Cross-selling through related product recommendations has always been a huge strength of e-commerce, with 35 per cent of Amazon’s revenue generated by its recommendation engine. In recent years, innovations in RFID-based solutions such as smart fitting rooms and mobile chatbots have opened the doors to automated product recommendations within physical stores. Whilst the technology is now available, there is still one more hurdle between brick and mortar stores and effective cross-selling. This is namely the fact that the best recommender systems require vast amounts of both personal and aggregated data to provide effective suggestions, and whilst this is at a surplus in e-commerce, physical stores traditionally struggle with data being limited as well as sparse.
Speaking at the ACM UMAP 2019 in Cyprus this month where it was presented, data scientists from Detego, who specialise in RFID-based software solutions for retailers, presented their proposed method of data-manipulation for in-store recommender systems with a paper titled: ‘Beggars Can’t Be Choosers: Augmenting Sparse Data for Embedding-Based Product Recommendations in Retail Stores’. The approach involves an alternative algorithm that leverages shopping-baskets and common-item combinations combined with point of sale information. Detego said this allows retailers to provide targeted recommendations with a 6.9 per cent increase in quality, aimed at individual stores, without having to maintain separate models for each location. When combined with the technology to deliver these product recommendations, retailers could see a substantial increase in sales in brick and mortar stores, whilst customers will see a more connected and engaging in-store experience.
“Customers who bought this also bought… is no longer a phrase reserved exclusively for customers of e-commerce platforms. Due to the adoption of RFID-based technologies, such as Detego’s Smart Fitting Room, personalised recommendations can also be presented to customers of brick and mortar stores. Moreover, Detego’s AI-based recommendation engine is tailored towards the specific requirements of fashion retail stores, such as fast-changing and varying product assortments,” said Matthias Wölbitsch, Detego data scientist.
With Detego now successfully rolling out the Smart Fitting Room application alongside their real-time inventory management software, this latest improvement is another opportunity for retailers to evolve their stores for the future. (PC)
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