How to Assess the Relevancy of 3rd Party Intent Data

Digital Marketing


At this point, most of us have realized that traditional, or “spray-and-pray” marketing approaches no longer work. We are now living in the Engagement Economy where buyers have more power than brands and expect personalized, relevant interactions at every touchpoint.

As Steve Lucas, Marketo CEO, said, to win in the Engagement Economy, marketers must listen and learn before they engage. Now, savvy marketers are turning to intent data to listen to their target audience.

What is Intent Data?

Intent data is time-based, information collected about a person’s activities which tell you the topics they are showing interest in or researching.

People are leaving digital clues about their interests and intentions in many places across the web, including when they visit your website or click on your emails (1st party data), visit websites you don’t own, and go to social networks to discuss business issues and products (3rd party data).

Intent data can help you keep the pulse on your target audience at every stage of the buying journey. This type of data can help you identify in-market buyers who haven’t yet engaged with you, prioritize your existing leads, nurture leads with personalized emails, invest in advertising more strategically, personalize your website, and more.

Most recently, SiriusDecisions acknowledged the value of intent data when they unveiled a new Demand Waterfall model at the SiriusDecisions Summit 2017.

The latest Demand Unit Waterfall has added two new stages—target demand and active demand—aimed at identifying target buyers before they have raised their hand or self-identified on your site.

Sirius Decisions Demand Waterfall 2017

According to SiriusDecisions’ recent blog post, ”third-party intent data can help determine which demand units not only are in ‘shopping’ mode, but also have experienced an identifiable event that means they could be in-market. This data then helps organizations determine where to aim their outbound marketing efforts.”

A Nascent and Confusing Market

Although the conceptual value of intent data is compelling, the solutions market is still maturing. As a result, it can be challenging for marketers to pick the right data provider who can meet their desired use cases.

The two biggest questions marketers have right now are:

  • What types of intent signals are most relevant to my business? In other words, which intent signals are the best predictors of purchase behavior?
  • How do I use intent data in practice?

In another post, I discussed one use case—how organizations can leverage social intent data for email marketing. In this post, I will share some thoughts on how you can assess the relevancy of intent data by asking three questions. By the end of the post, I hope that you’ll have a clear idea on how to evaluate intent data providers to pick the one that can help you achieve your desired objectives.

How Are Intent Data Providers Collecting Data?

One part of the question on data relevancy is about whether the data can accurately tell you who is behind an activity. The ability to accurately identify who is behind an activity depends on how the data provider is gathering that data and what places the data is coming from.

In general, there’s two approaches to gathering intent data:
1. Algorithmic / using web crawlers
2. Human-volunteered, human verified

There are several places where intent data comes from that generally falls into two categories:
1. Company websites, B2B publishers, business or technology-focused online communities
2. Public data from social media networks, i.e. Twitter

At this point, one category of providers are crawling B2B-focused and general consumer websites for activities that are related to business solutions. These data providers are using techniques that can generate intent data at the domain or company level.

Some of these data providers have placed cookies on their network of publishing partners’ sites to track site visitors’ consumption of content on certain topics. These cookies are associated with IP addresses. The data provider uses reverse IP-lookup to figure out which domains or accounts these visits came from. If there is a spike of activities within the same domain around a specific topic, then the domain is showing “surging interest” or intent on that topic.

One problem with using cookies to track site visits is that they are frequently deleted. As a result, intent signals around certain topics will be sparse.

Other providers are leveraging device tags (on PCs, tablets and phones) to track what devices are consuming certain content. Devices can be tied to specific businesses using reverse IP-lookup.

Using reverse IP lookup to identify “active” companies is not a perfect solution, because IP lookup can only resolve a fraction of IP addresses into accurate domain names, and even fewer outside of North America.

What Specific Content Has the User Interacted With?

For data to be used to pinpoint purchase intent, you need to trust that the content used to produce the activity is actually related to your solution.

It’s not enough for a web crawler to find content that matches your product category keyword —let’s say “network equipment”. Only a small portion of content on the web is going to be very high-focused, in-depth content that would help buyers make a purchase decision, and even less will show up high enough on search engines to drive significant traffic volumes.

At this point, while many publishers/sites are allowing data providers to scan their sites and aggregate the information, premium publishers like Gartner do not allow their sites from being included in these scans. For example, intent data providers are not able to scan Gartner’s website to see who is reading the latest Magic Quadrant on Business Intelligence software.

Some of the best intent data on the market today comes from well-known B2B content communities where buyers go to self-educate.

For example, BrightTalk is a well-known webinar content destination for business professionals in Marketing and Sales. Software developers go to sites by TechTarget (i.e. SearchCloudComputing) to learn about different technologies. Marketing Ops professionals visit software review sites like Capterra or G2Crowd when researching marketing technology like marketing automation platforms.

Because these places provide highly targeted content and users have to register / self-identify before viewing or download content, they are able to provide strong signals of intent. However, because a user must land on a specific site or a relatively small network of sites, you will be able to get intent data on just a subset of people.

