AI for the Enterprise

4 ways Watson’s improving your shopping experience

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Consumers are expecting more than ever from the places they shop. Shoppers expect to be segmented and treated like an individual, from online browsing to purchase, which has shifted to the retail landscape. Now, companies must build an entire experience not on segmentation, but on each individual consumer. In order to do this, they must sort through mounds of customer data to extract relevant insights to build that sought-after personalized experience.

Watson’s cognitive computing platform is one way that retailers are starting to improve the consumer experience. Watson uses natural language processing and machine learning to reveal insights from large amounts of unstructured data. By using Watson’s suite of APIs, companies have enabled retailers to individually segment consumers, personalize the experience, and guide shoppers to the product they most want or need.

We chronicled 3 companies that are using Watson to improve the shopping experience for consumers. We break it down into 3 categories: Segmentation, Personalization, and Guided Shopping.

Individual Segmentation
Challenge: 82% of CMO’s feel unprepared to deal with the data explosion. Source

Solution: Stat Social

StatSocial is the first in market to link multiple social profiles. This allows them to deliver a complete view of consumers across demographic, affinity, and personality insights. Using the Watson Personality Insights API, StatSocial mines through user-declared social content to identify the personality types, values and needs of hundreds of millions of consumers across the world. Stat Social’s clients can now understand, segment, and target their client’s consumers to create the ultimate personalized shopping experience.

Personalize
Challenge: 48 percent of shoppers surveyed in 2014 want on-demand, personalized promotions while online, and 44 percent want the same in the store. Source

Solution: Cognitive Scale

Cognitive Scale brings together structured and unstructured content to create personalized, relevant, real-time “cognitive insights.” These insights use Watson’s natural language processing capability to read and understand consumer social activities, location, and are combined with shoppers purchase history, browse behavior, and loyalty data. The output is “segment-of-one” insights that are delivered across all of a retailer’s shopping channels. This allows consumers to find and purchase relevant products.

Guided Shopping
Challenge: Nearly half of shoppers today describe themselves as preferring to shop online. Source

Solution: Vine Sleuth

VineSleuth’s Wine4.Me In-Store Wine Advisor takes the guesswork out of buying wine, empowering shoppers and increasing sales. Shoppers tell the application what they want in a wine (flavor profile, food pairing, price requirements and more) and Watson returns a custom curated, unbiased wine list and suggests food pairings for each shopper.

Solution: Fluid

Fluid’s Powered by Watson XPS solution is bringing personalized shopping to your fingertips. XPS helps customers quickly discover relevant clothing items based on their responses to a series of questions. Premier active-wear retailer, The North Face is using XPS to provide clothing recommendations to customers.

 

For the curious, we present a collection of API mashup examples that provide creative solutions to your unstructured data problems.


Offering Manager - IBM Digital Group

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Ellie Mahoney

I think it would also be great if when you go out shopping and have a particular thing in mind e.g. in a mall, that there is an app that tells you exactly what store has that and how to get there.

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