The goal of using data as a marketer is simple: You want to reach the right audience, with the right message, at the right time. When it comes to sophisticated targeting, historical data can only take you so far. Once you’ve established the patterns that your audiences are taking, how do you translate insight into action?
Enter predictive marketing and predictive modeling, the missing link between analytics and actual targeting. Today’s most innovative CMOs merge marketing and technology to craft a richer picture of their audience, and to better predict how they will behave next.
The Definition of Predictive Marketing
Predictive marketing is the process of better leveraging your data to fine-tune your relationships with your target audiences.
By gaining a deeper understanding of your audience over time, you can better understand—and predict—their needs. As Brian Kardon, CMO of Lattice Engines, points out in an interview for Quarry, the difference between analytics and predictive marketing is the ability to look ahead and take action. Predictive models can help address many of the biggest questions that marketers struggle with today:
“Many marketers […] are now looking for the new ‘new’ thing,” explains Kardon. “They are looking for an edge. However, from a performance standpoint, the best that marketing automation can do is provide a view into what happened in the past. It can show how prospects responded to various marketing channels or which campaigns performed better than others.”
Predictive marketing, on the other hand, helps all layers of a marketing organization, from the high-level decision makers to the on-the-ground media buyers, make informed, actionable decisions. Too much of marketing still boils down to guesswork. And with guesswork comes risk: what happens if one of your assumptions about your target audience is wrong? Predictive marketing allows you to leverage data to more accurately anticipate how your prospects and past customers will behave next.
For example, Amazon’s almost uncannily relevant product recommendations are powered by predictive algorithms—based on past purchases and shopping behavior. They are designed to constantly improve at guessing what shoppers will buy next.
“Marketers really want to figure out how to improve results going forward and ensure they are driving towards top-line revenue goals,” Kardon says. “Predictive marketing can help do just that and understand buying intent.”
This is an excerpt from our eBook on Predictive Marketing. Click here to learn how to predict behavior and get “one step ahead” of your audience.