How to Define and Use B2B Buying Signals to Drive Revenue

Signal-based go-to-market strategy has emerged as the defining theme for commercial teams in 2025. Traditional volume-based tactics have stopped yielding results, undermined by oversaturation of automated outbound, buyer fatigue, and the rise of dark funnels. In this article, we'll explore how this shift is reshaping B2B sales and marketing, and more importantly, how to leverage signal intelligence effectively to drive meaningful revenue growth.

The Collapse of Traditional Outbound Efficiency

Since 2023, GTM teams across industries have struggled to hit pipeline and revenue goals despite having unprecedented access to data. The prevailing approach focused primarily on building large lists of accounts, which were then targeted with bulk email campaigns using basic, templated messaging. This spray-and-pray methodology initially showed promise when adoption was low, but market dynamics have fundamentally changed.

Over the past year, conversion rates have dropped dramatically as email service providers implemented stricter policies to combat cold email abuse. Gmail, Outlook, and other major platforms now flag or block messages that exhibit patterns consistent with mass outbound campaigns. Meanwhile, buyers have grown increasingly sophisticated at filtering out generic outreach, with open rates plummeting and reply rates hitting record lows.

The data tells a stark story: teams report needing 2–3× more touches than in 2022 to book a meeting. Sales development representatives report spending more time than ever on outbound activities while seeing fewer conversations materialize. The economics of traditional outbound have simply broken down, forcing teams to rethink their entire approach to market engagement.

Buyer Control and the Dark Funnel

Today's buyers complete roughly 70% of their purchasing journey before ever contacting a salesperson, and they deliberately hide much of their research in private channels. This phenomenon, known as the 'dark funnel', encompasses buyer activity in online communities, private groups, review sites like G2 and TrustRadius, podcasts, and peer-to-peer conversations that leave no trackable digital footprint.

Traditional lead scoring models were built on the assumption that you could track buyer behavior through form fills, content downloads, and website visits. But these signals have become increasingly unreliable indicators of genuine buying intent. The rapid adoption of AI assistants exacerbates this challenge further, as these tools extract specific information on behalf of users without leaving the same digital breadcrumbs. Lead magnets like eBooks and gated whitepapers, once staples of demand generation, are becoming obsolete as AI can synthesize equivalent information from publicly available sources.

This shift in buyer behavior means that by the time a prospect becomes visible in your traditional funnel, they've already formed opinions about your category, evaluated alternatives, and potentially ruled you out. The companies that win are those that can identify and engage buyers earlier in this hidden journey, when they're still open to influence.

Market Saturation and Buyer Fatigue

Between 2020 and 2024, more than 75% of B2B technology categories experienced a doubling in the number of competitors, according to ICONIQ's 2025 State of GTM Report. Every niche has become overcrowded, with players competing for the same finite budget pools. This explosion of vendors means buyers face an overwhelming number of choices, making differentiation increasingly difficult.

The average B2B decision-maker is now exposed to thousands of advertisements and hundreds of outbound attempts each day. This relentless barrage creates cognitive fatigue and has trained buyers to develop sophisticated filtering mechanisms. They've learned to ignore generic messaging, delete obvious templates, and trust personal networks over vendor claims.

The convergence of market saturation and buyer fatigue creates a perfect storm where generic outbound channels (cold email, mass advertising, and static sequences) feel indistinguishable from spam. Buyers don't just ignore these approaches; they actively block senders, mark messages as spam, and develop negative associations with brands that persist with irrelevant outreach. What worked in 2019 doesn't just underperform in 2025; it actively damages your brand and burns through resources.

Why Signal-Based GTM Is Surging in 2025

Using buying signals directly addresses these systemic challenges by fundamentally shifting outbound from guesswork to real-time relevance. Instead of chasing volume, GTM teams use live behavioral and firmographic indicators to narrow their focus to the subset of the market that's genuinely ready for action.

Signal-based GTM works because it aligns your outreach with moments of genuine need. When a company raises funding, they're likely evaluating new tools to support growth. When they hire a new executive, that person often wants to bring in solutions they've used successfully before. When they post job descriptions mentioning specific technologies, they're signaling current or planned adoption. These moments represent windows of opportunity where your message will be welcomed rather than ignored.

The economic logic is compelling: by focusing resources on accounts showing active buying signals, you dramatically improve efficiency. Sales teams spend time on conversations that actually matter. Marketing budgets target prospects who are receptive. Customer success can proactively engage accounts showing signs of expansion. In effect, signal intelligence flips the high-volume, low-relevance approach to one that prioritizes timing, relevance, and buyer experience.

