Intent Data 101 and Demand Generation 2.0
The late motivational guru Wayne Dyer left us with the following quote, which was the core of his world-renowned philosophy: “Our intention creates our reality.” As marketers, we must first understand a target customer’s intentions if we want to be part of their reality. Fortunately, the emerging trend of intent data provides us with a powerful tool to help us accomplish this.
B2B marketers often struggle with lack of insight into their customers’ buying processes. Many have attempted to map out the buyer decision journey using overly-restrictive models of stages, phases or steps. In addition, the buyer’s process is not always logical or linear, and is often lengthy. It’s no wonder marketers are frustrated!
As I discussed in my previous blog post “Can You Predict The Buyer’s Intent?”, intent data is a technique that aggregates multiple data points to indicate when a prospect is interested in buying. Each impression or data point is a clue that paints a bigger picture of what a customer is thinking, their needs, and their strategic direction.
Intent data is:
- the next generation of demand generation,
- the 2.0 version of the traditional sales lead model
- an effective means of realizing efficiencies
With intent data, we can leverage the insights derived from plotting points and observing trends in order to anticipate a buyer’s next step. Ultimately, we are more empowered to tailor our marketing materials and messages for our target customers when we understand what moves them to act.
A recent resource from VentureBeat classifies intent data in two categories:
- Internal Intent Data (or “first-party data”): Activity captured on a company’s own website or application logs. This info is usually highly predictive for buying signals because the content is so directly relevant to the purchase decision. Internal intent data can include exactly what pages a prospect touched, which links they clicked on, and how long they spent on each page.
- External Intent Data (or “third-party data”): Collected by publisher networks at the IP level, or through user registration or cookies. Data could include articles a user reads, content they download, their site searches, and potentially even comments they leave.
Marketing analyst Laura Ramos describes the implications of intent data for marketers: “By looking at the data that prospects throw off as they research problems and new ideas, B2B marketers can tap into this data bounty to reveal hidden information about buyers’ interests, business relationships and backgrounds even before they visit their websites or provide any identifying information.”
Intent data helps marketers identify where potential customers are along the path of a purchasing decision, and provide them with more personalized content. With the information that intent data provides, we can position ourselves to prospects as providers of the desired product or service, at various touchpoints during their purchasing journey.
Through intent data, if we can understand buyer behavior and how their choices are being influenced, we are better positioned to help them make more informed purchasing decisions. A more specific implication of this is that a target account list can go from an overwhelming swath of flat data to a contoured landscape of the buyer spectrum, with hot spots of in-market buyers and untapped leads rising to the surface. Looking forward to the coming months, this is the type of ‘predictive marketing’ that will become increasingly prevalent.