Editorial
Intent data’s importance in the B2B marketing and sales disciplines continues to grow as more teams discover the extensive range of its uses and values. Yet the numerous use cases, various types of intent data, and the terms surrounding them all cause many practitioners who would likely benefit from intent data to remain puzzled by this new product category.
Intent Data Terms and Definitions
To help provide some clarity in a noisy field, here’s a list of 17 terms and definitions to help those B2B marketers new to intent data. By no means is this list comprehensive (that would require hundreds more definitions related to B2B sales and marketing), it simply comprises the most important and least understood terms arising in conversations I’ve had about intent data and how to effectively use it.
Further, I’ve chosen to list the 17 terms in a logical order, rather than alphabetically.
Intent data: Data generated by business users’ online content-consumption activities and aggregated to provide B2B marketing and sales teams with insight into which organizations are increasingly researching their product or service categories, and to what extent.
First-party intent data: Intent data gathered from your organization’s owned web properties, 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 cookies or some other method).
Third-party intent data: Intent gathered and provided by an intent data vendor. These typically come in three flavors: co-op data, bidstream data and standalone publisher data.
Bidstream data: Intent data gathered via ad exchanges across biddable online advertising inventory, which allows for unmatched coverage and volume of data, but with less analytical depth. Intent scores are typically based on webpage keywords.
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 webpages. This is higher-quality data than bidstream data, but with less coverage and data volume (though it offers greater coverage than standalone publisher data).
Standalone publisher data: Intent data collected exclusively from a publisher’s own portfolio of web properties. This data is often high quality but lacks the coverage (volume of data) of bidstream and co-op data vendors.
Intent keywords: A method of assigning intent to an organization based on the keywords located on the pages visited by members of that organization. Keywords are typically used by bidstream data providers.
Intent topics: Unlike keywords, intent topics refer to a method of assigning intent based on the context of a given webpage. Intent is derived using sophisticated models to predict the topic of content by analyzing the context and content within a page. For example, one webpage may discuss the stock market value of 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.
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Topic clusters: A grouping of related topics monitored to provide a more complete view of an organization’s interest.
Topic taxonomy: The classification of topics used to derive intent. As mentioned above, topic-based intent data (as opposed to keyword-based, which simply identifies keywords on a webpage) 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.
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.
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.
Use cases: The range purposes for which intent data may be used. Intent data has many — here are a few:
- Account-targeted digital advertising
- Account-based lead generation
- Lead/account scoring and routing
- Account prioritization
- Events planning and management
- Content marketing/message selection
- Customer retention and expansion
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 (data as a service providers). Others have intent data built into their proprietary activation points (software as a service).
F.I.R.E.: An acronym for fit, intent, recency and engagement. This has become a very popular model among B2B practitioners to organize their data. Below is a brief description of each data type, but you can read more about FIRE in this CMSWire article.
- 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.
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.
Intent-qualified account (IQA): An organization deemed qualified for follow-up based solely on its demonstrated interest in a specific product or service.
I’ve undoubtedly overlooked many terms that should’ve made the list. If you can think of any additional “terms to know” (or would like to correct/adjust any provided definitions), please include in the comments section.
Related Article: 5 Lesser-Known Ways to Use Intent Data for ABM
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