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Uncovering AI Tech within Fashion: Dressipi’s Personalised Outfits

By Sponsor

Jun 24, 2021


From shop windows and advertising campaigns, to in-store merchandising and social media influencer marketing, brands have been showcasing their products through outfits right from the beginning - they’ve always been an integral part of how to sell fashion. On the most basic level, outfits tell a customer a brand story and offer them inspiration for how they could wear products. It puts a product into an emotive and creative context which is really in touch with the everyday reality of how people wear clothes. As fashion retail moves increasingly online, adapting outfits to a digital way of shopping is a no brainer.

Personalisation is Paramount

Personalisation used to be a ‘nice-to-have’ but in today’s competitive world of online retail, that is no longer the case. The future of fashion retail is in blending the online with the offline, the digital with the emotional and the personal. Personalising outfit recommendations will enable retailers to offer an intuitive and differentiated shopping experience that really resonates with the customer.

Dressipi’s Personalised Outfits enable styling and product recommendations that are only completely relevant to each customer’s individual preferences and context. By going beyond showing what the model wears, they exponentially expand a retailer's scope for offering curated shopping experiences. With outfit recommendations for all occasions and the ability to recommend products to style with a customer’s previous purchases and preexisting wardrobe, personalised outfits inspire customers in a way that is most relevant to them, and showcases the versatility of products.

Dressipi’s technology is built by fashion stylists and scaled by AI - truly expressing a brand’s creative vision and producing outfit recommendations that are always the best for each customer, and are always on-trend and onbrand.

River Island, John Lewis & Belstaff: Keeping in line with brand DNA

Take our work with River Island, John Lewis and Belstaff - three very different retailers with three very different brand identities and visions. River Island styling tends to be more feminine, sexy and chic; core to John Lewis’ style is correct brand adjacencies and a more traditional way of styling pieces; and at Belstaff’s heart, is its luxury heritage and a flexible styling of timeless investment pieces. We work very closely with a brand’s creative team to ensure that their vision is understood and captured in Dressipi’s algorithms to tell a unique, curated brand story. Important here is the accuracy and specificity of product data - we tag each product from a unique taxonomy of over 1000 attribute values for truly exceptional outfit recommendations.

The Possibilities are Endless

Outfit recommendations that really resonate with both retailer and customer will transform the way we shop online. The sheer scope of what you can do once you personalise outfits to each customer’s individual preferences and context is incredible - being able to inspire with outfit recommendations for various occasions, send post-purchase emails with styling suggestions, and even incorporate a customer’s pre-existing wardrobe into recommendations, gives retailers the ability to create a brand experience that is really in tune with the everyday reality of the way people shop and dress.

Our clients have seen a 5% increase in incremental revenue as a result of Personalised Outfits, and the product tagging with fashion-specific attributes we have delivered for their product catalogues is a critical foundation for their retail transformation, leading the way for innovation both now, and in the future.

The future of digital shopping is personal, and intuitive to how each individual shops and dresses - retailers need to lay the foundations of their digital transformation and really offer something that speaks to each shopper on an emotive and individual level. Personalised Outfits are the solution.

Find out more about Dressipi and Personalised Outfits here.