It's 2025: What the heck are we doing with all these signals?

Building GTM Alpha Through Unique Data

I've been obsessing over something Clay calls "GTM Alpha" lately—the competitive edge you gain through unique data, custom plays, and GTM engineering that your competitors can't easily replicate.

And here's what I've realized: most companies are still playing checkers with basic intent data while the winners are playing chess with proprietary signal combinations.

The digital marketing landscape is absolutely chaotic right now. Your buyers are getting hammered with hundreds of messages every day, and here's the kicker—most marketers are using the exact same signals to reach them. Everyone's tracking the same "pricing page visits" and "whitepaper downloads" that every intent platform sells.

That's not GTM Alpha. That's GTM table stakes.

The companies I'm working with who are absolutely crushing it? They're not just collecting more data. They're building unique signal combinations that create sustainable competitive advantages. They're using AI to identify patterns their competitors can't see. They're moving faster from signal to action than anyone else in their space.

Let's get into how you build this kind of competitive moat.

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👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. If you’re one of the 27 people that have subscribed since last Sunday, hello! So glad you’re here—you’ve just joined 2k+ marketers who read Looped In every Sunday .

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The GTM Alpha Revolution

GTM Alpha isn't about having slightly better marketing tactics. It's about fundamentally changing the game through unique data and execution speed that competitors can't match.

Think about it like this: when everyone has access to the same Bombora intent data, the same G2 signals, the same LinkedIn Sales Navigator insights—where's your competitive advantage?

The answer: in the signals no one else is tracking.

And everyone’s first question is: what signals are more meaningful than others? And like most marketing answers…the answer is: it truly depends on you, your buyers, your brand, your market.

But in general, the most successful teams I'm seeing are combining:

• Proprietary first-party data from their product and customer interactions

• Custom third-party research using AI to identify non-obvious patterns

• Unique signal combinations that create compound insights

• Speed of execution that gets them to prospects before competitors even know there's an opportunity

This isn't just about being better at signal-based marketing. It's really about creating a systematic approach to competitive advantage that compounds over time.

Beyond Standard Signals: The Custom Research Revolution

Here's what's happening right now in the best GTM organizations: they're using AI and custom research to identify signals that don't exist in any vendor's database.

Instead of relying on standard signals like "company size" and "recent funding," they're building custom research engines that identify things like:

• Industry-specific stress indicators: Companies in fintech posting multiple engineering jobs after a regulatory announcement

• Competitive displacement signals: Prospects engaging with content about migrating away from specific tools

• Expansion opportunity patterns: Usage behaviors that predict which customers will upgrade within 60 days

• Market timing indicators: External events that create buying windows for their specific solution

I recently worked with a company that used AI to identify a pattern: B2B companies that hired a Head of Revenue Operations within 90 days of a Series B were 4x more likely to evaluate marketing automation tools within the following quarter.

Their competitors likely had no idea this signal existed.And by the time they figure it out, this company will be onto the next combo.

The Competitive Moat: Unique Signal Combinations

Standard intent data is becoming commoditized. Everyone can buy the same signals from the same vendors. But this ‘GTM Alpha’ concept comes from creating proprietary signal combinations that can't be easily replicated.

Here's how the best teams are thinking about it:

Layer 1: Standard Signals (Table Stakes)

• Website visits to pricing pages

• Content downloads

• Webinar attendance

• Basic intent data from vendors

Layer 2: Enhanced Signals (Competitive)

• Cross-channel behavior patterns

• Product usage correlations

• Social engagement sequences

• Custom intent combinations

Layer 3: Proprietary Signals (GTM Alpha)

• Industry-specific trigger events

• Competitive intelligence patterns

• Custom AI-identified correlations

• Unique data source combinations

The magic happens when you combine all three layers to create signal profiles that competitors can't access or understand.

AI-Powered Signal Discovery

This is where things get really interesting. The teams achieving GTM Alpha aren't just tracking more signals—they're using AI to discover signals that shouldn't logically correlate but do.

For example, one client discovered that companies with engineering teams using specific open-source tools were 3x more likely to need their API management solution—even though there was no obvious connection.

How did they find this? Custom GPT research.

They used AI to analyze thousands of data points across their best customers and identified patterns human analysts would never spot. Then they built automated research workflows to identify prospects matching these hidden patterns.

Here's the framework I'm helping clients implement:

1. Historical Pattern Analysis

Use AI to analyze your best customers and identify non-obvious commonalities:

• What technologies do they use that aren't in your ICP?

• What timing patterns exist in their evaluation cycles?