Teams implementing signal-based strategies typically see immediate improvements in key metrics. Reply rates jump from single digits to 20-30%. Conversion rates from initial conversation to qualified opportunity increase by 2-3x. Sales cycles compress as you're catching buyers when they're already in motion. Perhaps most importantly, the quality of conversations improves dramatically because your outreach demonstrates genuine understanding of the prospect's current context.

Defining the Right B2B Signals for Your Business

Buying signals typically fall into several distinct categories, each revealing different aspects of buyer readiness:

What Counts as a B2B Buying Signal

• Business changes create organizational shifts and shake-ups that drive buying decisions. These include raising funding rounds, making new executive hires, entering new markets, experiencing hiring surges or slowdowns, launching new products, opening new offices, or undergoing mergers and acquisitions. Each represents a moment when companies reassess their technology stack and operational processes.

• Techstack shifts signal changing technology priorities. Look for adoption of new software platforms, job postings mentioning specific tools or technical skills, participation in technology user groups, or migrations away from competitors. These indicators suggest both technical readiness and budget availability for new solutions.

• Behavioral signals reveal interest through action. When people at a target company engage with specific topics on social media, attend relevant industry events, post reviews on sites like G2, publish content about related challenges, or participate in online communities discussing your category, they're demonstrating active interest and research behavior.

• Industry-wide trends create shared challenges across multiple accounts simultaneously. Changing regulations, new compliance requirements, market downturns, surges in investor interest, technological disruptions, or macroeconomic shifts all trigger waves of buying activity as companies adapt to new realities.

• Direct engagement represents the strongest signals, typically falling under first-party data. This includes prospects engaging with your content, attending your webinars, signing up for trials, making repeated visits to your website, or directly reaching out through various channels.

How to Operationalize Signals

Not every signal matters equally for every business. A funding round might be highly predictive for infrastructure software but irrelevant for mature enterprise solutions. A CRO hire could be crucial for sales tools but less important for developer products. The key is identifying which changes most reliably indicate a genuine sales opportunity for your specific offering.

Step 1: Build a Clear Ideal Customer Profile (ICP)

Before you can identify meaningful signals, you need a precise understanding of what your ideal customer actually looks like. This foundational step is often undervalued, leading to wasted effort on misaligned prospects once GTM activities scale.

Your ICP functions as the definition of "best fit" that filters which signals matter. Start by analyzing your happiest, most successful customers. Document commonalities in their firmographic details: industry verticals, company size, revenue range, funding stage, geographic locations, and technology stack. But go deeper than demographics. Understand their buying cycle: how long did evaluation take, who was involved in the decision, what triggered their search, what alternatives they considered.

Conduct systematic market research to validate assumptions. Interview customers to understand why they chose you and what problems you solved. Talk to lost deals to learn why you weren't selected. Review win-loss data to identify patterns. Establish clear "must-haves" that define your sweet spot and explicit disqualifiers that help you avoid wasting time on poor fits.

If you're in the startup phase, distinguish between your current ICP based on existing traction and your aspirational ICP, the customer profile you're building toward. Your early customer base often includes companies that tolerate product gaps because they face acute pain, but these may differ significantly from the mainstream market you'll target twelve months from now.

Most critically, treat your ICP as a living document. Market conditions shift, your product evolves, competitive dynamics change, and new data emerges continuously. Revisit and refine your ICP quarterly at minimum, incorporating lessons from recent deals and changing market conditions. The best GTM teams make ICP refinement a regular discipline rather than a one-time exercise.

Step 2: Map Signal Triggers to Business Outcomes

With a clear ICP in hand, the next step is translating those ideal characteristics into observable events and triggers. Your ICP tells you about the pain points, challenges, and ambitions that make a company a good fit. Now you need to identify the specific happenings that hint at those attributes in action.

For example, if you're selling sales enablement software, a company hiring or promoting a Chief Revenue Officer signals ambition for revenue growth, a higher probability of displacing existing tools, likely availability of new budget, and a decision-maker who'll want quick wins. A surge in sales development job postings suggests they're scaling outbound and will need better tools to support that growth.

Example B2B Signal Mapping

Not every signal carries equal weight. Create a hierarchy based on both strength and timing. A funding round announcement suggests a company will invest in new technology and hiring, but a wave of job postings tells you it's happening right now. An executive hire indicates potential change; that executive actively engaging on LinkedIn about specific challenges confirms active evaluation.

