Demystifying B2B Intent Data

Breaking down intent data for the modern B2B marketer.

What is Intent Data? 

Intent data is behavioral information that indicates a potential buyer’s level of interest, or “intent,” to learn more—to buy a specific product or service. Sales and Marketing both leverage intent data. However, their intent signals vary greatly in both signal strength and scale. 

“Intent to Learn” vs “Intent to Buy”

B2B intent data can be broadly categorized into two primary buckets: 

“Intent to Learn” is a category of behavioral events that are largely focused on research types of activity. In B2B specifically, (and this is one area where B2B is very different from B2C), employees from all disciplines (IT, Marketing, Finance, C-Suite, etc.) are constantly engaged in active learning cycles. They need to stay up to date on the latest trends and technologies to remain relevant in the digital age. In the market in which we all now operate, the landscape is constantly evolving at amazing speed and B2B professionals must constantly learn. 

“Intent to Buy” is a category of behavioral events that are largely focused on purchase types of events. These are unique behavioral events that are too detailed in nature to be part of the continuing education/research events highlighted above. 

Intent Goals for Sales and Marketing

Intent Goals for Sales Use Cases:

• Deals/Revenue: To drive revenue in the near term (i.e., the next two quarters in most cases). To create open opportunities for AEs and SDRs.

• Focus on Efficiency: Sales leadership does not want to waste the time of expensive AEs and SDRs with false positive opportunities.

Intent Goals for Marketing Use Cases:

• Product Awareness: Driving product visibility and establishing familiarity with the company’s Ideal Customer Profile (ICP) or ABM targets.

• Brand Building: Creating a positive brand image & message in the minds of their market.

• Nurture Motions: Nurturing prospects through the top and middle of the sales funnel with omnichannel programs (i.e., email, display, social, etc.).

• Focus on Scale: Marketing must provide air cover and soften the ground so when a prospect is ready to buy, their company is top of mind.

Key Differences in How Sales and Marketing Use Intent Data 

Aspect	Intent to Buy	Intent to Learn
Primary Objective	Focused on identifying high-intent leads that are in an active purchase cycle.	Focused on identifying early-stage interest and creating brand awareness or engaging leads/contacts.
Stage of Funnel	Bottom of the funnel (BOFU)—identifying leads with purchase intent or readiness to convert.	Top and middle of the funnel (TOFU and MOFU)—engaging leads in research or consideration stages.
Key Actions	Direct outreach, personalized sales calls, demo requests, event invitations, syndicated content, etc.	Lead nurturing, brand marketing, retargeting, programmatic display campaigns, email campaigns, etc.
Data Sources	Product and pricing page visits, demo requests, downloads of case studies or product specs, peer review engagement, etc.	Open Web content consumption, social media interactions, industry research, home page website visits, etc.
Key Metrics	Response rates to outreach, meetings, and demos scheduled, open opportunities, deals closed, etc.	Engagement rates (CTR, email open rates), lead scoring, website visits, content interactions, etc.

“Intent to Buy” vs. “Intent to Learn”: Specific Examples of Behavioral Events 

Intent to Buy Events

Here are a few examples of intent to buy signals that are closely aligned with a sales use case. These behavioral signals are highly specific and not a causal/normal course of business research events.

• A prospect requesting a product demo

• A company viewing pricing pages or product specifications

• Engaging with customer success stories or case studies

• Requesting a free trial or consultation

• Contacting support teams with specific product questions

• Attending a product webinar

• Spending time on a peer-review site

• Downloading product white papers

Intent to Learn Events

Here are a few examples of intent to learn signals. These behavioral signals are research-oriented in nature and are not, as a standalone signal, indicative of an active buying motion.

• Reading research reports

• Consuming open web business content

• Viewing blog posts or videos on topics of interest

• Following a company or person on social media

• Engaging with content related to industry trends

• Consuming business news

• Following a company’s stock price/reporting

Integrating Learning Signals and Buying Signals of Intent Data 

The collaboration between sales and marketing is critical when using intent data to drive better outcomes. The old saying “it takes a village…” could not be a more appropriate metaphor for best practices in the utilization of B2B intent data. No single standalone intent signal has any significant value when used in isolation. 

Intent signals must be aggregated and modeled to be of measurable value to an organization. 

The single largest mistake we see over and over again is departments utilizing data (all forms of data, just not Intent) in isolation. This practice results in disjointed measurement and reporting and leads to more confusion, not clarity. For example, we see marketing organizations that create an account score in isolation. Then they throw accounts with high scores over the wall to sales for follow-up, with little context or transparency. This method simply does not work.

High-Level Best Practices 

• Intent Data Alignment and Transparency: All teams must align on the definition of “intent to learn” vs. “intent to buy” and document proper use cases associated with each intent signal.

• Data Sharing: Sales and marketing need to share insights and feedback and continually integrate and optimize for each intent type (event) that is most productive for its given task.

• Data Measurement: Each intent type must have measurable KPIs (Key Performance Indicators) to properly assign value to each signal.

• Company Firmographic Standardization: Company firmographic data must be normalized across platforms so outcomes can be measured and tied to specific events in the customer journey.

• Tools and Data Integration: Integrating both the data and tools (e.g., CRM, marketing automation, ABM platform, intent data) ensures both teams are working from the same data, helping to streamline lead handoff from one tool to the next to improve and measure conversion rates.

Conclusion 

B2B intent data is a powerful tool for both sales and marketing, but each team uses different signals, for different use cases, to achieve their goals. While sales teams focus on immediate opportunities (i.e. prioritizing leads that are ready to purchase), marketing teams utilize intent data to nurture early-stage prospects and move them further down the funnel.

Misalignment between intent data signals and the proper use of this data does more harm than good. It simply creates more confusion, mistrust, and thrash between the respective organizations. However, when used collaboratively, intent data can be a key driver of efficiency, personalization, and alignment across the entire sales and marketing organization. 

Happy Hunting