This article details the logic in how ShoeSize.Me gives recommendations for new brands and/or models to our database.
The ShoeSize.Me database contains over 5000 brands and over a million models across 18 different international size scales; all of which are linked by the size and fit data of real shoppers. With every recommendation ShoeSize.Me gives, our algorithm is learning. But what happens when a new brand or model is entered into our database? How does our algorithm determine what the best possible size recommendation?
Our machine learning technology process works in a 3 layered method. The core of which is the Purchase & Returns:
- Size Chart matching
- ShoeSize.Me's Shoe Shelf App
- Machine Learning via Purchase & Returns Data
1. Size Chart Matching
The ShoeSize.Me database contains an Official Size Chart for every brand in our database. We take the official size chart guidelines of each brand and compare and connect them to each other. On a basic level, this enables us to provide a recommendation that follows the official size guides that each of the brands uses and publishes. ShoeSize.Me is able to compare these charts and calculate a recommendation which acts as the first layer in how our algorithm learns and a starting point when our database has little or no previous data.
2. ShoeSize.Me's Shoe Shelf App (Optional)
We understand that sometimes having a certain level of control over technology and recommendations is desired - especially in cases like this where there is limited or no previous data. For this, we have created the Employee Shoe Shelf Builder App which is an optional tool to speed up the learning. This app enables you to provide direct links from your brand to other brands in which you know the sizing-differences for and these differences will be reflected in the Size Advisor recommendation. This provides additional data points to speed up the learning of our algorithm and will serve as another layer for our algorithm to learn from. With time, the data input may change as the algorithm learns and engages with more and more of your shoppers. Full details on this Shoe Shelf App can be found here and the ShoeSize.Me Onboarding Team is available to assist you and your team in using it!
3. Machine Learning via Purchase & Returns Data
While the Size Chart Matching and the Shoe Shelf App layers described above provide a basis for recommendations, the core long-term learning by our algorithm is done through machine learning via - primarily - the Purchase & Returns exchanges set up during onboarding. From the Purchase & Returns, our algorithm analyzes combinations of user input, purchase patterns and return patterns and combines those with product attributes that together enable ShoeSize.Me to calculate size recommendations. Through usage of the Size Advisor by your shoppers and with time, the algorithm learns more and more about sizing differences between your shoe models and their connections to other brands and retailers. The long-term learning curve achieved through the usage of the Size Advisor and the Purchase & Returns data follows an s-curve with a slow start, steep learning, and a sustainable plateau. There is no way to “skip” or fast forward this learning, but remember; ShoeSize.Me is constantly learning from the insecure and uncertain shoppers who are hurting your profits today. This is why it is important to keep the long-term, big picture in mind, which you review on your Performance Platform. The sooner you let the tech run on your website, the sooner you maximize results.
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