Why GTM Leaders Should Prioritize AI-Driven Intent Signals
AI-driven intent signals are transforming how GTM leaders identify, prioritize, and engage high-value prospects. By leveraging a unified view of buyer intent, teams can accelerate pipeline velocity, improve personalization, and maximize ROI. Early adopters are seeing significant gains in conversion rates and sales efficiency.
Introduction: The New Era of AI-Driven GTM
Go-to-market (GTM) leaders face unprecedented complexity as buying journeys lengthen, digital touchpoints proliferate, and organizational budgets tighten. In this environment, the ability to surface and act on buyer intent signals—especially those enhanced by artificial intelligence (AI)—can make the difference between lagging and leading. As enterprise B2B sales cycles become more opaque, traditional methods of qualifying, prioritizing, and engaging buyers are no longer enough. AI-driven intent signals offer a powerful solution, unlocking actionable insights that can transform every stage of your revenue engine.
Understanding the Shift: From Activity to Intent
Historically, GTM teams relied on overt buyer activities—like form fills, email opens, or webinar registrations—to infer interest. But these actions often miss unseen research, anonymous browsing, and early-stage exploration. Intent signals, by contrast, capture the underlying motivations and readiness of prospects through behavioral, contextual, and firmographic data. AI amplifies this shift by sifting through millions of digital breadcrumbs, identifying patterns, and scoring prospects based on likelihood to convert.
Types of Intent Signals
First-party intent: Actions prospects take on your owned properties (website, app, email, events).
Third-party intent: Signals from external sources—content consumption, peer reviews, social activity, and research on industry sites.
Contextual intent: Inferred from the context of actions, such as timing, frequency, and sequence of interactions.
Technographic and firmographic intent: Data about a buyer’s technology stack, company size, industry, and stage of digital transformation.
AI technologies can integrate, normalize, and analyze these diverse signals, revealing hidden opportunities and surfacing high-propensity accounts.
The Business Imperative: Why Intent Signals Matter Now
With B2B deals requiring more consensus, and decision-makers often working asynchronously, it’s increasingly difficult to track and influence buyer journeys. AI-driven intent signals equip GTM leaders with the foresight to:
Prioritize outreach based on real-time buying readiness, not just static scoring models.
Personalize engagement with relevant, timely messaging and offers tailored to detected interests and pain points.
Align sales and marketing by sharing a unified view of account activity and intent across all customer-facing teams.
Accelerate pipeline velocity by focusing resources on accounts with high conversion potential.
Reduce churn and drive expansion by identifying up-sell and cross-sell signals within the customer base.
Quantifying the Impact
Recent studies show that companies leveraging AI-powered intent data see:
Up to 40% increase in conversion rates for prioritized accounts.
Shortened sales cycles—by as much as 30%—due to more relevant and timely engagement.
Improved marketing ROI, with budgets allocated to high-intent segments that are most likely to close.
Higher customer lifetime value, as expansion opportunities are surfaced earlier and addressed proactively.
How AI Identifies and Scores Intent Signals
The sophistication of AI algorithms enables the extraction of intent from noise. Here’s how it works in a modern GTM stack:
Data Aggregation: AI ingests data from web analytics, CRM, marketing automation, external data providers, and social listening tools.
Signal Clustering: Machine learning models group related activities—such as multiple visits to a pricing page or comparison with competitor solutions—into "intent clusters."
Pattern Recognition: Algorithms detect patterns that historically correlate with purchase decisions, adjusting weightings based on industry, deal size, and buying role.
Scoring and Alerting: Prospects or accounts are scored continuously, with alerts triggered when intent thresholds are crossed or new buying signals emerge.
Predictive Recommendations: AI suggests next-best actions, content, or outreach timing based on intent profile and buyer stage.
Integrating AI Intent Signals Into Your GTM Strategy
To unlock the full potential of AI-driven intent, GTM leaders must rethink their processes and technology stack.
1. Unify Data Across Silos
Consolidate intent signals from sales, marketing, customer success, and product analytics into a single source of truth. This requires integration with CRM, MAP, and third-party intent platforms. Data unification ensures that AI models have the broadest, most accurate picture of buyer behavior—reducing blind spots and duplicative outreach.
2. Align Teams Around Intent-Driven Playbooks
Intent data is only as powerful as the actions it inspires. Build playbooks that prescribe specific actions for sales, SDRs, and marketing based on the type, strength, and recency of intent signals. For example, high-scoring accounts viewing comparison pages may trigger immediate SDR outreach, while low-intent accounts receive nurturing content. Clear escalation paths and SLAs ensure no signal is wasted.
3. Orchestrate Personalized Multichannel Engagement
AI-driven intent enables hyper-personalization at scale. Use detected interests to tailor outreach across email, social, chat, and digital ads. Contextual triggers—like a prospect revisiting pricing pages or engaging with competitor content—should inform timing, messaging, and offers. Personalization increases response rates and builds trust early in the buying journey.
4. Measure and Optimize Continuously
Regularly assess the effectiveness of intent-driven programs. Track KPIs such as engagement rates, pipeline velocity, win rates, and average deal size for high- versus low-intent segments. Use AI analytics to identify which signals best predict conversion and adjust scoring models accordingly. This feedback loop ensures ongoing improvement and alignment with revenue goals.
Common Challenges and How to Overcome Them
Despite the promise of AI-driven intent, GTM leaders often encounter obstacles:
Data Quality: Incomplete or outdated data can undermine AI models. Invest in data enrichment and hygiene to maximize accuracy.
Change Management: Teams may resist new workflows or distrust AI-driven recommendations. Drive adoption through enablement, training, and clear attribution of intent-driven wins.
Signal Overload: Too many signals can overwhelm teams. Prioritize only those with proven impact on deal progression.
Privacy and Compliance: Ensure all intent data usage complies with GDPR, CCPA, and other regulations. Clearly communicate data practices to prospects and customers.
Case Studies: AI-Driven Intent Transforming GTM Outcomes
Case Study 1: Enterprise SaaS Provider Accelerates Pipeline
An enterprise SaaS company integrated AI-powered intent data from multiple sources into their CRM. The result: sales reps identified in-market accounts 3X faster, prioritized outreach, and increased opportunity creation by 35% within six months. Deal cycles shortened as reps tailored messaging to detected pain points, resulting in a 20% increase in average deal size.
Case Study 2: Marketing Team Reduces Waste, Boosts ROI
A leading cybersecurity vendor used AI intent scoring to segment and target high-propensity accounts for ABM campaigns. By focusing ad spend and content syndication on these cohorts, marketing reduced cost per qualified lead by 45% and improved campaign ROI by 60%. Alignment with sales improved as both teams operated from a shared view of account intent.
Case Study 3: Customer Success Uncovers Expansion Opportunities
A cloud infrastructure provider leveraged AI-driven intent to monitor usage patterns and external research by existing customers. Early signals of expansion—such as increased product usage or competitive research—triggered proactive outreach. As a result, the company increased expansion revenue by 25% and reduced churn among at-risk accounts.
Future Trends: How AI Intent Signals Will Evolve
The pace of innovation in AI and intent data is accelerating. GTM leaders should prepare for:
Deeper personalization: AI will enable micro-personalization, dynamically adapting messages to individual stakeholders and their roles in the buying group.
Real-time orchestration: Intent signals will trigger automated, orchestrated plays across sales, marketing, and customer success with minimal human intervention.
Predictive forecasting: Advanced models will predict not only which accounts are in-market, but when and how to engage for maximum likelihood of conversion.
Expanded data sources: Integration of voice, video, and unstructured data will further enrich intent models, providing a 360-degree view of buyer needs.
Best Practices for GTM Leaders
Audit your current intent data sources and identify gaps.
Evaluate AI platforms based on data integration, signal accuracy, and ease of use.
Develop intent-driven playbooks aligned to your sales methodology (e.g., MEDDICC, Challenger, SPIN).
Foster a culture of experimentation—pilot, measure, and optimize intent-led initiatives.
Invest in change management and stakeholder alignment to drive adoption.
Monitor regulatory changes to ensure responsible intent data usage.
Conclusion: The Competitive Advantage of AI-Driven Intent
In an era of information overload and heightened competition, GTM leaders can no longer rely on static lead scoring or generic outreach. AI-driven intent signals provide a transformative lens, surfacing hidden opportunities and enabling precision engagement at scale. By prioritizing intent and embedding AI insights into every stage of your GTM strategy, you’ll accelerate pipeline, improve win rates, and build lasting customer relationships. The future belongs to those who see— and act on—intent before their competitors do.
Key Takeaways
AI-driven intent signals unlock actionable insights for every GTM function.
Success requires unified data, aligned teams, and continuous measurement.
Early adopters are achieving faster growth, higher ROI, and more resilient pipelines.
Introduction: The New Era of AI-Driven GTM
Go-to-market (GTM) leaders face unprecedented complexity as buying journeys lengthen, digital touchpoints proliferate, and organizational budgets tighten. In this environment, the ability to surface and act on buyer intent signals—especially those enhanced by artificial intelligence (AI)—can make the difference between lagging and leading. As enterprise B2B sales cycles become more opaque, traditional methods of qualifying, prioritizing, and engaging buyers are no longer enough. AI-driven intent signals offer a powerful solution, unlocking actionable insights that can transform every stage of your revenue engine.
Understanding the Shift: From Activity to Intent
Historically, GTM teams relied on overt buyer activities—like form fills, email opens, or webinar registrations—to infer interest. But these actions often miss unseen research, anonymous browsing, and early-stage exploration. Intent signals, by contrast, capture the underlying motivations and readiness of prospects through behavioral, contextual, and firmographic data. AI amplifies this shift by sifting through millions of digital breadcrumbs, identifying patterns, and scoring prospects based on likelihood to convert.
Types of Intent Signals
First-party intent: Actions prospects take on your owned properties (website, app, email, events).
Third-party intent: Signals from external sources—content consumption, peer reviews, social activity, and research on industry sites.
Contextual intent: Inferred from the context of actions, such as timing, frequency, and sequence of interactions.
Technographic and firmographic intent: Data about a buyer’s technology stack, company size, industry, and stage of digital transformation.
AI technologies can integrate, normalize, and analyze these diverse signals, revealing hidden opportunities and surfacing high-propensity accounts.
The Business Imperative: Why Intent Signals Matter Now
With B2B deals requiring more consensus, and decision-makers often working asynchronously, it’s increasingly difficult to track and influence buyer journeys. AI-driven intent signals equip GTM leaders with the foresight to:
Prioritize outreach based on real-time buying readiness, not just static scoring models.
Personalize engagement with relevant, timely messaging and offers tailored to detected interests and pain points.
Align sales and marketing by sharing a unified view of account activity and intent across all customer-facing teams.
Accelerate pipeline velocity by focusing resources on accounts with high conversion potential.
Reduce churn and drive expansion by identifying up-sell and cross-sell signals within the customer base.
Quantifying the Impact
Recent studies show that companies leveraging AI-powered intent data see:
Up to 40% increase in conversion rates for prioritized accounts.
Shortened sales cycles—by as much as 30%—due to more relevant and timely engagement.
Improved marketing ROI, with budgets allocated to high-intent segments that are most likely to close.
Higher customer lifetime value, as expansion opportunities are surfaced earlier and addressed proactively.
How AI Identifies and Scores Intent Signals
The sophistication of AI algorithms enables the extraction of intent from noise. Here’s how it works in a modern GTM stack:
Data Aggregation: AI ingests data from web analytics, CRM, marketing automation, external data providers, and social listening tools.
Signal Clustering: Machine learning models group related activities—such as multiple visits to a pricing page or comparison with competitor solutions—into "intent clusters."
Pattern Recognition: Algorithms detect patterns that historically correlate with purchase decisions, adjusting weightings based on industry, deal size, and buying role.
Scoring and Alerting: Prospects or accounts are scored continuously, with alerts triggered when intent thresholds are crossed or new buying signals emerge.
Predictive Recommendations: AI suggests next-best actions, content, or outreach timing based on intent profile and buyer stage.
Integrating AI Intent Signals Into Your GTM Strategy
To unlock the full potential of AI-driven intent, GTM leaders must rethink their processes and technology stack.
1. Unify Data Across Silos
Consolidate intent signals from sales, marketing, customer success, and product analytics into a single source of truth. This requires integration with CRM, MAP, and third-party intent platforms. Data unification ensures that AI models have the broadest, most accurate picture of buyer behavior—reducing blind spots and duplicative outreach.
2. Align Teams Around Intent-Driven Playbooks
Intent data is only as powerful as the actions it inspires. Build playbooks that prescribe specific actions for sales, SDRs, and marketing based on the type, strength, and recency of intent signals. For example, high-scoring accounts viewing comparison pages may trigger immediate SDR outreach, while low-intent accounts receive nurturing content. Clear escalation paths and SLAs ensure no signal is wasted.
