Intent data is complex.
I’ve been a product marketer in the B2B sales and marketing technology space for more than a decade, the last five of which I’ve spent focusing on intent data solutions. And I’m still learning new things all the time. This is partly because intent data solutions are always evolving. And it’s partly because in new tech categories, such as intent data, definitions are … well, let’s say they’re fluid.
Recently I’ve been updating our new-employee training. And the stickler for efficiency that I am, I figured the brief guide I wrote for them on intent data, outlining the various types, sources and scoring methodologies would help others, too.
Please note: The explanations below are from my personal perspective. I’m sure other intent data experts see these concepts differently, which is fine. My aim here is simply to categorize and describe the various elements of intent data in a way that will help current and future intent data users get more out of their intent investments.
Intent Data Types: 1st- 2nd- and 3rd-Party Data
First-Party Intent Data
First-party intent data include businesses’ online research activities indicative of buying intent acquired via your website, landing pages, social media profiles, CRM, marketing automation platform, etc. In other words, this is data you acquire directly through owned media, marketing and sales systems.
First-party intent signal examples may include:
- Website visits.
- Email responses.
- eBook downloads.
- Blog subscriptions.
- LinkedIn follows.
Second-Party Intent Data
« Second-party data » is a bit of a misnomer. It’s just a subset of third-party data, which is data sourced by another party. The distinction — according to common industry vernacular — is that it’s sourced via the third party’s owned media and database systems (i.e., it’s a third party’s first-party intent data).
The most common examples of « second-party intent data » are product review vendors who also sell intent data, as well as some media publishers who deem their intent products as second-party data. In the grand scheme of things (in my opinion at least), it doesn’t matter if it’s labelled second- or third-party intent. What matters is the quality of the data, how it’s sourced and scored, and whether you can effectively use it.
Third-Party Intent Data
Like I said, this is just intent data from a third party — usually an intent data vendor or a technology provider who offers add-on data or intent-driven solutions. This data may come via a channel partner of another intent data provider, or originate from public data or ad exchanges, or in combination with their own first-party sources.
Related Article: The Demise of the Cookie and the Rise of First-Party Data
Intent Data Sources
There are more than four source types of intent data, but these are the most common.
Ad Exchange Data
Intent data gathered via ad exchanges across biddable online advertising inventory, which allows for unmatched coverage and volume of intent signals. In fact, the volume can be challenging if you don’t align its use to a well-developed strategy and processes. Depending on the vendor, this category includes a wide range of signal derivation models and scoring methods (see sections below).
Co-op data is gathered from a collective of online properties owned by publishers, research firms, tech vendors, agencies, event firms and more. Coverage is typically quite broad with high signal volume (though less so than ad-exchange data). Co-op data typically uses topic-based tracking and trend-score models (see below).
Intent data collected exclusively from a publisher’s own portfolio of web properties. (I include review sites in this category, but they arguably could fall into their own.) The data in this case can be high quality but lacks the coverage of exchange-based and co-op data due to fewer overall web properties available to monitor (i.e., fewer businesses tracked and lower volume of signals).
Data derived by combing the public web (e.g., social media websites) to show which of your target accounts are engaging with competitors, specific keywords and events relevant to your product and service offerings. Unlike the other types, social/public intent data is less about content consumption and more about engagement activities — which makes it very valuable. However, insights may be less granular than some of the other types.
Intent Signal Tracking Methods
Topic-based tracking looks at an entire piece of content (e.g., article, web page, landing page, ebook, case study, etc.) to assess its relevance to one or more pre-defined subjects (i.e., topics). Such relevance is typically identified using machine learning, such as natural language processing (NLP), and is valuable for preventing “false positive” intent signals.
Keyword-based tracking looks for the use of exact words or phrases within a piece of content and/or its URL. If, for example, an article includes the keywords a marketer is tracking, and a business user reads the article, that activity then registers as an intent signal. The great thing about keyword-tracking is it allows you to track whichever keywords you’d like, which is especially helpful when trying to identify a target account’s interest regarding niche solutions.
These two methods complement one another quite well. And it’s a good idea to use them together, which helps ensure both signal specificity and contextual relevance. Read more about the differences between monitoring intent topics and keywords here.
Related Article: How to Improve SEO Through Keyword Mapping
Intent Scoring Models
This method assesses the recent online content consumption activity into a given subject against its historical baseline activity to show whether interest in that subject is rising or falling — and to what extent.
A simplified example: Over the last six months, “Acme Inc.” has on average taken 100 actions per week around “Talent acquisition software,” which may include consuming articles, downloading content, registering for events, etc. (i.e., intent signals). If in the most recent week Acme continued this 100-count trend, it may be assigned an intent score for “Talent acquisition software” of 50 — a baseline score indicating no change of interest. If, on the other hand, Acme’s intent signals around “Talent acquisition software” increased to 150, this would indicate a 50% surge in interest against the historical baseline. As such, Acme’s intent score for “Talent acquisition software” may increase to a score of around 75 (depending on how the algorithm weighs various signals).
Trend scores provide valuable context, allowing users to understand a business’s change in interest over time, which is obviously important when trying to understand intent to buy. The drawback is you don’t really know whether that jump to a 75-point score was due to 50 more weekly signals (up from 100) or 3 more weekly signals (up from 5).
This way of scoring intent simply counts the number of actions taken by a business that may be indicative of intent to buy.
A simplified example: During the last week, “Acme Inc.” visited 92 web pages containing the keyword “Talent acquisition software,” downloaded six ebooks relevant to talent acquisition, and clicked on two of your display ads. The event count would be 100 (and hopefully broken down by signal type, too). It may also provide the event count for the previous week and/or month for some comparative context.
Event counts provide helpful transparency into the volume of intent signals. And some intent data providers even break down counts by category (e.g., content consumption, leads generated, ads clicked, etc.). However, this scoring model makes it difficult for users to ascertain longer-term intent trends. What may seem like a surge in intent signals may instead be a return to normal signal activity after a recent dip caused by unknown reasons.
Related Article: 3 Ways to Improve B2B User Experiences With Intent Data
There isn’t a single best way to source, track or score intent data. Each method holds its own benefits that complement the shortcomings of the others. The organizations using intent data with the most success are those utilizing multiple intent sources and types. That’s why 71% of intent users are leveraging three or more intent data sources (Ascend2, titled “The B2B Marketer’s State of Intent Data).
To be sure, leveraging a range of intent data sources is the best way to ensure you get both the broad and in-depth intent insights you need to focus your efforts on the right organizations in the right way.
David Crane is VP of Marketing at Intentsify, a leading provider of intent data solutions. With a decade of tech-industry B2B marketing experience, David leads Intentsify’s go-to-market and messaging strategy.