35 Intent Data Terms Every B2B Marketing and Sales Leader Should Know

This article was originally published in January 2020 and has been updated in February 2021 by Jaymi Onorato.

Intent data’s importance among B2B marketing and sales disciplines is rising, as more teams discover the extensive range of its uses and value. In fact, research from Ascend2 reveals “94% of marketers agree to some extent that using intent data will give marketing and sales a competitive advantage in 2021.”

Yet as a new product category, intent data can be a complex. And many practitioners who would likely benefit from the data remain puzzled by all the terms surrounding the various differing types of intent data and its numerous use cases.

Intent Data Terms and Definitions

To help provide clarity in a noisy field, Intentsify had already published a list of intent data terms and definitions. After joining the company, I found this resource useful for gaining a foundational knowledge of intent as a marketing and sales tool but quickly realized that there are many more terms worth noting.

Expanding on the original list, here are 35 terms to help B2B marketers and sellers who are new to intent data. I focused on the most important terms as well as those I’ve found to frequently come up during conversations around intent data, but are the least understood among teams.

1. Account/Lead Prioritization: A popular intent data use case and process of selecting specific accounts/leads to focus on based on their likelihood of converting into sales opportunities. This can be part of the ideal customer profile (ICP) creation process or used to further prioritize accounts among your ICP target-account list.

Business-development reps (BDRs) often use intent data to prioritize accounts/leads in order to allocate their time and efforts more efficiently.

2. Account/Lead Scoring: A popular intent data use case in which marketing and sales teams assign values to accounts/leads based on myriad types of information, such as intent signals, email open rates, sales conversations, and much more. These scores then dictate which accounts/leads convert down demand-funnel stages, based on a specific scoring methodology.

3. Account-Based Marketing (ABM): A B2B marketing approach that focuses time, effort, and resources on specific organizations that are more likely to convert into sales opportunities, as opposed to targeting the total available market (TAM).

Marketing and sales teams often use intent data to support ABM strategies, achieving greater precision, efficiency, and results for their efforts.

4. Account-Based Orchestration (ABO): Organizational alignment between various functions across multiple departments for the purpose of executing an “account-based” approach throughout the entire funnel.

Organizations that lack orchestration often fail to successfully launch account-based programs due to the fact that the unique purposes of (and even cultural differences between) these functions typically aren’t well understood across the organization (read here for more on each function’s role in ABO).

5. Activation Point: The point at which intent data is leveraged for a specific purpose or use case. For example, a marketing automation platform is often the activation point for using intent data to better score leads and/or accounts. Some intent data vendors simply provide the data, leaving it up to you to use via whichever activation points you choose (e.g., data as a service providers). Others have intent data built into their proprietary activation points (e.g., software as a service).

6. Buyer Journey: The typical online “path to purchase” taken by a prospective account/lead. This journey usually consists of three main stages (though these can be—and often are—broken down further into more specific stages):
  1. Recognizing the need: Otherwise known as the awareness stage, this is where the prospective buyer typically conducts research to identify and understand the underlying cause of their business challenges.

  2. Exploring solutions: The prospective buyer has (or should have) a basic understanding of the pain points and challenges they need to address by this stage. Here they are looking for specific ways to solve such challenges and generate better results.

  3. Considering specific products: The prospective buyer has identified potential, general solutions (e.g., types of tools or services) and is now researching all of the available products and vendors that can address their problem.

7. Buyer Persona: A hypothetical representation of someone who embodies the characteristics of an individual most likely to benefit from your product/solution, usually based on your existing customers and market research.

Unlike your ideal customer profile (ICP)—which looks at the characteristics of an organization as an entity—buyer personas are created at the individual level. Therefore, such descriptions can include: preferred method(s) of communication, job responsibilities, seniority level, tools needed to do their job, goals and objectives, biggest challenges and pain points, etc.

8. Content Syndication: A popular intent data use case and form of third-party demand generation in which marketing teams leverage publishers/media partners to distribute branded content throughout publishers’ portfolio of web properties to reach broader audiences.

Content is usually gated by data-collection devices (e.g., contact forms, surveys) to gather contact information from targeted personas that can later be used by marketing and sales teams for more focused outreach. Marketing teams and publishers are increasingly using intent data to target their content syndication campaigns so that they generate leads from specific organizations (usually those among marketing team’s target-account list). (Read here for how to successfully launch a content syndication program using intent data).

9. Co-Op Data:Intent data gathered from a collective of online sources, including publishers, research firms, tech vendors, agencies, and event firms. Intent scores are typically based on topics assigned to specific web pages. This data can be more nuanced than exchange data, but with less coverage and data volume (though it offers greater coverage than standalone publisher data).

10. Exchange Data: Intent data gathered via ad exchanges across biddable online advertising inventory, which allows for unmatched coverage and volume of data, but often with less analytical depth (though this is not always the case). Intent scores are typically based on keywords but can also include topics assigned to web pages by natural language processing (NLP).

11. F.I.R.E.: An acronym for fit, intent, recency, and engagement. This has become a popular model among B2B marketing and sales practitioners to organize their data. Below is a brief description of each data type.
    • Fit: This category includes data that identifies whether an account (and/or persona) fits within your ideal customer profile (ICP).
    • Intent: The intent category includes data that’s collected about a business’s online behavior, which provides insight into the extent to which that business is likely to purchase your product or service.
    • Recency: Recency tells you when important activities and events occurred.
    • Engagement: Engagement data highlights accounts or leads that are actively engaged with your marketing or sales team.

12. First-Party Intent Data: Intent data gathered from your organization’s owned web properties, email systems, and social media accounts, often referred to as engagement data. It may be known data (e.g., when a user fills out a form) or anonymous data (e.g., tracked via a pixel or some other method).

