A footwear e-commerce startup that aims to disrupt our experience of shopping for shoes online.
There’s a basic problem with online shoe shopping & footwear e-commerce platforms for shoe shopping. E-commerce platforms for most other items might work well but for footwear, typical e-commerce platforms are not perfect.
You cannot guarantee if the shoe fits nor can you really get the feel of the shoe with your foot in it. It’s only after you buy the shoe(s), get them delivered, try them on, do you really decide whether you are going to keep it or not.
For online shoe retailers or e-commerce stores focusing on shoe shopping / footwear e-commerce, that presents a big problem in terms of handling returns? Zappos, (reportedly by Lightspeed ventures) has a 35% return rate for footwear e-commerce. A little bit more on the high return rates were documented here.
From a business point of view, this presents a huge operational risk and also lowers margins considerably. Shoefitr (another online shoe shopping platform / footwear ecommerce platform acquired by Amazon) also saw the huge opportunity to solve footwear e-commerce retailers’ high return costs.
The goal was to predict a shoe shopper’s fit in any given shoe as accurately as possible using technology. The first version of this solution was in the form of a kiosk that could be deployed at various malls. Once shoppers scan their feet by standing on the kiosk, the software would create an accurate outline of the foot and thereafter, a wireframe/pointcloud would be created with these images.
However, this wasn’t a very scalable solution and presented several challenges.
The platform needed to gather data feeds of various shoes available from retail and e-commerce stores in USA. Once those data feeds were obtained, an administrator would need to ensure that all shoe fit prediction algorithms were in place.
On the customer side, a mobile app would be required for shoppers to take multiple images of their feet. These images would need to be submitted via the mobile app so when the shopper visits the e-commerce website, their fit into any shoe could be as accurate as possible.
Finally, a customer facing e-commerce website was required that would allow for seamless, integrated, omni-channel e-commerce experience to shoppers.
Nisos Technologies solved these challenges by creating and curing data feeds from Zappos that fetched all shoes available from the e-tailer. These nightly feeds were then fed into a central database that allowed company administrators to adjust any shoe fitting recommendations if needed.
We also created an administrator web portal that allowed company admins to maintain the entire ecommerce site and supply chain. This included maintaining customer data, feet images, feet wireframes, fit recommendations, shoe sizes, widths, fits etc.
A mobile app was created that shoppers can use to register, login and maintain their profile information. The mobile app also allowed shoppers to take images of their feet and store it against their profile online.
Thereafter, shoppers could browse for any shoes available for purchase and be given accurate recommendations for how the shoe would fit their feet.
The company’s revenues depended on affiliate revenues from various shoe retailers. We embedded CommissionJunction code for this purpose.
- A fully functional, robust iOS mobile app
- A scalable backend that can support millions of users
- A secure web portal that complies with all PCI DSS concerns
- Data feeds that connect to Zappos
- Affiliate revenue generation via CommissionJunction
Java, HTML5/CSS3, iOS, Postgres, JQuery
Duration: 4 months
Total Effort: 16 man-months