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By: Regina Gray, VP, decision sciences, Experian; Phyllis Schneble, EVP, marketing & sales, dLife; Michelle Zweig, VP, product leadership, The Nielsen Company
Think of predictive analytics as a quantitative treasure map leading the way around media bogeys and budget switchbacks to the ultimate prize: loyal consumers. Life just keeps getting more complicated for marketers as consumers exercise greater control over how they receive marketing messages, from channels to devices, and as corporate applies more pressure for better margins.
Predictive analytics can help three ways: by providing 1) a deeper understanding of customers to guide the marketing spend, 2) guidance on selecting messages and vehicles for marketing communications, and 3) key metrics for measuring campaign impact. To illustrate the power of this methodology at work, here’s a case study of how predictive analytics shaped a program targeting the diabetes community.
There are 24 million diabetics in the United States, and unfortunately, one in three children born this year will develop diabetes in their lifetime. That incidence is even higher -- one in two children -- for minority groups. Diabetes accounts for 31 percent of health care costs in the United States, and approximately $175 billion in annual spending.
Diabetics, like any group that shares an interest or concern, view the world through the prism of their disease, which influences things like food-purchasing patterns. As might be expected given the concern with blood sugar levels, Nielsen uncovered high penetration items, including dietetic candy, sugar substitutes, lemon/line diet soda, diet cola, dry beans, mineral supplements, medications/remedies, gelatin mixes and salad dressing. Conversely, calorie-dense items or foods with high sugar content -- like brownie mixes and regular cola -- indexed low for diabetics.