Go-to-market articles and insights

We're not Reddit marketing experts. We're a B2B GTM team that kept seeing the same thing from different angles: buyer conversations on Reddit that weren't happening anywhere else, AI answers citing Reddit threads over vendor content, and data suggesting the channel deserves more attention than most B2B teams give it.

This is Part 1 in a series where we evaluate Reddit as a B2B GTM channel. This piece covers why we think it deserves serious consideration. Part 2 gets into the practical side: how to actually show up without getting ignored or banned.

Where your buyers are actually talking

Reddit is one of the most-visited websites in the United States, with 121.4 million daily active users as of Q4 2025. It's where your buyers go when they want an honest answer, not a vendor blog. A peer conversation with someone who's used the product and has no reason to sell them anything.

The modern B2B buying journey is largely self-directed. Buyers complete most of their research before they engage with sales, and they're turning to communities where the answers aren't sponsored. According to Reddit's advertising materials citing Comscore data from June 2025, 61% of B2B decision-makers are active on Reddit, and 38% of those buyers aren't on LinkedIn at all. Reddit also cites that 72% of tech decision-makers use the platform for peer reviews and 49% for active product research. (These figures come from Reddit's own marketing, so take them with appropriate context.)

The intent is different from other platforms. When someone types a question into r/marketing or r/revops, they're looking for an answer to a specific problem. That research posture makes Reddit one of the highest-intent channels in B2B. The smaller, more focused communities like r/revops, r/CRM, and r/marketingops consistently carry more purchase intent per conversation than the large ones.

What's possible on Reddit

Reddit supports more than comments: long-form posts, live AMAs, newsletter distribution, and paid advertising. The posts that drive engagement are specific, backed by data, and written from direct experience. The model is to publish the kind of content your marketing team usually saves for gated assets and give it away for free.

Comments are probably the most underrated GTM activity on the platform. When a thread in r/SaaS asks "what tool does your team use for X?", that's a public buying signal. A useful, honest response gets upvoted, indexed by Google, and cited by AI systems for years. The challenge is volume. It gets overwhelming fast. Tools like Reddit Pro help by surfacing threads that match your target language so you're responding to the ones that matter, not trying to read everything.

The format that works: give a concrete answer first, cover tradeoffs honestly, and end with a soft mention — "we cover more of this in our newsletter if you want to go deeper." Value first, link last.

On the paid side, Reddit offers subreddit-level targeting that puts your message in front of communities defined by professional interest. CPCs run 50-70% lower than LinkedIn for comparable B2B audiences ($0.50-$2.00 vs. $7-$12). Reddit's ad business grew 74% year-over-year in Q3 2025, reaching $2.2 billion in total annual revenue. That cost gap won't hold forever.

Does Reddit content age better?

A LinkedIn post has a shelf life of 24 to 48 hours. A Reddit comment can rank in Google for years and gets pulled into AI-generated answers indefinitely. According to Semrush's analysis of 150,000+ AI citations, Reddit is the most-cited source across major AI platforms including ChatGPT, Perplexity, and Google AI Overviews.

Google has increased Reddit's visibility in search results. Threads now regularly rank on the first page for B2B software comparisons. Tailscale's Reddit engagement, built through months of technical participation with no promotional agenda, has produced more than 1,300 subreddit discussions ranking in Google search results and thousands of monthly referral visits. All organic, no ad spend.

For organic GTM, the asset doesn't depreciate when you stop spending.

The trust factor

Buyers know the difference between a case study written by the company and a peer recommendation from someone with no stake in the outcome. According to Reddit's own research (a July 2024 survey of 1,250 business decision-makers), 90% of Reddit users trust the platform to learn about new products and 74% say it influences their purchasing decisions. These are Reddit's numbers, not independent research, but the directional signal is hard to ignore.

When someone from your team shows up in r/revops and gives a useful answer, it reads like expertise, not advertising. The companies doing this well treat Reddit as a trust-building channel that makes every other part of their funnel work better.

Companies worth studying

Shopify built r/Shopify into a community hub with over 274,000 members. Team members participate across multiple subreddits without a promotional agenda. The community now generates its own brand advocacy and Google-ranked discussions without Shopify having to push it.

Tailscale built credibility in r/sysadmin, r/devops, and r/networking — communities hostile to vendor promotion — by answering technical questions with no product mention for months. Organic recommendations followed from community members, not from Tailscale itself.

How would you even measure this?

Standard attribution can't capture how Reddit works. Most influence happens through passive consumption. Someone reads a comment, closes the tab, and books a demo three weeks later through branded search. The Refine Labs research on dark social documented a 90% gap between software-attributed and self-reported revenue. Reddit sits firmly in that gap.

There's also a built-in tension. Your instinct is to wire everything up: UTM codes, tracked URLs, attribution pixels. But Reddit punishes that. The community can smell a tracked link, and the more you optimize for measurement, the less your engagement looks human.

