February 16, 2026
Deal Intelligence
Leverage real-time competitive intent signals to drive faster, higher-converting B2B sales outcomes.
Intent data has become table stakes in B2B sales. Every vendor promises "actionable signals" that tell you when accounts are in-market. Every platform claims their scoring model surfaces the hottest opportunities at exactly the right time.
The problem is that most intent signals fall short in predictable ways. They're too general, arrive too late, lack relevance to your specific market context, or fail to map to the roles that actually matter in your deal cycle.
For revenue teams building signal-based go-to-market motions, the question is not whether to use intent data. It's how to prioritize which signals actually deserve your reps' attention—and which are just noise dressed up as insight.
This post lays out a framework for evaluating and prioritizing intent signals based on proximity to the buyer, specificity of the action, and relevance to your actual sales motion. It's built from years of working with revenue operations teams to structure account prioritization systems that scale without drowning sellers in false positives.
Account prioritization comes down to four things: Account, Contact, Context, and Timing.
Account and contact data are relatively easy to get. Every data provider can tell you company size, industry, tech stack, and who the VP of Sales is. These are table stakes. They help you build lists, but they don't tell you when to act.
Context and Timing are the hard parts to source consistently—and that's what most "intent signals" are trying to approximate. Context tells you why an account might be receptive right now. Timing tells you when to engage before the window closes.
The gap between what intent vendors promise and what they deliver usually comes down to four failure modes:
1. The signal is too general
"This company is researching sales enablement" doesn't tell you if they're evaluating vendors or if one person downloaded a whitepaper. Broad topic surges lack the specificity needed to trigger outbound action.
2. Not relevant in the company's actual market context
A hiring surge at a company that just raised a Series C is different from a hiring surge at a bootstrap profitable company managing cash carefully. Most intent tools don't account for these nuances.
3. Not relevant to a specific role
Account-level signals tell you something is happening at a company, but not who cares. If your buyer is the CFO and the signal is product team hiring, the connection is unclear.
4. Shows up too late to act on
Many intent signals are backward-looking. By the time Bombora tells you an account spiked on your category last month, they may have already talked to three competitors. As 6sense's research on the Dark Funnel has shown, most B2B buying happens invisibly until vendors are invited in. Late signals miss the early research phase entirely.
Understanding these failure modes helps explain why not all intent signals are created equal—and why proximity to the actual buyer matters more than volume of data.
The framework I use prioritizes intent signals based on proximity to known buyers and specificity of action. Contact-level signals that match specific buyer personas rank higher than account-level signals that indicate general activity. Direct actions rank higher than inferred behavior.
This hierarchy flows from highest to lowest priority:
What it is: Known people taking high-value actions in your own systems—emails opened, pages viewed, product usage, demos attended, content downloaded.
Why it ranks highest: You know exactly who did what, when they did it, and what they engaged with. There's no inference layer. The person is identified and the behavior is specific.
Tools and measurement: This is what you're tracking with Segment, HubSpot, Marketo, GA4, or your product analytics platform. If you're a PLG company, product usage signals (feature adoption, frequency, expanding usage to new team members) are the strongest first-party signals you have.
Example in practice: A VP of Sales at a Series B SaaS company views your pricing page twice in one week, then downloads a competitive comparison guide. This is a direct, attributable signal that this specific person is actively evaluating. Your SDR should be reaching out within 24 hours.
Why this matters: First-party engagement signals function as triggers for communication, not just scoring. When a known contact in your ICP takes a meaningful action, that's an immediate outbound or automated touchpoint trigger.
What it is: Your known contacts doing something indirect that creates an opportunity—most commonly, job changes. Someone you've engaged with moves to a new company in your ICP.
Why it's high priority: The person is known to you, the action is concrete, and the context is clear. Job changes create natural re-engagement windows. According to UserGems' data, buyers who move to new companies are 3x more likely to bring their preferred vendors with them, and the first 90 days in a new role are prime buying windows.
Tools and measurement: UserGems specializes in job change tracking and buyer re-discovery. You can also track this manually through LinkedIn Sales Navigator alerts or by monitoring changes in your CRM contact data (title changes, company changes, email bounces).
Example in practice: A champion from a closed-won deal at a previous company just became VP of Revenue at a faster-growing company in your ICP. She knows your product, trusts your team, and is now empowered to make buying decisions. This is one of the highest-probability opportunities in your pipeline.
Why this matters: First-degree inferred signals essentially extend your first-party data by following your known contacts to new opportunities. The relationship context carries over even though the account is new.
What it is: Single, concrete actions taken by specific people at target accounts—captured by third-party tools that monitor external behavior.
Why it's valuable: You're seeing what individual buyers are doing, not just what's happening at the account level. The action is specific enough to inform messaging and timing.
Tools and measurement: Deal Intelligence (our platform) tracks buyer persona activity like your ICP connecting with competitors' AEs on LinkedIn, engaging with competitor content, or showing up in relevant buying committee org changes. Champify tracks when past champions become active in new buying cycles. Common Room aggregates buyer activity across community platforms, Slack groups, and social channels.
Example in practice: Deal Intelligence flags that a Director of Sales Ops at a target account connected with your top competitor's AE on LinkedIn yesterday. You can see the specific person, the specific competitor, and the timing. Your rep can now reach out with context: "I noticed you're evaluating sales tools—here's how we compare to [competitor] on the dimension that matters most to teams like yours."
Why this matters: These signals give you both the who and the what. You're not inferring from aggregate behavior. You're seeing discrete actions by identifiable buyers, which enables personalized, timely outreach.
