Big Data in Retail Examples

5 Big Data in Retail Examples with Big Paybacks

Big data is delivering some big results for retailers.

Macy's says that better use of more data is a key competitive advantage and cites its retail big data program as a strong contributing factor in boosting the department store's sales by 10 percent annually. Sterling Jewelers attributes a 49 percent increase in sales during the last holiday season to its retail big data. Kroger CEO David Dillon refers to his retail 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 their 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 industry, plenty of obstacles remain.

In meeting with a number of executives I've found that data and analytics are getting a lot of interest, but many of these retailers struggle with some common challenges. Things like how to identify data-driven use cases, how to apply new types of (generally unstructured) data and how to harvest data for improved decision making.

Big data in retail is anything but out of the box. This is a disruptive technology without packaged solutions. Sure, you can acquire analytics technologies. But they only work if you first understand and hypothesize how previously hidden data can be harvested and applied to business processes.

Without first understanding the challenges and mapping the opportunities, analytics tools become another shelfware solution with a disappointing payback and short lifespan.

From over two decades of retail consulting experience, I've found that successfully deploying these solutions begins by identifying use cases and business decisions which benefit from new information. But I also recognize this is easier said than done as big data in retail examples or use cases are a function of your creative thinking. To stimulate that thinking, consider the following examples.


Hotel Chain Uses Data to Increase Bookings

Bad weather reduces travel, which then reduces overnight lodging. That's not good news if you are 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. It then built an algorithm which factored weather severity, travel conditions, time of the day and cancellation rates by airport and airline among other variables.

With these 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.

The payback was 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 insights and geo-based mobile marketing campaigns Red Roof Inn achieved a 10% business increase in one year.


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.


Music distributor Applies Data for Demand Planning

Record label EMI uses data and analytics 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 information is aggregated by demographics, locations and subcultures. That 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.


Financial Services Company Scores New Clients

After incurring low win rates for new client acquisitions, a financial services firm turned to data to better identify which new client opportunities warrant the most investment. The company supplemented its customer demographic information 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%.


Retailer Creates Pregnancy Detection Model

In a near infamous example, retailer Target correlated its baby-shower registry with its Guest ID program 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 expansion strategy built on data. It 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 women 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, they 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. These women increased baby product purchases, and Target achieved double digit revenue increases.

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.

Big Data

Go Big or Go Home

These big data in retail examples can be extrapolated in many ways. From using weather patterns to predict in-store sales to combining data from web search trends. Or using website browsing patterns, social network traffic 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 data and analytics will design products that are more embraced by consumers, better anticipate and respond to market shifts, and engage consumers with predictable results. This also 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 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 this technology opportunity, increase market dominance and essentially relegate smaller competitors to "the role of convenience stores."

He makes a well supported argument as there is a clear competitive advantage from applying data for improved marketing conversions, product availability, customer experiences or other retail CRM objectives. Any one of these benefits will likely empower a technology leader to outperform technology laggards.

However, 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 their propensity toward innovation and willingness to adapt.

The industry is incurring profound change and smaller businesses can 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."