The most reliable approach is also the simplest. Add a free-text field (not a dropdown) to conversion forms asking how the person heard about you, and train reps to ask the same question on calls. "I saw your comment in r/revops about attribution" tells you more than any dashboard. Also use extended attribution windows. Reddit's influence typically materializes 60 to 90 days after first contact.

Is the window closing?

Most competitors haven't built a Reddit presence yet. The ones who are there often show up wrong, treating it like a broadcast channel. Reddit rewards real expertise and penalizes anything that feels like advertising. The bar for differentiation is still low.

The AI citation advantage, the search visibility, the buyer trust. It's all still available in most B2B categories. The cost gap between Reddit and LinkedIn won't hold forever, and the time to start building is before your competitors figure out the same thing.

So what next?

The harder part is knowing how to actually show up without getting ignored, downvoted, or banned. Which accounts to use, which subreddits to prioritize, how to write a comment that earns trust, and how to build a system your team can maintain. That's what we'll cover in Part 2.

‍

Your investment team spent three months evaluating a $20M software company. The financials looked solid. The market was there. The product worked. Everything checked out until you sat down with their sales and marketing leader and asked a simple question: "Show me how you actually acquire customers."

What they showed you was a spreadsheet. Not a system. Not a process. Just a sheet with projected pipeline numbers that looked like they came from thin air. When you dug deeper, you found what was actually driving deals: the founder. His network. His ability to get on the phone and close. The company had a scattered marketing motion, no recent thought leadership building brand, and no repeatable customer acquisition process.

You killed the deal.

This isn't an edge case. Most companies in growth mode operate this way. They start with founder-led sales, and fewer companies actually evolve beyond this stage. They grow through founder hustle, not systems. And when you're evaluating acquisition targets or trying to understand the quality of a portfolio company's growth engine, you need a framework to spot the difference between real growth and spreadsheet theater.

The good news: you can evaluate this in diligence. The better news: you can use the same framework to assess and improve any company you acquire.

What a modern growth engine looks like

A real growth engine has layers. Each layer is a system that works independently but connects to everything else. When all layers are operating, growth is predictable and repeatable. It doesn't depend on one person's rolodex.

Here's the simplified system to show how we connect all the pieces:

• GTM Foundation is the base. This is your positioning, messaging, and ideal customer profile. Does the company know who they actually sell to, and can they articulate why customers should buy from them. Messaging either stays consistent across the team or it doesn't. Positioning either gets written down or it lives in the founder's head.
  
• Content is what fuels awareness and demonstrates expertise. This is case studies, thought leadership pieces, nurture sequences, educational content. Content can be produced on a schedule or sporadically. It can align to the buyer journey or just be random top-of-funnel noise.
  
• Campaigns are how you put your strategy in motion. Campaigns can be structured around segments, buyer stages, or specific value props. Done well, campaigns have specific objectives, measurable outcomes, and keep your actions focused and intentional.
  
• Channels are where your audiences live, and serve as the pipelines that you activate to increase awareness and drive engagement. This is linkedIn, email marketing, reddit, paid ads, etc.
  
• Activate and Capture is how you convert the 5% of your audience that’s ready to act. This is a lead capture form on your landing page, lead magnets your audience bites on in direct social outreach, or your customer who replies to the monthly newsletter with a referral. 
  
• RevOps Underneath. Are systems connected or siloed? Do you know your metrics or are you guessing? Do you have a CRM that actually tracks contacts, deals, and engagement across the stack, or are you managing spreadsheets masquerading as systems? RevOps isn't about having the fanciest tools. It's about having a platform that connects your foundation to your measurement, so you can see what's working and what isn't.

Growth is messy, non-linear, and multidisciplinary. Frameworks and systems add clarity and structure that help control the chaos. At scale, companies have to have systems or they break. At early stage, the hope is that if you build the right system from the beginning, you avoid the death march of trying to retrofit it later.

Every company we speak with has a different level of maturity across the framework. The trick is knowing when the existing setup is a long-term liability or an immediate opportunity.

How Do You Actually Spot This in Diligence?

The real work of GTM diligence is asking the right questions about each layer and knowing when you're looking at real answers versus vague reassurance. Spreadsheets are easy to write down. Knowing when to call BS on what you're being told is the hard part.

Here are the five questions we use to structure the conversation:

1. What channels are actually working for growth? Not "what channels do you want to use." What's generating deals right now, and how much pipeline is each channel creating? 
2. How do you create content today? Who's responsible? Is there a schedule? Do people actually use it to sell, or does it sit on a shelf? Does it align to your buyer's journey, or is it random top-of-funnel noise?
3. Walk us through a recent marketing campaign. Who planned it? How did it get organized? What were you trying to achieve? What actually happened? You'll learn more about how a company actually operates from one campaign than from any document they send you.
4. For your active pipeline, what are the different lead sources? Break it down. If it's 90 percent founder referrals and 10 percent everything else, you know what you're inheriting.
5. What's the average deal cycle duration for pipeline in the last 12 months? Not a single deal. Look at the last 12 months. If it's chaotic, you're seeing founder-dependent selling. If it's predictable, you're seeing a system.

The key is listening to how they answer. Do they describe a process, or do they describe people? Do they reference metrics, or do they tell you what they think should happen?

Case Study: The Software Company We Walked Away From

Last year, our team spent two months evaluating a tech-enabled services company that had household name clients, a solid delivery team, and reasonably unique software that was under invested in recently. On paper, it looked solid. Mid-market B2B SaaS business, $20M ARR, good margins, established customer base. The investment thesis made sense.

So we dug into the growth engine.

GTM Foundation was weak. Positioning existed but wasn't consistent across the team. Their content engine was minimal. Campaigns weren't structured. Channels were a mystery. 

More importantly, the company had one person capable of selling. Not one person doing most things. One person the business actually depended on. He was the founder. He closed deals, maintained relationships, knew how the business worked. And he wanted out.

He was tired. He'd been running on fumes for three years, ready to hand it off and move on, and we knew this going in. But what the company underestimated was the degree to which their lack of any growth engine handicapped the deal.

We evaluated what it would take to fix this post-close. Build a GTM foundation. Create content. Structure campaigns. Enable a sales team to replace what one person was doing. 

We walked away.

Six months later, the business was acquired by a competitor. Founder checked out like he said he would. Customers started churning. The revenue that looked solid in the data room turned out to be fragile. Every month, the business eroded a little more. The acquirer is now managing decline instead of managing growth.

The issues we identified weren't unsolvable. They were all things we could have fixed with our operating engine. But the founder was unrealistic on valuation, and the deal numbers didn't support what it would cost to fix it. Not all deals work out.

What the acquirer didn't spend: two months of diligence with operators who knew what to look for.

What they're spending now: managing the fallout.

Putting It Together: What the GTM Layers Tell You

When you evaluate a target company, you'll find different combinations:

Strong across all layers. This company has a real growth engine. Post-close, you're scaling and optimizing, not building from scratch. Low risk. Lower post-close investment.

Weak foundation, strong execution. This one is tricky. The team executes well, but execution depends on people. Positioning and messaging aren't locked down. Red flag. When that person leaves, execution falls apart. Moderate risk. Plan for significant change management post-close.

Weak execution, strong foundation. You're buying a platform with solid messaging and positioning. Campaigns and channels aren't built out yet. But you have something to build on. Moderate risk. High post-close investment in building execution, but you're not starting from zero.

Weak across all layers. This is spreadsheet theater. Real growth doesn't exist. You're buying a founder and hoping his network scales. It won't. High risk. Either massive valuation adjustment or pass.

How We Approach GTM Diligence

Other firms skip GTM assessment in PE diligence. Not because it's unimportant. Usually because it's unclear how to evaluate it without being a marketing expert yourself. So it gets skipped, or it gets delegated to consultants who'll hand you a 50-page report that doesn't help you make a decision.

We approach it differently.

Trelliswork joins your deal team as GTM operators during diligence. We sit in management meetings with you and the target company. We help you understand what actually exists and what doesn't. We ask the specific questions that separate real growth from founder-dependent hustle. We help you build a roadmap for what needs to happen post-close if you move forward.

Here's what that engagement looks like:

Two management meetings. We join your team in the room with the target company's leadership. We sit alongside your deal team as an extension of your team, not as external consultants. We dig into their channels, campaigns, and metrics. We ask about process and tooling. We help you get a clear picture of what's actually built versus what's aspirational. You understand the gap between where they are and where they need to be.

A realistic post-deal roadmap. Then we work with you to build a specific GTM roadmap for this company, this market, these people. Not a generic template. A plan that's actually executable. What needs to happen in month one. What needs to happen in quarter one. What gets fixed versus what gets rebuilt. What you're inheriting versus what you're building.

You close the deal with clarity about what you're acquiring and what it actually costs to scale it.

The Cost of Missing This

You can discover this in diligence. You price accordingly. You go in with eyes open about what you're buying and what you'll need to invest post-close.

Or you miss it. You assume that because the company has grown, they have systems. You close the deal. You onboard the company into your portfolio. And somewhere around month three or month four, you realize the growth engine doesn't actually exist. Now you're managing a broken revenue machine while you should be scaling it.

The valuation adjustment you didn't make in diligence becomes the operational headache you own in year one.

Evaluate the layers. Ask the hard questions. Decide what you're actually buying.

That's how you spot the difference between real growth and a spreadsheet.

Ready to derisk your next deal?

Value creators and deal teams use Trelliswork to pressure-test growth engines before the close. We embed with your team during diligence, identify the gaps that don't show up in a data room, and build realistic post-close roadmaps so you know exactly what you're buying and what it costs to fix.

See how we work with investors or get in touch to talk about your next deal.

A Different Kind of Restructuring

Every few months, another headline announces agency layoffs. The narrative focuses on AI displacement and budget cuts. But there's a different story playing out at mid-sized independent agencies.

