Buying Intent Signals: How Martin Markov Gets 40 to 60% Reply Rates From Live Buyer Behavior Using Clearcue

TL;DR

• Buying intent signals predict pipeline more reliably than any database you can buy.
• The quality of your list outperforms the quality of your message, and the experiments back it.
• Competitor engagement and job postings are the most undervalued signals in B2B.
• Treat the market as a live stream of behavior, reviewed and acted on every week.
• Let AI monitor signals around the clock while humans keep control of strategy and feedback.

Most teams still buy a list, blast it, and blame the copy when nobody replies. The harder truth sits in the data. Research from the Ehrenberg-Bass Institute with the LinkedIn B2B Institute finds that only about 5% of B2B buyers are in-market at any given time. Buying intent signals point to a sharper starting place, one that helps you find that 5% through real buyer behavior rather than a static list. On the Edbound With Kinner podcast, host Kinner Sacchdev sat down with Martin Markov, founder of ClearCue, to unpack how signal-driven prospecting actually works. 

Why static ICP lists quietly stopped filling pipeline

For years the playbook was simple. Build a list of accounts that match your ideal customer profile, then run outreach until something lands. Martin watched that approach lose its edge while scaling his own agency to 65 people.

The problem is structural. Every team buys from the same providers, so the same contacts receive the same outreach across the same channels. The list looks full, yet the inboxes behind it are saturated.

Email feels the strain first. As open and reply rates slide, email slips into a secondary channel, useful as a follow-up once a more direct path has been tried.

A static list also assumes that fit equals readiness. Matching an ICP shows that someone could buy. Readiness is a separate question, and it changes by the week.

There is a freshness problem too. Large databases are full of contacts who have gone quiet, including people who have not opened the platform in months. A smaller list of currently active buyers tends to be worth far more than millions of stale records.

What changes when you accept this:

• Bought databases become a backup, not the engine.
• Fit becomes a filter, and timing becomes the trigger.
• A small active list beats a large dormant one.

This echoes a recurring theme on our podcast about building a signal-driven GTM stack.

What counts as a buying intent signal

A buying intent signal is an observable behavior that shows someone is moving toward a purchase in your category. It can be engaging with a competitor, viewing your profile, or posting a role that describes your product. The signal points to direction, not a stated plan.

Martin uses a simple image to explain it. A person standing outside a bakery is probably hungry. You do not need them to announce it. Their behavior already tells you. Real-time buyer intent works the same way, showing up in actions long before anyone fills out a form.

That distinction matters for execution. A signal is specific to your product and your buyer. One company's strongest signal can be pure noise for another, which is why a generic list of triggers rarely transfers between businesses.

Martin shared one example that makes this concrete. A company selling management software for buildings found that a competitor opening an office with very specific criteria was a gold signal. Acting on it booked six enterprise-level demos in the first week of using the product.

Topic monitoring is another source worth setting up. Listening for the specific problems and keywords your buyers discuss surfaces interest early, often before they ever land on your site.

Useful signals tend to share three traits:

• They are recent, ideally inside the last few days.
• They connect to a problem your product solves.
• They reflect behavior, not just firmographics.

Why the list outperforms the message every time

Most teams respond to weak results by rewriting the message. Martin's experiments point the other way. When the list is right, even an average message performs.

"Every time we make an experiment like having the right list versus like perfect message or perfect channel or whatever, the list is winning even though the message is not like the best one." - Martin Markov, Founder - ClearCue

This reframes where effort should go. Hours spent perfecting subject lines deliver little when the list is full of people who are not in-market. The same hours spent qualifying the list compound over time.

It also reflects how sales math actually works. Given the choice between talking to a thousand people to close ten deals, or talking to ten of the right people to close those same ten, every founder picks the second.

This is the core of intent based selling. You qualify the behavior first, then let the message follow it.

The buying intent signals most B2B teams overlook

Some of the strongest signals hide in plain sight. Martin highlights three that teams routinely miss, the three first-look signals.

The first is your own network. People who comment on your posts or view your profile are already paying attention. When that engagement spikes and the person fits your ICP, it is a strong first-party signal and an easy place to start.

The second is competitor engagement. Someone interacting with a competitor is often in-market. There is a limit, though. A person who engages with the same competitor signal seven, eight, or nine times is usually a customer or a close contact, which turns it into a disqualifying signal. Engagement spread across multiple competitors is the stronger read.

The third is hiring signals for sales. Job descriptions are an undervalued source of intent. A posting that asks a new hire to rebuild a content workflow with AI tells you exactly what that company is about to buy.

