Black Friday is by far one of the busiest days in the retail industry. It typically brings in a large percentage of a company’s yearly sales, so businesses place a high priority on ensuring the day is a success. Last year, a whopping 101.7 million Americans shopped on Black Friday—roughly one-third of the entire U.S. population. According to Forbes, consumer spending is expected to rise 47 percent compared with last year, with nearly seven in 10 consumers shopping that weekend. Here’s how businesses are using big data to drive greater sales on this day and throughout the holiday season.
Black Friday shoppers will likely be out in greater numbers this year, and retailers are preparing enticing offers to ensure purchase. To do so, they’re using big data to create timely, personalized offers based on what they know about customer behaviors. Retailers can accomplish this by comparing historic sales data to real-time information about brick-and-mortar and online sales, assessing current purchasing trends, and adjusting prices accordingly to maximize revenue.
In the past, companies have taken this approach using limited data sets, such as demographics, store locations, or weather patterns. Now, competitive retailers are leveraging larger, more diverse sets of big data, gaining a comprehensive view of the customer and delivering custom offers at just the right moment. If a customer has an existing relationship with a brand—whether they’ve purchased products, participated in a loyalty program, searched for products on the website, or tweeted about it—the data from these interactions should be carefully factored into Black Friday offers.
Some retailers are getting even more sophisticated with their big data analytics. By using geolocation information from social media posts to discover when a customer might be in the vicinity, stores can deliver tailored offers at the right time (and price) to induce a purchase. Others are using video analytics to assess in-store shopper behavior and determine what store layout produces the greatest results.
Real-time analytics can provide timely insights into product returns, allowing companies to continually improve them and make them more efficient. Some retailers leverage big data insights to reduce their return rate, which can constitute as much as 30 percent of online purchases—and on a high-volume day like Black Friday, such returns can have an even greater impact on the bottom line.
Big data insights may indicate, for instance, that a large chunk of returns are initiated by serial returners who tend to have specific customer personas. Certain issues, such as product quality or alignment with customer expectations, may be a factor as well.
By understanding the trends behind order returns, a company can make proactive, informed decisions on how best to minimize them well before the big day. A business can also tap big data analytics to improve the reverse logistics involved in completing a return, thereby ensuring better customer satisfaction with that process.
Staffing is a major concern around this time of year, and analytics can be beneficial in this area too. Businesses can use big data to predict the ideal number of seasonal workers to hire—as well as the potential revenue generated by doing so. In that way, and through other workforce optimization techniques, a retailer can efficiently deploy resources to specific areas in a store to boost sales and ensure that customers receive a consistently excellent level of service.
Retailers conduct a considerable amount of data collection and analysis throughout the year that contributes to the success of this major event. By gathering as much customer information as possible and using it to form sophisticated insights, a business can intelligently predict the most compelling offers to make. That way, they improve their ability to generate revenue and can capitalize fully on the sales potential of this day. For this reason, big data analytics is of great value to businesses—and not just during the holiday season, but throughout the year.
Find out more about the best strategies for using analytics in retail.