These firms aren't shrinking. They're restructuring around a simple insight: the work clients pay premium rates for (strategy, relationships, business insight) is different from the work that eats up most of their headcount (content production, campaign execution, technical delivery).

So they're splitting the difference. Keep the strategic layer. White-label the execution layer to specialized GTM partners who do that work faster and cheaper.

Legal Services Got Here First

Law firms faced this inflection point before marketing did.

If you've used traditional outside counsel before you know what it feels like to get a $1,700 invoice for a 30 minute phone call and an updated legal doc. Everyone knew this industry was ripe for disruption, it just wasn’t possible until today's AI came along to handle the legal busywork.

Harvey AI went from zero to an $8 billion valuation by becoming the execution layer for law firms. Partners kept client relationships and strategic counsel. Harvey handled the document review, research, and drafting that used to employ armies of junior associates.

This same dynamic is in play now with GTM firms and marketing Teams.

The Economics Forcing This Conversation

Mid-sized agencies typically run 60-70% of their headcount in execution roles: content writers, campaign managers, SEO specialists, designers, developers, analytics people. These roles are necessary, but they're where margins get compressed.

Execution work is increasingly commoditized. Clients benchmark your deliverables against competitors producing similar outputs with smaller teams and better tooling. Meanwhile, your execution staff expects annual raises, benefits costs climb, and turnover runs 20-30% annually.

You're running hard just to stay in place. The strategic work is where your differentiation lives, but execution overhead consumes capacity you could invest there.

Why Agencies Can't Build This Internally

Three reasons.

The talent market works against you. Engineers who understand AI-native workflows command $300,000+ packages. Even if you land someone good, they need infrastructure and time to build something useful. Most agencies underestimate the investment.

Your team can't retool fast enough. Your content team isn't becoming prompt engineers in a month. Training takes time you don't have, and clients won't subsidize your learning curve.

Your business model fights the change. Agencies built on billable hours struggle when AI compresses delivery timelines. If a project that took 40 hours now takes 15, do you bill for 15 and take the revenue hit? Its an option, but it doesn't represent the value you are creating.

How White-Label Partnerships Work

You keep: Strategy and planning, client relationships, creative direction, business development.

Your partner handles: Content production, campaign management, technical implementation, SEO and paid media, analytics infrastructure.

Everything ships under your brand. Clients interact with your team. The partnership stays invisible.

The Delivery Engine Behind It

A good GTM partner brings more than bodies. They bring a delivery engine: an existing framework, workflow, and system you plug into.

Consider what goes into publishing a single, high quality SEO article. You need a content calendar, keyword strategy to inform topics, and then a workflow for each piece: outline creation, gathering unique perspectives from the client, drafting, adding visuals, internal review, a featured image, client approval. Then internal links, optimization for the buyer journey stage, and finally publication. That’s a lot of moving parts for one article.

At Trelliswork, we use our 10/80/10 framework. The agency owns the first 10% (client relationship, strategic direction) and the last 10% (client review, final approval). We handle the 80% in the middle: all the execution and orchestration that turns strategy into deliverables. Content interactions get packaged up and shipped ready for the agency to present. The end client never sees Trelliswork. The agency maintains full ownership of the relationship while we run the engine underneath.

The Financial Impact

A $15 million agency with 22 employees might look like this:

Current state:

• 8 people in strategy/client services: ~$960K loaded cost
• 14 people in execution/production: ~$1.5M loaded cost
• Operating margin: 12-15%

After restructuring:

• Keep 8 strategy/client services people: ~$960K
• White-label execution (typically 45-55% of internal cost): ~$750K
• New operating margin: 18-23%

That's roughly $750K in annual savings. The flexibility matters as much as the savings: scale execution through your partner when you win big accounts, reduce scope without layoffs when accounts churn and simplify your bench management stress. Your cost structure becomes variable instead of fixed.

What Separates Good Partnerships From Bad Ones

Integration quality means your partner operates as an extension of your team. Shared project management, direct communication, aligned accountability. If you're spending hours coordinating handoffs, you've traded one overhead problem for another.

Strategic depth means your partner brings expertise that improves your work. They've seen patterns across dozens of clients and know what's working. They should elevate your strategy, not just fill orders.

The Competitive Window

Agencies restructuring now are building partner relationships and improving margins while competitors maintain the old model. That advantage compounds. Better margins fund better business development. Better business development wins more clients.

The agencies that wait will face the same economics eventually, but they'll be playing catch-up with less runway.

Making the Shift

First, map your current economics. What percentage of headcount sits in execution roles? What's your fully-loaded cost per deliverable type?

Second, identify the right partner. Look for GTM firms that handle the full execution stack under one relationship. Evaluate integration capabilities, not just deliverable quality.

Third, plan the transition. Start with a single service line or client segment, prove the model works, then expand scope gradually.

The agencies winning in 2026 are maintaining execution quality for clients while shifting their best people to think more strategically. To get there, it means retooling your delivery model. White-labeling is one option, but the shift is structural, not transactional.

