Most companies start their intent data journey focused on sales leads and marketing demand generation.
The right data can do much more. In fact, it has applications in nearly every major discipline of marketing today. These include:
"Wait! What??!" you're saying?
That's right. If you understand the range of potential use cases, then the question "How to buy intent data" becomes both more complex - picking the best data for multiple uses - and simpler - the investment has a much more substantial ROI.
We'll dig into each of the use cases briefly below, but first, it's important to understand how different data methods and models impact the use cases.
The full range of use cases hinges on a couple of key technical details. First, are you using account-level or contact level™ intent data? Second, does the data provide specific, granular insights?
Most third-party intent data only provides account-level information. This is a really important distinction because so many of the other use cases hinge on having job titles of the actual people taking action. This is VERY different from the common hybrid of names pulled from a database to match your target personas for accounts where account level activity has been observed.
Even knowing the individual, though, doesn't help if you don't have detailed, granular insights from which you can infer important information including the likely stage in the buying journey, the problem they're trying to solve, colleagues on the buying team, competitors they're engaging with, etc.
Bidstream data vendors often provide granular keyword level information (but only observe account-level activity, and face accuracy and potential data propriety issues.)
Publishing coop data generally provides account-level data only, and only broad "topics" of activity. The topic distinction is minor sounding but really important to selecting data that enables many of these other use cases.
So keep in mind that many of these use cases may seem impractical based on your current data or what you've worked with previously. That's another reason why understanding the details of what's in the data black box is important. It's also why buying data that's embedded in another platform (like many of the ABM offerings) might severely restrict your options.
Let's look at each of the use cases quickly.
The same data should be able to support all these requirements!
Simply knowing who's active, taking what kind of action, in those accounts matching your ideal customer profile (ICP) lets you focus your nurture and outbound efforts efficiently. Increasingly the BDR and SDR functions are marketing assets and intent data's most common use case is to inform and streamline their efforts.
Imagine data insights into what problems buyers are trying to solve, the solutions they're considering, the job functions and seniority that are involved at each stage in the journey....and even how these trends are evolving over time?
Using intent data for product marketing can help accumulate and understand these insights over time - perhaps with the help of an analyst. And it provides a high-level quantitative data set that's often missing from the product marketing tool kit.
What are the important intersections of key term, job function, seniority, and problem to solve with each of your competitors? With contact level intent data you can often discern this as well as competitor engagement by industry, geography, company size and more. Intent data can power competitive marketing, even including sophisticated signals like job postings that list specific technologies.
Typically public relations relies on "beats" to target likely journalists with pitches. And it's largely lost in the noise. Intent data can help turn that around. It can help PR professionals identify which journalists are active around which topics and competitors, and to inform pitches so they're timely and relevant.
The end of third-party cookies changes the ad game. That's not so angst producing for companies that use contact level intent data and who leverage that data to put active prospects into carefully segmented customer match/custom audiences for efficient and effective targeting based on their activity. For many companies, this is a high-priority use case that's not yet on the radar. Even retargeting can be improved with intent data.
ABM is a well-understood use case for intent data, and lots of data is sold as integrations with ABM platforms. It helps prioritize accounts and understand activity within target accounts that might indicate active projects.
It should go further, though, to inform marketing and sales efforts with insights into the buying team and buying journey to help personalize the ABM experiences.
Whether simple lead scoring to prioritize accounts or to focus rep activities, or more sophisticated propensity to buy modeling, intent data provides a range of additional factors beyond form fills, email opens, and other routine marketing automation metrics.
What's happening in the market? Are more CMOs now considering a certain kind of software? Are more CEOs now involved in cybersecurity issues as their boards demand assurance? Is the EU catching up in activity around certain topics? Are the important benefits shifting as some that had helped close deals fade in importance as new ones arise?
Intent data can help answer these questions for your team - building a quantitative, large-scale understanding of market trends. But savvy companies can take this a step further and build their own proprietary published research on the insights they collect through their intent data. It's a great, underutilized, thought leadership use case for intent data.
Personalization is about more than name and company. Ultimately it's about delivering an experience that feels natural and perfectly aligned with where a buyer is in their research and journey. Intent data should support a process of programmatic personalization - of course, contact level data is key here because personalized account level marketing is oxymoronic.
Maybe you serve content dynamically based on industry, company size, persona, or other segmenting factors. What if you could go deeper? Imagine a competitor comparison page that naturally reflects the competitors with whom you know a buyer has engaged? Or features that speak to the problems you know they're trying to solve? Or even the type of content that's more appropriate based on inferences you can draw about their stage in the buying journey? Contact level intent data that delivers granular insights can support all of this.
The premise of conversational marketing is to engage site visitors in a natural, helpful, informative way. Intent data can supplement that by providing contextual data on competitor interactions, problems to solve, and more. This helps your team craft more nuanced and effective chat experiences that will resonate.
Virtual and in-person events are great B2B marketing plays, but also quite resource intensive. Intent data can help with events you're organizing, sponsoring, attending or simply observing. You can use data to identify those whose interests overlap (topics, speakers, etc.) to target and nurture for registration. It's also possible to observe who's engaging with competitors' events and target them as well. Finally, overlaying intent data on registration info will help your sales team more effectively set meetings (or accelerate sales to close before a competitive event.)
Leveraging intent data for all these use cases requires some technology capabilities as well. A fully integrated marketing data stack (1st, 2nd, 3rd party data, enrichment, contextual technographic™ data, and other components will amplify the benefits.
Data visualization technology will simplify the tasks for the analysts on the marketing team, and foster collaboration with impactful, easy-to-digest insights.
Finally, a robust data infrastructure (beyond the typical Marketo/HubSpot and Salesforce tech stack) will facilitate all these exciting use cases of intent data for marketing - all with more nuance and at scale.
Don't worry if you're not even considering data for all these use cases initially. Knowing about them, and weighing the future potential, will help you select the right data for your requirements and can broaden the adoption, energy and return on the data investment. Leading companies start with one or a couple of similar use cases AND a plan for the priority and steps to roll the data out to others.