Using intent data, marketers can refine their ICPs, and target their campaigns more accurately to audiences who are most likely ready to buy. For Sales, the advantages are potentially greater still, enabling reps to time their engagements right and shorten sales cycles, among other things.
Still, intent data is an emerging data source that has its limitations. Only by knowing those limitations can you ensure real value from investing in intent data — and avoid the frustration and disappointment of Gartner’s “trough of disillusionment.”
First, here’s a quick reminder of basic categories of intent data (see here for a more thorough explanation):
– Anonymous 1st Party Behavioral: People visiting your website are identified by their IP address, which is then mapped to their company name.
– Known 1st Party Behavioral: If a visitor to your website fills out a form with their email address, they are considered “known”.
– Anonymous 3rd Party Behavioral: People visiting other websites that you don’t own, but which indicate some relevance.
– Known 3rd Party Behavioral: If those 3rd party website visitors provide their email address, they become “known” to the website owner.
So for example: 3rd party behavioral data is highly unstructured — and the volume is massive. As a result, very few companies possess the budget or expertise to integrate such data into their existing marketing and sales processes.
That’s why many Marketing and Sales organizations use predictive platforms to help sift through the noise and determine which 3rd party topics are actually relevant, and integrate those insights into their Marketing Automation and CRM platforms. Anonymous 3rd party topic data can be incorporated into predictive account scoring models to determine prospective accounts’ likelihood to buy. This information is used to identify target accounts for outbound initiatives as well as prioritize new inbound inquiries from people within high scoring accounts.
Known 3rd party topic data can also be incorporated at the person level with persona scoring models.
The most common mistake B2B Marketing & Sales make with Intent Data
To effectively use intent data you need the means to process it — for example, as mentioned above, using predictive technology.
But even more fundamental is having the right data in the first place. Too often, companies adopting new tools like intent data simply aren’t prepared to leverage it as a result of faulty or insufficient underlying data.
For example, one common use case for intent data is to deliver personalized content based on the persona of a given user on your website. It’s at this point in particular that many marketers stumble and fail to actualize the full potential of intent data.
Nascent personalization and nurture efforts leverage job titles to segment inbound leads. But job titles in the B2B space particularly are not standardized, change frequently, and often give no real insight into the seniority, buying power or even specific functions the person serves within their company.
This often results in improper categorization of people, leading to unqualified leads being sent to Sales, and “personalized” content being delivered to leads for whom it is in fact irrelevant.
But knowing the level of ostensible “intent” for a user without understanding the context behind their interest in a topic simply doesn’t tell you anything useful.
For example, someone reading this blog is showing an interest in the topic of intent data. If they then go on to download our intent data white paper, that interest will peak further.
Clearly a qualified prospect, right? Not necessarily.
If that reader is a B2B Sales or Marketing professional — particularly someone with buying power or influence within their company — then that topic interest is indeed meaningful, as it likely indicates at least some level of interest in potentially buying intent data. But what if they’re actually an industry analyst, or a student reading up about intent data, with no intent whatsoever of actually buying anything? Clearly such a user is not a prospect.
Intent data alone can’t tell you who the person is that’s ostensibly showing interest in your topic/topics. To know that information, you need other more “basic” forms of prospect/customer data. Without that data, you can’t begin to make real use of intent.
To get value from intent data, be realistic
The conclusion? Intent data should be treated like any other kind of data. Is it often very useful? Absolutely. Should your Sales and Marketing rely solely on intent data in finding and targeting the right audiences? No.
Intent data is a great way to validate your marketing and sales strategies. It’s an excellent additional piece of intelligence to help pinpoint which prospects to target and when. On the other hand, intent data is like any other single data point: by itself it doesn’t tell you all that much. Intent data is no silver bullet — you can’t know for sure what your prospect is thinking. But add it into your data and intelligence mix, and intent data certainly has the potential to significantly improve your demand generation.
As with any new demand gen, those B2B Sales and Marketing practitioners who approach intent data with open eyes and reasonable expectations will ultimately be the ones who benefit from it the most.
Learn everything you need to know about intent data for B2B Sales and Marketing, and boost your demand gen. Download our free white paper: