When I spoke with Kathleen Schaub last summer, we entered into an in-depth conversation surrounding the buyer’s journey -- different from the sales funnel, but also an important process for marketing practitioners to understand. While the sales funnel focuses on the internal process to move a prospect from lead to customer, the buyer’s journey focuses on external thinking and buyer perspective.
So what is it? Predictive analytics looks at buyer behavior, such as previous decisions they’ve made, to predict a future outcome or decision. Schaub uses an example of Beehive Geyser at Yellowstone National Park to help illustrate this concept. The eruptions of this geyser lack a pattern or predictability, but just before it erupts, a minigeyser appears. This signals to park rangers that an eruption is about to occur so they are able to notify visitors to watch the show moments before it begins. In a similar way, marketing teams can look for tiny indicators through the use of predictive analytics to identify key triggers and upcoming events.
4 key takeaways can be drawn from Schaub’s report:
Technology continues to improve and become readily available and inexpensive.
Data is everywhere. Data, facts and numbers are key for any marketing teams to build campaigns and support those tough decision making.
It’s all about how you analyze the numbers.
Predictive analytics is a different way to utilize data to support your marketing team and predict buyer’s decisions. It’s not about the data that you collect, but rather how you analyze and leverage it to predict an outcome.
Success requires abandoning convention.
Schaub talks to many marketing practitioners who tell her that they make decisions based off of tradition, instincts, and/or fear of changing their ways. If your marketing team is stuck in a rut or if you want to make a big move, this requires ignoring fears to swing for something new.
Marketing is about humans.
A human’s decision making process isn’t always predictable. Humans are complex. They may have visible patterns but they can always be swayed but outside influences and/or factors. Incorporating a system of analytics is necessary to understand how complex decision are made by customers.
Schaub comments in her report, “if companies truly want to place customers in the center of their universe, as they claim they do, then they must use predictive analytics to reduce bias and reveal hidden truths about who buyers really are, what they really want, and how they really behave.”
And at Juniper, we’re doing just that with our Marketing, Analytics and Data Science, or MAD Science team.
Brian Cooper, who heads up the team, stresses the importance of focusing on data analytics to support our customers, “Digital disruption is enabling organizations to collect and analyze disparate data sources in one data architecture. Analyzing our customers’ historical purchase behaviors, partnering with third party companies to gather intent data, and managing our customers’ digital footprint is critical to enabling Juniper to build predictive models that enable us to bring products and services that are relevant for each of our customers.”