How More Immediate Sales Data Analyses Can Help You Capture Last-Minute Back-to-School Shoppers

The window for back-to-school (BTS) shopping is shrinking. Research from the National Retail Federation found that families are now doing their shopping later in the summer, often just a couple weeks before school starts. This means that retailers have fewer chances to influence purchasing decisions. And when there are more buyers in play and less time to reach them, the competition heats up.

Parents and retailers alike go into high-stress mode when school comes back in session, and these late summer shoppers could be motivated by a number of factors — e.g., their class schedules were announced at the last minute or they read a report about the killer deals they can score later in the summer. Whatever the case, retailers can't afford to wait them out.

However, while retailers used to face these challenges blindly, they have a new and powerful way to lure in scrambling shoppers: actionable data held by banks, payment processors and card networks. By relying on real-time (or close to real-time) information about BTS purchasing trends, companies can adjust their sales strategies to beat the competition and win over last-minute buyers.

Data-Driven Retailers Get an ‘A’

Timely sales data is actionable. Retailers that analyze data as customers shop can see what works and what doesn’t, and then they can adjust their strategies on the fly to capitalize on whatever strategy works best in the moment. Delayed sales data, while useful in setting a broader strategy for the next BTS season, can’t help retailers score with shoppers in the short timeframe they have left this year.

Time-sensitive sales require retailers to understand and adapt to customer needs quickly. According to a survey conducted by research firm IDC in 2017, 77 percent of executives believe their lack of timely data prevents their businesses from capitalizing on opportunities. Another 54 percent of executives said that this same data drought limits their operational efficiency.

Retailers with better data can shift spending to tactics and content that drive more immediate sales. For example, one seller might have three banner ads for the same promotion, each focused on a different item (e.g., shoes, pencils, and backpacks). Knowing which ad drove the most sales over the past week would allow that retailer to double down on a successful tactic and further improve the next week’s sales.

Know Your Data, Know Your Customer

Immediate, actionable data like this is great, but not all data is created equal. Marketing executives should consider three quality factors as they begin using real-time feedback to improve their bottom lines. The key factors are truth, trust and actionability.

1. Truth and High-Quality Data

Beware of data that could be skewed by a limited field of customers. Some buyer data that comes from regional banks, while valuable in the right context, might not apply to broader demographics in other markets. "Truth" here means data that's related to actual customer behavior rather than proxy information like location data.

Data should represent your whole customer base, both existing and prospective. While data that you gather from the most loyal customers might be quite useful to keep those shoppers engaged, it doesn't help with new customers or switchers. To measure statistically significant differences in ad performance, collect large sample sizes. This is somewhat limited by an individual retailer's resources and methodologies, but when conducting a study of a particular campaign, companies should always request expected match rates and sample size estimates. Learning that 20 percent of customers responded to a campaign is far more valuable when that means 100,000 out of 500,000 than when it means 200 out of 1,000.

2. Trust and Privacy Concerns

Advertising is a conversation consumers are having with your brand; when those consumers participate in that conversation, they're showing that they trust you enough to let you know who they are. Before making any decisions based on that new information, consider not only what the data says, but also where and who it comes from.

New regulations and standards, such as the European Union's GDPR, require companies to be extremely cautious about how they use and store data. Consumers, too, are more critical of brands that don't steward their data with care. Minimize risks by keeping personally identifying information and individual-level credit card data on the other side of the proverbial wall. You don't need to see such sensitive individual data to gain valuable insights. Retail shops account for just 4.8 percent of data breaches, but no company wants to make the headlines for the wrong reason.

3. Actionability and Detail

To optimize digital media for sales, drill beyond overall campaign results to see which ads, publishers, audiences, geo-targets and other factors drive results. This type of granular analysis often reveals that some aspects of a campaign drove incremental sales when other aspects created no lift at all.

For example, a retailer might have driven a 2 percent sales lift during the first half of its BTS campaign, which, on the surface seems fairly good. But if the digital ads were bought through four DSPs and analysis shows that only one DSP generated the sales lift, the retailer then has actionable insight.

With tax-free weekends mostly in the rearview mirror, competition among retailers to capture the final sales rush before the end of summer is ramping up. By turning to real-time data analysis and adjusting digital media buys while campaigns are still in flight, retailers can maximize sales during this vital time of the year.

Thomas Noyes is the founder and CEO of Commerce Signals, a marketing measurement platform that helps retailers unlock insights from payments data in near real time.