3. Orchestrate Personalized Multichannel Engagement
AI-driven intent enables hyper-personalization at scale. Use detected interests to tailor outreach across email, social, chat, and digital ads. Contextual triggers—like a prospect revisiting pricing pages or engaging with competitor content—should inform timing, messaging, and offers. Personalization increases response rates and builds trust early in the buying journey.
4. Measure and Optimize Continuously
Regularly assess the effectiveness of intent-driven programs. Track KPIs such as engagement rates, pipeline velocity, win rates, and average deal size for high- versus low-intent segments. Use AI analytics to identify which signals best predict conversion and adjust scoring models accordingly. This feedback loop ensures ongoing improvement and alignment with revenue goals.
Common Challenges and How to Overcome Them
Despite the promise of AI-driven intent, GTM leaders often encounter obstacles:
Data Quality: Incomplete or outdated data can undermine AI models. Invest in data enrichment and hygiene to maximize accuracy.
Change Management: Teams may resist new workflows or distrust AI-driven recommendations. Drive adoption through enablement, training, and clear attribution of intent-driven wins.
Signal Overload: Too many signals can overwhelm teams. Prioritize only those with proven impact on deal progression.
Privacy and Compliance: Ensure all intent data usage complies with GDPR, CCPA, and other regulations. Clearly communicate data practices to prospects and customers.
Case Studies: AI-Driven Intent Transforming GTM Outcomes
Case Study 1: Enterprise SaaS Provider Accelerates Pipeline
An enterprise SaaS company integrated AI-powered intent data from multiple sources into their CRM. The result: sales reps identified in-market accounts 3X faster, prioritized outreach, and increased opportunity creation by 35% within six months. Deal cycles shortened as reps tailored messaging to detected pain points, resulting in a 20% increase in average deal size.
Case Study 2: Marketing Team Reduces Waste, Boosts ROI
A leading cybersecurity vendor used AI intent scoring to segment and target high-propensity accounts for ABM campaigns. By focusing ad spend and content syndication on these cohorts, marketing reduced cost per qualified lead by 45% and improved campaign ROI by 60%. Alignment with sales improved as both teams operated from a shared view of account intent.
Case Study 3: Customer Success Uncovers Expansion Opportunities
A cloud infrastructure provider leveraged AI-driven intent to monitor usage patterns and external research by existing customers. Early signals of expansion—such as increased product usage or competitive research—triggered proactive outreach. As a result, the company increased expansion revenue by 25% and reduced churn among at-risk accounts.
Future Trends: How AI Intent Signals Will Evolve
The pace of innovation in AI and intent data is accelerating. GTM leaders should prepare for:
Deeper personalization: AI will enable micro-personalization, dynamically adapting messages to individual stakeholders and their roles in the buying group.
Real-time orchestration: Intent signals will trigger automated, orchestrated plays across sales, marketing, and customer success with minimal human intervention.
Predictive forecasting: Advanced models will predict not only which accounts are in-market, but when and how to engage for maximum likelihood of conversion.
Expanded data sources: Integration of voice, video, and unstructured data will further enrich intent models, providing a 360-degree view of buyer needs.
Best Practices for GTM Leaders
Audit your current intent data sources and identify gaps.
Evaluate AI platforms based on data integration, signal accuracy, and ease of use.
Develop intent-driven playbooks aligned to your sales methodology (e.g., MEDDICC, Challenger, SPIN).
Foster a culture of experimentation—pilot, measure, and optimize intent-led initiatives.
Invest in change management and stakeholder alignment to drive adoption.
Monitor regulatory changes to ensure responsible intent data usage.
Conclusion: The Competitive Advantage of AI-Driven Intent
In an era of information overload and heightened competition, GTM leaders can no longer rely on static lead scoring or generic outreach. AI-driven intent signals provide a transformative lens, surfacing hidden opportunities and enabling precision engagement at scale. By prioritizing intent and embedding AI insights into every stage of your GTM strategy, you’ll accelerate pipeline, improve win rates, and build lasting customer relationships. The future belongs to those who see— and act on—intent before their competitors do.
Key Takeaways
AI-driven intent signals unlock actionable insights for every GTM function.