• What external events preceded their buying decisions?

*Bonus points if you also do this same exercise for opps that from the outside looked like ICP fit, but in the end they were Closed Lost. This will create an interesting venn diagram of data.

2. Custom Research Automation

Build workflows that automatically research prospects for your unique signals:

• Industry-specific stress indicators

• Technology stack combinations

• Leadership change patterns

• Market timing factors

3. Competitive Intelligence Integration

Monitor what signals your competitors are missing:

• Which accounts are they targeting?

• What triggers are they responding to?

• Where are the gaps in their coverage?

Speed As Competitive Advantage

Here's something most people miss: GTM Alpha isn't just about better data. It's about speed of execution.

When you identify a unique signal, you have a narrow window before competitors catch on. The teams winning are those who can move from signal identification to prospect engagement in hours, not days.

I worked with a company that built an automated system to identify when their target accounts posted specific job openings. Within 2 hours of a posting going live, their sales team was reaching out with relevant case studies and offering to connect the hiring manager with similar customers.

Their close rate on these opportunities was 67%.

Why? Because they were having valuable conversations before prospects even realized they needed to evaluate solutions.

Building Your GTM Alpha System

Phase 1: Identify Your Unique Data Advantages

Start by auditing what data you have access to that competitors don't:

• Product usage patterns: How do successful customers behave differently?

• Customer success data: What early indicators predict expansion?

• Support ticket analysis: What pain points drive solution searches?

• Sales conversation insights: What triggers cause prospects to engage?

Phase 2: Custom Signal Research

Use AI to identify non-obvious patterns:

• Analyze your best customers for hidden commonalities

• Research external factors that influenced their timing

• Identify technology combinations that predict fit

• Map industry events that create buying windows

Phase 3: Competitive Intelligence

Monitor your competitive landscape:

• What signals are competitors tracking?

• Where are the gaps in their coverage?

• What unique positioning can you claim?

• How can you move faster than they can?

Phase 4: Automated Discovery

Build systems that continuously identify new opportunities:

• Real-time signal monitoring across multiple sources

• AI-powered pattern recognition for emerging trends

• Automated research workflows for prospect qualification

• Speed optimization for signal-to-action timing

The Compound Effect of GTM Alpha

The beautiful thing about building GTM Alpha through unique signals is that it compounds over time. Every unique pattern you discover becomes part of your competitive moat. Every speed improvement widens your advantage. Every automated workflow scales your capability.

I recently worked with a B2B software company that implemented this approach. In 6 months, they:

• Identified 12 unique signal patterns their competitors seemingly weren't tracking (or at least were too slow to take action on)

• Reduced signal-to-action time from 3 days to 2 business hours

• Increased conversion rates by 340% on signal-driven outreach

• Built a 6-month lead over competitors in their key market

But here's the kicker: their competitors still don't understand how they're doing it. They see the results but seemingly can't replicate the system.

That's GTM Alpha.

Turning Signals Into Sustainable Advantage

Signal-based marketing used to be about responding to intent. GTM Alpha is about creating and controlling the conditions that generate intent.

The companies I'm working with who are absolutely dominating their markets have moved beyond reactive signal response. They're using unique data combinations and AI research to identify opportunities before prospects even realize they need solutions.

They're not just playing the game better—they're changing the rules of the game entirely.

This isn't about having more data. It's about having better data, unique insights, and faster execution than anyone else in your space.

Ready to build your own GTM Alpha system?

Start by identifying one unique signal combination your competitors aren't tracking. Use AI to research patterns they're missing. Build workflows that let you act on opportunities faster than they can.

The window for competitive advantage through unique signals is still open. But it's sure to be closing fast.

See ya next week,
Kaylee ✌

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Building Your GTM Alpha Toolkit

What unique data sources can you access that competitors can't?

Look beyond standard intent platforms. Your product usage data, customer success metrics, and support interactions contain signals others don't have access to.

How can you use AI to identify non-obvious patterns?

Use custom GPT research to analyze your best customers for hidden commonalities. Many successful signal combinations aren't intuitive—they're discovered through pattern analysis.

What's your signal-to-action speed?

The fastest teams move from signal identification to prospect engagement in under 2 (business) hours. Every hour you wait gives competitors more opportunity to reach the same prospects.

How do you continuously discover new signal opportunities?

Build automated research workflows that identify emerging patterns and market changes. GTM Alpha requires constant evolution of your signal strategy.

Want to discuss building your own GTM Alpha system? Drop me a line at kaylee@demandloops.com.