"Predictive analysis has a long and profitable history with direct mail," says Arthur Middleton Hughes in a Pro article at MarketingProfs. "Millions of dollars have been saved by focusing on those customers most likely to buy—and not mailing to those who are unlikely to be interested."

Only recently, though, have email marketers begun to realize how predictive analysis can help them target likely buyers and keep unsubscribe rates as low as possible, Hughes says.

He points to the results that he and Anna Lu presented at the Predictive Analytics World conference in February. Using Next Best Product (NBP) analysis, a marketer selected 273,334 likely responders from a list of 2.5 million subscribers; a control group of 20,000 was selected randomly. All received the same email offer for the same product. Here were the interesting results:

  • Emails to the targeted group (273,334) netted 842 sales (0.31%) and 273 unsubscribes (0.10%).
  • Those to the control group (20,000) weren't nearly as successful, with 3 sales (0.02%) and 260 unsubscribes (1.30%).

That last figure is especially important, Hughes says. "If 273,334 had been selected by a random select and sent the same email as the first group," he explains, "a total of 3,553 might have unsubscribed—if we apply the unsubscribe rate of the 20,000 control group."

The Po!nt: Forecast your fortune. By heightening relevance with predictive analysis, you can improve sales rates for each email campaign, slow your unsubscribe rates, and boost revenue!

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