Marketers aren’t short on intent data. They’re short on clarity.

After all, if everything is a signal, nothing is...

We're drowning in signals but starving for conversions.

Over the past year writing this newsletter, and working with dozens of B2B SaaS companies, there’s no denying it: marketers have more intent data than ever before but are struggling to turn that data into actual pipeline and revenue. The unbundling of ABM platforms has democratized access to signals, but it's created a new problem - signal saturation without clear paths to action.

As I've been exploring in my recent articles on signal-based marketing and the new ABM regime, the challenge isn't collecting signals anymore. It's knowing which ones actually matter when it comes to driving GTM Alpha – a term coined by Clay and being described as: the act of earning a competitive edge via unique data, custom plays, and GTM engineering.

---

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 1k+ readers who get Looped In delivered to their inbox every Sunday .

---

The Signal Saturation Problem

We've reached a tipping point in B2B marketing where the tools and data sources available to us have absolutely exploded. It’s wild. As I wrote about in "ABM, the new regime," the great unbundling is happening. Companies are moving away from all-in-one ABM solutions that cost $100K+ annually toward specialized, point solutions that each provide their own unique signals.

This shift has created both opportunity and chaos. On one hand, we have access to more granular data than ever before. On the other, we're facing signal paralysis with our inability to move from data collection to action because there's simply too much to process.

One CMO I worked with recently said: "We're tracking over 200 different signals across 15 different tools, but we still can't reliably predict which accounts are ready to buy."

I’ve been calling this part of the process, “orchestration”. This CMO above is struggling with orchestration, across more than just marketing. For any of you that have read Emily Kramer’s analogy about pie, I’m about to give you a doozy of an analogy about orchestras. Sry not sry.

Like a symphony orchestra, achieving GTM Alpha requires coordinating multiple elements that individually make noise but together create harmony. I’m trying to get clients to think about it like this:

1. The Conductor (Leadership): In an orchestra, the conductor doesn't play any instrument but directs the entire ensemble - determining tempo, dynamics, and bringing in each section at the right moment. Similarly, achieving GTM Alpha requires leadership that doesn't get lost in any single signal but instead focuses on coordinating all elements.

2. Different Instruments (Signal Types): Just as an orchestra has strings, brass, woodwinds, and percussion - each with their unique sound quality - your signal strategy has various types (website engagement, product usage, buying committee activation, competitive research, etc.).

3. Sheet Music (Signal Framework): Musicians follow a score that tells them when to play and when to remain silent. Your signal framework similarly determines which signals receive attention at different stages of the buyer's journey.

4. Timing (Signal-to-Action Cadence): In orchestral music, timing is everything - a note played too early or too late ruins the harmony. With GTM Alpha, the timing of your response to high-intent signals similarly impacts the liklihood of conversion.

5. Harmony (Cross-Functional Alignment): When all instruments play their parts correctly, they create harmony greater than any single instrument. When sales, marketing, and customer success teams act on the right signals in coordination, they achieve GTM Alpha.

I’m not quite ready to drop this analogy yet. Is it excessive? Sure. But does it create a visual in the minds of execs that they keep 👏 on 👏 referencing 👏? Yep.

Why High-Intent Signals and GTM Alpha Go Hand in Hand

When Clay talks about GTM Alpha, they're referring to the competitive advantage companies gain by seeing and acting on data that competitors can't access or don't understand. It's not just about slightly outperforming your competition—it's about fundamentally changing the game through unique data and customized plays.

High-intent signals are the cornerstone of GTM Alpha because they provide that unique data advantage. While most companies focus on generic firmographic targeting or basic intent, companies achieving GTM Alpha are orchestrating a symphony of hyper-specific signals that indicate genuine buying momentum:

1. From static ICP targeting to dynamic signal orchestration - Rather than targeting broad categories like "all SaaS companies who’ve recently raised funding," GTM Alpha teams coordinate precise signals to identify companies that are showing immediate buying intent, such as those with usage-based pricing who just posted new customer success jobs.

2. From mass outreach to precision targeting - Instead of sending thousands of generic messages, GTM Alpha means coordinating the exact right message to the right prospect at precisely the moment they're most receptive—like identifying when a VP-level finance executive views your pricing page twice after a demo.

