EBay is testing whether one of the most effective approaches to shopper engagement exists outside the aisle by adopting an algorithm that may be best recognized for picking actual rhythms.
Its new app service, called Interests, takes a simple approach to opening “getting-to-know-you” conversations with shoppers: a questionnaire that results in individualized homepages, featuring themes and items selected specifically for each shopper.
If this approach is music to consumers’ ears, there’s a good reason. It’s similar to the Spotify music-streaming model that uses a variety of feedback and data to better recommend songs and weekly playlists to members.
In eBay’s case, shoppers are asked to answer questions such as “What do you love?” and “What’s your style?” Like Spotify, eBay relies on algorithms to match its users’ chosen interests with their browsing patterns, along with the purchase insights gathered from its 171 million shoppers. (Spotify also cross-references data among users who have similar playlists, recommending the songs some listeners play often to others who have not played them at all.)
Data is not industry-specific, so there’s no reason eBay shouldnt look outside retail for data best practices. Here are five other non-retail brands that set a good example.
Learning From Banks, Social Media, Movies
Instagram: In March 2016, Instagram introduced an algorithm that shifted how member posts appeared in its feeds. Previously, it had listed posts in chronological order, but then changed its ranking to engagement levels, prioritizing the kinds of posts members were likely to spend more time with based on previous activities. The cause was opportunity. Instagram had determined its users were missing an estimated 70% of posts when they were listed chronologically, and many of those posts would be highly relevant. Retailers can present their online stores similarly by customizing landing pages with items their loyalty program members reviewed or purchased. To keep intrigue alive, they could throw in an item the shopper had lingered over long ago, and add new-to-store merchandise that would complement recent searches.
Delta Air Lines: Walmart has patented facial-recognition technology to identify shopper pain points (and happy moments) during checkout, but Delta is taking pain to heart. It used heart-rate monitors on volunteer fliers to track their heartbeats at 11 stressful moments along the travel passage, from finding a parking spot and boarding the plane to collecting baggage. Delta also tracked where on the planes travelers looked, to be more intuitive, using biometrics and bio data. If volunteers agreed to wear monitors for Delta, then why wouldn’t they for retailers? Merchants can choose how to collect or identify those emotional moments along the experience chain, and the biometrics are quite telling.
The Dorchester Collection Hotels: This luxury hotel group analyzed online review and social media data to figure out why the number of weddings, but not overall spending, was rising at its Hotel Bel-Air and Beverly Hills Hotel. It detected anxiety was a major contributor, distracting bride, groom and the wedding parties from the hotel’s various experience offerings. Dorchester used these findings to ease aspects of wedding planning, from providing its logo and designs to recommended invitation printers to partnering with Rodeo Drive dress shops that offered guest discounts to brides. It also began marketing its wedding services to stand apart from rivals. Retailers could apply the same data analytics to identify the most challenging shopping periods, such as the holidays and back-to-school, and create specific services that ease the tension that sometimes overshadows the experience of these important events.
MoviePass: In return for unlimited movie access, the app-based subscription service can follow which movies its members are going to see, as well as where and when. Its goal is to gather enough insights from members to suggest upcoming films or recommend partner shops or eateries near the multiplex. A moviegoer could, for example, receive a notification offer for a free side at a neighboring restaurant. Data partnerships are not untested in retail, though a look into the shopper journey suggests they explore beyond the obvious places people pass on their way to and from the store. A supermarket, for example, could benefit from a data-sharing partnership with a home-improvement chain, because DIY projects (especially kitchen ones) are often punctuated by premade or carryout meals. And the DIY chain would benefit from the positive perceptions it gets for suggesting the meals.
These companies, like Spotify, are striving to use data to create the equivalent of the greatest hits in customer experience. Nothing is stopping retailers from doing the same; they just need to know the rhythm, and algorithms, of their shoppers.