Refash case study
Refash was an opportunity to test whether the shopping rewards platform could reach a whole new audience by focusing on a single vertical in an otherwise untapped market segment. By selecting fashion retailing, we determined not only to target a product that would resonate with then current trends, but contain a habit forming killer content feature. It was a whole new approach to an existing by not wide-spread product, and this allowed us to work differently and with modern product methodologies (theory of change, product canvas, design sprint).
We started with a team of four before enlisting an additional front-end superstar to help us launch, and an affiliate manager to set up our retailer relationships—product manager, fashion marketing specialist, developer, designer).
We eventually used a design sprint to design out our main feature, however we started at the beginning using the Theory of Change to define the initial problem to tackle, a shortcoming among similar products—creating a sticky feature. We determined the target customer’s behavioural patterns and how to create a core feature that was most likely to engage them. With the goal set, we researched potential customers, their shopping and internet behaviour, before determining our target demographic.
Only then with an understanding who we were designing for and what behaviours we needed to understand. We started our design sprint with a much tighter brief, focusing our energies on one feature that would make our product stand out. The design sprint ended with a quick prototype used to validate our early idea so we could proceed with confidence.
The design sprint led to a design phase where we wireframed the overall customer journey while conducting further research into the content creators we would need for our platform. At this point we hired a branding designer to focus solely on creating a look that would speak to our audience. Once the initial brand was nailed down with logos, colour and UI assets, we moved on to stickers and other assets identified during the build.
From the beginning our aim was to be mobile first. Almost all initial wireframes were sized for mobile, and the first iteration of the front-end was mobile only. We built our prototypes for mobile and ran our tests on mobile. While we had a sense of how the app would look on desktop, particularly the movement of content to the wider viewport, I did not begin desktop desks until well into the project when the front-end developer needed them.
The shoppable look
The team focused most of our efforts on creating the shoppable look, a feature that leveraged Instagram before their own shoppable post. Fashion influencers would be able to not only import their existing Instagram photos, but tag clothing items with deep-links to the real products. Our rewards platform would take over from there. We would follow up by unlocking this feature to all customers.
The shoppable look solved two problems we had identified. The first was our initial goal—to create a feature that would make Refash a destination for fashion shoppers. Unlike similar reward products, we would create our traffic based on content rather then a simple directory. The second reason was discovered through our research into fashion apps—far too often fashion influencers only shared a look with some brands seldom tagged. We kept finding great looks but couldn’t then find the outfit.
The competition was split. Instagram wasn’t allowing product tagging, one app that did was complicated and didn’t leverage the platform popular for influencers, and one that gave rewards lacked the content entirely. We had identified a unique opportunity that would be explored eventually.
Core reward shopping experience
Refash’s core feature is of course the rewards platform, giving customers points for purchases made through our affiliate links that could be redeemed for gift cards at their favourite fashion retailers.
The existing engine built for Quidco, Qipu and Shoop relied on paying out cashback, a mechanism we found not all customers understood (or it was a matter of perception). Prior to beginning the project we conducted a series of multivariate tests to find which method resonated with customers best, pitting points against pure cashback. Points won easily with a statistical significance.
Some modifications would be required to enable the backend code up to work in points, and we had some bandwidth to take a fresh approach to the collection and redemption experience.