Innovations in online behavioral data have driven a rapid evolution in GTM strategies, which are now incredibly sophisticated and dynamic. These new strategies have had great results, causing businesses to be much more scientific in the ways in which they focus their resources and communicate their messages.
Unfortunately, the dynamic nature of these new data sets make GTM strategies really difficult to execute. And data-driven strategies are only as good as an organization's ability to action them across all customer and prospect-facing teams.
Intent data is no different. Intent solutions have significantly influenced B2B marketing, sales, and customer success strategies over the past few years. Providing everything from broad market intelligence to account-specific research insights, intent data is helping B2B teams better understand their prospects' and customers' needs. Yet, several critical roadblocks are preventing GTM teams from gaining intent data's full value.
Reason #1: Lack of Adequate Signal Coverage
The more sources and types of intent data used, the more accurately informed your strategy. That’s because more intent signals ensure greater coverage of your target audiences' online research activities and buying behaviors, while also allowing you to verify signals derived from each source.
The problem is today's market is filled with solutions that leverage only one or two intent sources and derive intent signals using only a single methodology. This is a problem. No one or two sources of intent can cover all your target markets’ research activities. You’ll not only be missing signals, but you’ll also fall into the trap of giving too much weight to signals that aren’t important – all resulting in inaccurate intelligence.
You must use a solution that aggregates multiple, differentiated sources, tracking methods and evaluation models. This provides greater coverage and multifaceted perspectives of your buyers’ priorities.
Reason #2: Using Disparate Intent Solutions Isn’t Effective
The seemingly simple solution is to aggregate multiple intent data feeds and/or solutions, which is much easier said than done. In theory, stitching together several intent data feeds will provide the breadth of online research coverage and signal accuracy required to gain comprehensive account intelligence that can drive successful GTM strategies.
However, this is nearly impossible to do manually. The varying range of derivation methods and evaluation models makes comparing data feeds incredibly time consuming. Not only does this waste resources but you also likely won’t be able to digest disparate feeds, synthesize them into useable intelligence, and act on them before they're no longer relevant. If your team tries to do this manually, you're going to struggle to:
- Quickly set up, synthesize and analyze intent signals from multiple feeds to get a thorough understanding of buyers.
- Distribute intent-driven intelligence to teams in charge of acting on it
- Ensure the end users of the intelligence can quickly and effectively act on the intelligence you’re delivering.
Reason #3: Generic, Manual Modeling Prevents Intent Precision
Almost all intent data models derive intent signals by monitoring selected intent topics and/or keywords. The accuracy and value of intent solutions are often hindered by:
- A limited number of intent topics/keywords available to monitor. If an intent solution can’t track a large, varied, custom list of topics and keywords that are relevant to your business solution and offerings, it’s not that valuable to your team.
- Inability to select the right combination of topics/keywords (i.e. human error). Beyond ensuring a solution gives you the means to choose relevant topics and keywords to monitor, you need to ensure that someone on your team is tasked with selecting and continuously updating those topics/keywords. This requires a level of knowledge and time commitment that not every team has accounted for or are capable of doing it at all. Instead, these teams are relying on the intent provider to dictate what they should be tracking and they have even less of an understanding.
- Equally weighted topics and keywords. Likely the biggest problem. Even if GTM teams have knowledgeable users with the time to select and update the proper topics and keywords to track, the intelligence will be skewed if all the topics/keywords are given the same weight. Intent models should give greater weight to more relevant topics and keywords according to the customers’ specific needs. These are the shortcomings that prevent precision that B2B teams desperately need to effectively and efficiently market to their prospects. In a time where teams are constantly being asked to scale efforts and do more with less, the need for precision is paramount.
Intent data was sprung on the market quickly and there are so many teams invested in its promise and even more solutions launched that promised a seamless, intelligence-based customer journey. The truth is there are not many solutions that can offer outstanding intent data and solutions to act on the intelligence.