When these signals are read correctly, the results show up fast.

"When we're using these signals, we have between 40 and 60 % reply rate, which is ridiculously high in the space." - Martin Markov, Founder - ClearCue

A quick map of where to look:

• Your network: comments, reactions, and profile views.
• Competitors: engagement across several accounts, not one repeated contact.
• Job boards: the language inside the description, not just the title.

Operationalizing these sources is where revenue ops and sales ops come in, a topic earlier guests have gone deep on.

Build Your Own Buying Signal System And Talk To This Podcast Episode's AI Brain

Access Martin Markov's signal-based prospecting framework to build pipeline from buying intent signals, reaching buyers who are actually in-market instead of blasting static lists. Then speak to this podcast's AI Brain to map the exact signals for your business.

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From quarterly campaigns to a live signal stream

Most teams still run go-to-market as scheduled campaigns. A push every quarter, sometimes every month for the more aggressive teams. Martin describes a different rhythm, where the market sends behavior continuously and the team responds as it happens. He frames it as a move away from static lists and filters toward catching behavior and acting on it the moment it appears.

This reshapes the growth role. The job becomes less about building lists and more about watching behavior, sorting it into buckets that have already proven to convert, and acting quickly. For a head of growth or a GTM engineer, the scorecard shifts from how many lists were built to how fast the team turned behavior into booked conversations.

Signal based selling rewards speed. A signal from the last 48 hours is far more valuable than one from last quarter, because intent fades. The team that responds first reaches the buyer while the interest is still warm. It is the same principle behind a signal-based selling playbook shared by an earlier guest: read the behavior, then act on it.

The message can follow the behavior too. Martin's team writes a message per behavior, tied to the pain point a signal reveals, and these tested templates consistently outperform AI-generated personalization.

What a live approach looks like in practice:

• Behavior is monitored continuously, not reviewed monthly.
• Proven behaviors get their own response path.
• Speed of response becomes a core metric.

How to build a buying intent signal system that runs every week

A signal system is built, then tuned. Martin is direct about the starting point. No one knows in advance which signals will work, so the system has to learn through experiments run at speed, an approach echoed in a learning-first prospecting framework from a past episode. AI works best here as an amplifier of human judgment, not a shortcut around it.

Set up your starting signals

Begin with what you can see today.

• First-party: engagement on your own content and profile.
• Competitive: people interacting with rival products.
• Hiring: job posts that describe your problem inside their requirements.

Then route intent qualified leads to the right channel. LinkedIn tends to outperform email for this work. LinkedIn caps most accounts at roughly 100 connection requests a week, closer to 200 on premium, which makes list quality decisive. A weak list burns those connections. A clean list of intent qualified leads turns them into conversations. Email still has a place as a fallback when a connection request goes unanswered, though it carries less weight as a first touch.

Run the weekly feedback loop

Signals decay, so review is built into the system rather than bolted on. This is where AI earns its place. Agents collect and analyze behavior around the clock, then surface what they find for a human to approve or correct. You provide the strategy and the judgment. The system adapts and promotes new signals as old ones fade.

The amplifier effect is real. Martin described running a single prompt through his platform's research layer and getting in five minutes the kind of deep audience analysis that used to take two days of manual work. The human still sets the question. The system does the heavy lifting.

One more piece of advice from the episode is worth keeping. When you start a new go-to-market motion, treat it like a mini startup inside your company. Set the legacy process aside and build from scratch, so speed and experimentation are not slowed by old systems.

Know whether it is working

A few numbers tell you the system is earning its place.

• Reply rate on signal-sourced lists, measured against your cold baseline.
• Share of flagged signals that convert into a real conversation.
• Time from signal to first touch, kept inside 48 hours.
• How often signals are refreshed, since stale ones quietly decay.

Pipeline follows the signal

The shift Martin describes is practical. Pipeline becomes more predictable when you read who is showing intent and act while it is fresh. The list does the heavy lifting, the message supports it, and the system keeps learning every week.

That same logic applies to how you create and distribute content. Every piece you publish and every conversation you spark generates first-party signals about who is paying attention. Capturing and acting on those signals is where content turns into pipeline, the same way strong content helps you earn visibility in AI search.

This is the execution layer Edbound AI is built for. Edbound AI helps B2B teams stand up content hubs that attract the right audience, distribute across SEO, email, and LinkedIn, and surface the engagement that signals real intent. The result is a content to distribution to pipeline motion that runs with consistency, at scale, without burning out a small team.

Build the system once, and the signals keep working for you.

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