By Paramita Patra Published on : Sep 15, 2025
A sales team is chasing leads, armed with data. Despite their effort, most outreach ends in silence. Why? Because the leads they’re targeting may not be in a buying cycle, may not have budget approval, or may not be interested. This is where intent-based profiling steps in, changing the game.
Intent-based profiling is the process of identifying and analyzing digital signals that reveal a prospect’s readiness to buy. You can use intent data to understand where a prospect is in their journey and tailor outreach. For example, a company researching “cloud data migration tools” is displaying a clear intent that they may soon invest.
This article will talk about the importance of intent-based profiling in B2B.
Below are the key steps on how intent data is collected and analyzed.
Data collected directly from channels such as websites, emails, or product demos.
Example: A SaaS company tracks which whitepapers are downloaded most frequently. If a CIO downloads a “Data Security in Cloud Migration” guide, it signals interest in cybersecurity solutions.
Data is purchased or accessed from external platforms that monitor online behaviors across multiple websites.
Example: An AdTech firm uses third-party intent data to learn that a cluster of manufacturing companies is actively searching for “IoT supply chain automation.”
Monitoring actions like webinar attendance, repeat visits to pricing pages, or engagement with case studies.
A cloud solutions provider sees an enterprise repeatedly visiting its pricing page over two weeks, implying strong intent.
Adding context about the company’s size, industry, and technology stack to refine targeting.
Example: If a healthcare company is researching “HIPAA-compliant cloud storage” and is already using outdated infrastructure, this combined insight enhances the precision.
Using ML to process intent signals and predict which accounts are most likely to convert.
Example: A marketing automation platform leverages AI to score accounts based on recent activity like competitor comparisons, industry trends, and keyword searches.
Feeding intent data into CRM systems enables sales and marketing teams to act quickly.
Example: A sales rep receives an alert in their CRM that a target account has just engaged with multiple “cloud migration challenges” blogs.
Below are key ways intent-based profiling strengthens both ABM and sales strategies.
Intent signals allow marketing teams to spot accounts actively researching solutions, ensuring campaigns target those most likely to engage.
Example: A cybersecurity vendor identifies that a Fortune 500 bank is searching for “zero-trust security frameworks”. The ABM team prioritizes this account and tailors campaigns.
Sales and marketing teams can align messaging to the buyer’s journey stage, making outreach relevant.
Example: A cloud storage provider notices a mid-sized healthcare firm reading case studies on HIPAA compliance. The sales team follows up with a personalized demo.
When sales reps know which accounts are actively in-market, they avoid wasting time on cold prospects and engage faster with warm leads.
Example: A SaaS company sees a prospect repeatedly comparing competitor solutions online. The sales team steps in with positioning and proof-of-value.
Intent signals allow ABM campaigns to adapt in real-time, targeting accounts when they are most receptive.
Example: A logistics software provider uses intent data to trigger LinkedIn ads for accounts actively searching “AI in supply chain optimization.” Sales follows up with tailored case studies.
Below are key challenges in using intent data.
Challenge: Companies collect vast amounts of signals, but not all indicate genuine buying intent. Noise from irrelevant activities can dilute focus.
Solution: Use AI-driven tools to filter signals and apply scoring models that prioritize high-intent behaviors.
Example: A SaaS provider sees signals about “cloud migration.” By using AI filters, the team identifies that only enterprise-level firms visiting pricing pages multiple times are genuine prospects.
Challenge: Not all intent data sources are reliable. Some signals may be outdated, misleading, or based on generic searches rather than genuine buying research.
Solution: Validate intent signals with first-party data (web analytics, CRM engagement) and cross-check with trusted third-party providers.
Example: A marketing automation company cross-verifies external “AI in marketing” keyword searches with its own webinar attendance list to confirm genuine buyer interest.
Challenge: Collecting and using intent data must comply with privacy regulations like GDPR and CCPA.
Solution: Partner with compliant data providers and be transparent about data usage. Focus on aggregated signals where necessary.
Example: A HRTech company ensures compliance by sourcing intent data only from GDPR-certified providers and informing prospects how data is used in campaigns.
Challenge: Intent signals can be misread. For example, an employee researching a topic may not be the decision-maker, or the search might be for academic purposes.
Solution: Enrich signals with firmographic data to verify account-level relevance and decision-making authority.
Example: A supply chain software vendor refines raw intent data by layering it with firmographic details, ensuring that research on “AI logistics tools” comes from senior operations staff within targeted industries.
Challenge: Sales and marketing often disagree on how to interpret and act on intent data, leading to misalignment.
Solution: Establish shared KPIs, regular data review meetings, and playbooks for acting on specific signals.
Example: A FinTech solutions provider holds weekly alignment sessions where both teams review intent data dashboards, agree on high-priority accounts, and coordinate outreach strategies.
The future of intent-based profiling is not just about collecting signals but integrating them intelligently. One who adopts this mindset will gain a competitive edge and future-proof their strategy. Start integrating intent-based profiling and watch your pipeline evolve into a driver of growth.
