For decades, brands have relied on market research to shape strategy and test theories about what customers want. Sometimes marketers go with their gut. Sometimes they scale up a successful strategy. But today, data intelligence has paved the way for brands to analyze consumer behavior with a specificity that unveils what customers are doing at the end of the value chain.
“You are what you buy, as opposed to what a survey says,” says Mark Craig, assistant vice president of marketing science for Ansira. “A lot of the work we do is a myth-busting exercise.”
By analyzing consumer behavior at the transactional level, a brand can see exact purchases at an exact point in time — and benefit from a clear picture of who its true customers are. With this information, brands can hone their strategies, messages, and channels to effectively engage with customers, leaving the guesswork — and costly marketing missteps — behind.
“By looking at the data, we can show brands where opportunity exists and where they shouldn’t waste their time,” Craig says.
Beauty Brand’s About-Face
Market research is almost always a good place to start, particularly in the absence of an in-house team or agency that can assist with segmentation. But with the ability to analyze all types of data — such as first-, second-, and third-party data — brands can dig deep and deliver relevant marketing experiences.
One of the most transformative changes resulting from transactional data happened with a prominent national beauty brand. For years, the company marketed to what it assumed was its target customer: a twentysomething female with a household income around $40,000 who’s driven by value. Based on this profile, the competitive set was Walgreens, CVS, and Walmart.
Every marketing decision, from point-of-sale materials to promotional offers, was directed at her.
However, after running an initial segmentation to validate the beauty brand’s organizational belief of who its customer was, the data unveiled a completely different picture. The actual customer proved to be a fiftysomething female with an average household income of $70,000 who’s seeking both value and experience. The true competitive set is Ulta.
The data showed that this popular brand had been off base for years.
Convenient Store’s Coffee Quotient
A well-known convenience store chain had a similar eye-opening experience. After market research painted the customer profile to be construction workers who shop for Gatorade and breakfast items for their crews, the transactional data revealed a much different story.
Although stores in Texas do see an influx of construction workers, most of the brand’s stores are located in Las Vegas and New York. The customer profile for these areas: white- and blue-collar workers popping in for coffee on their way to work.
Imagine how different the marketing became with this data-driven intel.
While understanding customer intent and predictive behaviors is valuable, the certainty that transactional data provides can be game-changing. In marketing, truth trumps assumption.
When a brand knows who’s purchasing what, it can better utilize marketing spend — and avoid wasting resources on a campaign that seems right but doesn’t hit the mark.