Marketing Analytics Benefits Consumers, Yet FTC Is Doubtful

The goal of marketing is to provide consumers with a product or service offer that will be of interest to them wherever they are. Everyone gets marketing offers. Predictive analytics increases the likelihood that those offers will be valuable to the consumer. The Federal Trade Commission (FTC) isn’t so sure, however, and focused on marketing analytics at its Workshop on Alternate Scoring Products.

The FTC’s interest is to ensure that marketing analytics doesn’t get used for eligibility (which is already regulated, as it should be, and something that no responsible marketer would do). It also makes certain that there’s transparency between brands and consumers on what marketing data is used and why. Those guidelines are fair enough, and it’s up to every marketer to ensure that our practices are of the highest standard and build trust and confidence with consumers. 

I know that many digital marketers think that things like segmentation and predictive targeting are recent inventions, but this kind of work has been going on for decades. What are new are the increased amounts of data, the marketing automation, and the campaign and data management technologies that allow segmentation and predictive targeting to happen with more precision and in real time. 

Direct Marketing Association (DMA) VP of Government Affairs Rachel Thomas presented at the FTC workshop, and explained why these practices work so well through the following examples. It’s because they all engage customers in offers that have meaning for them.

1. In 1888 Sears predicted that consumers living in the rural West would be more interested in the catalogs they sent because they wouldn’t have access to stores with those products.
2. In 1912 L.L. Bean predicted that people who had Maine hunting licenses but lived out of state would be interested in a hunting gear catalog, so the brand purchased lists of licensees from the State of Maine.
3. Microsoft recently released some research showing that “consumers are absolutely desperate for more personalization during their purchase journey.” Consumers don’t want to encounter gaps between brands’ online, mobile, and in-store presences; they want seamless experiences wherever they encounter a brand.
4.  A department store might look at what customers purchased at a particular store, through its website, from its mobile site, and otherwise, and then analyze those purchases in comparison to others who have bought those items. Using predictive analytics, the department store will guess whether a customer is more likely to want a coupon for jewelry versus kitchen appliances versus clothing.
5. Nonprofits use predictive analytics to keep fundraising costs down by focusing on the people most likely to donate. They also use predictive analytics to home in on populations in greatest need of assistance and tailor their approaches to engaging those in need. For example, The Humane Society, World Vision, and others create statistical (demographic) pictures of major donors and then search for new donors that have similar profiles. This keeps marketing costs lower and puts more donations to work on the cause.
6. Predictive analytics are also important for finding new customers, donors, supporters. If a company knows that the customers who are most likely to buy purple shoes are female, age 30-34, living in big cities, it might go to a marketing information service provider and ask what other products females of that age in big cities are likely to be interested in. This helps a company decide whether it should send those consumers a coupon for a red dress instead of a blue dress.

The importance of data provided by consumers to various brands, and the exchange of data between firms is what drives our data-driven marketing economy, valued at $156 billion in 2012 alone, according to a recent academic ”Value of Data” study from the Data-Driven Marketing Institute.

While marketers know that smarter marketing means happier customers and higher revenue, we must never forget that our self-regulated industry is a privilege afforded only to those who maintain high ethical standards and persistent respect for all audiences. If data is the fuel that powers modern marketing, then we must be cognizant of the need to steward the use of data responsibly. As DMA’s Thomas says, “Self-regulation means doing what is right before someone tells you that you must.”