When we're unsure about our options in a brick-and-mortar store, we usually turn to a sales assistant who can help us choosing products that match our tastes and needs. However, today, many consumers prefer to shop online, where the offer is extremely wide. This presents challenges for both consumers and retailers.
Consumers need to find what they're looking for quickly: indeed it's not uncommon to abandon a purchase if the search for the perfect item feels endless, like falling into a bottomless Alice in Wonderland-style rabbit hole. Time is crucial, especially for those shopping on a mobile phone with a low battery or browsing with a slower internet connection. Retailers, on the other hand, need to recommend items swiftly before the shopper moves on to a competitor's website.
Personalizing user experiences and driving engagement can help retailers win more customers. To achieve this, they must have a top-notch recommendation engine that can match products with individual customers effectively.
Recommendation engines often rely on Artificial Intelligence (AI), particularly machine learning algorithms. These systems analyze vast amounts of data - such as user behavior, preferences, past interactions, purchase history, and contextual information like date, location, and device used - to make personalized suggestions. By leveraging AI, these engines continuously learn and improve, becoming more accurate over time. They are widely used across industries like e-commerce, streaming services, and social media to enhance user experience and boost engagement.
The fashion industry, in particular, has benefited from various technologies, including AI-driven recommendation features. Another innovation that some retailers have adopted is the swipe-able format and feedback loop, inspired by popular dating apps like Tinder, Bumble, and Hinge. These platforms use machine learning algorithms to recommend potential matches based on user behavior and preferences, coupled with swipe-able interfaces that let users rate potential romantic candidates.
The latest fashion app to adopt this sort of interface is FashWire, one of the three B2B2C platforms (with GlossWire for beauty products and PawWire for pets) owned by The Wires.
The platform's AI tailors product suggestions for shoppers, enhancing the user experience by predicting preferences and displaying personalized items. As the AI learns more about a shopper's taste, its recommendations become increasingly accurate. The swipe-able feature, similar to the swipe-right or swipe-left mechanism in dating apps, allows users to browse personalized product recommendations based on their feedback. Thanks to user feedback, the "Trending Now" section of the FashWire app is dynamically updated, highlighting the newest apparel, footwear, and accessories with each app visit
This feature, developed in partnership with tech company SnapSoft, will also be integrated also in GlossWire. AI will curate a selection of over 60,000 items (between FashWire and GlossWire) from more than 800 global brands spanning 60 countries. AI becomes therefore a necessity for managing the products of the emerging and established brands in the two fashion and beauty spaces.
Yet also brands will benefit from this application: AI will indeed provide real-time analytics to brands, allowing them to optimize their offerings. This real-time data will represent a game-changer for brands, allowing them to instantly monitor consumer behavior and feedback, fine-tune their product offerings and messaging, and make decisions that can effectively convert browsers into buyers, encourage repeat purchases, and build customer loyalty.
While these swipe-able technologies might not produce romantic matches, pairing the right products with the right consumers will still be a match made in heaven for the platforms and brands involved.
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