DSteelers

Simplify Gift Card

Comment

Project Inspiration

The Gift Card Market is burgeoning, currently valued at $899.3 Billion and projected to hit $2.3 Trillion by 2030. Surprisingly, 47% of American adults possess unused gift cards, amounting to approximately $23 billion in unspent funds. To address this issue, our project aims to serve as a timely reminder for users when they're in proximity to a store where they can utilize their gift cards effectively.

Functionality

Our application serves as a convenient reminder tool for users by notifying them when they're near a store where they can utilize their gift cards. The notification includes essential information such as the store's location, distance from the user's current position, and user ratings to facilitate informed decision-making.

Key Features:

  1. Automatic Gift Card Loading: Leveraging YOLOV8 object recognition model, we enable automatic loading of gift cards by recognizing brand logos. While our training time was limited, achieving a 78% accuracy rate was a notable accomplishment.

  2. Proximity-Based Notifications: Utilizing a combination of APIs including Google Maps, Google Places, and Geolocation, our application triggers notifications when users are near stores where they can redeem their gift cards.

  3. Gift Card Organization: Users can categorize their gift cards within the application, enhancing organization and accessibility.

Technology Stack

Our application's foundation is built upon React for the frontend, ensuring a seamless user experience. To facilitate brand logo detection and recognition, we've integrated the powerful YOLOV8 Image Detection and Segmentation Model. This model enables precise identification of brand logos, enhancing the automatic gift card loading feature.

Furthermore, we've leveraged various APIs including Google Maps, Google Places, and Geolocation to provide robust functionality. These APIs enable us to deliver accurate location-based services, proximity notifications, and essential information such as store ratings and distances from the user's current location.

Technical Challenges

One of our primary challenges was training the YOLOV8 model to accurately recognize brand logos within a short timeframe, achieving a commendable 78% accuracy.

Proud Achievements

We take pride in developing a solution that can potentially benefit millions by facilitating the efficient utilization of gift cards, thereby reducing wastage and maximizing their value.

Future Enhancements

While our current version addresses the immediate need for gift card utilization reminders, we envision several avenues for future improvements:

  • Expiration Alerts: Notifying users about impending gift card expirations.
  • Fraud Protection: Implementing measures to safeguard against gift card fraud.
  • Holiday Gifting Ideas: Providing tailored gift card suggestions for holiday seasons.
  • Personalized Recommendations: Offering personalized gift card recommendations based on user preferences and spending habits.
  • Crawling Web for Available Gift Cards: To expand the usability of our application, we propose a feature to crawl the web and fetch available gift cards from various sources. This feature enhances user accessibility to a wide range of gift card options, ensuring they have ample opportunities to make use of their platform.

Example: Utilizing Grok for Twitter to Fetch Free Gift Card Offers: We propose integration of Grok, a powerful Large Language Model with access to Twitter to search for tweets containing mentions of free gift card offers. By analyzing tweets in real-time, our application identifies and compiles a list of available free gift card promotions shared within the Twitter community. This information is then presented to users, providing them with additional opportunities to acquire gift cards without any monetary commitment.

Share this project:

Updates