When online services tap into your data under the guise of offering a more personalized set of recommendations, the results can often be disappointing. You, after all, know that you are greater than the sum of your parts and the data that you’re freely giving away is a valuable commodity. What if technology could learn from you, and present you with what you want based on your preferences or tastes as they evolve rather than the vague desires of someone ‘like’ you?
In our Future of Retail report, PSFK Labs has identified the trend of Adaptive Personalization, where technology companies are developing new consumer-facing platforms that employ adaptive algorithms to learn shopper preferences over time, offering a set of tailored recommendations that leverage past behaviors and current context to begin to anticipate future need. Read below to see what services are reshaping the retail marketplace.
Online auction website eBay is partnering with fashion recommendation website Dressipi to present select shoppers with a personal online gallery of clothing. EBay visitors fill out a questionnaire examining metrics like age, size, body shape, preferred colors, favorite designers, and even the amount of skin they want to reveal. The system behind the UK-based Dressipi site then creates a specific ‘fashion fingerprint’ based on the shopper’s data, and eBay is able to better curate products displayed on the shopper’s Fashion Gallery. Dressipi is also able to recommend products manually with a team of stylists who review shoppers’ ‘fingerprints’ and suggest pieces that will fit best. Dressipi and eBay hope to help shoppers cut down on search time by matching them with personalized recommendations in minutes.
Nara Logics is a recommendation service that tailors search results for restaurants and hotels based on personal tastes and interests rather than search history. Individuals with a smartphone can download the app and start receiving recommendations by entering their favorite cuisines, preferred neighborhoods, and price range. Nara matches diners’ data with restaurants that match their criteria and as dinners continue to use the application, they can ‘thumbs up’ or ‘thumbs down’ restaurants they do and don’t like. For its part, Nara’s software unpacks and sorts web page data containing restaurant and hotel reviews to find commonalities and patterns in a user’s preferences. Nara creates what it calls a ‘neural network’ that continues to evolve based on existing reviews and user preferences. The company hopes to deliver a higher level of satisfaction than other search engines in a shorter amount of time.
E-commerce recommendations service StyleSeek is implementing a ‘style game’ that culls valuable information from shoppers and feeds it into an algorithm designed to create a customized experience for every shopper. When users visit the site, they are prompted with swatches of pin-board like images to pick from that best represent their style. StyleSeek creates ‘Shopper DNA’ based on an individual’s personal style rather than collaborative filtering that compares a shopper’s style to that of people who bought similar products. In under 30 seconds, a user’s StyleDNA is defined and relevant products are recommend. StyleSeek hopes to compete against social recommendation engines that require users to ‘follow’ others, providing a shared style perspective rather than a tailored one. StyleSeek told PSFK that their users tend to have a conversion rate that is three times higher than the industry average.
In the fourth edition of the Future of Retail Report, PSFK Labs brings together two interconnected themes and eleven key trends that provide a foundation for the modern shopping experience. The findings are brought to life with best-in-class examples, actionable strategies and leading questions to inspire leading retailers and brands. Join us at our San Francisco conference on Nov. 21st where talks from retail innovators will bring the key themes to life.
Photo by IBN Live