Document your signal taxonomy systematically. Create a framework that categorizes each signal type, explains why it matters for your business, assigns a priority score, and defines the appropriate response. For instance, a Series B funding round might be a Tier 2 signal (moderate priority, warrants monitoring), while a new CRO hire plus sales development job postings could be a Tier 1 signal (high priority, immediate outreach).

Include both positive and negative signals in your framework. Signs of contraction, leadership departures, or technology consolidation might indicate poor timing or increased risk. This prevents wasted effort on accounts that look good on paper but face challenges that make them unlikely to buy.

Step 3: Establish Signal Collection Infrastructure

Once you've defined the triggers worth tracking, you need systems to monitor and capture these events at scale. Manual monitoring might work for a handful of accounts, but signal-based GTM requires automation and comprehensive coverage.

Leverage data providers like LinkedIn Sales Navigator, Crunchbase, and BuiltWith to track firmographic and technographic changes. Set up alerts and integrate APIs where possible to receive real-time notifications rather than checking manually. Use social listening services to monitor company news, press releases, funding announcements, and executive changes.

Set up systematic monitoring of review sites like G2, TrustRadius, and Capterra. Track not just reviews of your own product, but competitors' reviews as well. Negative sentiment about a competitor can signal opportunities, while positive reviews can inform competitive positioning.

Integrate your first-party data sources comprehensively. Connect your CRM, website analytics, product usage data, email engagement metrics, and marketing automation platform. Build workflows that flag significant behaviors: repeat website visits to pricing pages, specific feature adoption patterns, email engagement spikes, or content consumption that indicates evaluation stage.

Ensure your signal collection infrastructure prioritizes both coverage and timeliness. You want to capture as many relevant signals as possible across your target market, and you need to receive them quickly enough to act while they're still fresh. Automate the flow of signals into your CRM or create notification workflows that surface high-priority signals immediately to the right team members.

Step 4: Activate Signals Across Your GTM Motion

Collecting signals without systematic activation renders them useless. You need processes that translate signals into prioritized actions and measurable outcomes.

Implement signal scoring that ranks incoming triggers by likelihood of conversion. Weight scores based on signal strength, account fit with your ICP, and recency. Combine multiple signals to create composite scores, an account showing three moderate signals simultaneously might warrant higher priority than one with a single strong signal from six months ago.

Build routing rules that surface the highest-value signals to your team through their existing workflows. Create dedicated views in your CRM for signal-driven accounts. Generate automated "hot lead" lists that refresh daily. Consider creating Slack or Teams notifications for Tier 1 signals that require immediate response.

Design playbooks that specify how different signal types should be approached. A funding round signal might trigger a congratulatory message focused on growth enablement. A CRO hire could prompt outreach offering a peer benchmarking conversation. Job posting signals might lead with talent challenges and productivity improvements. The messaging, timing, and channel should match the signal context.

Establish regular review cycles to evaluate signal performance. Track which signals consistently convert to pipeline and revenue versus which generate activity without outcomes. Monitor metrics like signal-to-conversation rate, conversation-to-opportunity, and velocity of signal-driven deals compared to others. Gather qualitative feedback from your sales team about which signals produce the best conversations.

Use these insights to continuously refine your signal framework. Remove or deprioritize signals that create noise without results. Double down on signals that consistently produce pipeline. Adjust scoring weights based on conversion data. Update playbooks based on what messaging resonates. The best signal-based GTM systems improve over time through systematic learning and optimization.

Getting Started with Signal-Based GTM

Tracking and collecting relevant data across hundreds or thousands of businesses is not trivial to set up and maintain. Building custom integrations, maintaining data freshness, deduplicating signals across sources, and properly automating the entire workflow can be prohibitively complex and expensive for most teams.

Building the Intelligence Layer with Saber

This challenge is precisely why we built Saber. Our AI agents continuously monitor data sources that are typically hard to access and synthesize them into actionable signals your team can use immediately. The platform automatically identifies crucial events and prioritizes accounts that are genuinely worth pursuing, based on its understanding of your business, product, market, and ideal customer profile.

Each insight comes with complete sourcing, so every signal can be traced back and verified. This transparency ensures your team can confidently act on signals while maintaining the context needed for relevant, personalized outreach.

Teams at DataDog, Wiz, and Cloudflare have used Saber to accelerate their revenue growth by shifting from volume-based outbound to precision targeting based on real-time buying signals. If you're looking to explore modern signal-based GTM, we'd love to show you how Saber can transform your approach.