This byline by Infer’s Sean Zinsmeister was originally published on Marketing Land.
Columnist Sean Zinsmeister takes a deep dive into intent data, explaining how you can use it to increase your predictive power and revenue.
As big data increasingly becomes more accessible, marketers are looking for ways to make it more scalable and actionable in order to better target prospects in various stages of the buyer journey. Intent data is synonymous with this topic, but it understandably causes a great deal of perplexity for many marketers.
It can be difficult to sift through all the terminology: It’s variously referred to as activity, behavioral, internal intent data, or external intent data. Pairing intent data with other customer signals — like those housed within a company’s marketing automation system — provides an especially unique opportunity for businesses to understand and leverage customer insights.
Nonetheless, it’s a topic that will continue to gain steam as more companies look for new ways to identify and predict where customers are in their buying journey.
Defining intent data and its uses
To start, it’s helpful to define common terms for a clear understanding of the various types of intent data out there, and how they can be applied. Simply put, intent data is information collected about a person’s or company’s activity. For the most part, it falls in one of two main categories, each of which best serves a different purpose: