The Modern Intent Data Stack

A few weeks ago, I wrote about how intent data–behavioral signals that indicate a potential buyer's interest and readiness to purchase–is blurring the line between inbound and outbound motions . In this week’s newsletter, I intend (mind the terrible pun) to discuss how a great intent data stack should look in practice.

We'll explore the foundation of intent data: data acquisition from sources like job boards, social media, and firmographic databases, and we'll dive into data enrichment techniques that fill in the gaps and provide a more complete understanding of your prospects. Next, we'll look at signal intelligence engines, which use advanced algorithms and machine learning to distill raw intent data into actionable insights. Finally, we'll discuss bringing it all together and how to action the insights gained from intent data to drive sustainable revenue.

Let’s dive in.

Data Sources?

The foundation of the modern intent data tech stack lies in the acquisition of high-quality data. Certain data requires more enrichment and processing than others, but by casting a wide net and gathering insights from multiple touchpoints, you can build a comprehensive, multi-dimensional view of your target accounts and their buying intent.

One extremely powerful source of intent data are professional communities and forums like LinkedIn groups, Slack communities, Discord servers, Dev.to , etc. These platforms provide a wealth of user-generated content and discussions that can offer insights into the challenges, interests, and priorities of your target audience.

By monitoring relevant conversations and engagement within these communities, you can identify key topics and trends that are driving interest and intent among potential buyers. For example, if you notice a surge in discussions around a specific pain point or emerging technology within a LinkedIn group or Discord server, this could indicate a growing need or interest in solutions related to your offerings.

To effectively harvest these community-based intent data sources, social listening and community management tools that can help you track relevant conversations, identify key influencers, and streamline your engagement efforts are your friends. By integrating these insights into your broader intent data tech stack, you can gain a more comprehensive view of your target accounts and tailor your outreach and content strategies accordingly.

Another valuable raw data source are job boards like Indeed and Glassdoor, as well as LinkedIn. By monitoring changes in job titles and new job listings at target accounts, you can spot early signs of buying intent, such as the hiring of a new executive or the expansion of a relevant department. LinkedIn can help here too, helping surface changes in job titles, company affiliations, and professional interests of key decision-makers.

Another crucial component of the intent data landscape is traditional intent data providers like Bombora and 6sense. These platforms collect and aggregate behavioral data from a vast network of syndicates, allowing you to track content consumption patterns and identify accounts that are actively researching topics related to specific solutions.

More recently, web traffic deanonymization services like Penguin AI and RB2B have emerged as a new frontier in intent data acquisition. These tools try to unmask the identities of anonymous website visitors, enabling you to tie digital behaviors directly to specific accounts and decision-makers. While the effectiveness of these services is spotty, they represent an intriguing avenue for collecting high-intent person-level web traffic data.

Data Enrichment

Once you've acquired a solid foundation of intent data from various sources, the next step is to enrich and refine this information to extract maximum value for your sales and marketing efforts. Data enrichment involves enhancing your raw intent data with additional context, firmographic details, and contact information to create a more complete and actionable picture of your target accounts and decision-makers.

The common approach to data enrichment is through traditional data providers like ZoomInfo and Clearbit. These platforms offer extensive databases of company and contact information, allowing you to fill in missing details about target accounts like industry, company size, revenue, and roles.

In addition to third-party data sources, you should be leveraging your own (first-party) data to enrich your intent signals. By integrating your CRM, marketing automation, and customer success platforms with your intent data stack, you can gain awesome insights into the engagement history, pain points, and buying stage of your target accounts, triangulating it with third-party data to create a stronger signal.

To make the most of enrichment, you should have a good data management and integration strategy in place. That means investing in a CDP or data orchestration tool that can help you consolidate, clean, and normalize data from multiple sources, ensuring a consistent and reliable foundation for your intent-driven initiatives.

By combining high-quality intent data with rich contextual information and first-party insights, you can create a more complete and actionable view of your target accounts, enabling you to engage them with the right message, at the right time, through the right channels. In the next section, we'll explore how signal intelligence engines can help you take your enriched intent data to the next level.

