How to use intent data across the entire enterprise

Apr 28, 2020 | Author Ed Marsh

Tl;dr - Companies that subscribe to buyer intent data as an integrated add-on feature to other marketing software often sacrifice important value and data capabilities. Leveraging data across the entire enterprise requires attention to three factors: the right data stack, organizational mindset, and the martech stack.

Beyond Demand Generation - Using Buyer Level Intent Data

Most folks think of buyer intent data - whether contact level™ data, account level, or a hybrid like persona level data, as being for primarily for demand generation. That's often the point of entry into organizations. Demand gen teams tend to have the budget for data and activation tactics, and data is often perceived as a tool to discover new, unknown, activity.

Let's call that Intent Data Gen 1.

As companies move through an intent data maturity model they develop Gen 2 and Gen 3 approaches. Often championed by an informal data savant - a Marketing Data Master Distiller - this progression gradually introduces the potential of intent data to departments and functions across the entire company.

While this rarely originates in an explicit strategy, there are three common factors to environments where it evolves organically.

  1. Data Stack
  2. Organizational Mindset
  3. Martech Stack

Enterprise Wide Intent Data Stack

To foster enterprise wide adoption, data must, in fact, be effective across the enterprise - in other words for marketing, sales, success and other functions. That sounds straightforward, but for many reasons is often not the case. Data factors to consider include:


How is data collected? Is it common surge data from a limited range of collaborative publishers that rely on IP address resolution? Does it only include account level information? Does it rely on third-party cookies?  Does it provide detailed, granular insight or simply some indeterminate association with a vague topic?

To be effective across the enterprise, intent data must provide the detail and insights that support use cases across departments. Too often data models provide only vague information which might be enough to prioritize ABM account efforts, but not to inform activity in other departments.


How is data delivered? When it's embedded in a platform, like ABM software or as an add-on for verified contact databases, the simple nature of delivery almost guarantees that it won't be used across the company. It's reach and scope are naturally limited.

Data should be available as files which can be analyzed, ingested or uploaded according to the requirements of each department and function. In some cases that might mean integration with marketing automation. In other cases it might be best managed with periodic off-line files.


The accuracy of intent data is a fascinating data science and statistical question. It's also a function of the model, and the interpretation and analysis. (Take a deep dive into intent data accuracy here.)

It's important that each department understand the context of the data to inform reasonable expectations about accuracy as the basis for activation steps.


Lots of data is simply focused on demand generation. It may be used to target syndicated content, to inform custom audiences, support event marketing or other marketing steps. Often, though, it doesn't address the specific data needs of other departments. Therefore it doesn't add much value. Unsurprisingly it's not adopted.

There are distinct differences between data that's useful across the enterprise. For instance, Prospecting™ Intent Data, Buyers Journey™ Intent Data and Customer Lifecycle™ Intent Data each serve different purposes and are engineered to collect and deliver different insights that are specifically relevant to each function.

Full Stack

Finally the ability to fully leverage data across the company improves as the richness of data improves. A full range of 1st, 2nd and 3rd party intent data combined with enrichment and validation sources improves the full texture of the insight. That often raises a question of what should be included and how much intent data costs. More on that here.

Organizational Mindset

The second factor is the organizational mindset. How siloed are departments? In organizations where marketing is accountable primarily for MQLs which they just toss over the wall to sales, it's unusual to see enterprise wide adoption of behavioral intent data. In contrast, where there are appropriate SLAs between revenue functions including marketing, sales and customers success, then the culture is often more conducive and it's more natural to collaborate around data.

Purchase intent data normally enters organizations through the marketing function. To spread effectively throughout the company there are two important conditions. There must be:

  • regular communication and structured collaboration between marketing and sales, and marketing and success
  • someone in the marketing department must have a compelling curiosity, vision, and intense passion for using the data in creative ways - and share suggestions, insights and success stories across the company

That's why at we celebrate those special people that we call Marketing Data Master Distillers.  Their vision and passion often represent the "X factor" that distinguishes companies with very mature intent data strategies from those with more rudimentary, single use-case activations.

Want to learn how one marketer took their data program to the next level? Listen to our podcast episode featuring Cybereason's Director of Marketing Chris Taylor describing his "Raptor" project.

Capable Martech Stack

Finally, it takes the right technology to ingest, unify, analyze, activate and surface key insights to the right people at the right time. Even a complete data stack, in an organization with a collaborative mindset and a passionate data project leader, will disappoint if the marketing data orchestration infrastructure is lacking.

Certainly there are companies that achieve remarkable enterprise-wide results with behavioral intent data using a fairly rudimentary martech stack of CRM and marketing automation. What they lack in automation to scale the data they make up in hard work - often in marketing data operations. (Check out the podcast above to hear from Chris about how he wrote his own python script to pull it all together!) Consistent manual analysis and segmentation can achieve a lot when it's consistently practiced.

The easier, more elegant solution is to use a customer data platform (CDP) to ingest and unify the entire data stack, run ongoing rules and analysis, and orchestrate prospect and customer journeys at scale. This can be designed to ensure that not only is data activated efficiently for individual use cases like account-based marketing and custom audience PPC programs, but is also applied to target account sales intelligence, customer churn reduction and other company wide initiatives. The right CDP solution lets marketing manage the data efficiently, and trigger actions and alerts in various systems to ensure that the right message is in front of the right people (internal and external) at the right time.

Great Companies Leverage Intent Data Beyond Leadgen

While it's simple to provide intent data to BDRs as a plug-in integration with contact databases and other tools, that often costs companies huge opportunities to fully leverage data across the organization.

That cost comes in failure to optimize prospect experiences, buying journeys and customer experiences.

Using data fully across the enterprise doesn't have to cost more, it just takes a commitment to the right data, the right organizational mindset, and the right data management tools or processes. 

If your company is already using data, then think about what it would take to use it more broadly. If you're just starting to explore data, then consider the pros and cons of an enterprise-wide approach vs. an add-on to another system.


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