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How it works
The tool can process prompts from users’ even in colloquial language and convert it into tailored product recommendations. The company claims that Shopping Muse can also understand modern trends and phrases like “cottagecore” or “beach formal.” Users can ask the tool questions like “What should I wear for a summer wedding?” or “Can you recommend pieces for a minimalist capsule wardrobe?”.
To offer personalised recommendations, Shopping Muse looks at the context of the user’s shopping experience, the direct question(s) it is being asked and the content of the conversation. The algorithms use data from the retailer’s product catalogue, along with the shopper’s on-site behaviour. This includes clicking certain products and adding products to carts. The algorithms also look at real-time and known preferences shown by the consumer.
The algorithms may even consider a user’s past purchase and browsing history with that retailer if they are logged in to the site. For example, this will also include any purchases made by a user in person and are connected to their account by providing the cashier their phone number or email.
Apart from helping users to search for clothes with phrases, Shopping Muse can also recommend items that the users are unable to describe what they’re looking for.
Mastercard explains that “using integrated advanced image recognition tools, retailers can recommend relevant products based on visual similarities to others, even if they lack the right technical tags.”
The company also noted that fashion is the first use case for the tool, while this technology can extend into other categories, like furniture and grocery.
In a statement to TechCrunch, the CEO of Dynamic Yield by Mastercard, Ori Bauer said: “Personalisation gives people the shopping experiences they want, and AI-driven innovation is the key to unlocking immersive and tailored online shopping. By harnessing the power of generative AI in Shopping Muse, we’re meeting the consumer’s standards and making shopping smarter and more seamless than ever.”
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