The client is a technology startup that has emerged as a leading player in the local restaurant discovery space, covering major cities along the East Coast of the United States. The company was founded in 2014 with the goal of creating a collaborative marketing platform for local restaurant owners.

• Limited Marketing Budget: Smaller restaurants could not afford the same marketing campaigns as large chains, limiting their visibility.
• Inconsistent Footfall: Restaurants struggled to attract customers on slow days and off-peak hours.
• Lack of Data: Owners had limited access to actionable data on customer behavior, preventing them from making informed business decisions.
• Low Engagement: Existing discovery apps lacked a compelling reason for repeat use beyond simple searching.

QBurst developed a comprehensive gamified mobile application that serves as a two-sided platform connecting diners and restaurants. The solution incorporates an Optimal Discount Computation Algorithm that determines dynamic discounts based on factors such as app usage, day, time, and weather. This ensures that discounts are beneficial for both users and restaurants. The entire system's intelligence relies on Python for its backend algorithms and Location Services for core proximity and check-in functionality. The three main components of the solutions are:

• User Application: An engaging iOS and Android app that allows users to spin a virtual wheel to win discounts, earn points for check-ins and reviews, and upgrade user levels. It also features integrations with Google Maps and Uber for seamless navigation.
• Merchant Application: A dedicated application that enables restaurant owners to customize their business page, post promotions, and view GPS-verified ratings and reviews.
• Admin Web Application: A centralized platform for the client to manage users, restaurants, and discounts, and to send push notifications and view real-time analytics.

• Optimal Discount Algorithm: The backend features a proprietary algorithm that calculates dynamic discounts in real-time based on a variety of data points, including day of week, time of day, and weather conditions.
• Scalable Infrastructure: An Elastic Load Balancer distributes traffic between API servers, and auto-scaling ensures the app remains available even during peak usage. MongoDB replica sets provide data redundancy and high availability.
• Engaging Gamification: The application was built with features like points, user levels, and social sharing to promote user engagement and drive repeat usage.
• Robust Analytics Engine: The system's advanced analytics engine provides valuable insights to merchants on customer behavior, spend patterns, and visit durations.

• Exponential Growth: In just 18 months, the user base exploded from 100+ to 250,000+, and the number of participating restaurants grew from 8 to over 1,500 across 16 cities.
• Increased Footfall and Revenue: The application successfully drove traffic to local restaurants on slow days, helping them increase footfall and revenue.
• High User Engagement: The gamified experience led to a high engagement rate, with a 25% Monthly Active User (MAU) rate, a key indicator of the app's success.
• Actionable Insights: Restaurants gained access to valuable data, enabling them to make smarter business decisions regarding promotions and operations.