Big Data Use Cases in Retail

Dynamic pricing across multiple channels is not new, but big data allows for a much more refined set of indicators for price elasticity in comparison with traditional influencers such as time and availability. Other indicators include the weather, the location, the complete buying profile of a customer, and the social media presence of a customer (prescriptive analytics).

Fuzzy matching helps when people search for jobs, hotels, secondhand cars, houses and other goods with many characteristics. Fuzzy matching pairs results that are almost fitting, like a dark blue car instead of a black one, or a hotel on a different Greek island from the one requested (prescriptive analytics).

Counter-dynamic pricing is the opposite of dynamic pricing. If big data analytics can refine price-elasticity models, the optimization algorithms can also be reversed and put to use for consumers. Personal analytics help determine the best moment to buy goods or services for the lowest price, and carry out the order immediately (prescriptive analytics).

Fraud detection is important in most industries. Specifically for the retail sector, one can think of tracking fraud in returns and abuse of customer service, or credit risk for larger purchases, based on, for example, uncovering fraud rings, the social media activity of customers and detecting patterns (descriptive analytics).

Dynamic forecasting complements traditional demand and supply chain forecasting, taking more external factors into account, such as traffic conditions, weather forecasts, shop video feeds suggesting demand, and sensors (moving from sensors attached to checkpoints and monitoring objects [products, materials, parts] to sensors attached to every single object to be monitored) (predictive analytics).

Recommendations are the mostly widely adopted big data use in the retail sector. Based on what other customers have bought, a customer could also be interested in another product. This is also called “best next offer” recommendation. Recommendations can benefit from a much broader context, not only checking which combinations are most likely, but also, based on a very fine-grained “graph analysis,” identifying a closely related peer consumer group. It can also work with people, such as when LinkedIn recommends other people to connect to. It can additionally be used with locations, based on common or complementary characteristics (descriptive analytics, prescriptive analytics).

Retail data can be monetized by selling it upstream to partners and suppliers, to give them more insight into how their products are selling and in which circumstances. Information on, for instance, buying patterns throughout the day, in relation to the weather and how busy stores are, helps suppliers optimize their marketing (general management).

Market basket analysis traditionally matches products that are purchased together. Big data adds more context, including time of day, music played in a store, store visit duration, weather, length of queue and so forth (descriptive analytics).

Mall experience gamification is a new concept, based on smartphone location data (or data from any other location-aware device). Visitors to a mall can be tempted to buy more and stay longer by tracking their movements through a mall and sending them special offers — for instance after checking in at more than 10 stores (descriptive, predictive analytics, prescriptive analytics).

Real-time offers respond in real-time to changing patterns in visitor numbers. Not only online, where dynamic promotions were pioneered, but also in the “real world.” For instance, in airports, shops can put different items on sale based on flights to or from specific locations. Shops can change their promotions not only based on the weather forecasts or commuter streams, but also based on more fine-grained algorithms, including social media analytics (what are people talking about in the neighborhood) (predictive analytics, prescriptive analytics).

Shopping cart defection is not a new form of analysis in the online retail sector. But big data enables consideration of many more factors, next to clickstream analysis (diagnostic analytics).

Loyalty management benefits from big data by extending channel reach from point of sale, Web and call center to include mobile and social capabilities. Rewards may be accrued by more than purchases; people may also earn them for being good product or brand ambassadors. Rewards may also come from more than personal contributions — social relationships may be included (descriptive analytics).

Multichannel location analysis involves researching and evaluating optimal locations in which to develop profitable retail stores in conjunction with e-commerce and mobile commerce market analyses. It includes the use of store location analysis techniques, including the analog method, gravity-modeling multiple regression analyses, and the use of geographical information systems and e-commerce and mobile commerce trade market analysis tools to analyze multichannel trading in geographic areas (descriptive analytics).

Real-time store task management helps with the allocation of staff to, for instance, shelf restocking, customer service, checkout support and order picking, based on actual customer traffic, as determined by, for example, video analytics (prescriptive analytics).

Customer-centric merchandising helps retailers improve their product- and supply chain-centricity. Instead of selecting products based on the offerings of their suppliers and pushing them to customers, big data analytics help identify customer needs and aid the selection of new products that could increase a provider’s “wallet share” on the basis of demand (descriptive analytics).

(Courtesy of Gartner Leader’s Toolkit: Big Data Business Opportunities From Over 100 Use Cases)