How Retailers Use Big Data to Gobble Up Sales
Billions of dollars will be spent this weekend as the Thanksgiving holiday gives way to Black Friday, Cyber Monday, and the beginning of the end-of-the-year shopping extravaganza. For retailers eager to get “back in the black,” big data analytics provides a great opportunity juice profits by successfully converting on ample sales opportunities.
Black Friday has become an American shopping tradition, and the day that, ostensibly, retailers replace the red ink (losses) in their books with the black variety that indicates profit. Of course, the American consumer spends untold billions at stores every weekend of the year, but the days after Thanksgiving are unique due to the heavy transaction volume, the official start of holiday shopping, and the deep discounts retailers offer on big-ticket items.
The volume of sales this weekend is expected to be massive. According to the National Retail Federation, 137.4 million Americans are expected to shop online or in stores over the four-day holiday, up from 135.8 million last year. Consumer confidence is high, thanks to a 5.2% increase in the median income of American workers (per the Census Bureau) and all-time highs reached on stock market indices.
The noble American consumer will do his part to bravely battle the effects of tryptophan poisoning and line up in the wee hours of the morning to nab amazing Black Friday deals on TVs, tablets and matching Christmas sweaters for the whole family. But after this initial sales buzz wears off, it’s up to the retailer to reel in the sales, and that’s where big data analytics will separate the winners from the losers.
All this adds up to a huge sales opportunity for retailers and e-tailers alike. Merchants have worked for months to prepare for this weekend, including ramping up inventory and staffing levels, fortifying e-commerce sites, and updating merchandising and marketing systems.
On the data front, there’s a lot of work to do too, and numerous opportunities to goose sales with intelligent application of analytics. There are a number of common analytic systems that retailers have built with big data, such as recommendation systems, Customer 360 systems, path-to-purchase, and market basket analysis–and this weekend will see those systems put to the test.
But there are also big challenges. According to Jeff Evernham, director of consulting for North America at Sinequa, just getting the data in place to sufficiently take advantage of the Black Friday opportunity is easier said than done.
“There is huge potential that many retailers struggle to take advantage of,” Evernham says, specifically around transaction and customer behavior data. “Often this goes unrealized because the disparate systems and diversity of data make it difficult to bring shopping and purchasing information together–to say nothing of incorporating unstructured data (such as that from social media sites) to gauge how shoppers are feeling and responding.”
Retailers that can unify their data and apply analytics to it “will have a definite advantage” in terms of “pricing, messaging, and campaigns for the remainder of the holiday shopping season,” Evernham says.
Shoppers aren’t the only ones who will have a busy weekend. Your friendly neighborhood data scientists will also be hard at work, says Mike Upchurch, Founder and Chief Operating Officer of Fuzzy Logix.
“As the busiest days of retail–online and in-store–approach, retailers are doing their best to predict what will fly off the shelves and what might not,” Upchurch says. “This requires an analysis of historical sales data paired with real-time information about the sales of thousands of separate stock keeping units (SKUs) across thousands of stores. The data is large and very dynamic.”
In the past, retailers would be able to keep only thin slices of historical data, and match that up on a per SKU basis against variables like shopper demographics, store locations, or weather patterns. But thanks to the power of today’s parallel processing systems, data scientists are able to slice and dice the data at a much finer level, which results in better and more accurate predictions on shopper behavior.
That analysis will directly transmit to Black Friday and Cyber Monday offers that retailers make. “Buying patterns of shoppers will affect the content you see online and how it’s priced,” Upchurch says. “Careful shoppers can play the game and get good deals, but for many, the work by data scientists could hand retailers a win by driving impulse buys and using low margin items as bait to drive high margin additional purchases.”
Omni-Channel or Bust
Every major retailer today has an omni-channel program in place to essentially include store shelves as part of the online supply chain. As Black Friday bleeds into Cyber Monday and the hot items fly off the shelves, retailers will count on machine learning algorithms running on platforms like Hadoop to identify the omni-channel opportunity and optimize the placement of goods across the extended supply chain.
To see how omni-channel can boost the bottom line, one need look no further than big box behemoth Best Buy. According to a recent Wall Street Journal article, Best Buy’s e-commerce system would list any product as out of stock if they weren’t in one of the company’s warehouses, even if they were on the shelf of a store.
However, since implementing omni-channel in 2012, the stores now “do double duty as e-commerce warehouses,” according to CEO Hubert Joly, and half of the company’s online orders now are shipped from a store or picked up there. The retailers margins have rebounded, and as a result, the company is actually competitive with Amazon.
To be sure, there are many more uses of big data in the retail industry. But as the annual American shopping spree commences, it’s clear that retailers have a lot to be thankful for when it comes to data and analytics.