Get In Front of the Competition: Use Intent Data to Find the Accounts That Drive Your Business

B2B markets are more competitive than ever. They’re crowded, commoditized, noisy, and everybody is trying to knock on the same doors. So how do you break through the cacophony?

The key is to not wait and reach out to your accounts with everyone else but to reach them when they are still early in the buyer’s journey. And that’s where leveraging intent data comes in.

With intent data, you can see who is showing interest in your company or your competitors before they respond to your marketing efforts through form fills for content downloads or event RSVPs. You can create orchestrated plays as soon as they are in-market when they are searching. So you can deliver fresh ideas based on knowledge and understanding of an account’s industry and unique business issues before everyone else.

What is intent data?

Intent data is an indicator of an account’s current level of interest in your company or category. It uses artificial intelligence to track online activity and uncover the topics that companies are actively consuming across the web.

There are two types of intent. First-party intent tracks what accounts do on your site. While third-party intent identifies the kind of content an account consumes on sites other than your own website.

Why use intent data?

As I’ve noted previously (see my blog post “How To Attract and Engage the Attention of Your B2B Audience”), buyers don’t want to be marketed to, and they’ll avoid forms and do everything they can to stay anonymous. So when they do their research anonymously online on third-party websites, it’s hidden from Sales and from marketing automation scoring.

By leveraging intent data, you can understand who is researching anonymously. Here’s why that’s important.


• Identify in-market accounts. Spikes in interest in a specific topic can identify when accounts are in-market. This helps you find the magic moments when buyers — who normally resist Marketing and Sales outreach — actually want to hear from vendors like you.

• Identify good fit accounts. The content that an account tends to read could indicate how qualified it is for your solution. For example, if you sell cybersecurity software, an account where employees show significant interest in that topic is likely to be a better fit than one where employees show no interest.

• Identify relevant topics. Your Sales team can use intent data to understand what topics a specific account will find most relevant, and your marketing team can use it to understand the trending topics your market cares about.

How does intent data work?

Demandbase collects intent from a broad array of sites, using four steps to derive intent data:

1. Collect content consumption events.
The event is a signal that contains the visitor’s IP address, an anonymized cookie, a timestamp, and geo-location information (usually just metro or country). There is no personal information.

Collecting these events is straightforward for vendors that use their own site. For others, the methods vary. Demandbase does it by plugging into the B2B advertising bidstream for over 2 million sites (close to 500 billion content events a month).

2. Deanonymize and identify the account.
A key technology is the ability to identify accounts that are on a specific web page. In cases where the data comes from a publisher that requires a login, the publisher may know the company from their registration data. In others, the intent provider deanonymizes the traffic using cookie and IP information. As always, accuracy and match rates are both important.

3. Analyze the content of the page.
The next step is to analyze and classify the content of the page into specific keywords or topics. This is best done via natural language processing of the content on the page, though some vendors simply read the SEO meta tags (which aren’t designed to be indicative of buying intent) to identify the page content.

Artificial intelligence plays an important role to understand the context and relevance of the page, not just the keyword. For example, does “lead scorer” refer to someone identifying hot B2B leads or the top basketball player in a game? And even if the specific word doesn’t show up on the page, if related concepts show up, then it can still indicate interest.

Some technology vendors just read site page meta tags. But with natural language processing, Demandbase can analyze and classify the content on the page, while understanding its context.

4. Identify patterns and trends.

With a massive data set of specific accounts reading specific content topics through the previous steps, the algorithm can now apply machine learning to identify true intent patterns.

  • How many relevant articles did they read?
    – More articles imply stronger interest.
  • Over how many different days?
    – A potential buyer would have sustained interest.
  • How old were the articles?
    – New articles are likely news of the day while older articles imply vendor research.
  • How recently did they read the articles?
    – Timing is everything: Intent goes cold, fast.
  • How rare are the relevant keywords in the articles?
    – Rarer, more specific keywords imply the account is further into the buyer journey.
  • Do they have intent in other topics that disqualifies them?

Scale matters when it comes to intent data. There are over 100,000 possible topics, and a single relevant article does not indicate a potential buyer. You need to find trends and patterns across the account.

Baseline vs. trending intent

Depending on the algorithm your provider uses, you may see two kinds of intent patterns: baseline intent and trending intent.

Baseline, or weekly, intent shows general interest in a keyword or keyword set over time, which can indicate broad awareness and interest in the topic across the account.

Trending intent — sometimes called surging intent — indicates when an account is showing an increased interest in a keyword or keyword set. It’s useful for showing when an account may be moving into a purchase cycle. Some vendors update this daily, others weekly.

“When evaluating intent data providers, make sure you understand what data sources they use and the science behind their algorithm — it can make a big difference to your results.”

A case study: Scoring helps Coupa spot opportunities.

Coupa’s all-in-one Business Spend Management platform helps technology companies manage spend across every aspect of their organization. When they first started their ABM program, they turned to Demandbase because it provided so many pieces of the ABM puzzle. It allowed them to get campaigns up and running faster. 

When it came time to choose their target accounts, they married Engagement Minutes and Demandbase offsite intent with their inhouse Ideal Customer Profile (ICP) scores. The end result: targeted, strategic account lists that both the Marketing and Sales teams were excited to work on together, and which have become the heart of its ABM strategy. Their ABM program now fuels a full third of all Account Development Rep opportunities, making it an important contributor to the business.

Key takeaway

If intent is the new lead, then more intent signals provide more leads. If your organization partners with multiple intent vendors, they should ultimately be used together to shine the light on the correct accounts with higher levels of certainty. When in doubt, always ask the intent provider for the science behind the collection to feel confident that the intent data collected is worthy of operationalizing.

Excerpted from The Clear & Complete Guide to Account-Based Experience, by Jon Miller.

Chief Marketing and Product Officer, Demandbase

As Chief Marketing and Product Officer of Demandbase, I'm responsible for delivering Demandbase’s product vision to delight customers and fulfill its mission of transforming how B2B companies go-to-market. I have a long history of establishing and leading some of the most notable marketing technology companies. Most recently, I was co-founder and CEO of Engagio, the leading Account-Based Orchestration Platform. Earlier, I co-founded and held the position of Chief Marketing Officer for Marketo (acquired by Adobe).