A decade ago, intent data promised to revolutionize the go-to-market (GTM) landscape. For a lot of teams, the reality was budget fatigue, limited activation, and disappointment. This outcome was not a failure of the data itself, but a failure of the early model. It was oversold before the technology or GTM teams were ready to operationalize it.
Flash forward, and intent data is experiencing a second wave. This transformation is fueled by technological maturity, superior accuracy, and an obsession with operationalization. The conversation has shifted from hype and dashboards to precision and predictable revenue.
This article outlines the transformation of intent data’s definition and the approach required to turn intent into a predictable GTM asset today.
The Disconnect of the First Wave
The initial iteration of intent data—the “First Wave”—was defined by critical failures that created a massive gap between signal detection and real-world action. This is the root cause of the broken trust we see today:
1. Account-Level Fog
First-wave intent data was siloed at the account level. It could tell you that “Company X is researching ABM,” but it lacked the persona-level precision needed to identify the actual buying group members: the CFO, the RevOps lead, or the Marketing Director.
Sales was handed lists of “hot accounts” with no context on who to call or what their role-specific pain points were. This ambiguity forced sellers to cold call lists with minimal relevance, leading to poor conversion rates and the quick erosion of trust in the data source.
2. The Operational Chasm
Early intent data was sold as a standalone dashboard, not an integrated system. It lacked the necessary architecture to flow into data warehouses, CRMs (like Salesforce), MAPs (like Marketo), and media platforms.
This created a massive signal-to-action gap. Data lived in isolation, requiring manual extraction, processing, and prioritization—a task that is simply unsustainable for efficient RevOps teams. We were using intent solely as a reporting metric rather than a workflow trigger surrounded by context.
3. The Personalization Misstep
Early adopters often mistook intent signals for a personalization script. This led to “creepy” outreach messages like, “I saw you downloaded X…” or “I noticed you were researching Y…”
The reality is that intent data provides context, not copy. It tells you who is showing interest, what they care about, and how deeply they are engaging. Real personalization is achieved by combining that signal with human empathy and language, which is then used to inform the sales narrative and content creative.
Defining the Second Wave: Precision and Activation
The current generation of intent data, this second wave, is defined by technical maturity and a focus on integration.
1. Persona-Level Intelligence
Modern intent platforms monitor granular signals to identify buying group intent. This allows teams to target specific roles within an account—for instance, serving cost-efficiency messaging to the CFO while focusing on pipeline velocity for the Demand Gen Manager. This level of precision is non-negotiable for complex B2B sales cycles.
2. Signal as a Workflow Trigger
The fundamental shift is how the data is used. Intent data is now the engine of signal-based revenue operations. It’s no longer enough to know an account is interested; the system must automatically trigger a specific action and provide additional context.
When a high-value signal is detected, the modern system is able to:
- Triggers a nurture sequence in the CRM.
- Sends an instant notification to the BDR/AE.
- Flows the account into an exclusion list for content syndication.
- Populates an ad audience, shareable on media platforms.
This automation replaces manual guesswork and hands sellers the next-best-action instantly, drastically improving seller efficiency and timeliness. For more information on how we use our intent data and automation to assist our marketing, sales, and revenue operations processes, check out our eBook titled “How Intentsify Uses Intentsify: Volume II.”
3. Stay Up to Date on How Your Team Can Utilize AI and Dynamic Scoring
Look into how your team can leverage AI to go beyond basic keyword matching. Today, teams are using dynamic scores that track complex research patterns and integrate external intelligence (like funding rounds or executive changes) to create a highly accurate, predictive prioritization engine. This ensures that you are focusing resources only on accounts with the highest purchase readiness.
The New Rules of Re-Engagement
The second wave requires clear boundaries and a disciplined strategy. Intent data must function as a performance amplifier, not a silver bullet.
Teams re-embracing intent data today are succeeding by following these operational rules:
- Lead with Clarity: Both Sales and Marketing must agree on what each intent signal represents (e.g., “Competitive search is an MQL trigger, not a sales-ready lead” or “This job title is staying up to date on industry happenings, not within our ICP”).
- Integrate Intentionally: Never silo intent data in dashboards. Bring it into the systems your teams are already in: CRM, MAP, and media platforms.
- Prioritize, Don’t Personalize: Use intent data primarily as a prioritization engine. Let the data tell you who is active. Let your human creativity and content tell them why they should engage and how you can help them succeed.
- Close the Feedback Loop: Continuously track which intent signals and content themes are most predictive of closed-won revenue, and use that insight to refine your models.
Intent data stopped being a “product to buy” and became a strategy to build around. By focusing on architecture, precision, and alignment, teams can transform intent from a source of frustration into the foundation of a predictable, efficient revenue engine.
FAQs
What is the critical technological difference between the “First Wave” and “Second Wave” of intent data that guarantees better ROI today?
The critical difference is the shift from a dashboard-based data source to an integrated workflow trigger. The Second Wave uses intent data to power real-time automation. When a high-value signal occurs, the system automatically triggers sequences in the CRM or ads platform, eliminating manual prioritization and ensuring timely action. This significantly reduces the signal-to-action gap, which is key to better ROI.
Given the historical distrust of intent data lists among sellers, how do we rebuild trust and ensure Sales actually adopts this new system?
You rebuild trust by replacing raw lists with context and next-best actions. The system should flow the intelligence—not just the data—directly into the sales environment. Sales reps should see which specific personas are active, what topics are surging, and be served a concrete, relevant outreach sequence. When the system consistently gives them the next-best-action that leads to qualified conversations, adoption will follow.
How does investing in persona-level intent data future-proof our GTM strategy against competitors who are still using basic account-level signals?
It provides a fundamental precision advantage. While competitors waste budget targeting an entire company, you are focusing resources exclusively on the active buying group personas and their role-specific pain points. This not only increases conversion rates but also prepares your system for the integration of next-generation signals (like Dynamic Scores and Business Event Data), ensuring your GTM engine is continuously evolving and staying ahead of the buyer’s journey.
