How Can GTM Teams Turn Buyer Signals Into High-ACV Deals?

Enterprise deals rarely collapse overnight. Instead, they drift.

A champion goes quiet. A promising opportunity stalls in pipeline review. Weeks pass with no meaningful engagement, and sales teams are left debating the same question: Is this deal still alive?

For decades, the answer to that question relied on instinct. Experienced sellers learned to read subtle cues, such as email tone and meeting cadence, and make educated guesses about a deal’s trajectory.

But modern buying behavior has quietly broken that model. As explored in Why AI Search Killed Your Old Funnel and MQLs, the traditional B2B sales funnel has shifted upstream, and much of the buying journey now happens outside your areas of visibility.

Today’s B2B buyers research vendors long before contacting sales. They often evaluate multiple options across review platforms and communities, and ask AI chatbots before a single discovery call happens.

The new challenges for revenue teams now are:

1. How do we spot real buying momentum before the deal shows up in our CRM?
2. How do we move from reactive to proactive prospecting strategies?
3. How do we differentiate our products and messaging from competitors in an AI-first world?

The answers increasingly lie in reading buyer-signal patterns in real time and turning those signals into timely, relevant sales action.

If your go-to-market GTM team is seeking faster deal velocity, clearer pipeline prioritization, and most importantly, larger, higher-confidence enterprise deals — this article’s for you.

What happens when software seller instinct meets real buying signals?

Signals don’t replace intuition; they refine it.

Revenue teams often encounter moments when instinct and data tell different stories. 

James Roth, Chief Revenue Officer at ZoomInfo shared an example where an enterprise deal appeared to stall. After several weeks of silence, the account executive assumed the prospect had chosen a competitor and began shifting attention elsewhere.

But buyer signals told a different story.

The account showed:

• Increased research activity around competing solutions
• Multiple website visits from a VP of IT security
• A newly hired CTO who had purchased the same solution in previous roles

These signals indicated internal evaluation was still underway and happening quietly.

Instead of walking away, the sales team re-engaged with messaging tailored to the new CTO’s priorities. The deal closed within 30 days at six-figure average contract value (ACV).

“Buyer silence doesn’t mean buyer disinterest. Signals reveal what’s actually happening behind the scenes.”

James Roth
Chief Revenue Officer of ZoomInfo

Why patterns matter more than individual signals

Individual signals rarely tell the full story. They become powerful when they form patterns. These patterns reveal when an account is moving from exploration to evaluation — the critical window when the right outreach can influence deal trajectory.

Enterprise buying decisions typically involve:

• Multiple stakeholders
• Long evaluation cycles
• Parallel vendor comparisons

Bruno Basic, GTM leader at DualEntry, experienced this firsthand when his team assumed a prospect cared about one use case — until behavioral signals revealed repeated interest in a completely different product capability.

“Signals beat hunches. We assumed one use case; website intent said otherwise.”

Bruno Basic
GTM Leader of DualEntry

The insight for sales teams is simple: Recognizing signals is only the first step. The harder challenge is operationalizing them.

Many organizations already have access to dozens of buying indicators from hiring trends to product research activity — yet still struggle to translate those signals into pipeline impact.

Why do most GTM teams struggle to turn signals into revenue?

Signal adoption rarely fails because of technology. It fails because of operational ambiguity, and because not all intent signals are created equal. Different signal types reveal different stages of buyer interest, and without that context, teams struggle to prioritize the right accounts.

In practice, revenue teams often work with three different categories of signals:

• First-party signals: Show engagement with your own properties such as website visits, product usage, or marketing interactions. These signals often appear later in the buying journey.
• Second-party signals: Deliver behavioral insights shared through trusted partner ecosystems
• Third-party signals: Show external buying activity across research platforms, review sites, and broader digital behavior
  
  

Contrary to first-party signals, second and third-party signals surface earlier research activity before buyers ever engage directly with your brand.

Lisa Kelly, Chief Growth Officer at Nue.io, describes the difference between high-performing signal programs and those that stall.

“The biggest separator is whether intent is treated as decision infrastructure or just an interesting signal.”

Lisa Kelly
Chief Growth Officer of Nue.io

In many organizations, signals appear in dashboards but lack clear next steps.

