Intent Monitoring: Why It’s Not Predictive
- Predictive analytics and account-based marketing are two of the hottest areas in B2B
- Intent monitoring is often considered predictive, but doesn’t actually use data in an algorithmic way
- Intent monitoring will grow in popularity as it continues to or serve as a “lightweight” alternative to predictive analytics
Recently, my colleague Kerry Cunningham and I conducted a SiriusDecisions forum in Cambridge, Massachusetts, called “The Role of Predictive Analytics for Demand Creation and Account-Based Marketing” – and had an amazing turnout. The number of participants was not surprising, as account-based marketing (ABM) and predictive analytics are two of the hottest topics in B2B. In SiriusDecisions’ 2016 State of Account-Based Marketing study, 87 percent of respondents stated that ABM is extremely or very important to their overall marketing initiatives. In addition, 29 percent said they were investing in predictive analytics in the next 12 months.
During the forum, Kerry and I discussed several challenges that B2B marketers face when leading traditional or account-based demand creation programs (e.g. not having enough leads, having too many leads, not converting leads efficiently). We then introduced eight different predictive use case applications that can help B2B organizations address these challenges, focusing on a few of those applications in some detail. One of these was intent monitoring.
Keep in mind, intent monitoring is not predictive – it’s descriptive and fact-based. Predictive applies a statistical approach so that organizations can analyze past performance and infer likely future performance. Therefore, predictive applications almost always provide some measure of likelihood of an outcome happening (e.g. tier A accounts are three times more likely to convert than tier B accounts). In contrast, intent monitoring does not use a statistical approach to predict anything. Instead, intent monitoring scrapes and aggregates evidence of how people within target accounts are researching topics that could be aligned to solutions. It then looks for changes in the frequency and intensity of those activities. The notion is that if many people within a target account are all searching certain keywords, consuming many pages of content, downloading relevant white papers and participating in online forums and social media in a relevant way, there might be an active buying opportunity.
While intent monitoring should not be classified as predictive, it is a valuable resource for B2B marketers and sales reps. It can help identify potential accounts to focus on and accelerate a sales cycle. But it won’t be fruitful unless you have playbooks or actions that are ready to be implemented based on surging intent. One of the differentiating benefits of intent monitoring over predictive is that it’s operationally easy. There’s nothing to install or integrate, and no models to build or algorithms to refine.
Many predictive vendors include intent monitoring in a comprehensive package to help clients predict who is a likely ideal customer (among other use cases), but when they might appear to be in market, too. Having said that, in the past 12 months, we’ve seen a handful of vendors focused exclusively on intent monitoring (many stemming from their B2B data roots). This marketplace will continue to grow during the next 18 to 24 months, and we should anticipate early adopter acceleration during the first half of 2017.
And no, I did not use predictive analytics to come to this conclusion. I used intent signals, the old-fashioned way – from talking to many leading B2B marketers and vendors.