By Paramita Patra
Published on 15th, Sep, 2025
A sales team is chasing leads, armed with data. Despite their effort, most outreach ends in silence. Why? Because the leads they’re targeting may not be in a buying cycle, may not have budget approval, or may not be interested. This is where intent-based profiling steps in, changing the game.
Intent-based profiling is the process of identifying and analyzing digital signals that reveal a prospect’s readiness to buy. You can use intent data to understand where a prospect is in their journey and tailor outreach. For example, a company researching “cloud data migration tools” is displaying a clear intent that they may soon invest.
This article will talk about the importance of intent-based profiling in B2B.
Below are the key steps on how intent data is collected and analyzed.
Data collected directly from channels such as websites, emails, or product demos.
Example: A SaaS company tracks which whitepapers are downloaded most frequently. If a CIO downloads a “Data Security in Cloud Migration” guide, it signals interest in cybersecurity solutions.
Data is purchased or accessed from external platforms that monitor online behaviors across multiple websites.
Example: An AdTech firm uses third-party intent data to learn that a cluster of manufacturing companies is actively searching for “IoT supply chain automation.”
Monitoring actions like webinar attendance, repeat visits to pricing pages, or engagement with case studies.
A cloud solutions provider sees an enterprise repeatedly visiting its pricing page over two weeks, implying strong intent.
Adding context about the company’s size, industry, and technology stack to refine targeting.
Example: If a healthcare company is researching “HIPAA-compliant cloud storage” and is already using outdated infrastructure, this combined insight enhances the precision.
Using ML to process intent signals and predict which accounts are most likely to convert.
Example: A marketing automation platform leverages AI to score accounts based on recent activity like competitor comparisons, industry trends, and keyword searches.
Feeding intent data into CRM systems enables sales and marketing teams to act quickly.
Example: A sales rep receives an alert in their CRM that a target account has just engaged with multiple “cloud migration challenges” blogs.
Below are key ways intent-based profiling strengthens both ABM and sales strategies.
Intent signals allow marketing teams to spot accounts actively researching solutions, ensuring campaigns target those most likely to engage.
Example: A cybersecurity vendor identifies that a Fortune 500 bank is searching for “zero-trust security frameworks”. The ABM team prioritizes this account and tailors campaigns.
Sales and marketing teams can align messaging to the buyer’s journey stage, making outreach relevant.
Example: A cloud storage provider notices a mid-sized healthcare firm reading case studies on HIPAA compliance. The sales team follows up with a personalized demo.
When sales reps know which accounts are actively in-market, they avoid wasting time on cold prospects and engage faster with warm leads.
Example: A SaaS company sees a prospect repeatedly comparing competitor solutions online. The sales team steps in with positioning and proof-of-value.
Intent signals allow ABM campaigns to adapt in real-time, targeting accounts when they are most receptive.
Example: A logistics software provider uses intent data to trigger LinkedIn ads for accounts actively searching “AI in supply chain optimization.” Sales follows up with tailored case studies.
Below are key challenges in using intent data.
Challenge: Companies collect vast amounts of signals, but not all indicate genuine buying intent. Noise from irrelevant activities can dilute focus.
Solution: Use AI-driven tools to filter signals and apply scoring models that prioritize high-intent behaviors.
Example: A SaaS provider sees signals about “cloud migration.” By using AI filters, the team identifies that only enterprise-level firms visiting pricing pages multiple times are genuine prospects.
Challenge: Not all intent data sources are reliable. Some signals may be outdated, misleading, or based on generic searches rather than genuine buying research.
Solution: Validate intent signals with first-party data (web analytics, CRM engagement) and cross-check with trusted third-party providers.
Example: A marketing automation company cross-verifies external “AI in marketing” keyword searches with its own webinar attendance list to confirm genuine buyer interest.
Challenge: Collecting and using intent data must comply with privacy regulations like GDPR and CCPA.
Solution: Partner with compliant data providers and be transparent about data usage. Focus on aggregated signals where necessary.
Example: A HRTech company ensures compliance by sourcing intent data only from GDPR-certified providers and informing prospects how data is used in campaigns.
Challenge: Intent signals can be misread. For example, an employee researching a topic may not be the decision-maker, or the search might be for academic purposes.
Solution: Enrich signals with firmographic data to verify account-level relevance and decision-making authority.
Example: A supply chain software vendor refines raw intent data by layering it with firmographic details, ensuring that research on “AI logistics tools” comes from senior operations staff within targeted industries.
Challenge: Sales and marketing often disagree on how to interpret and act on intent data, leading to misalignment.
Solution: Establish shared KPIs, regular data review meetings, and playbooks for acting on specific signals.
Example: A FinTech solutions provider holds weekly alignment sessions where both teams review intent data dashboards, agree on high-priority accounts, and coordinate outreach strategies.
The future of intent-based profiling is not just about collecting signals but integrating them intelligently. One who adopts this mindset will gain a competitive edge and future-proof their strategy. Start integrating intent-based profiling and watch your pipeline evolve into a driver of growth.