High-performing GTM teams instead define:

• Who owns each signal type
• How quickly sellers should respond
• What messaging aligns with the buyer’s stage
• How signals influence account prioritization

Leslie Venetz, Founder of The Sales-Led GTM Agency, emphasizes that signals only drive pipeline when they are interpreted correctly. Timing your sales motion is what creates movement.

“Intent signals only convert when you understand where the buyer sits in their awareness journey.”

Leslie Venetz
Founder of The Sales-Led GTM Agency

Treating every signal as “ready to buy” often results in poorly timed outreach — and missed opportunities.

If signals reveal when buyers are evaluating, they can also reveal how large the opportunity might become.

That’s where the connection between signals and average contract value (ACV) becomes particularly powerful.

How do software buyer signals influence deal size and expansion opportunities?

Buyer signals don’t merely identify active deals; they reveal emerging strategic priorities inside an account.

Here’s how ZoomInfo created a threefold expansion of a target account:

ZoomInfo reports that accounts exhibiting strong signal activity generated 2.7× larger deal sizes compared with accounts without those signals.

In one case, an existing customer using a basic product started showing signals by:

• Hiring large numbers of SDRs
• Referencing “sales efficiency” and “digital transformation” 17 times in earnings calls
• Researching automation technologies

Together, those signals pointed toward a broader transformation of the sales organization.

Instead of treating the renewal as routine, the account team repositioned the conversation around scaling sales productivity.

Signals didn’t create the opportunity. But they revealed it earlier than the team would have naturally realized it.

While industry experts show how signals influence deals in practice, software buyer reviews reveal something equally important: Where signal tools succeed and where they fail — inside real GTM teams.

The G2 take: What 500 G2 reviews reveal about buyer-intent platforms and GTM challenges

Analysis of 500 G2 reviews in the Buyer Intent Data Providers category surfaces a clear pattern:

Buyers don’t just want signals. They want signals they can trust and act on immediately.

Three expectations dominate across reviews:

1. Signal accuracy
2. Integration with existing GTM workflows
3. Actionable context for sellers

Tools that surface buyer activity insights without connecting them to execution tend to generate lower enthusiasm from users.

This emphasis on accuracy and actionability reflects a broader challenge for revenue teams: Not all signals are equally reliable or operational.

“Intent data is only valuable if it reflects real buying behavior. What makes G2’s signals actionable is that they come directly from in-market buyers actively researching solutions on G2.”

Andrea Youmans
Director of Product Marketing, Buyer Intent at , G2

Andrea explains that signals derived from actual buyer research activity give revenue teams clearer visibility into which accounts are actively evaluating solutions. When these signals are delivered in real time and integrated into existing GTM tools, they allow teams to prioritize accounts earlier, improve pipeline quality, and ultimately drive higher ACV.

While software buyers consistently praise signal platforms that help them spot in-market accounts and prioritize sales outreach with greater precision, they also reveal a clear set of frustrations.

The 7 biggest SaaS sales frustrations GTM teams are talking about

A closer investigation of reviews highlighting user dislikes exposes recurring patterns and operational challenges.

1. Signal overload without prioritization
Teams receive dozens of alerts but lack a clear framework for determining which accounts deserve immediate attention.

2. Signals that stop at dashboards
Insights exist, but they do not trigger workflows inside CRM or sales engagement platforms.

3. Fragmented GTM data ecosystems
Signal data often sits outside CRM systems, forcing sellers to manually connect insights across tools.

4. Difficulty identifying buying committees
Signals tied to individual contacts often miss the broader account-level momentum.

5. Limited attribution to pipeline impact
Leaders struggle to connect signals directly to revenue outcomes.

6. Delayed response to buying activity
Signals arrive after buyers have already progressed deep into evaluation.

7. Skepticism around signal accuracy
When signals appear inconsistent or noisy, adoption drops quickly.

Why GTM signal strategies fail

Source: G2 review analysis between September 2025 to March 2026

So, what do GTM teams want from buyer intent data providers?

Beyond common frustrations, the review dataset surfaces several broader patterns about how teams evaluate signal platforms.

1. Execution matters more than analytics

Buyers consistently value tools that trigger sales action over those that simply visualize buyer activity. Signals that integrate with CRM tasks, outreach sequences, or account prioritization workflows receive stronger feedback.