Signal Intelligence Engine

So now that we’ve got intent data and contextual information in place, the next component of a modern intent data stack is a signal intelligence engine. These advanced platforms leverage artificial intelligence, machine learning, and natural language processing to analyze vast amounts of intent data, uncovering hidden patterns, relationships, and opportunities that may not be immediately apparent.?

Signal intelligence engines can ingest intent data from a wide range of sources, including web traffic, content consumption, social media engagement, and more. By applying sophisticated algorithms and predictive models to this data, these platforms can identify the most relevant and actionable signals of buying intent, helping you focus your efforts on the accounts most likely to convert. There aren’t any off-the-shelf signal intelligence engines in the form I’ll describe, so most of this currently has to be done in house. Fortunately, we’re building one, so stay tuned!

One key advantage of signal intelligence engines is their ability to provide a more nuanced and comprehensive view of intent beyond simple keyword searches or content consumption. By analyzing the full context of an account's behavior, including the topics they're researching, the competitors they're considering, and the decision-makers involved, these platforms can help you understand the true depth and quality of an account's intent, as well as their likely buying stage and readiness to engage.

Another powerful feature of signal intelligence engines is their ability to identify and track intent signals across multiple channels and touchpoints. By stitching together intent data from various sources and devices, these platforms can provide a more complete and unified view of an account's journey, helping you orchestrate a consistent and relevant experience across every interaction.

In addition to providing insights and predictions, signal intelligence engines can also help you activate your intent data through seamless integrations with your existing sales and marketing tech stack by pushing intent insights and account prioritization directly into your CRM, marketing automation, and ABM platforms. We’ll discuss that next.

Actioning Your Signals

Activation is what ultimately converts intent into concrete business outcomes. I like to divide actioning into three discrete categories: activation, analytics, and AI/ML.

Activation

Activation involves the direct application of intent data to drive specific sales and marketing actions. Here’s how this can be implemented:

• Integrating with sales and marketing tools: Link your intent data with CRMs, marketing automation platforms, and ABM tools to push real-time insights to your sales and marketing teams–ensures immediate access to actionable data, enabling timely and relevant interactions with prospects
• Intent-triggered marketing campaigns: Design automated marketing campaigns that activate based on specific user behaviors and intent signals, such as visiting certain pages or downloading content
• Personalized sales outreach: Enable sales teams to tailor outreach efforts based on intent data insights.

Analytics

Analytics focuses on interpreting intent data to understand and improve business strategies. Here’s how analytics can be applied:

• Performance analysis: Use intent data to analyze the effectiveness of various sales and marketing strategies–and refine appropriately.
• Funnel mapping: Apply analytics to trace the path of prospects through the sales funnel such that you can optimize touchpoints
• Segmentation and targeting: Analyze intent data to segment audiences based on behavior and interest levels for more efficient allocation of resources to high-intent groups.

AI/ML

Machine learning utilizes intent data to forecast future behaviors and automate data-driven decisions. Here are some applications:

• Predictive lead scoring: Score leads based on likelihood to convert, using a combination of behavioral data and historical outcomes.
• Churn prediction and prevention: Predict which customers are at risk of churning so you can take preemptive action to retain customers through targeted offers or outreach.
• Automated real-time decisions: Integrate ML to make/suggest strategy based on current intent signals. For instance, adjusting content delivery or personalized offers in real-time to align with the user's current engagement level and intent.
• Forecasting market trends: Analyze patterns and trends within intent data to predict shifts in market dynamics and inform content strategy.

Wrapping up

The intent data stack I laid out above is just one of many configurations that should help the best GTM teams harness the power of intent data to better land, expand and retain. This is not a rigid formula, so by all means mix and match–most teams won’t be able to build their own intent engines.

Also keep in mind that building a successful intent data stack isn’t a one-time effort. It requires ongoing investment, experimentation, and optimization to make sure you're staying ahead of the curve and extracting maximum value from your data. That means regularly evaluating new data sources, refining your enrichment and signal intelligence processes, and continuously testing and iterating on your activation strategies.

It’s also important to approach intent data as part of a holistic, customer-centric strategy rather than a single tactic. By integrating intent insights with other key data points like customer feedback, engagement history, and lifetime value, you can create a more comprehensive and nuanced view of your target accounts and deliver a more seamless and personalized experience across every touchpoint.