The sales performance of a fashion product is highly dependent on its ability to be combined with other products in the range to create an outfit. Determining complementary products not only helps to better understand and plan the demand, but also provides a huge opportunity for product recommendations.
Therefore Dr. René Götz, Senior Data Scientist at adidas, gave a presentation about the exciting topic „How product and consumer data can be used for deriving fashion outfits.“
Because „outfit recommandations could be challenging“, René and his team analyze different data sources to provide the best outfit combination for their customers.
Watch an excerpt from the presentation here, in which he explains more about the procedure at adidas:
René’s talk was divided into two parts. In the first minutes he gave an overview on relevant data sources and preprocessing techniques. After that, René presented Machine Learning methods, that can make use of the data to produce the final recommendations.
Although the data analysis is often a big secret in a company that is only discussed internally within, René shared at the DATA festival in Munich a lot of insights and thoughts of his team.
This is why his presentation received a lot of encouragement and questions from other data scientists.
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