To turn its bold vision into reality, abof needed the very best e-commerce platform—and a joint offering from IBM and Metail proved to be the ideal solution.
Today, abof is engaging customers with a seamless, personalized shopping experience across all touchpoints. Whether shoppers choose to browse online or on mobile—via their desktop browser or the abof mobile app—they are welcomed with a continuous flow of fashion content, stories and style tips, all of which are linked to the latest trends from abof’s online catalogue.
“Our homepage has been designed with a similar look-and-feel to websites like Pinterest and Instagram,” explains a spokesperson. “Visitors can scroll through curated content and when they click through to a specific story, they are presented with photos of similar looks and invited to shop the story—leading them to a specific section of our catalogue, where they can browse and buy products.
“We find that presenting visitors with this kind of story-driven, ‘snackable’ content—as opposed to a huge wall of products—is a much better way to capture their attention, encourage them to spend more time thinking about and viewing our products, and ultimately inspire them to make a purchase. Of course, if a customer just wants to browse our main catalogue, then they can easily do that as well.”
When a customer finds a piece of clothing that they like, they can see how the garment might look when worn, using the Metail virtual fitting room tool. Users simply click a “Try me on” icon displayed next to a product, enter their height, weight and bra size, and the tool uses the data to generate a custom “MeModel” to visualize how the clothing will fit on their unique body type. Users can even mix and match different items of clothing to create a head-to-toe look. What’s more, the solution can provide personalized size recommendations based on the measurements supplied by the user and sizing charts from abof.
Currently, the experience is available to female shoppers, with a launch for male customers planned in the near future. Ultimately, abof plans to integrate sharing capabilities with the tool, enabling users to post the outfits they have created to social media.
Behind the scenes, abof is capturing rich data on customer preferences and measurements, which it will use to refine garment sizing and product recommendations.
“The aim is to match consumers to garments that fit both their body type and style preferences,” says a spokesperson. “By taking user data and feeding it into machine learning algorithms, we can start to build a more accurate picture of what products and sizes are likely to sell best with each customer. This will allow us to provide a much more personalized shopping experience that encourages consumers to buy. And better fit guidance will allow us to reduce return rates—boosting customer satisfaction and delivering cost savings for abof.”