What it is: People at an account collectively or anonymously taking actions that suggest buying interest—without knowing exactly who's doing what.
Why it's lower priority: You know something is happening at the account, but not who's driving it or how serious it is. These signals are useful for scoring and prioritization, less useful as direct outbound triggers.
Tools and measurement:
Example in practice: Bombora flags that an enterprise account in your ICP has shown a 3x surge in content consumption around "sales compensation management" over the past two weeks. You don't know which people are researching, but you know the account is in-market. This triggers your SDR to enrich contacts at that account and launch a targeted outbound sequence.
Why this matters: Account-level inferred intent functions as a trigger for contact enrichment and account scoring, not immediate outreach. You're using these signals to decide which accounts to research and build contact lists for, not which individuals to call today.
What it is: Observable changes in company fundamentals—hiring surges, funding rounds, leadership changes, revenue growth signals.
Why it's even lower priority: These changes indicate capacity or potential need, but don't indicate active buying behavior. A company raising a Series B might buy your tool, but they're not necessarily in-market right now.
Tools and measurement:
Example in practice: A company in your ICP just raised a $20M Series B and is hiring aggressively across go-to-market roles. They're scaling, which suggests potential budget and urgency. But this is a positioning signal, not a buying signal. It tells you they're worth prioritizing in your outbound strategy, not that they're evaluating tools this week.
Why this matters: Firmographic changes help with account prioritization and segmentation. They're useful for deciding which accounts to add to your outbound lists or which existing accounts to move up in priority. They don't tell you when to strike.
What it is: Proprietary scoring models that aggregate multiple signals into a single number or grade—often combining firmographic fit, technographic data, web activity, and third-party intent.
Why it's lowest priority for outbound triggers: Black-box scores are helpful for sorting and list-building at scale, but they obscure the underlying signal. You don't know why an account scored high, which makes it hard to personalize outreach or validate the urgency.
Tools and measurement: Most major data providers bundle intent scores:
Example in practice: Your sales engagement platform flags 50 accounts with "high intent scores" this week. The score tells you these accounts are worth calling, but it doesn't tell you who to call, what to say, or whether the signal is stale. Your SDRs still need to enrich contacts and craft messaging—the score just helped prioritize which accounts to work.
Why this matters: Composite scores are useful for high-volume list building and routing decisions ("which 100 accounts should this SDR focus on this month?"), but they're a starting point, not an action trigger. Human or AI-driven research still needs to happen before outreach.
In practice, this prioritization hierarchy determines two things: triggers for communication and triggers for research.
Contact-level signals (tiers 1-3) function as triggers for communication. When a known person or a specific identified buyer takes an action, that action should trigger outreach—whether automated (email sequence, Slack message, calendar invite) or manual (personalized email, LinkedIn message, phone call).
Account-level signals (tiers 4-6) function as triggers for contact enrichment and account scoring. When an account shows aggregate intent or experiences a firmographic change, that should trigger research: enrich the buying committee, identify champions, build a contact list, update account scoring, or add the account to an outbound campaign.
This distinction matters because many revenue teams treat all intent signals the same way—as outbound triggers. When SDRs get a list of 200 accounts with "high intent" and no clear point of contact or specific action to reference, the outreach is generic and the conversion rate is low.
The framework helps you route signals appropriately:
AI tools are increasingly marketed as "signal orchestration" platforms—automating the workflow from signal detection to outreach. The promise is that AI can monitor signals, draft personalized messages, and execute outbound at scale without human intervention.
In practice, AI tools are better at orchestration once the signal surfaces than at generating the signal itself.
AI is effective at:
AI is less effective at:
The role of AI in signal-based selling is to reduce manual work in acting on signals, not to replace the strategic work of choosing which signals to act on. Teams that over-rely on AI-generated outbound without validating the underlying signal quality end up with high-volume, low-conversion spray-and-pray motions.
Signal-based selling has been a growing consideration for revenue operations and GTM builders over the past year or two. What's changed recently is that executives are joining the conversation.
HubSpot CEO Yamini Rangan has posted multiple times this month about the shift to signal-based GTM. In a recent LinkedIn post, she argued that the future of B2B sales is about "orchestrating around signals" rather than static lead routing and linear funnels.
Jason Lemkin at SaaStr has been writing about the death of the MQL and the rise of signal-based engagement models. His thesis: MQLs were always a proxy for buying intent, and now we have better proxies.
Tido Capital's research on signal-driven GTM argues that the best-performing revenue teams are shifting from activity-based (calls, emails, meetings) to signal-based (engagement, intent, fit) prioritization.
The reason this matters now is that buyers have gone dark. The average B2B buying committee does 70% of their research before engaging with vendors, according to Gartner's research on The New B2B Buying Journey. Cold outbound to static lists is losing effectiveness. Signal-based engagement—reaching out when buyers are actively researching—is the adaptation.
If you're building or refining a signal-based go-to-market motion, the framework above gives you a way to evaluate and prioritize the dozens of intent tools flooding your inbox.
Start by asking which tier of signal you're trying to solve for:
Then ask whether the signal is specific enough to inform messaging. If your SDR can't personalize an email based on the signal ("I noticed you..."), the signal is probably too general to drive high-conversion outreach.
Finally, ask whether the signal is fresh enough to matter. Intent that's 30 days old is not intent—it's history. The best signals have a short half-life and require fast action.
Signal-based selling is not about collecting more data. It's about collecting the right data and acting on it faster than your competitors. The teams that win are the ones that can distinguish actionable signals from noise—and prioritize their reps' time accordingly.
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Leverage real-time intent signals to drive faster, higher-converting B2B sales cycles.