AngelHack is co-hosting the Retail Disruptathon, sponsored by Sears in Chicago on September 28- 29th at 1871.
Launch your retail startup in 2 days and get big fast! at the speed of a start-up and at the scale of sears
• $20,000 total in prizes!
Come join the brightest minds in Chicago to help create the next intersection of Social, Local, Mobile and Retail through Shop Your Way Local! Be a part of the Retail Disruptathon, meet your peers, form a team, collaborate and create an awesome local shopping experience! Hack our iOS, Android, Web apps and make the world work YOUR way!
Date: 28–29 September Participant Portfolio
Participants could be designers, developers, product managers - basically all the hackers in town!
• GRAND PRIZE: $15,000
2nd place: $3,000
3rd place: $2,000
Use our APIs, design your own features to make Local Shopping convenient and convert foot to site traffic. Some of the features you can work on include:
Peer-to-Peer Crowd Sourced Delivery: Design and develop an idea which facilitates coordination of delivery of orders within users belonging to a common locality.Local Shoppers’ Community: Create a social platform for members to share reviews, recommendations and feedback on local products and shops with fellow members. Additional APIs that can be used: Yelp, TripAdvisor, ShopZilla etc.Local Gifting: A platform where members can send gifts from local shops to fellow members on special occasions.Local Gamification: Engage members on a social platform through interactive and visually attractive games to further understand their preferences and provide personalized deals.Member Initiated Shop On-Boarding Strategy: An add on feature which brings local shops on-board by members’ choice through Shopin’(Check-in on SYWL).Local Commerce Analytics: Analyzing prices, purchases and preferences design and develop a predictive analytics dashboard displaying best seller, predictive prices and seller comparison statistics.Recommendation Engine: Build a recommendation engine to provide recommendations to members based on their preferences and previous purchases or purchases of fellow members with similar interests.