Top 5 Intent Data Trends Shaping Lead Generation Success in 2025

B2B lead generation is undergoing significant transformation. As we navigate 2025, using intent data strategically has become necessary, not just nice-to-have, for demand generation managers who want predictable pipeline growth.

What makes 2025 different? Three major changes are reshaping effective lead generation: stricter data privacy rules, higher expectations for personalized experiences, and a fundamental shift in success metrics—moving from counting leads to measuring revenue impact.

Inbox Insight’s method integrates intent data through a systematic Planning > Activation > Measurement process. This approach, detailed further in our step-by-step strategies for actioning intent data, doesn’t just find potential buyers; it identifies who should buy tomorrow and helps marketing teams connect with them at the perfect moment.

Hear Ross Howard and Bombora’s Nina Interlandi Bell discuss this framework and explore step-by-step strategies in the full webinar:

For demand generation managers adapting to these changes, the message is straightforward: intent data isn’t just another tool—it’s the foundation for a smarter, more efficient approach to lead generation.

Trend 1: Strategic precision – moving beyond volume to value

The new approach

B2B lead generation is shifting from broad market coverage toward precise targeting of high-potential segments defined by much more than basic firmographics.

As Ross Howard puts it: “The best customers are not just who buys from me today, it’s who should buy from me tomorrow.” This reverse pyramid thinking starts with identifying ideal accounts first, then working backward to define realistic campaign scope.

Why it matters

This precision allows teams to focus resources on accounts with the highest conversion probability and revenue potential, significantly improving ROI. Instead of spreading efforts across a massive Total Addressable Market (TAM), teams can concentrate on segments that will drive the most value.

“If you start with the right companies in mind, how do you do that?” he asks. The answer comes from understanding the distinction between your total addressable market, your serviceable addressable market, and your serviceable obtainable market—who you can realistically sell to within a specific timeframe.

Data integration

Effective targeting requires combining different data types:

  • Qualitative insights: CRM data, sales feedback on deal velocity, buying committee composition
  • Quantitative data: Intent signals, firmographics, technographics, engagement metrics

The strongest targeting strategies blend these elements to create a complete picture of ideal customers.

Practical application

Implement a “reverse pyramid” approach to account targeting:

  1. Define your best potential customers based on value and fit
  2. Use TAM/SAM/SOM analysis to set realistic campaign scope
  3. Prioritize resources based on intent signals and fit scoring
  4. Create detailed briefings with insights about how buyers make decisions

“Being realistic with where they should spend so that they don’t spread themselves too thin or that they don’t go after enough accounts,” he says. “All of that should become part of the discussion to understand what their goals are and understand within their audience who they want to attack right now.”

Trend 2: Combining first-party insights with third-party signals

The new reality

As privacy regulations strengthen and cookies disappear, demand generation teams are building stronger first-party data strategies alongside third-party intent signals. This combined approach offers a more complete view of buying behavior than either source alone.

Why it matters

First-party data provides detailed, consented insights into known contacts and anonymous visitors on your properties, while third-party data (like Bombora’s) shows research activities happening elsewhere. Together, they create a powerful view of buying intent.

“We also, as publishers, have first-party data—I would split between anonymous visitors and known visitors—and have a look at what you’re capturing at the moment,” notes Howard. This first-party component becomes increasingly valuable as third-party tracking faces limitations.

The publisher advantage

Publishers have a natural edge here, as they routinely collect rich engagement data across their audience. By properly tagging content and tracking interactions, publishers can build valuable intent signals that complement co-op data.

“The way we do it is to tag those topics so that we can see which companies care about which content from which brands,” he explains. “We rank it by the format of the content. We rank it by the channel that it came to the website on, and we crunch it with machine learning to look at which companies and contacts are most interested in that topic area.”

Practical steps

Develop a combined data strategy by:

  1. Tracking and analyzing your first-party behavioral data (website visits, content downloads)
  2. Adding content tagging to understand topic interests
  3. Connecting first-party signals with third-party intent sources
  4. Setting baseline measurements to spot significant changes in research behavior

Even without fancy technology, he says: “I would suggest that you dig around with the teams you have in place to understand how we can determine which companies are visiting which content on the website, what that content is about, and start building your own database.”

Trend 3: Coordinating multi-channel outreach based on intent

The new approach

Static campaign lists are being replaced by dynamically prioritized account lists activated across multiple channels simultaneously based on current intent signals. This ensures marketing efforts align with actual buying behavior in near real-time.

Why it matters

Reaching the right accounts when they’re actively researching, with coordinated messaging across touchpoints, dramatically increases engagement and conversion rates. Companies showing high intent on specific topics are much more likely to respond to relevant outreach.

“If you have a list of domains in your email database, you can match on domain to people from those companies,” Howard notes. This approach works across channels: “Most paid social now has the ability to match on company domain name as well… You can then match that to any data point that you need to get an advert in front of somebody.”