3. From one-size-fits-all signals to industry-specific triggers - Companies achieving GTM Alpha develop unique "signal libraries" for different industries and buying roles, orchestrating different actions based on the specific signals that matter most in each context.

4. From reactive to predictive engagement - And lastly, GTM Alpha means orchestrating your signals to not just respond to intent but predict it, allowing you to engage prospects before competitors even recognize the opportunity.

It’s not achieved through a single tool or tactic, but through the orchestration of a signal strategy that continuously evolves. Like conducting an orchestra where each instrument must enter at precisely the right moment, your signal strategy must coordinate multiple data sources and trigger the appropriate responses with perfect timing. And truthfully, it will also be an iterative process. The reality, especially today, is that there is no permanent competitive advantage.

The Signal-to-Alpha Framework

After helping multiple clients navigate the signal saturation problem, I've developed a framework that has consistently helped teams identify which signals actually merit immediate action to drive conversion.

1. Unique Signal Identification

Before assessing signal strength, start by thinking through which signals your competitors might not be tracking. Start by considering:

Proprietary Data Advantages

• What unique data sources do you have access to that competitors don't?

• Are there industry-specific signals only visible to insiders?

• Can you combine common signals in uncommon ways to create proprietary insights?

Hyper-specific Signal Definition

• Instead of broad signals like "visited pricing page," define granular ones like "Berlin cafe owner viewed B2B payment integration page twice after DoorDash onboarding"

• Create compound signals combining firmographics with real-time intent (e.g., "usage-based pricing company with newly posted customer success jobs")

• Develop custom ratios between signals that predict conversion

2. Signal Strength Assessment

Once you've identified potentially unique signals, evaluate them along three dimensions:

Recency, Frequency, and Depth

• How recent was the signal? (Within 24 hours = highest priority)

• How frequently has the account shown this signal? (One-off vs. pattern)

• How deep was the engagement? (Passive viewing vs. active participation)

Multiple Stakeholder Engagement The involvement of multiple stakeholders from the same account, especially when they include decision-makers, dramatically increases signal value. Look for:

• Senior stakeholder engagement following junior stakeholder research

• Multiple departments engaging with the same content

• Different stakeholders exploring different aspects (technical, financial, etc.)

Technical Validation Actions These signals indicate serious intent and often precede purchase decisions:

• Demo follow-up questions

• Sandbox or free trial activity (especially testing core features)

• Integration documentation downloads

• API exploration

3. Signal-to-Revenue Correlation Analysis

The next step is understanding which signals have historically led to closed deals for your specific company and product. This is where GTM Alpha companies excel - they've done the work to understand their specific signal patterns and continuously experiment with new correlations.

Here's a simple approach I've used with clients that doesn't require a data science degree:

1. Win/Loss Signal Audit: Take your last 10 closed-won deals and your last 10 closed-lost deals. Document all signals exhibited by each in the 30 days prior to close (consider making this 90-180 days for longer sales cycles).

2. Pattern Recognition: Which signals appeared more frequently in won deals vs. lost deals? Those are your high-correlation signals.

3. Timing Analysis: Did certain signals consistently appear immediately before closing? These are your "trigger signals" that should prompt immediate action.

4. Continuous Experimentation: Set up a system to test new signal combinations monthly and compare results - this prevents your signal advantage from being commoditized.

One client discovered that when a VP-level executive at a prospect company viewed their pricing page twice within a 48-hour period following a demo, they closed the deal 78% of the time. That became their golden signal for an immediate in-pipe play.

4. Action Threshold Determination

Not every signal deserves the same action. Creating a tiered response system ensures you're allocating resources appropriately.

The key is matching the investment in response with the signal's proven correlation to revenue. The real advantage comes not just from better signals, but from creating plays and workflows that competitors can't easily replicate - even if they eventually discover the same signals.

Practical Example: Real-World High-Intent Signal Prioritization

Let me share a practical example from a recent project with a B2B SaaS company selling marketing automation software that was working toward creating a stronger competitive moat.

The Before State:

• Tracking 150+ signals across their tech stack

• Treating all signals equally for scoring purposes

• SDRs overwhelmed with "high intent" accounts that weren't converting

• 17.6% conversion from qualified-hand-raiser to closed won.