Success requires unified data, aligned teams, and continuous measurement.
Early adopters are achieving faster growth, higher ROI, and more resilient pipelines.
Introduction: The New Era of AI-Driven GTM
Go-to-market (GTM) leaders face unprecedented complexity as buying journeys lengthen, digital touchpoints proliferate, and organizational budgets tighten. In this environment, the ability to surface and act on buyer intent signals—especially those enhanced by artificial intelligence (AI)—can make the difference between lagging and leading. As enterprise B2B sales cycles become more opaque, traditional methods of qualifying, prioritizing, and engaging buyers are no longer enough. AI-driven intent signals offer a powerful solution, unlocking actionable insights that can transform every stage of your revenue engine.
Understanding the Shift: From Activity to Intent
Historically, GTM teams relied on overt buyer activities—like form fills, email opens, or webinar registrations—to infer interest. But these actions often miss unseen research, anonymous browsing, and early-stage exploration. Intent signals, by contrast, capture the underlying motivations and readiness of prospects through behavioral, contextual, and firmographic data. AI amplifies this shift by sifting through millions of digital breadcrumbs, identifying patterns, and scoring prospects based on likelihood to convert.
Types of Intent Signals
First-party intent: Actions prospects take on your owned properties (website, app, email, events).
Third-party intent: Signals from external sources—content consumption, peer reviews, social activity, and research on industry sites.
Contextual intent: Inferred from the context of actions, such as timing, frequency, and sequence of interactions.
Technographic and firmographic intent: Data about a buyer’s technology stack, company size, industry, and stage of digital transformation.
AI technologies can integrate, normalize, and analyze these diverse signals, revealing hidden opportunities and surfacing high-propensity accounts.
The Business Imperative: Why Intent Signals Matter Now
With B2B deals requiring more consensus, and decision-makers often working asynchronously, it’s increasingly difficult to track and influence buyer journeys. AI-driven intent signals equip GTM leaders with the foresight to:
Prioritize outreach based on real-time buying readiness, not just static scoring models.
Personalize engagement with relevant, timely messaging and offers tailored to detected interests and pain points.
Align sales and marketing by sharing a unified view of account activity and intent across all customer-facing teams.
Accelerate pipeline velocity by focusing resources on accounts with high conversion potential.
Reduce churn and drive expansion by identifying up-sell and cross-sell signals within the customer base.
Quantifying the Impact
Recent studies show that companies leveraging AI-powered intent data see:
Up to 40% increase in conversion rates for prioritized accounts.
Shortened sales cycles—by as much as 30%—due to more relevant and timely engagement.
Improved marketing ROI, with budgets allocated to high-intent segments that are most likely to close.
Higher customer lifetime value, as expansion opportunities are surfaced earlier and addressed proactively.
How AI Identifies and Scores Intent Signals
The sophistication of AI algorithms enables the extraction of intent from noise. Here’s how it works in a modern GTM stack:
Data Aggregation: AI ingests data from web analytics, CRM, marketing automation, external data providers, and social listening tools.
Signal Clustering: Machine learning models group related activities—such as multiple visits to a pricing page or comparison with competitor solutions—into "intent clusters."
Pattern Recognition: Algorithms detect patterns that historically correlate with purchase decisions, adjusting weightings based on industry, deal size, and buying role.
Scoring and Alerting: Prospects or accounts are scored continuously, with alerts triggered when intent thresholds are crossed or new buying signals emerge.
Predictive Recommendations: AI suggests next-best actions, content, or outreach timing based on intent profile and buyer stage.
Integrating AI Intent Signals Into Your GTM Strategy
To unlock the full potential of AI-driven intent, GTM leaders must rethink their processes and technology stack.
1. Unify Data Across Silos
Consolidate intent signals from sales, marketing, customer success, and product analytics into a single source of truth. This requires integration with CRM, MAP, and third-party intent platforms. Data unification ensures that AI models have the broadest, most accurate picture of buyer behavior—reducing blind spots and duplicative outreach.
2. Align Teams Around Intent-Driven Playbooks
Intent data is only as powerful as the actions it inspires. Build playbooks that prescribe specific actions for sales, SDRs, and marketing based on the type, strength, and recency of intent signals. For example, high-scoring accounts viewing comparison pages may trigger immediate SDR outreach, while low-intent accounts receive nurturing content. Clear escalation paths and SLAs ensure no signal is wasted.
