Big Data in Retail Examples

In my experience, successfully deploying a big data solution begins by identifying use cases and business decisions which benefit from new information. This is easier said than done as big data for retail use cases are a function of your creative thinking. To stimulate that thinking, consider the following retail big data examples.

1. Hotel Chain Uses Big Data to Increase Bookings
   Bad weather reduces travel, which then reduces overnight lodging. That’s not good news if you’re in the hotel business. However, Red Roof Inn turned this trend on its head. The hotel chain recognized that cancelled flights leave travelers in a bind and in need of a place to sleep overnight. The company sourced freely available weather and flight cancellation information, organized by combinations of hotel and airport locations, and built an algorithm which factored weather severity, travel conditions, time of the day and cancellation rates by airport and airline among other variables. With its big data insights, and recognition that travelers will be using mobile devices for this use case, the company used Search, PPC and SoLoMo mobile campaigns to deliver targeted mobile ads to stranded travelers and make it easy for them to book a nearby hotel. This big data payback is compelling. Flight cancellations average 1-3% daily, which translates into 150 to 500 cancelled flights or around 25,000 to 90,000 stranded passengers each day. With its big data and geo-based mobile marketing campaigns Red Roof Inn achieved a 10% business increase in one year.
2. Pizza Chain Earns More Dough in Bad Weather
   Somewhat similar to the above example, a pizza chain uses a mobile app and mobile marketing techniques to deliver coupons based on bad weather or where power outages leave consumers unable to cook. This mobile and location-based marketing campaign achieves a 20% response rate.
3. Music distributor Applies Big Data for Demand Planning
   Record label EMI uses big data to measure and forecast product demand. After distributing or leaking music, the company measures consumption on its own social networks and additionally acquires third party listening pattern data from popular music streaming services, song identification apps or 'second screen' social media collators. The data is aggregated by demographics, locations and subcultures and helps the music distributor deliver pinpoint advertising and forecast product demand with a high confidence level. This concept is applicable to other retailers who can also aggregate feeds from social networks to build an understanding of how new products will be received by new or existing markets, or even how their products and company reputation are perceived among the public.
4. Financial Services Company Scores New Clients
   After incurring low win rates for new client acquisitions, a financial services firm turned to big data in order to better identify which new client opportunities warrant the most investment. The company supplemented its customer demographic data with third party data purchased from eBureau. The data service provider appended sales lead opportunities with consumer occupations, incomes, ages, retail histories and related factors. The enhanced data set is then applied to an algorithm which identifies which new client leads should receive additional investment and which should not. The result has been an 11 percent increase in new client win rates while at the same time the firm has lowered sales related expenses by 14.5%.
5. Retailer Creates Pregnancy Detection Model
   In a near infamous retail big data example, retailer Target correlated its baby-shower registry with its Guest ID program in order to determine when a shopper is likely pregnant. Target's Guest ID is a unique consumer ID that tracks purchase history, credit card use, survey responses, customer support incidents, email click-throughs, web site visits and more.The company has a retail technology management strategy that supplements the consumer activities it tracks by purchasing demographic data such as age, ethnicity, education, marital status, number of children, estimated income, job history and life events such as when you last moved or if you have been divorced or ever declared bankruptcy. By comparing shoppers who registered on the baby shower registry with the purchase history from their Guest ID, the retailer discovered changes in shopping habits as the woman progressed through her pregnancy.For example, during the first 20 weeks, pregnant women began purchasing supplements like calcium, magnesium and zinc. In the second trimester, pregnant women began buying larger jeans and larger quantities of hand sanitizers, unscented lotion, fragrance free soap and cotton balls; often extra-big bags of cotton balls. In total, the retailer identified about 25 products purchased by pregnant women.

   By applying these purchase behaviors to all shoppers Target was able to identify women who were pregnant even though these women had not notified Target – or often anybody else – they were pregnant. Target used this discovery to create a pregnancy prediction model which assigned a pregnancy prediction score to shoppers. The retailer was then able to distribute baby product promotions to a very specific customer segment, timed to stages of pregnancy, and the financial results were off the charts. Not only did these women make new baby product purchases, but knowing that significant life events change a consumers overall shopping habits, Target was able to grow its revenues from $44 billion in 2002 when the analysis started to $67 billion in 2010.

   While the retailer does not publicly comment on this program, Target's president, Gregg Steinhafel, is on record sharing with investors that the company's "heightened focus on items and categories that appeal to specific guest segments such as mom and baby" heavily contribute to the retailers success.Notwithstanding the consumer privacy and public relations considerations which must be deliberated, this is a powerful lesson for retailers.