In the United States alone retailers rake in a staggering $4.5 trillion in sales annually. While that’s a big number, don’t let it fool you. Retail is a tough business. The average grocery and food retailer, for instance, reports net margins of just 2.71 percent.
With so little room for error, retailers are understandably focused on tweaking price, assortment and placement as they strive to drive up average purchase values, improve customer retention, and maximize sales per square foot. Now more than ever before, they’re looking to Big Data and advanced analytics to hone these metrics and provide guidance.
Recently, Brick Meets Click (BMC), a consultancy that helps grocers find growth opportunities, huddled with a group of experts to identify 12 areas where companies can solve retail problems with Big Data. The entire report is available here, but I’ll focus on a couple that jumped out at me – namely because these resonate in a world where store managers and floor personnel are increasingly equipped with sensors and tablets that enable them to input performance numbers in real-time, or access insights that can impact everything from promotion forecasts to planograms.
Identify profitable items to promote. According to the study, most promotional items don’t actually grow category sales or lift store profits. And if promos fail to attract profitable new customers, they actually end up costing retailers money. To solve this, the organization recommends ferreting out successful promotions – those that actually contribute to store performance – by analyzing sales by item and category, along with gross profit dollars to produce a list of promotions that drive performance. Then, use this internal Big Data to run zoned ads at each location, and increase your mix of promotions that share the characteristics of those on the list.
Reduce out-of-stocks on promoted products. Products sold in a promotion often have twice the out-of-stock levels than regularly priced products. This leads to as much as a 15 percent loss of sales for out-of-stock items, not to mention frustration for customers who respond to promotions only to find shelves empty. The study suggests tracking actuals against forecast in the first few hours of the promotion, and then adjusting the forecast based on this real-time data to avoid out-of-stocks. Here, cloud-connected devices like tablets or RFID sensors can import data directly from the sales floor into analytics programs resulting in real-time insights and continuous forecasts.
Increase sales-per-square foot with customer tracking. Point-of-sale (POS) and tracking systems produce a lot of data that have long been available to every retailer. But until recently that data has been difficult to transform into actionable insights that measure merchandising effectiveness in the aisle. New tools built to analyze Big Data can automate that task, making it easy to establish a sales-per-shopper baseline and then track sales-per-shopper after merchandising changes are made – opening the door to measuring the revenue impact of those changes.
Having worked with some of the largest supermarket chains in the United States, I can tell you modern retailers are shopping for answers to some of their most vexing problems via Big Data. In fact, one of our customers (a multi-billion, publicly traded American foods supermarket chain) is pulling in receipt-level information from their POS systems 5 times per day in order to do daily comp store analytics and accurately identify trends at the SKU level. Using Tidemark's Big Data Finance capabilities, they are able to access public domain information from resources like National Oceanic and Atmospheric Administration (NOAA) and Bureau of Labor Statistics, integrating it into their forecasting and planning system and allowing for correlation to weather events by region to apply seasonality to their predictive forecasting modeling.
If you’re interested in more research and uses cases for Big Data Finance, check out our new research paper or stay tuned to our blog. I’ll cover other stories on how retailers unlock performance in the near future.