3. Orchestrate Personalized Multichannel Engagement
AI-driven intent enables hyper-personalization at scale. Use detected interests to tailor outreach across email, social, chat, and digital ads. Contextual triggers—like a prospect revisiting pricing pages or engaging with competitor content—should inform timing, messaging, and offers. Personalization increases response rates and builds trust early in the buying journey.
4. Measure and Optimize Continuously
Regularly assess the effectiveness of intent-driven programs. Track KPIs such as engagement rates, pipeline velocity, win rates, and average deal size for high- versus low-intent segments. Use AI analytics to identify which signals best predict conversion and adjust scoring models accordingly. This feedback loop ensures ongoing improvement and alignment with revenue goals.
Common Challenges and How to Overcome Them
Despite the promise of AI-driven intent, GTM leaders often encounter obstacles:
Data Quality: Incomplete or outdated data can undermine AI models. Invest in data enrichment and hygiene to maximize accuracy.
Change Management: Teams may resist new workflows or distrust AI-driven recommendations. Drive adoption through enablement, training, and clear attribution of intent-driven wins.
Signal Overload: Too many signals can overwhelm teams. Prioritize only those with proven impact on deal progression.
Privacy and Compliance: Ensure all intent data usage complies with GDPR, CCPA, and other regulations. Clearly communicate data practices to prospects and customers.
Case Studies: AI-Driven Intent Transforming GTM Outcomes
Case Study 1: Enterprise SaaS Provider Accelerates Pipeline
An enterprise SaaS company integrated AI-powered intent data from multiple sources into their CRM. The result: sales reps identified in-market accounts 3X faster, prioritized outreach, and increased opportunity creation by 35% within six months. Deal cycles shortened as reps tailored messaging to detected pain points, resulting in a 20% increase in average deal size.
Case Study 2: Marketing Team Reduces Waste, Boosts ROI
A leading cybersecurity vendor used AI intent scoring to segment and target high-propensity accounts for ABM campaigns. By focusing ad spend and content syndication on these cohorts, marketing reduced cost per qualified lead by 45% and improved campaign ROI by 60%. Alignment with sales improved as both teams operated from a shared view of account intent.
Case Study 3: Customer Success Uncovers Expansion Opportunities
A cloud infrastructure provider leveraged AI-driven intent to monitor usage patterns and external research by existing customers. Early signals of expansion—such as increased product usage or competitive research—triggered proactive outreach. As a result, the company increased expansion revenue by 25% and reduced churn among at-risk accounts.
Future Trends: How AI Intent Signals Will Evolve
The pace of innovation in AI and intent data is accelerating. GTM leaders should prepare for:
Deeper personalization: AI will enable micro-personalization, dynamically adapting messages to individual stakeholders and their roles in the buying group.
Real-time orchestration: Intent signals will trigger automated, orchestrated plays across sales, marketing, and customer success with minimal human intervention.
Predictive forecasting: Advanced models will predict not only which accounts are in-market, but when and how to engage for maximum likelihood of conversion.
Expanded data sources: Integration of voice, video, and unstructured data will further enrich intent models, providing a 360-degree view of buyer needs.
Best Practices for GTM Leaders
Audit your current intent data sources and identify gaps.
Evaluate AI platforms based on data integration, signal accuracy, and ease of use.
Develop intent-driven playbooks aligned to your sales methodology (e.g., MEDDICC, Challenger, SPIN).
Foster a culture of experimentation—pilot, measure, and optimize intent-led initiatives.
Invest in change management and stakeholder alignment to drive adoption.
Monitor regulatory changes to ensure responsible intent data usage.
Conclusion: The Competitive Advantage of AI-Driven Intent
In an era of information overload and heightened competition, GTM leaders can no longer rely on static lead scoring or generic outreach. AI-driven intent signals provide a transformative lens, surfacing hidden opportunities and enabling precision engagement at scale. By prioritizing intent and embedding AI insights into every stage of your GTM strategy, you’ll accelerate pipeline, improve win rates, and build lasting customer relationships. The future belongs to those who see— and act on—intent before their competitors do.
Key Takeaways
AI-driven intent signals unlock actionable insights for every GTM function.
Success requires unified data, aligned teams, and continuous measurement.
Early adopters are achieving faster growth, higher ROI, and more resilient pipelines.
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