Location based coupon distribution system

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A mobile phone app and backend system The app alerts users when they are in a geographic location near restaurants that they like and provides them with a coupon to incentivize them to go to that restaurant. The end users preferences are defined on the mobile app and stored in the backend system. The backend system allows commercial customers like restaurants to define restaurant locations that need to be targeted and allows them to define what promotional coupons should be targeted to each location, it gives them a place to place and store the content for the coupons and provides a pricing table for each coupon displayed on the end users phone. End users are given incentives to promote the app to restaurants that they frequent if that restaurant signs up for the program. 2 files attached. One is a AI rough cut on initial db structure. The other is an initial block diagram showing some of the main functional areas. AI analysis: ### Key Points - Research suggests a database structure with tables for users, preferences, restaurants, coupons, and a referral system to incentivize promotion. - It seems likely geofencing triggers alerts based on user preferences, with restaurants defining locations and coupons in the backend. - The evidence leans toward using spatial databases (e.g., PostGIS) for location data and JSON for flexible pricing tables, with a referral table linking users to restaurant sign-ups. ### System Overview This system is a location-based marketing platform that alerts users when they’re near preferred restaurants, offering coupons to encourage visits. User preferences are set in the app and stored in the backend. Restaurants define targeted locations and coupons via the backend, including pricing tables displayed on users’ phones. Additionally, users earn incentives for referring restaurants that join the program, tracked through a referral mechanism. --- ### Survey Note: Comprehensive Analysis of Database Structure for Location-Based Restaurant Coupon App with Referral Incentives This note provides a detailed examination of the database structure for a mobile phone app and backend system that alerts users near preferred restaurants with coupons and incentivizes them to refer restaurants to the program. It covers system design, database schema, technical components, and comparisons to existing solutions, ensuring a thorough understanding as of April 5, 2025. #### Introduction to the System The system involves a mobile app using geofencing to notify users when they’re near restaurants matching their preferences, offering coupons to drive visits. Preferences are defined in the app and stored in the backend. The backend allows restaurants to set targeted locations and promotional coupons, including content and pricing tables for display on users’ phones. A unique feature is the referral program, rewarding users for promoting the app to restaurants that sign up, similar to referral mechanics in apps like Uber Eats or Yelp. #### System Design and Functionality The system’s core functionality includes: - **User Alerts**: The app uses GPS-based geofencing to trigger notifications when users are near preferred restaurants, supported by spatial data queries ([Unleashing the Power of Spatial Databases](https://medium.com/@dezsays/unleashing-the-power-of-spatial-databases-navigating-location-based-data-59e209116dfb)). - **User Preferences**: Users set preferences (e.g., cuisine types) in the app, stored in the backend for personalized targeting, akin to Shopular’s interaction-based offers ([5 Proximity-Based Deal-Finding Apps for Retail Brands](https://streetfightmag.com/2016/02/09/5-proximity-based-deal-finding-apps-for-retail-brands/)). - **Restaurant Management**: Restaurants define locations and coupons via the backend, with pricing tables displayed on users’ phones, similar to Foursquare’s business tools ([14 of the Best Location Based Marketing Apps for Business](https://clarkstjames.com/location-marketing/14-of-the-best-location-based-marketing-apps-for-business/)). - **Referral Incentives**: Users earn rewards (e.g., bonus coupons) for referring restaurants that sign up, tracked through a referral system, a mechanic seen in referral programs like Uber Eats ([How to Design Coupon and Voucher Management System](https://www.geeksforgeeks.org/design-coupon-and-voucher-management-system/)). - **Coupon Display**: Coupons with pricing details incentivize visits, displayed when users are in proximity, mirroring SnipSnap’s approach ([5 Proximity-Based Deal-Finding Apps for Retail Brands](https://streetfightmag.com/2016/02/09/5-proximity-based-deal-finding-apps-for-retail-brands/)). #### Database Design The backend requires a database to manage users, preferences, restaurants, coupons, and referrals. The schema below is informed by coupon systems and location-based services: | Table | Field | Type | Description | |----------------|-------------------|--------------------|--------------------------------------------------| | User | user_id | INT | Primary key, auto-incremented | | User | name | VARCHAR(100) | User name | | User | email | VARCHAR(100) | Email address, unique | | User | referral_code | VARCHAR(20) | Unique code for referring restaurants | | User | created_at | TIMESTAMP | Timestamp of registration | | Preference | preference_id | INT | Primary key, auto-incremented | | Preference | type | VARCHAR(50) | Restaurant type (e.g., Italian, Mexican) | | UserPreference | user_id | INT | Foreign key to User | | UserPreference | preference_id | INT | Foreign key to Preference | | Restaurant | restaurant_id | INT | Primary key, auto-incremented | | Restaurant | name | VARCHAR(100) | Restaurant name | | Restaurant | address | VARCHAR(200) | Physical address | | Restaurant | latitude | DECIMAL(9,6) | Latitude for geofencing | | Restaurant | longitude | DECIMAL(9,6) | Longitude for geofencing | | Restaurant | radius | INT | Geofence radius (meters) | | Restaurant | created_at | TIMESTAMP | Timestamp of creation | | Restaurant | referred_by | INT | Foreign key to User (referring user), nullable | | Coupon | coupon_id | INT | Primary key, auto-incremented | | Coupon | restaurant_id | INT | Foreign key to Restaurant | | Coupon | code | VARCHAR(50) | Unique coupon code | | Coupon | discount | DECIMAL(10,2) | Discount value (e.g., 10% off) | | Coupon | expiry_date | DATE | Expiry date of the coupon | | Coupon | pricing_table | JSON | Pricing details (e.g., meal discounts) | | Coupon | created_at | TIMESTAMP | Timestamp of creation | | Referral | referral_id | INT | Primary key, auto-incremented | | Referral | user_id | INT | Foreign key to User (referrer) | | Referral | restaurant_id | INT | Foreign key to Restaurant | | Referral | reward_type | VARCHAR(50) | Reward (e.g., bonus coupon, points) | | Referral | reward_status | ENUM('pending', 'completed') | Status of reward distribution | | Referral | created_at | TIMESTAMP | Timestamp of referral | - **Spatial Data**: PostGIS or similar spatial extensions support geofencing with latitude/longitude and radius fields, enabling proximity queries ([Unleashing the Power of Spatial Databases](https://medium.com/@dezsays/unleashing-the-power-of-spatial-databases-navigating-location-based-data-59e209116dfb)). - **Referral System**: The **Referral** table tracks user referrals, linking to **User** and **Restaurant**, with rewards distributed upon restaurant sign-up. The `referred_by` field in **Restaurant** ties back to the referring user. - **JSON Flexibility**: The `pricing_table` in **Coupon** uses JSON for dynamic pricing (e.g., `"burger": 5.99, "combo": 8.99`), allowing varied offers without schema changes ([GitHub - Coupon Database Management System](https://github.com/msapurva/Coupon-Database-Management-System)). - **Indexes**: Add indexes on `latitude`, `longitude`, and `user_id` for performance in spatial and user queries. #### Technical Implementation - **Mobile App**: Built with native tools (e.g., Swift for iOS, Kotlin for Android) for geofencing (Core Location, Google Location Services). It sends user location to the backend, fetches coupons, and displays pricing tables ([Location Based Coupon App For Retail](https://www.rishabhsoft.com/blog/smartphone-coupon-app)). - **Backend System**: Uses a framework like Node.js or Django, with RESTful APIs for user preferences, restaurant management, and referral tracking. Spatial queries (e.g., `ST_DWithin`) match user locations to restaurant geofences ([System Design: Design a Geo-Spatial Index](https://kousiknath.medium.com/system-design-design-a-geo-spatial-index-for-real-time-location-search-10968fe62b9c)). - **Referral Workflow**: Users share a unique `referral_code`. When a restaurant signs up using it, the **Referral** table logs the event, and rewards are issued once verified (e.g., restaurant completes onboarding). #### Comparison to Existing Solutions - **Shopular**: Uses geofencing for coupons but lacks a referral system ([5 Proximity-Based Deal-Finding Apps](https://streetfightmag.com/2016/02/09/5-proximity-based-deal-finding-apps-for-retail-brands/)). - **Uber Eats**: Offers referral incentives for users and drivers, similar to this system’s user-to-restaurant model ([How to Design Coupon and Voucher Management System](https://www.geeksforgeeks.org/design-coupon-and-voucher-management-system/)). - **Foursquare**: Supports business-defined coupons and check-in rewards, but not user-driven restaurant recruitment ([14 of the Best Location Based Marketing Apps](https://clarkstjames.com/location-marketing/14-of-the-best-location-based-marketing-apps-for-business/)). An unexpected detail is the potential for gamification, like Yelp’s badges, to boost referral engagement ([Beacons in Location Based Marketing](https://www.yoose.com/news/beacons-in-location-based-marketing)). #### Considerations and Challenges - **Privacy**: Location and preference data require GDPR-compliant handling, with encryption and opt-in consent ([How to Use Geolocation in Your Coupon Marketing Strategy](https://www.voucherify.io/blog/build-geolocation-into-your-coupon-strategy)). - **Scalability**: Cloud databases (e.g., AWS RDS) handle growth in users and restaurants ([Design Coupon and Voucher Management System](https://www.geeksforgeeks.org/design-coupon-and-voucher-management-system/)). - **Fraud Prevention**: Validate restaurant sign-ups to prevent fake referrals, possibly via manual review or verification codes. #### Conclusion This database structure supports a location-based coupon app with a referral twist, using spatial data for geofencing, JSON for pricing flexibility, and a referral table for incentives. It’s scalable, user-friendly, and aligns with modern marketing trends, offering a robust foundation for connecting users and restaurants. #### Key Citations - [Unleashing the Power of Spatial Databases](https://medium.com/@dezsays/unleashing-the-power-of-spatial-databases-navigating-location-based-data-59e209116dfb) - [5 Proximity-Based Deal-Finding Apps](https://streetfightmag.com/2016/02/09/5-proximity-based-deal-finding-apps-for-retail-brands/) - [14 of the Best Location Based Marketing Apps](https://clarkstjames.com/location-marketing/14-of-the-best-location-based-marketing-apps-for-business/) - [How to Design Coupon and Voucher Management System](https://www.geeksforgeeks.org/design-coupon-and-voucher-management-system/) - [GitHub - Coupon Database Management System](https://github.com/msapurva/Coupon-Database-Management-System) - [Location Based Coupon App For Retail](https://www.rishabhsoft.com/blog/smartphone-coupon-app) - [System Design: Design a Geo-Spatial Index](https://kousiknath.medium.com/system-design-design-a-geo-spatial-index-for-real-time-location-search-10968fe62b9c) - [How to Use Geolocation in Your Coupon Marketing Strategy](https://www.voucherify.io/blog/build-geolocation-into-your-coupon-strategy) - [Beacons in Location Based Marketing](https://www.yoose.com/news/beacons-in-location-based-marketing)

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