The Process: We applied the Signal-to-Alpha Framework to determine which signals actually correlated with deals closing. After analyzing their past 24 months of closed-won deals, we discovered three "golden signals" with the highest correlation to revenue:

1. Multiple stakeholders from the same account viewing implementation guides within a 7-day period (83% correlation to closed-won)

2. Technical decision-maker reviewing API documentation after initial demo (76% correlation)

3. Finance stakeholder viewing pricing page multiple times after a demo (71% correlation)

The Action Plan: We redesigned their signal response matrix to prioritize these three golden signals:

• Golden Signal 1: Triggered immediate outreach from the AE with an offer for a technical implementation call

• Golden Signal 2: Triggered personalized outreach from a solutions engineer

• Golden Signal 3: Triggered outreach with custom ROI calculator and payment terms options

The Results:

• Conversion from qualified-hand-raiser to opportunity increased from 17.6% to 28.2%

• Sales team reported higher quality conversations

• AEs focused their time on accounts showing these specific signals, resulting in 22% higher productivity* for the accounts in the test group.

The key wasn't adding more signals or more sophisticated technology. It was identifying which signals actually mattered for conversion and creating a clear action plan for each - exactly what GTM Alpha companies do. *To be clear, there were definitely other changes in the org happening at the same time, it’s a startup after all. It’s not like we paused all other experimentation for this one, but I’d still say huge gains for the company regardless.

GTM Alpha Through Signal Orchestration

GTM Alpha isn't just about having a slightly better marketing or sales process - it's about creating a fundamental competitive advantage through your go-to-market strategy. High-intent signal orchestration is the key that unlocks this advantage.

Just as a world-class orchestra requires both exceptional musicians and a masterful conductor, achieving GTM Alpha requires both quality signals and strategic orchestration. Companies that master this recognize that:

1. Signal orchestration creates strategic asymmetry - While competitors are playing checkers with basic lead scoring, you're playing chess by orchestrating multiple signals across channels, timing, and audience segments to create patterns they can't easily replicate.

2. The competitive edge is in constant evolution - What generates GTM Alpha today will be table stakes tomorrow. True GTM Alpha companies build systems that continuously discover new signal combinations through ongoing experimentation.

3. Unique data + executable playbooks = sustainable advantage - It's not enough to identify signals competitors miss - you need to translate those insights into plays your team can run that deliver measurable outcomes competitors can't match.

4. The GTM alpha window is shrinking - As tools democratize access to data, your window for capitalizing on signal advantages is getting shorter. The competitive moat now comes from how quickly you can identify new signals, test their efficacy, and scale successful patterns.

Building Your Signal Orchestration System

So…wanna get started on this tomorrow? Here are five steps to build your foundation:

1. Develop unique data combinations: Move beyond standard third-party intent data to create proprietary signal combinations that competitors can't easily replicate. Think through 1P, 2P, and 3P data sources.

2. Create signal-to-playbook mappings: Design documented plays for each high-value signal pattern that precisely outline who does what, when, and how. Just like your traditional SLAs.

3. Implement continuous experimentation cycles: Establish a regular cadence to test new signal combinations and response strategies, measuring their impact on pipeline velocity and conversion rates. I prefer having standing weekly’s with the team, and a lively dedicated Slack channel.

4. Build cross-functional signal response teams: Break down silos between marketing, sales and customer success to create coordinated responses to high-value signals. This works better if everyone is aligned.

5. [Things I’m still dreaming of] Invest in signal orchestration infrastructure: Ensure your tech stack can identify correlations between signals and outcomes automatically, allowing you to scale what works.

The Future of Signal Orchestration

The companies achieving true GTM Alpha in 2025 won't be those with the most sophisticated intent data providers or the most comprehensive signal libraries. They'll be the ones that have built organizational muscle around signal orchestration - the ability to continuously identify, test, and scale new signal-response combinations.

As data becomes more democratized, GTM Alpha will increasingly derive from execution speed rather than information advantage. The question shifts from "what do we know that others don't?" to "how quickly can we transform signals into revenue-generating actions?"

By building a system that orchestrates signals across your go-to-market motions, you create a competitive advantage that's much harder to replicate than any single data point or tactic.

What signal orchestration systems are you building? Have you found combinations of signals that reliably predict purchase intent for your business? I'd love to hear about your experiences in the comments. ✨

Here’s to growth! See ya next week,
Kaylee