Ready to explore what this could look like for your agency? The first step is understanding your current cost structure and where the leverage points are.

Frequently Asked Questions

What does it mean to white-label GTM for agencies?

A partnership where GTM firms handle execution (content, campaigns, SEO, paid media, analytics) under your agency’s brand. Clients never see the partnership. You keep strategy and client relationships.[1] 

How much can agencies save by white-labeling execution?

45-55% reduction in execution costs. For a $15M agency, that’s roughly $750K annually. Savings come from eliminating salaries, benefits, tools, and 20-30% turnover costs.

What functions should agencies keep in-house vs. white-label?

Keep in-house: strategy, client relationships, creative direction, business development. White-label: content production, campaign management, technical implementation, SEO/paid media, analytics.

How is agency white-labeling different from traditional outsourcing?

Traditional outsourcing sends discrete tasks to the lowest bidder. White-label partnerships integrate the partner into your team with shared systems, direct communication, and accountability. Good partners improve your strategy, not just execute orders.

Will clients know we're using a white-label partner?

No. All deliverables ship under your brand. Clients interact only with your team. The partnership stays invisible.

What should agencies look for in a white-label GTM partner?

Integration quality (shared project management, direct communication, aligned accountability) and strategic depth (cross-industry expertise that improves your work). Avoid partners requiring heavy coordination or lacking full GTM stack experience.

Model Context Protocol (MCP) servers let you manage your CRM through natural language instead of endless clicking. You'll learn what MCP servers do, how to create deals and run reports without opening HubSpot, and what limitations to expect while the technology matures.

Adding a deal to HubSpot takes about 17 clicks and two and a half minutes. You navigate to the right pipeline, tab through edit fields, fat-finger something, delete it, try again. By the time you're done, you've lost momentum on whatever you were actually doing.

Now imagine speaking a 7 second update and having the deal created automatically. The pipeline is correct. The close date is set. The contact is associated. You never opened HubSpot.

That's what Model Context Protocol servers make possible. They put a conversational layer between you and your business systems so you can talk to tools like HubSpot in plain English instead of clicking through menus.

Behind the scenes

Think of MCP servers as translators. When you ask a question or give a command in plain English, the MCP layer converts that into the correct API calls for whatever system you're connecting to. The responses come back in readable form rather than raw data.

With a standard API integration, you need to know the exact endpoint, the correct formatting, the required fields. You're writing code or at minimum configuring something technical. With MCP, you describe what you want and the protocol handles the translation.

The Model Context Protocol documentation covers the technical architecture if you want to go deeper. The short version: MCP creates a standard way for AI applications to connect with external data sources and tools. Instead of each integration requiring custom code, platforms can publish MCP servers that any compatible AI interface can use.

HubSpot recently released a bi-directional MCP server. This means you can both read data from HubSpot and write data back to it. You can ask "show me all deals closing this month" and also say "create a new deal with these details."

Worth noting: write access through HubSpot's MCP server is currently in beta. Your company or partner admin needs to opt in before you can create or update records through the connection. Read access works immediately once you connect.

The headless CRM experience

The term "headless" comes from software architecture, where you separate the interface from the underlying functionality. A headless CRM means you get the data management and pipeline tracking without being forced into the CRM's interface.

For teams that spend their days in Claude, Slack, or Notion, this is welcome. Instead of context switching into HubSpot to update a deal, you update it from wherever you already are. The CRM becomes infrastructure rather than a destination.

Here's an actual prompt that creates a complete deal record:

"Add a new deal to HubSpot called 'NewCo GTM Assessment', assign it to me, associate the deal with Robert Duncan, set this to first deal stage in our pipeline, close date Mar 31, and set the lead source to 'Client Referral', deal type new business"

That single request replaces opening HubSpot, navigating to the pipeline, clicking "Add Deal," filling in the deal name, setting the amount, selecting the pipeline stage from a dropdown, picking a close date from the calendar widget, searching for and associating the contact, setting the deal type, configuring the lead source property, and finally clicking save.

The MCP server processes the whole thing, creates the deal record, makes all the associations, sets every field, and confirms what it did. Seconds instead of minutes.

‍

What this looks like in practice

Setting up the HubSpot MCP connection in Claude means enabling the connector in your settings. Once connected, you'll authorize specific tool functions the first time you use them. After that initial setup, the connection persists.

The real value shows up in recurring work. Updating deal stages, adding notes, changing amounts, associating new contacts. Each of these tasks involves multiple clicks and screen navigation in the traditional interface. Through MCP, they become single requests.

The time savings compound quickly. If you're managing a pipeline with 20 active deals and touching each one even once per week, you're looking at hours reclaimed monthly.

Notice in the example prompt that you can reference contacts by name ("Robert Duncan") rather than hunting for record IDs or email addresses. You can use relative terms like "first deal stage in our pipeline" instead of memorizing exact stage names. The MCP layer handles the translation to HubSpot's internal structure.

