you get complete guest intelligence:Data Sources Flow In
Guest (identity & behavior) + Ordering (history & customization) + Loyalty (tags & tiers)
Food Intelligence Processes
The Ingredient Intelligence layer analyzes everything through the lens of food—what they can eat, what they prefer, what they avoid.
What This Means in Practice
For Menu Personalization
A guest opens your app. Before they search, you already know:- Their restrictions: Wheat-free, peanut allergy
- Their preferences: High-protein, prefers lunch portions
- Their patterns: Usually customizes, price-sensitive
- Their favorites: Ordered the Power Bowl 6 times
For Campaign Targeting
You’re launching a new high-protein grain bowl. Who should see the promo?| Traditional Targeting | Food-Informed Targeting |
|---|---|
| ”Active loyalty members" | "Wheat-free + high-protein segment" |
| "Frequent visitors" | "Guests who order protein modifications" |
| "High spenders" | "Guests whose favorites are being discontinued” |
For Staff Alerts
A VIP guest walks in. Your host stand shows:Sarah M. — Gold member, 24 visitsThe staff can greet her by name and recommend something she can actually eat.
- Wheat allergy (verified)
- Prefers: Power Bowl, Stir-Fry
- Usually adds: extra protein
- Note: Birthday this month
For Product Development
Which dishes should you add to the menu?| Question | Food Data Answers |
|---|---|
| ”What’s missing?" | "12% of guests are wheat-free but only 3 dishes fit" |
| "What’s underperforming?" | "This dish matches many profiles but has low reorder rate" |
| "What’s trending?" | "High-protein customizations up 40% this quarter” |
The Data Model Summary
| Domain | What It Knows | Key Insight |
|---|---|---|
| Guest | Identity, demographics, behavior | Who the person is |
| Food | Ingredients, nutrition, allergens | What they can eat |
| Ordering | Transactions, customizations | What they chose |
| Loyalty | Points, tiers, tags | How valuable they are |
- Anonymous visitors into understood guests
- Transaction data into dietary insights
- Loyalty tiers into actionable segments
- Generic menus into personalized experiences

