It is the Achilles' heel of every online fashion shop: the return rate. For years, the average was about 40 to 50 percent. Berlin-based company Fit Analytics (formerly UPcload) wants to change that with its online size calculator. Founded in 2010, the company uses self-learning algorithms and a big-data approach to recommend the appropriate size to each individual user. Currently, the application is in use at shops such as Adidas, Puma, Hugo Boss, Tommy Hilfiger, ASOS and The North Face to name only a few. Here, Steffen Poralla, head of operations, gives more insights into system requirements and costs.
What exactly does Fit Analytics offer its customers?
Our customers include more than 100 online shops worldwide, which sell clothing and fashion. We help their customers with our online sizing guide which helps them to find the right size within only a few seconds. The sizing guide is embedded in the product sites of the online shops and can be selected by clicking: After the customer entered some basic information about himself (for example height, age and weight) our sizing guide generates an individual sizing recommendation with the help of anonymized purchasing and return information. The online sizing guide is available for almost all product categories (outerwear, jackets, pants, jeans skirts, shoes etc.) and can be used for men’s- women’s- and kidswear.
Who are your competitors on the market?
Our competitors are amongst others Fits.me, TrueFit and Virtusize. At Virtusize, customers first have to measure a garment from their closet before it gets compared to the desired article, while Fits.me and TrueFit calculate the body measurements of the user and then recommend a size based on that. This also reflects our approach and we also still calculate the body measurements of the users in the background – but we don’t only enrich them with clothing data, we also use aggregated purchase and return data, which significantly improves the quality of our recommendations and “socially” validates them.
What distinguishes you from your competitors?
The solution provides several advantages for our customers: The sizing guide helps them to significantly decrease fit-related returns, while at the same time the user receives more safety while shopping online which results in increasing conversion rates and a higher shopping cart value. Through numerous A/B tests we could validate that the sizing guide decreased returns up until 8% and at the same time the conversion rate increased up until 6%.
Which conditions must customers provide to be able to use Fit Analytics?
Initially, we don’t need much more than the product feed of our partner shops – which then gets imported to our data base and since this already contains the data of thousands of fashion brands from around the world, in most cases we can provide a high assortment coverage – we solely can’t cover categories like underwear and swimwear right now. But as we get numerous requests at the moment, the respective partner shop should command a certain traffic so our self-learning algorithms get enough data to practice and improve. The more sizing guides are in use, the better and the more precise are their recommendations.
What investment is necessary for using Fit Analytics?
The necessary (financial) investment of our partner shops is exceedingly small and risk-free: We can not only take over the whole integration and implementation process of the sizing guide so only small IT resources on the customer’s part must be provided, but after going live we also purely invoice based on the performance: We only charge a fee, when an article was bought after using the sizing guide. If the article gets returned later on, we refund the fee – regardless of the return reason.
What’s on the future agenda of Fit Analytics?
We develop and optimize our sizing guide on an ongoing base and further want to increase our coverage. In addition, we develop new features like size recommendations exclusively based on the personal purchasing history of the user without the need to open the guide on site (“freehand recommendation”). Furthermore, we want to expand our product palette by business intelligence services, which are meant to help our customers to understand their customers better.