Rewards Deals for Chains Only

Problem Context

Users rely on rewards programs to save money at restaurants and stores. They need to quickly find participating locations, but the official tools bury relevant deals under clutter, duplicates, and irrelevant local spots. This forces them to manually check each listing, wasting time and missing discounts.

Pain Points

The interface lacks filters to remove local spots or sort by chain restaurants. Users waste 10–15 minutes per search instead of 2, and manual workarounds (refreshing pages, different browsers) fail. The frustration builds until they consider abandoning the program entirely.

Impact

Time wasted adds up to hours per week, and missed discounts cost real money. The longer the problem persists, the less likely users are to check for deals at all, reducing their savings and engagement with the rewards program.

Urgency

This is a daily annoyance for active rewards users. Without a fix, they’ll either stop using the program or spend even more time on inefficient workarounds, both of which hurt their savings and the program’s value to them.

Target Audience

Anyone who uses rewards programs for dining, travel, or shopping—especially frequent users of chains like Starbucks, McDonald’s, or Chipotle. This includes budget-conscious consumers, travelers, and families who depend on discounts to stretch their spending.

Solution Approach

A web app that scrapes and organizes rewards program locations into a clean, filterable interface. Users input their rewards program (e.g., Starbucks), and the tool displays only chain locations, sorted by distance or deal type. No clutter, no duplicates—just the deals they need.

Key Features

1. Deal Type Sorting: Lets users sort by discount type (e.g., free item, percentage off).
2. Map View: Shows locations on a map with deal details.
3. Saved Favorites: Users bookmark their top deals for quick access.

User Experience

Users open the app, select their rewards program, and see a clean list of chain locations with deals. They filter by distance or deal type, then tap a location to see details. No scrolling through irrelevant listings—just the deals they want, in seconds.

Differentiation

Unlike official tools, this app focuses only on chain locations and deals, with no clutter. It’s faster than manual checks and more reliable than broken native filters. The data is updated daily to ensure accuracy, and the interface is designed for speed.

Scalability

Starts with dining rewards, then expands to travel (hotels, airlines) and retail (groceries, pharmacies). Premium features like price tracking or loyalty hacking tips can be added later. The scraper can be updated to support new programs as they launch.

Expected Impact

Users save 10+ hours/month and never miss a discount again. Businesses (e.g., rewards programs) benefit from higher user engagement. The app becomes a must-have tool for anyone who relies on rewards to save money.