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FTC Workshop: DMA Shows How Marketing Analytics Benefits Consumers


Post Date: March 19, 2014
By: Rachel Thomas

I presented at the FTC Workshop on “Alternative Scoring Products” today, and aimed to educate and guide FTC regulators on how marketing and predictive analytics has been a core part of the marketing ecosystem – and adding value to consumers – for decades.

The FTC workshops are a way for regulators to hear from industry, consumer advocates and other interested parties as part of their information gathering process.  Their interest in “alternative scoring” seems to be related to both a fear that marketing scores are being used for eligibility (which no responsible marketer does), and to recent inquiries around “data brokers” and the level of transparency between consumers and marketers.

I emphasized this morning that the goal of marketing in every case is to meet consumers where they are with an offer for a product or service that will be of interest to them.    Everyone gets marketing offers.  Predictive analytics make it more likely that the offer will be valuable to the consumer.

This has been going on for decades. I shared a few examples:

  1. In 1888, Sears predicted that consumers in the rural West would more likely be interested in the catalogs they sent, because they wouldn’t have access to stores with those products.
  2. In 1912, LL Bean predicted that people who had Maine hunting licenses but lived out of state would be interested in a catalog of hunting gear, so he purchased that list of licensees from the State of Maine.
  3. Microsoft recently released some research showing “consumers are absolutely desperate for more personalization during their purchase journey.”   Consumers don’t want to encounter gaps between a brand’s online, mobile, and in-store presence – they want to have a seamless experience wherever they encounter that brand.
  4.  A retailer might look at what a customer has 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 vs. kitchen appliances vs. clothing.
  5. Nonprofits use predictive analytics to keep fundraising costs down by focusing on the people most likely to donate, and to home in on populations in greatest need of assistance and tailor their approaches to engaging those in need.   The Humane Society, World Vision and others create statistical (demographic) pictures of major donors and then searching for new donors that fit 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 most likely to buy purple shoes are female, age 30-34 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, to help 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.

At the end of the day, I assured the FTC, this is about relevant advertising. No more.    Whether this seems shocking or magical or mundane, it’s important to remember what it’s all being used to accomplish: Marketing data is used for marketing purposes ONLY – to predict likelihood of responding to a marketing offer for a product.  At the end of the day, the biggest impact that it will have on a consumer’s life is whether that individual gets an ad or offer that is relevant to her interests…or one that is not.

The full testimony and hearing are available online here.

What are some other good examples of our how consumers benefit from marketing and predictive analytics?  Please let us know in the comments section below.

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1 Comment

  1. Robert Burns says:

    Historically (1963 forward) models were built to predict undeliverable mail – back then often 15% of a campaign. High undeliverables were suppressed. That saved a lot of trees, postage, and mis-directed expense.

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