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For business-to-business (B2B) marketers, predictive technology is the elephant in the room—it’s a buzzed-about topic, but few organizations are actually leveraging the full potential of predictive tools. eMarketer analyst Jillian Ryan’s April 2016 report, “Predictive Analytics in B2B Marketing: Using Data Decisively, at Every Stage of the Funnel,” zeroes in on the value of predictive marketing when it’s implemented across the funnel. Ryan shares her advice for marketers still new to the process.
eMarketer: What is the current state of predictive technology adoption?
Jillian Ryan: I’m skeptical about adoption, but it is growing. A lot of brands might actually be using predictive tools without realizing it because it might already be bundled into whatever marketing technology or marketing automation they’re using.
eMarketer: Are marketers actually using predictive insights for campaigns or ads? And if not, in what ways should they be leveraging them?
Ryan: Predictive analytics is certainly a buzz phrase, and while a lot of B2B brands are doing it, they’re not doing it in a holistic way. They’re looking at one single phase or part of the customer journey, and not across all the stages. As for how predictive tools should be used, common applications include finding new prospects, lead scoring, increasing lifetime customer value through upselling or churn prediction, and personalization or content recommendation.
eMarketer: How do you differentiate between sophisticated automation technology and predictive technology? What does it take to be considered “smart” technology?
Ryan: The brands that are doing predictive well are the ones that have figured out automation, because predictive means taking it one step further. It requires a forward-looking analysis where marketers look at what happened, but from there it isn’t about guessing. Predictive has a level of accuracy—it’s not a hunch since it’s in the model. That’s where it goes from automation into predictive.
eMarketer: What are some of the data challenges associated with predictive marketing?
Ryan: Machine learning and artificial intelligence [AI] are the bright, shiny objects surrounding predictive technology, and companies get caught up in the lure of something that’s sexy, new and innovative. But before they can do predictive marketing, they have to do predictive modeling and analytics, which lies in the [data].
Data from various customer touchpoints as well as third-party data must be brought into a centralized location, normally a data management platform. Once the data is clean, it can be used to build a model of successes and failures that will predict which actions have a higher propensity to succeed.
eMarketer: Now that you’ve wrapped up your report on predictive tools, what’s the key thing that you want marketers to know about the technology?
Ryan: The biggest takeaway should be that even though predictive is part of the vernacular right now, it doesn’t mean it’s going to be mainstream in application. We have a while before that happens. There’s a ton of money being invested, and it’s following the same route as automation. Five or 10 years ago, automation wasn’t important in a B2B environment, and now it’s a necessity. Predictive will follow suit.
eMarketer: What should marketers be aware of when it comes to predictive tools?
Ryan: It’s not science fiction. It’s not as though companies are going to start using predictive analytics and all of a sudden, there’s this shiny crystal ball that’s going to predict all the deals that are going to close. It doesn’t give marketers omniscient, God-like power. It equips marketers with information that they need to be smarter and more efficient.
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