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.
What is adaptive analytics?
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.
How is it different from predictive analytics?
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.
Why am I just hearing about it now?
Adaptive analytics requires the continuous processing of a ton of data, which until recently, was unfeasible for most organizations. With the growth of machine learning and the ability to process more data faster, adaptive analytics is increasingly a reality. So as machine learning continues to mature and become more prevalent, so will adaptive analytics.
It also depends on rich and accurate prospect and customer data. So as B2B marketers get better and cleaner CRM data, as well as adopt more sophisticated marketing attribution, they will be in a better position to really use the capabilities of adaptive analytics.
What are some applications of adaptive analytics for B2B marketers?
Let’s say you are tracking behind on your monthly opportunity numbers, so you want to plan a marketing campaign to engage your best leads/contacts. Typically, you’d look at lead scores (which are based on things like firmographics and website engagement) and attribution data (X% of leads who came from this channel converted into opportunities or customers). Using this data, you could make a data-backed bet that if you engage them with a campaign, X% of them would convert to an opportunity. While this is a huge step forward from just using your gut or assuming that everyone in your target market behaves the same, data-driven marketers are still left wanting.
As martech vendors grow and add customers, they collect troves of data. Through machine learning algorithms that can process mountains of data in relatively little time, they can identify similar companies with prospects in similar situations. When they engaged them with X content via X channel, X% of them converted. When they engaged them with Y content via Y channel, Y% of them converted. Adaptive analytics enables marketers to utilize these types of insights to plan campaigns with more confidence, as well make adjustments in real time.
What’s necessary to use adaptive analytics?
Here’s a pretty common adoption process for B2B martech: Website/CMS/Web Analytics > CRM > Marketing/Email automation > Attribution > Predictive Analytics > Adaptive Analytics.
Because adaptive analytics uses your prospect and customer data, and then leverages an aggregated group of data with machine learning, it’s important that your prospect and customer data is accurate first. That means clean CRM data and rich attribution data.
Once you have the foundational pieces in place, adaptive analytics can help you develop better insights and do more efficient marketing.