1. DIRECTLY COLLECTED DATA (Raw Input)
Identity & Account

Full name
Email address
Phone number
Date of birth
Profile photo (if added)
Password / authentication method (Google, Apple, Facebook login)
Linked social account data (name, email, profile picture from OAuth provider)

Device & Technical Data

Device model and manufacturer (e.g., iPhone 16 Pro vs. iPhone SE)
Operating system and version
App version
Screen resolution
Battery status (some apps collect this)
Device language and region settings
Device advertising ID (IDFA on iOS, GAID on Android)
IP address
Wi-Fi network name (SSID)
Carrier / mobile operator
Available storage
Whether device is rooted/jailbroken

Location Data

GPS coordinates (precise, if permission granted)
Background location (if "always on" permission granted)
Approximate location via IP address (even without GPS)
Wi-Fi triangulation location
Bluetooth beacon data (in-store proximity detection)
History of all locations where app was opened

Order Data

Every item ever ordered
Date and time of each order
Order size and total price
Restaurant location for each order
Order method (in-store, drive-thru, delivery, curbside)
Customizations (no pickles, extra sauce, etc.)
Delivery address
Time between order placement and pickup/delivery
Reorders and frequency of specific items

Payment Data

Payment method (cash, credit card, debit card, Apple Pay, Google Pay, gift card)
Card type (Visa, Mastercard, Amex)
Partial card number / last 4 digits
Whether tips were given (delivery orders)

App Behavior Data

Time spent in app per session
Screens visited and navigation path
Items viewed but not ordered (browsing behavior)
Search queries within the app
Coupon and offer interactions (opened, redeemed, ignored)
Push notification interactions (opened, dismissed, ignored)
Time between receiving and redeeming an offer
App open frequency

Loyalty Program Data

Points balance and history
Rewards redeemed
Tier/level in loyalty program
Response to bonus point campaigns

Survey & Feedback Data

Post-visit ratings and reviews
Complaint history
Customer service interactions

2. INFERRED DATA (Derived from Raw Data)
Household & Family

Estimated number of people in household (based on order size)
Presence of children (Happy Meals, kids' items, toy selections)
Estimated age range of children (toy themes correspond to age groups)
Family structure (single, couple, family)
Dietary restrictions or preferences within household

Home & Work Location

Home address (restaurant used in evenings/weekends)
Work address (restaurant used on weekdays at lunch)
Commute route (restaurants ordered from in sequence)
Commute method (drive-thru = car commuter; walking distance orders = pedestrian)
Work schedule (shift times inferred from meal timing)
Work-from-home days (midday orders at home location)

Travel & Mobility

Vacation destinations (orders in unfamiliar cities)
Travel frequency (how often away from home location)
Road trips (orders at highway locations)
Business vs. leisure travel (weekday vs. weekend away from home)
Whether you own a car (drive-thru usage)

Financial Situation

Income estimate (average spend per visit, visit frequency)
Financial stress signals (switching to cheaper items, fewer visits, heavy coupon use)
Response to price increases (behavioural change after menu price hikes)
Coupon dependency (high coupon usage = price-sensitive)
Device model as wealth indicator (iPhone 16 Pro Max vs. budget Android = strong income signal)
Payment method sophistication (Apple Pay / contactless = higher income / tech adoption)
Willingness to pay for premium items (McPlant, premium burgers vs. value menu)

Social Life & Relationships

Co-location with others: if two accounts order simultaneously at the same location, they can be linked as a social pair or group
Group size inference (large orders at unusual times = social gathering)
Regularity of group orders (weekly friend meetups, family dinners)
Whether you eat alone vs. with others (single-item orders vs. multi-person orders)
Social influence (do you order the same items as your linked social accounts?)
Dating patterns (two-person dinner orders on Friday/Saturday evenings)

Health & Lifestyle

Diet quality (frequency of fast food, caloric patterns)
Vegetarian/vegan indicators (plant-based item orders)
Alcohol-adjacent behaviour (late-night orders after pub hours)
Sleep patterns (very late or very early orders)
Physical activity inference (gym proximity + light meals vs. sedentary + large meals)
Stress eating patterns (increased orders during stressful periods)

Psychological & Behavioural Profile

Impulsiveness (spontaneous orders vs. pre-planned)
Brand loyalty (visit frequency, response to competitor promotions)
Price sensitivity (reaction to discounts, coupons, value meals)
Routine vs. variety seeker (always orders the same vs. tries new items)
Tech adoption level (early adopter of app features, contactless payment)
Emotional state signals (comfort food orders: more fries, desserts, larger portions)

Work & Schedule

Working hours (meal timing patterns)
Shift worker vs. 9–5 (irregular meal times)
Weekend vs. weekday worker
Lunch break duration (time between ordering and pickup)

Just happened to me lol.

1. I had lunch today
2. hungry

how?

its called tent and rich and homeless people do it all the time

is this femcel propaganda?

nerd fact: oil is made from algae and plankton not a dinosaurs (sadly)

we are surrounded by things, that in fact, not exist

we the strong should work towards this future, when there are no poor ***s and gangsters

you can literally go out and touch grass, then you can continue walking to forest/beach or whatever nature is in your vicinity

you are not locked in your home