Focusing on buying committees

Activation strategies now target multiple stakeholders within an account rather than individual leads. This approach addresses the reality of complex B2B buying committees with 8-12 decision-makers per purchase.

He advises: “As a lead is generated with us, we are looking to actively find other people from the same companies and engage the buying group across as many channels as possible.”

Practical application

Transform your lead generation approach by:

  1. Using intent data to prioritize accounts for multi-channel campaigns
  2. Aligning messages and content with the specific topics showing intent
  3. Coordinating outreach across channels (email, ads, sales) for priority accounts
  4. Targeting multiple stakeholders within important accounts
  5. Spacing communications to avoid overwhelming prospects

“If you generate a lead and that’s the first touch point, we should prioritize that across the rest of our channels,” he advises. “Doing that creates more engagement in all channels and you get an uplift across it.”

Trend 4: Measuring success through pipeline impact

The new standard

Lead generation measurement is moving beyond simple lead counts (MQLs) to focus on buying committee engagement and marketing’s influence on pipeline speed and revenue. This represents a fundamental shift in how demand generation teams show their value.

Why it matters

Proving marketing’s contribution means showing how intent-driven activities speed up deals and affect revenue, justifying spending and building trust with sales. Without this connection to revenue, lead generation might be seen as just a cost center rather than a growth driver.

“The ultimate justification has to happen their side,” Howard notes. “They need to look at which ones are progressing within their sales team and which ones are converting into revenue. So there needs to be a certain amount of collaboration.”

Advanced scoring & attribution

Leading programs use account-level scoring models that combine intent, fit, and engagement signals to prioritize efforts. These models often feed into attribution systems that properly credit marketing’s contribution.

He describes their approach: “We look at partnering with customers to give them a lead scoring model on top of our campaigns—this is Inbox’s Demand BI. It’s essentially a scoring model that allows us to tag what they’re doing on IFP, what they’re doing on Bombora, and what the buyer persona is for this topic area—like how senior they are and what role they have in decision-making.”

Practical steps

Improve your measurement approach by:

  1. Working with sales to understand their main pipeline metrics
  2. Creating account-level scoring that tracks buying committee engagement
  3. Connecting marketing activities to opportunity progress and revenue
  4. Using attribution models that recognize multiple touchpoints
  5. Focusing on quality metrics (conversion rates, deal velocity) over volume

As he explains: “If you can equate that to how much it’s moving that pipeline, they will thank you for it.” Inbox Insight has seen results, with one client reporting “12% higher conversion to SQL, which for their pipeline and for their revenue is amazing.”

Trend 5: AI-enhanced intent analysis and campaign efficiency

The new capability

AI and Machine Learning are increasingly applied to process intent signals, analyze behavior patterns, and improve campaign efficiency. The focus is more on analysis and process automation than prediction—at least for now.

Why it matters

Managing multiple intent data sources and large datasets makes AI valuable for marketing teams. It helps extract useful insights, automate repetitive tasks, and prioritize efforts more effectively, allowing people to focus on strategic activities.

Howard describes his team’s evolution: “We used to tag that content manually, but we now tag that by natural language processing.” This automation has greatly improved their ability to scale intent-based programs.

Practical applications

Current AI applications in intent-driven lead generation often include:

  • Analyzing behavior patterns across large datasets
  • Automating content tagging and categorization
  • Prioritizing accounts based on multiple signals
  • Simplifying campaign setup and execution

Getting started

Add AI to enhance your intent-based programs:

  1. Use AI tools to analyze patterns in first-party and third-party intent data
  2. Implement automated content tagging for better intent signals
  3. Build scoring models that use multiple data points for smarter prioritization
  4. Automate routine campaign setup and reporting
  5. Use AI to make your team more effective rather than replacing human judgment

He suggests starting simple: “You can start this stuff off manually if you have a really good folder structure on your website… It’s still useful intent data, it’s still useful knowledge of which companies are going to which sections on the site and which content topics.”

Conclusion

B2B lead generation will be shaped by five important trends: strategic precision through better targeting, combined first and third-party data strategies, coordinated multi-channel activation, revenue-focused measurement, and smart use of AI.

For demand generation managers, the way forward is clear: refine your targeting approach and data strategy, then build a coordinated plan across channels. Measure success through pipeline impact rather than lead volume, and see how AI can make your team more effective.

Intent data isn’t a magic solution—it requires thoughtful application through a systematic Planning > Activation > Measurement process. But when properly implemented, as shown by Inbox Insight’s approach, it becomes the foundation for predictable pipeline growth.

The future belongs to teams who can master these trends, turning the complexity of modern B2B buying into an opportunity to connect meaningfully with the accounts that matter most.

Generate highly qualified leads, empowered by intent data. Learn more about Inbox Insight’s lead generation service and talk to an expert.

Aikma Stirling-Stainsby

Aikma is a senior SEO expert with six years of experience, specialising in content auditing, planning, creation, and repurposing – especially for B2B marketers. He’s particularly skilled in working with transcript-based content and first-party research. Outside of work, he’s into tech, automation, and football.
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