Ad-hoc reporting without the formatting fights

Anyone who has built reports inside HubSpot knows the frustration. You want a specific view of your data, but the report builder has opinions about how that data should look. Column limits. Visualization restrictions. Export formats that require cleanup before they're usable.

MCP changes this. Instead of conforming to what HubSpot's reporting interface allows, you describe what you want to see and get it back in whatever format makes sense.

"Show me all deals by stage with associated contacts and last activity date, sorted by days since last touch."

The response comes back as structured data you can use however you'd like. No clicking through configuration screens. No wrestling with chart types that don't fit your data.

The real gain here is iteration speed. In HubSpot's report builder, changing a filter or adding a column means navigating back through configuration screens. With MCP, you refine your query conversationally. "Actually, filter that to just deals over $50k" or "Add the deal owner column" becomes a quick follow-up rather than a multi-click detour.

The tradeoff is a lack of standardized reporting while you're in iteration mode. You want your data to build on itself over time, to tell a story with trends and patterns, not start from scratch every week. This is where the opportunity to rethink what you really want your data to tell you comes in.

We're finding a middle ground that works right now. We keep a base set of reports that run clockwork week over week. They drive accountability and data checks. Then all the exploration off of that data goes back to MCP prompts, augmenting the data story for that particular week.

‍

‍

Hubspot's MCP Limitations

MCP servers are still maturing. You'll encounter quirks. Sometimes the connection needs re-authorization. Sometimes the AI layer needs gentle reminders that it does have access to the tools you've configured.

The technology requires the right setup. You need Claude's desktop app or similar interface that supports MCP connections. You need the specific MCP server for the tool you're connecting to. Not every platform has released one yet.

Write operations require beta access through your HubSpot admin. If you're testing this personally, you may be limited to read operations until your organization enables the beta features.

One thing to expect: the MCP tool connections reset periodically and require re-authentication. Part of this is because these integrations are actively evolving. Just be prepared to reconnect from time to time as things stabilize.

There's also a learning curve in how you phrase requests. Being specific helps. The example prompt above works well because it includes everything in one request: deal name, amount, pipeline stage, close date, contact association, lead source, and deal type. The more complete your prompt, the more accurate the result.

What this means for GTM operations

Nobody loves their CRM. This is an industry-wide truth. The systems exist because pipeline visibility matters, because forecasting requires data, because revenue operations need structure. But the interfaces are friction-heavy by design. They prioritize data capture over user experience.

MCP servers let teams interact with CRM data from wherever they already work. If your revenue team lives in Slack and Claude and Gmail, they can now update HubSpot from those environments. The CRM still does its job. You just don't have to live inside it anymore.

That connects to something we keep seeing in go-to-market operations. The tools that win are the ones that reduce busywork, not just move clicks from one interface to another.

For teams evaluating their tech stack, MCP compatibility is worth paying attention to. The platforms investing in these connection protocols are the ones building for where workflow is actually heading.

Getting started

If you want to experiment with MCP servers and HubSpot, the workflow is similar whether you're using Claude or ChatGPT:

1. Install Claude's desktop application (or use ChatGPT's interface)
2. Navigate to settings and enable the HubSpot connector
3. Authorize the connection to your HubSpot instance
4. Start with simple read queries to test the connection
5. Contact your HubSpot admin about enabling beta write access
6. Move to write operations once beta access is confirmed

‍

The initial setup takes about fifteen minutes. After that, you're working in a different mode. Less clicking, more doing.

‍

Frequently Asked Questions

Q: Do I need to be technical to use MCP servers with HubSpot?

No. The entire point of MCP is removing the technical barrier. You interact through natural language prompts like "create a new deal called X and assign it to me." The protocol handles the translation to HubSpot's API structure. If you can describe what you want in a sentence, you can use MCP.

Q: What is a headless CRM?

A CRM you interact with through APIs or AI prompts instead of its native interface. You get the data management without living inside the software.

Q: Is MCP reliable for daily use?

It's maturing. Connections reset periodically and need re-authentication. Best for teams comfortable with occasional troubleshooting as the technology stabilizes.

Q: Can MCP create deals in HubSpot?

Yes, but write access is in beta. Your admin must enable it. Read access works immediately. Once enabled, you create deals with a single prompt.

‍

‍

Every B2B marketing leader has heard the warnings by now. Google traffic is declining. ChatGPT is replacing search for research. Your buyers are asking LLMs for vendor recommendations, and you have no idea if your brand even shows up.

The SEO tool vendors see this shift too. SEMrush, Ahrefs, and newcomers like Mentions are rolling out "AI visibility" features that promise to track how often your brand appears in LLM responses. The pitch is compelling: if your buyers are using AI to research solutions, shouldn't you know whether ChatGPT recommends your competitors instead of you?

We decided to test these tools across our own properties and several client sites. Not to write an official vendor comparison (yet), but to answer a practical question: is there any real benefit to investing in AI visibility tracking today?

