Every time you use a supermarket loyalty card, you’re doing more than getting discounts—you’re giving away data. British firm Dunnhumby is one of the best-known pioneers of data-aggregating supermarket loyalty card programs thanks to groundbreaking work with Tesco in the UK and Kroger/Ralphs in the United States.
But like many innovators, Dunnhumby faces a dilemma. Marketing data has become democratized over the years, and it’s easy for retailers and ecommerce brands to do in-house analytics which previously required outside assistance. For companies like Dunnhumby, which come from the world of multi-year contracts and on-loan consultants, that’s a big sea change.
Guillaume Bacuvier, a former Google advertising executive, joined Dunnhumby as CEO in 2017. His mandate is to help the company navigate a sea change in marketing and retail data science.
While Dunnhumby’s bread and butter is their work with big-name firms like Coca-Cola, GlaxoSmithKline and Whole Foods, the company is now expanding into new areas: Working with mid-size and smaller companies, for instance, as well as building out a consumer data science platform.
This summer, AList spoke with Bacuvier at a Dunnhumby conference in Chicago about the company, its future plans, and the marketing data landscape in general. Here’s what he had to say:
Audience Targeting Has Multiple Uses
When it comes to the retail world, purchase history—and the inferences that can be drawn from it—is king. This isn’t just because it can be used to predict future purchases, but because it can be leveraged for additional purposes like advertising.
“If you’re a large consumer goods company and you’re about to launch a product,” Bacuvier says, “[Look at] audience targeting, which is built on the kind of data that a retailer has. It’s quite powerful because retailers have a lot of purchase history about people. That’s usually a good predictor of work, whether you’re in the market for a new chocolate bar that you’re about to launch or a new brand of detergent. What Dunnhumby has done—and we’re not the only ones in the market to do this—is help retailers build media revenue streams and advertising revenue streams that come on top of our core retail business.”
Marketing Data Co-ops For Smaller Companies
Bacuvier notes that one challenge facing smaller retailers—that is, retail chains with multiple locations that might not necessarily be Walmart or Kroger sized—when it comes to big data is that they simply have less data to work with than their larger competitors. He says one possible way for smaller companies to gain leverage is through data co-ops. “Co-op models are not foreign to the retail industry,” Bacuvier adds. “They’ve often had co-ops when it comes to dealing with their supplier base and build co-ops to gain purchasing power and get better prices.”
Possible models for marketing data co-ops, for instance, could include regional retailers teaming up to aggregate their data for better insights and advertising inventory.
GDPR As An Opportunity
Marketers and data brokers are still struggling with the European Union’s GDPR regulation, which changes the way customer and user information is processed. Europe’s new guarantees of data portability and information retention are much more stringent than the United States’. Bacuvier says this presents an opportunity.
Speaking of data portability, Bacuvier notes that customers could potentially take loyalty card records and transfer them to competitors.
“This is disruptive in many ways. One is obviously that the kind of intrinsic value of that data set, for whoever is owning that loyalty program, is exposed because all of a sudden, it’s not as protected as before. It is much easier and creates a kind of competitive environment for one company to potentially win over customers from another by being able to very quickly absorb all their historical transaction data—therefore be able to offer something personalized far more quickly than they would before. It also will require us to create a whole ecosystem to enable data portability.”
The Value Of Consumer Data Is Changing
In the past, in-store consumer data was primarily used to create customized promotions and help companies predict the future to varying degrees of accuracy. However, the rise of ecommerce means that consumer data can now be leveraged for advertising and other secondary uses.
“We use data science to help companies create revenue out of that data,” Bacuvier says. “Media is one example where you can create additional revenue streams from the data that you have. Or it could be monetizing in retail; it’s quite a classic as you can commercialize that data back to your supplier base in the form of insights or market research or business intelligence. Many suppliers are willing to pay for it because [it’s] the only way they can really understand what’s going on with consumers.”