Business-to-business (B2B) brands now has a staggering amount of data available, and the goal of the marketing department is to use that data to deliver more effective results. Enter predictive marketing, which uses machine learning to deliver more accurate insights across the funnel to encourage sales from existing and new customers, per the new eMarketer report, “Predictive Analytics in B2B Marketing: Using Data Decisively, at Every Stage of the Funnel.”
Predictive marketing is forward-looking analysis. It’s really about how you take the backward-looking components and make them forward-looking to predict outcomes.
Goals for predictive analytics span across the customer funnel. The primary objectives, according to a VB Insight study of US marketers:
- customer acquisition (33%)
- measuring customer behavior and audience insights (17%)
- ad/campaign effectiveness (17%)
- calculating and improving customer lifetime value (17%)
- customer retention (17%)
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 a 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.