The short answer: not yet.

The tools show promise, but the data infrastructure isn't there. More importantly, anyone claiming they've cracked the code on how to manipulate LLM visibility is lying. These systems are still largely a black box, and the theories about how to game them are just that: theories.

What we found, and what you should focus on instead.

The Current State of AI Visibility Tools

The concept makes sense in theory. Just like you track keyword rankings in Google, you should track "prompt rankings" in ChatGPT, Claude, Gemini, and Perplexity. You want to know how often your brand appears, what prompts trigger mentions, and how you stack up against competitors.

The tools we tested claim to deliver these insights. In practice, they provide dashboard scores without the substance you need to act on them.

Zero Visibility Doesn't Mean Zero Impact

If you're not a household brand with massive search volume, expect to see mostly zeros in your dashboard. The tools assign you an "AI visibility score," but provide almost no context for what drives that number.

One client site we tested scored a 0. Their direct competitor scored a 35/100. Sounds concerning, right? Except there's no breakdown of which prompts drove that 35, how many actual appearances that represents, or what content made them visible. You can't reverse engineer success when the underlying data isn't exposed.

This isn't like traditional SEO, where you can identify long-tail keywords, see monthly search volume, and build an optimization plan. The LLM vendors aren't sharing prompt data at scale. OpenAI doesn't provide a "Search Console for ChatGPT." Neither does Anthropic for Claude, or Google for Gemini.

The AI visibility tools are trying to fill a gap that's mostly still empty.

The Data Problem Is Foundational

These tools are building on quicksand.

Google Search Console exists because Google wants site owners to improve their content. Better content creates better search results. The incentive structure works for everyone.

LLM providers haven't opened up that same transparency yet. You can't see which prompts mentioned your brand, how often you appeared in responses, what context surrounded those mentions, or whether users took action afterward.

Some tools are scraping what they can or running test prompts at scale to simulate visibility. But it's a thin dataset compared to the depth available for traditional search. Until the LLM vendors provide real transparency, these tracking dashboards are measuring shadows.

One Thing Works: The ICP Exercise

Mentions does something valuable that has nothing to do with tracking. Their onboarding takes a crack at creating your ideal customer profile after some general questions. It identifies competitors, articulates differentiators, and maps the questions buyers naturally ask based on services and does a decent job of it so you can quickly edit or replace what it creates. 

This exercise matters. It pushes you to think about brand positioning in the context of conversational queries, not just keyword strings. If someone asks an LLM, "What tools help B2B companies improve pipeline visibility without adding headcount?" how should your brand be described in that response?

That's a strategic question worth answering, regardless of whether you can track the results.

But after building that foundation, the tool primarily tracks prompts that explicitly mention your company name. Of course you appear in those results. The more valuable question is whether you surface in category-level or problem-level queries where your name isn't mentioned at all.

Those are the prompts that drive net-new awareness. And the tools can't reliably track them yet.

Traditional SEO Vendors Are Adding Bolt-On Features

SEMrush and Ahrefs are layering AI visibility modules into their existing platforms. On the surface, this seems efficient. You already pay for these tools, so why not get AI metrics in the same dashboard?

The risk is they're applying an SEO framework to a fundamentally different problem. AI visibility isn't just about keywords and backlinks. It's about how your brand narrative gets synthesized into conversational responses. It's about authenticity, context, and the authority signals that LLMs use to decide what's worth citing.

If you optimize for trigger words without understanding how LLMs construct answers, you might improve a score that doesn't correlate with actual buyer influence.

What You Should Do Instead

If the tracking tools aren't ready, what's the alternative? Focus on the fundamentals that drive AI visibility, whether you can measure it precisely or not.

Build Content That LLMs Want to Reference

LLMs are trained on public content and retrieve from indexed sources during inference. If your content is thin, generic, or keyword-stuffed, it won't surface in AI responses no matter what your dashboard says.

Write content that demonstrates real expertise. Address specific buyer problems with depth. Provide clear points of view backed by experience. This is what gets cited when an LLM synthesizes an answer.

Forget the tricks. There are no tricks yet. Anyone who tells you they've reverse-engineered the ranking algorithm for ChatGPT is selling you something they don't have. These systems are black boxes, and the theories floating around are mostly speculation dressed up as strategy.

What works is the same thing that's always worked: create content valuable enough that it becomes a source of truth in your space.

Make Your Content Crawlable and Structured

LLMs need to access your content to reference it. That means basic technical hygiene matters more than ever.

Ensure your site is crawlable. Use clear URL structures. Format your pages with proper headings, lists, and semantic HTML. Make it easy for both traditional search engines and AI systems to parse what you're saying.

If you have key service pages, product explainers, or methodology documentation, structure them clearly. Use headings to break up sections. Include definitions for important terms. Link to authoritative sources that support your claims.

This isn't new advice. It's just more important now.

