Adaptive analytics is a new term for most B2B marketers. I interviewed Dave Rigotti, VP of marketing at Bizible, to get some insight into the newest technology in the B2B marketing analytics space. In the interview, he explains what adaptive analytics are, why it matters for B2B marketers in 2017 and beyond, and what it takes to get started.
Adaptive analytics comprises marketing data and insights that leverage real-time capabilities and algorithms to provide the most accurate information in the moment.
Adaptive analytics are useful for both the marketing and sales teams because it can help inform each team when it’s optimal to engage and with what content. Because adaptive analytics leverage prospect and customer data, it’s important for orgs to have accurate CRM and multi-touch attribution data before adaptive analytics can really be effective.
Today, predictive analytics is first and foremost used for more effective go-to-market targeting. It looks at your customer data and, based on certain traits like firmographics, tells you which prospects look most like your existing customer base, and are therefore more likely to be qualified.
Adaptive analytics, on the other hand, informs optimization and adjustments to prospects that you’re already engaging. It looks at your past interactions and the interactions of companies like yours marketing to audiences like yours, and tells you what the best next engagement is. Should you send them a mailer or an email? Should you send them middle of the funnel content or bottom of the funnel content? Should the sales team reach out? These are questions that adaptive analytics tells you.
Essentially, predictive analytics tells you who to target with your marketing; adaptive analytics tells you whether your marketing is working and how to adjust if it’s not working.
Adaptive analytics requires the continuous processing of a ton of data, which until recently, was unfeasible for most organizations.