2. Trust determines adoption

Signal quality directly determines whether sales reps actually use the signals to prioritize outreach. When signals appear noisy, inaccurate, or difficult to validate, SDRs and account executives lose confidence in the data and revert to traditional prospecting methods like cold outreach and manual account research.

High-confidence signals tied to clear buyer behaviors are far more likely to influence outreach prioritization and deal strategy.

3. Account-level visibility is becoming essential

Signals tied to individual contacts often fail to capture buying committee behavior. Software buyers increasingly prefer platforms that reveal activity across entire accounts.

4. Signals must support multiple GTM roles

Buyer-intent signals are used differently across the revenue organization, and platforms that support multiple roles tend to deliver the most value.

• CROs: identify high-probability deals earlier to improve pipeline quality and uncover larger ACV opportunities
• Revenue operations: unify fragmented data sources and connect signals to forecasting, pipeline analytics, and GTM workflows
• Demand generation: detect early buying activity to activate targeted campaigns and accelerate account engagement
  

Signal platforms that address all three needs simultaneously tend to deliver the strongest revenue impact.

What’s the new north-star metric for revenue teams?

For years, pipeline volume served as the primary success metric in B2B sales.

But pipeline size alone often hides deeper problems — poor qualification, stalled deals, and inflated forecasts.

Signal-driven revenue teams are shifting toward a different measurement framework: signal-qualified pipeline.

Instead of asking how much pipeline exists, leaders ask:

How much pipeline shows real buying activity?

Deal velocity often becomes the earliest indicator of success.

ZoomInfo reports that signal-informed deals closed 40% faster than deals pursued without those signals.

When sales engages buyers during active evaluation, conversations move faster, and outcomes become more predictable.

How revenue leaders can move from hunches to signal-backed high ACV

Signals alone do not transform revenue operations; execution does.

The revenue teams seeing the strongest results are building shared signal frameworks across marketing, sales, and RevOps.

Their playbooks tend to follow five principles:

• Prioritize signals tied to measurable revenue outcomes
• Embed signals directly into seller workflows
• Align outreach to buyer awareness stage
• Unify signal data across GTM systems
• Track signal influence on velocity and ACV

Signals don’t eliminate the need for human judgment. But they dramatically reduce the guesswork.

And in a buying environment where evaluation often happens silently, the ability to interpret those signals early may be the difference between chasing deals and closing them.

Frequently asked questions about buyer intent signals

Q1. What are buyer signals in B2B sales?

Buyer signals are observable behaviors that indicate an account may be evaluating a solution. These signals can include competitor research, product page visits, hiring activity, leadership changes, or engagement with industry content. Revenue teams analyze these signals to identify which accounts may be entering an active buying cycle and prioritize outreach accordingly.

Q2. How do buyer signals help increase average contract value (ACV)?

Buyer signals reveal strategic initiatives happening inside an account. For example, signals such as hiring sales teams, researching automation tools, or discussing transformation initiatives often indicate larger operational changes. When sales teams recognize these patterns early, they can position broader solutions, leading to larger deal sizes and expansion opportunities.

Q3. Why do many GTM teams struggle to operationalize buyer signals?

Many organizations struggle to operationalize buyer signals because signals appear in dashboards but are not connected to workflows. Without clear ownership, prioritization rules, and CRM integration, sales teams may see signals but lack guidance on how to act on them. High-performing teams solve this by linking signals directly to outreach triggers, account prioritization, and revenue forecasting.

Q4. What is a signal-qualified pipeline?

Signal-qualified pipeline refers to pipeline opportunities supported by real buying activity. Instead of measuring pipeline purely by volume, revenue teams evaluate whether accounts are showing meaningful buying signals such as research activity, stakeholder engagement, or technology evaluation. This helps leaders focus on pipeline quality rather than pipeline quantity.

Q5. How can GTM teams turn buyer signals into revenue impact?

To translate signals into revenue outcomes, GTM teams must operationalize them across sales, marketing, and RevOps workflows. This typically involves:

• Prioritizing accounts based on signal strength
• Triggering sales outreach automatically
• Aligning messaging with the buyer’s stage
• Tracking signal influence on pipeline and deal velocity

When signals are embedded into daily workflows rather than dashboards, they become actionable revenue intelligence.

If you want to understand what buyers are signaling across your category and how to turn that insight into action, explore G2’s buyer signals.

Edited by Supanna Das