13. Ideal Customer Profile (ICP): A description of a hypothetical organization most likely to become a valuable customer, which allows marketing and sales teams to allocate resources to organizations sharing the same characteristics.

These characteristics often comprise a range of information types, from firmographic data (e.g., company size, geographic location, annual revenue) to technographic data (e.g., current tech investments) to intent data.

14. “In-Market” Company/Account: An organization that is actively researching subjects relevant to your business (e.g., specific challenges, solutions, brands), and thus showing intent to buy a product/service in your category.

Certain data collected about an organization’s online behavior (e.g., content consumption) can highlight its interest in your specific solution(s). This can consist of first-party intent data (e.g., monitoring that organization’s visits on your owned web properties) and/or third-party intent data (e.g., vendors monitoring content consumption on a range of external web properties).

15. Intent Data: Data generated by business users’ online research and content-consumption activities, and aggregated to provide B2B marketing and sales teams with insight into which organizations are increasingly showing interest in specific product or service categories, and to what extent.

16. Intent Data Source: The originating source from which an intent signal derived. Signals can be collected across various types of intent data sources, which include:
  1. First-party data (e.g., website visits, email CTRs)
  2. Third-party intent data
    1. Exchange data
    2. Co-op data
    3. Publisher data
    4. Peer review site data
    5. Public/social data

17. Intent Keywords: A method of assigning intent to an organization based on the keywords located within the copy of pages visited by members of that organization. In some cases, providers simply track keywords within the URL, which is much less accurate. (Read here for our guide on selecting and monitoring intent data keywords).

18. Intent Monitoring/Tracking: The action of observing accounts’/companies’ online behaviors to reveal evidence of their “intent” and readiness to buy a specific product/service.

19. Intent-Qualified Lead (IQL): A lead (or contact) within an organization deemed qualified for follow-up based solely on the organization demonstrating interest in a specific product or service.

20. Intent-Qualified Account (IQA): An organization deemed qualified for follow-up based solely on its demonstrated interest in a specific product or service.

21. Intent Signal/Event: An action that signals a user’s interest in specific topics or keywords, such as downloading an eBook, reading a blog post, or registering for an event.

22. Intent Topics: Unlike keywords, intent topics refer to a method of assigning intent based on the context of a given web page. Intent is derived using sophisticated models to understand the content topics by analyzing the context of all the page's content. For example, one web page may discuss the stock market value of a specific HR technology company and another page may contain a blog post ranking the best HR tech solutions. Monitoring keywords for intent may weight a user’s visit to each site equally, whereas monitoring based on topics would consider the context of each page, and therefore give more weight to the blog post discussing the best HR tech solutions. (Read here for our guide on selecting and monitoring intent data topics).

23. Natural Language Processing (NLP): A subset of artificial intelligence (AI) focusing on interactions between human (natural) languages and computers. Specifically, programming computers to process large amounts of language data.

24. Originating Locations: The geographic location from which an intent signal was derived.

25. Programmatic Advertising Campaign: Another intent data use case and the process of using natural language processing (NLP) and other types of machine learning to automate the buying of digital advertising space, as opposed to using the traditional process of purchasing directly from publishers.

Advertisers can use intent data to improve digital-ad campaign performance by ensuring impressions are served only to organizations that have been actively consuming content relevant to your business.

26. Public/Social Data: External data gathered via the public web (e.g., social media platforms). This data provides greater coverage (volume of data) into online activities outside of your own digital properties (e.g., social media engagement, competitor mentions) but may lack the granularity of other intent sources.

27. Publisher Data: Intent data collected exclusively from a publisher’s own portfolio of web properties. This data can be high quality but lacks the coverage (volume of data) of exchange-based and co-op data.

28. Research Activity/Behavior: Online actions that indicate a user’s interest in specific topics or keywords, thereby informing intent signals and scores.

29. “Spiking” or “Surging” Intent: Online activity indicating an organization’s heightened interest in a specific topic, set against that organization’s historical baseline of interest regarding that topic/keyword.

30. Target Account: An organization that fits your ideal customer profile (ICP) and you’re actively trying to engage, because it’s more likely to convert into a sales opportunity.

31. Target-Account List (TAL): A list of organizations you’re actively targeting via marketing and sales efforts, typically because they fit your ICP (though they may be selected based on further criteria). Targeting this list ensures marketing and sales resources aren’t wasted trying to engage and convert organizations that are unlikely to buy your solutions.

32. Third-Party Intent Data: Intent gathered and provided by an intent data vendor. These typically come in five flavors: co-op data, exchange data, publisher data, peer review site data, and public/social data.

33. Topic Clusters: A grouping of related topics monitored to provide a more complete view of an organization’s interest.

34. Topic Taxonomy: The classification of topics used to derive intent. As mentioned above (e.g., Intent Topics), topic-based intent data (as opposed to keyword-based, which simply identifies keywords on a web page) relies on a much more sophisticated method of measuring intent signals. Consequently, each new topic must undergo an extensive AI-enabled training and testing process (via natural language processing) before being added to the taxonomy.

35. Use Cases: The range of purposes for which intent data may be used. Intent data has many—here are a few:
  • Account-based demand generation or content syndication
  • Account prioritization
  • Account-targeted digital advertising
  • Content marketing/message selection
  • Customer retention and expansion
  • Events planning and management
  • Lead/account scoring and routing

Like any new (and complex) tool, becoming more familiar with the common terminology around intent data will help teams more effectively and efficiently execute their solution(s). You don’t need to memorize each definition to drive impact, but understanding how each term relates to intent data will help set the foundation for success from your long-term investment.

This is a fluid list, and we encourage you to leave a comment below if you can think of any additional intent data “terms to know.”

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