More kudos to Mentions in this feature area, they provided the most depth on suggestions of structured content that might improve your “score.” Most were obvious: write about the topics that involve your services or customer problems identified in their ICP analysis.  However, Mentions also attempted to diagnose general some visibility problems with your brand and suggested content pieces unrelated to services (e.g. write a blog post specifically focused on who is Trelliswork so that the LLMs can fill in the gaps on what they glean from pages, FAQs, and services pages).

Link to Authoritative Sources (And Earn Backlinks)

LLMs weight authority when deciding what to include in responses. That authority comes partly from who links to you and who you link to.

Build relationships with credible sources in your industry. Contribute to publications that matter. Get cited in research reports, analyst briefs, and case studies from recognized firms.

When you publish your own content, link out to authoritative sources that support your points. This isn't just good practice for readers. It signals to AI systems that your content exists in a network of credible information.

Define How You Want to Be Described

Think about how you want an LLM to describe your company when someone asks about your category. What's your core differentiation? What problems do you solve that competitors don't? How would you explain your value in two clear sentences?

Document this. Make it public. Repeat it consistently across your site, case studies, thought leadership, and any content you control.

LLMs synthesize from available sources. If your positioning is clear and consistent everywhere, that's what gets reflected in AI responses. If it's muddled or contradictory, the LLM will struggle to represent you accurately.

Test Your Own Visibility Manually

You don't need a paid tool to understand your AI presence. Open ChatGPT, Claude, Perplexity, or Gemini. Ask the questions your buyers would ask. See what shows up.

Try variations:

• "What are the best tools for [your category]?"
• "How do B2B companies solve [problem you address]?"
• "What should I look for when evaluating [your solution type]?"

Does your brand appear? If yes, how is it described? If no, look at what does appear. What made that content authoritative enough to reference? What sources get cited?

Reverse engineer those patterns. Look at the structure, depth, linking behavior, and positioning of the content that wins. Then build your own content strategy around those observations.

This is manual and time-consuming. But it's more actionable than a dashboard score you can't interpret.

Promote Your Content in Traditional Ways

AI visibility doesn't replace traditional distribution. It complements it.

Keep promoting your content through email, social, partner channels, and any other distribution you've built. The more your content gets read, shared, and linked to, the stronger the authority signals become. Those signals matter for both traditional search and AI discoverability.

Don't abandon what works in pursuit of a new metric you can't control yet.

The Vanity Metrics Problem

You could add these AI visibility tools to your stack today, get a score, and have no idea what it means or what to do about it.

If your score is high, great. But why? If it's low, what's the actual fix? The tools don't provide enough depth to connect visibility to action.

This is dangerous for marketing leaders who need to justify spend and show progress. A static or declining AI visibility score without context creates pressure to "do something" without clarity on what that something should be.

You risk adding another dashboard that looks important but doesn't drive real decisions. That's the definition of a vanity metric.

Our score has ranged from 30-80%, but when you dig deeper – you start to see why. It is giving us credit for questions and response that really aren’t real. No one would ask these questions about Trelliswork:

What to Watch For as These Tools Mature

These tools will  no doubt get a lot better as LLM providers open up more transparency. When that happens, AI visibility tracking will become essential infrastructure, just like SEO tools are today.

What needs to happen for these tools to cross the threshold from "interesting" to "must-have":

Real prompt-level data. You need to see which specific prompts triggered your brand, how often, and in what context. Not aggregated scores, not when your brand was in the question from the start, but granular visibility into what's working to find you in the haystack.

Actionable recommendations. The tools need to analyze why certain content surfaces and provide specific guidance on what to change. "Improve your AI visibility score" isn't helpful. "Add more structured data to your service pages and link to these three authority sources" is.

Competitive context that matters. Knowing your competitor scored higher is useless without understanding what they did to earn that score. The tools need to surface the content, structure, and positioning differences that drive visibility gaps.

Validation that scores correlate with outcomes. Until there's proof that a higher AI visibility score leads to more inbound interest, pipeline, or revenue, these metrics remain theoretical. The tools need to connect their scores to business impact. We all expect this to change quickly so that the LLM providers can monetize beyond a paid chat interface.

Set a calendar reminder to revisit this space in 3-6 months. 

Where We Land

AI visibility tools are trying to solve a real problem. Buyer behavior is shifting toward AI-supported research, and you need to understand your presence in that environment.

But the infrastructure to track and optimize that presence is still too early. The tools from Mentions, SEMrush, and Ahrefs show the right strategic thinking. They understand what needs to be measured. They're building the frameworks and interfaces. The underlying data layer just isn't robust enough yet to deliver actionable value for most B2B brands. Although, as noted above we think Mentions.so is leaping ahead because it appears to have been designed from the start for this task. We are excited to watch this platform continue expanding.

If you're a high-volume, high-recognition brand, you might extract some directional insights. For everyone else, you're better off investing in the fundamentals : deep content, clear positioning, strong technical structure, and authentic authority building.

We'll keep testing these tools as they evolve. When the data catches up to the dashboards, we'll be the first to tell you. Just not today.