How Do You Want to Utilize Intent Data?

A list of active accounts without named contacts restricts its usefulness to marketing activities such as programmatic ad targeting. If you want deeper funnel applications such as lead prioritization or personalized emails, you need to get intent data at the individual / contact level.

As I mentioned above, certain B2B content communities/media companies can provide you with individual/contact-level intent data based on consumption of highly relevant content. Another place to get granular, contact-level intent data is through social media.

There’s a wealth of behavioral data that exists within social media platforms. On Twitter, you can identify potential buyers based on their tweets and following relations. On LinkedIn, you can find potential buyers by looking at their job titles, companies, group affiliations (i.e. Marketo User Group in Seattle, WA), the influencers they follow, and the articles they share and comment on. On Quora, you can look at which users are asking questions related to a specific product category. On, you look at the users who attended certain meetups.

At this point, companies, like Socedo, can associate activities on Twitter to the leads in one’s marketing automation database, and tell you which of your existing leads is researching your product category or business space based on their Twitter activities in real-time.

For example, if you’re selling business intelligence software to BI professionals who are using SQL Server, you’ll be able to see whom in your database is engaging with other BI/data visualization vendors, who just joined a professional group for SQL users, and who just followed one of your competitors.

Conversion Data on Socially Engaged Leads

Now you might think that Twitter is a really noisy place and wonder how many relevant signals you can actually get.

To answer this question, you’ll want to look at compare conversion rates for leads who have taken relevant actions on social media versus your baseline (all leads). The specific conversion metric you choose will depend on your use case. For example, if you want to use intent data to nurture your leads / personalize emails to increase engagement, you may look at your lead-to-MQL rate and expect a higher lead-to-MQL rate for leads who’ve taken relevant social actions.

Some of our customers have provided us with list of leads and let us analyze their data. Once we’ve added historical social media activities onto lead records, we can segment our customers’ leads by behaviors, and use conversion rates (by segment) to determine the extent to which intent signals from social media are predictive of purchase behavior.

In all cases, we found that socially engaged leads are more likely to convert compared to the typical or average lead in an organization’s database.

One of our customers—one of the largest technology companies in the world—is currently tracking leads’ Twitter activities around their own product lines, competitors in the cloud computing space, and a few key topics in their space.

They found that leads who have engaged with their own Twitter handles are at least 2.5 times more likely to convert into Marketing Qualified Leads (MQL) compared to their baseline conversion rate. In addition, they found that leads who have engaged with their competitors are about twice as likely to convert to MQLs compared to their baseline.

Last but not least, they found that when a lead engages with one of their tracked keywords two or more times, the lead is at least twice as likely to convert into an MQL compared to someone who has not taken any social actions.

By lead scoring social actions that have higher conversion rates than their baseline, the company’s marketing team can pass additional MQLs to their sales team and increase their contribution to pipeline.

Another company—a provider of M&A, P&E and VC transactions data for investors—found that leads who engaged with any of their tracked keywords are 29% more likely to convert compared to their baseline. Also, leads who talk about certain topics (i.e., blockchain, crowdfunding) and follow certain competitors (@cbinsights) are 50% to 200% more likely to convert compared to their baseline. In this case, a conversion is someone getting a demo of their platform.

These are just a couple of results I’ve seen. I am not trying to convince you that social intent data will be a strong predictor of purchase behavior for your particular audience. The important thing is that with any intent data, you want to run some tests and see if the contacts or accounts the provider flag as “showing intent” are in fact “better leads” than your typical lead. You’ll be able to answer the question for yourself by looking at performance metrics like leads’ response rates and conversion rates.

To understand whether an intent data provider can support your specific use case, I recommend asking the following questions:

  1. What data sources do you provide? What websites/online communities/social networks are you getting this data from?
  2. Are you providing account/company level data or individual contact level data?
  3. Has this data been verified by humans (i.e. via registration)?
  4. How granular is the data? Can you provide me data on specific actions people have taken (i.e. an entire Tweet, verified intent to purchase CRM in next 12 months) or do you just provide data at the topics/category level?
  5. Do you have your own technology to collect the data or do you license your data from other providers? What’s your specific data gathering methodology (i.e. website crawlers/scrapers, cookies, device tags, reverse IP address lookup, user volunteered data, API access from public sources like Twitter)?
  6. What is your data match rate? What percentage of contacts in a typical CRM or marketing automation database can be matched back to the data providers’ database
  7. How often do you update or refresh this data?
  8. What format does the data come in? Can I consume it through direct integration with my marketing automation or CRM system? Is it available through an API?
  9. Can I use this data to automatically trigger workflows in my system of engagements? For example, can I use this data to put contacts/leads into different email tracks, for programmatic advertising, or use this data in my lead scoring system?

Do you have any experience using external intent data in your marketing programs? I’d love to hear about your experience in the comments below.