Big Data in Retail Examples

5 Retail Big Data Examples with Big Paybacks

Big data is delivering some big results for retailers.

Macy's says that its big data program is a key competitive advantage and cites big data as a strong contributing factor in boosting the department store's sales by 10 percent. Sterling Jewelers attributes a 49 percent increase in sales during the last holiday season to big data. Kroger CEO David Dillon refers to his big data program as his "secret weapon."

McKinsey analysis of more than 250 engagements over a five year period revealed that companies that put data at the center of the sales and marketing decisions improved their marketing ROI by 15 to 20 percent.

But despite some impressive paybacks and what may be a game changer in the retail industry, plenty of obstacles remain.

In meeting with a number of retail executives I've found that Big Data is getting a lot of interest, but most of these executives struggle with some common challenges – such as how to align big data with use cases, how to identify new types of (generally unstructured) data and how to harvest big data for improved decision making.

Big data is anything but out of the box. This is a disruptive technology without packaged solutions. Sure, you can acquire big data technology, but without understanding and hypothesizing how previously hidden data can be harvested and applied to business processes, challenges or opportunities, big data becomes another shelfware solution with a disappointing payback and short lifespan.

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.

Go Big or Go Home

These big data for retail examples can be extrapolated in many ways — from using weather patterns to predict in-store sales to combining data from web search trends, website browsing patterns, social networks and industry forecasts to predict product trends, forecast demand, pinpoint customers and optimize pricing and promotions.

Understanding the correlation between your product sales and otherwise undetected factors such as the weather, pop culture, social media trending, your competitors and consumer sentiment can allow you to tap into these environmental events with specific actions that lead to improved financial performance.

Retailers that leverage big data will design products that are more embraced by consumers, better anticipate and respond to market shifts, and engage consumers with predictable results. This means fewer stockouts, higher visit to buy ratios, bigger basket sizes and other performance measures that can be improved with better data.

Big Data Not Just For Big Companies

Retail thought leader Gary Hawkins suggests that big data may actually create a retail oligopoly. Writing in the Harvard Business Review, Hawkins poses the likelihood that big data may "kill all but the biggest retailers." He suggests that large retailers, with their larger IT budgets and resources, can capitalize on the big data opportunity, increase market dominance and essentially relegate smaller retailers to "the role of convenience stores."

Notwithstanding Hawkins well supported argument as well as big data's very real opportunity to improve marketing, product availability, retail CRM, or the customer experience, and thereby outperform retail competitors, it's my strong belief that the new retail pecking order will be less determined by the size of the retailer's IT budget and more by the retailer's propensity toward innovation and agility.

The retail industry is incurring profound change and smaller businesses often show more agility than larger retailers. As Darwin taught us "It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change."