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A typical business-to-business (B2B) brand now has a staggering amount of data in its arsenal, and the marketing department’s goal is to use that data to deliver more effective results than ever before. Enter predictive marketing, which uses machine learning to deliver more accurate insights across the funnel to encourage sales from existing and new customers, as explored in a new eMarketer report, “Predictive Analytics in B2B Marketing: Using Data Decisively, at Every Stage of the Funnel.”
Predictive marketing is “rooted in forward-looking analysis,” said Rolf Olsen, chief data officer at Mindshare North America. “Predictive marketing is really about how you take the backward-looking components and make them forward-looking to—for lack of a better word—predict outcomes.”
Goals for predictive analytics span across the customer funnel. A VB Insight study found that 33% of US marketers polled cited customer acquisition as the most important, but right behind that, with 17% each, were four other primary objectives: measuring customer behavior and audience insights, ad/campaign effectiveness, calculating and improving customer lifetime value and customer retention.
Further, in an April 2015 survey of US B2B marketers conducted by OnTarget Consulting & Research for predictive intelligence platform 6Sense, 43% of respondents used predictive analytics to get insights about where prospects are in the sales funnel. According to the Forbes Insights study mentioned above, 26% of marketing executives in North America said that a benefit of predictive marketing was better funnel conversions.
Predictive analysis can achieve these goals by learning from patterns within the data that are derived from customer touchpoints—every interaction that a B2B decision-maker has had with a company. “Predictive technology learns from data to render predictions for each individual in order to drive decisions,” said Dr. Eric Siegel, founder of Predictive Analytics World and author of “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, Or Die.”
According to an April 2015 study of marketing executives in North America from Forbes Insights and predictive analytics vendor Lattice, the most common types of data used for predictive marketing included website data (47%), demographics (44%), digital transactions (41%) and social (39%).
Every application of predictive analytics, whether it is for marketing or for quantifying the risk of fraud, crime, or health or finance outcomes, follows the same two-part structure. First, understanding what is being predicted, and second, figuring out what to do with that prediction.
eMarketer corporate subscription clients can view the full report here.
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