The pipeline construct in B2B marketing has a dual nature. On the one hand, it is a true reflection of how buying groups move through purchasing hardware, services, and software. Concretely, it is true that companies, departments inside companies, and individual decisionmakers must be first made aware of a problem; then understand that a category of solutions to that problem exist; become aware of the vendors offering these solutions; at some point engage with the marketing and sales resources of one or more of those companies; and finally transact. Once they are a customer, they continue to update their experience of the company, perhaps adding services based on other perceived needs.

However, in most cases, the buyers and influencers who make up the customer buying group are indifferent to or unaware of whether they are interacting with a vendor’s “marketing,” “sales,” or “customer success” teams. To them, there is a brand, and that brand either meets or exceeds expectations, or does not. They simply want the best product and service at the best price, with the lowest risk (no one got fired for buying Company A) and do not want to jump through hoops to do so.

The reflection of this customer-centric pipeline inside the typical vendor is distorted but still relevant. For a typical B2B vendor—think Microsoft, Caterpillar, Oracle, Goldman Sachs, GE, etc.—the pipeline is divided into discrete stages, each made of either leads or opportunities, with different values and forecasted close dates. Typically, a “lead” is marketing’s responsibility, and an “opportunity” is owned by sales—but it’s critical to note that to a customer, these categories are irrelevant. This “lead / opportunity” split is a legacy of how B2B marketing and sales has typically functioned: Marketing “generates demand” and sales “closes deals.” The best way to think about “generated demand” in a software system is as a “hand raiser”—someone who has poked their head above water and can now be pursued. That hand raiser “becomes” an opportunity when they have been nurtured and developed, and at that point, the opportunity will gain momentum and hopefully turn into real revenue. Of course, leads and opportunities are both abstractions and simplifications of what is really going on.

We all want to measure marketing ROI

ROI (return on investment) continues to be a hot topic for B2B marketing and sellers, for obvious reasons. An accurate ROI (one that is non-duplicative, counterfactual, and based on a financial outcome) is extremely useful, because it allows all investments to be traded off against one another, particularly at the marginal or “last dollar” basis. If my marginal ROI for paid social is 1.1, and my marginal ROI for events is 0.9, then I should increase my paid social budget and decrease my events budget. Critically, ROI as an outcome metric allows marketing to be traded off against any other investment—at least in theory.

B2C companies are arguably closer to an ROI view of the marketing world. For large consumer brands like Coke, marketing mix models (MMMs) are constantly updated to provide ROAS (return on advertising spend) for various channels. The curves these models output are then used to remix dollars up, down, and across the funnel to maximize some objective—usually total revenue. However, MMMs are slow and prone to omitted variable bias—meaning that lurking, unknown variables, if left out of the model, can drive unrealistically rosy assessments of marketing’s performance.

B2B companies can’t generally use MMMs to measure marketing’s effectiveness (some try, and they “sort of” work, but that’s a topic for another day.) The same structural dynamics that lead to a pipeline view of the world make MMMs—which depend on large volumes of frequent time series data, including daily sales and marketing spend by region—ill-suited for B2B, namely:

• Long sales cycles (months to years, typically)
• Large transaction sizes, few transactions (chunkiness)
• Complex buyer groups
• Poor data quality when humans are involved (events, field sales, partner channels)

The pipeline, rooted in a database view of the world, is both a cause of and a solution to measuring ROI for B2B firms. It is the cause of the problem when it is taken too literally—that is, that the “lead” is a real thing that someone “generated.”

At some point in the foggy history of corporate marketing, “marketing attributed sales” became a commonly used term. This probably happened when someone in sales asked someone in marketing what value they were providing, which, by corollary, meant how many leads were being handed off.

Now, we commonly speak of “marketing attributed sales” as those opportunities that started with a marketing-generated lead. This means, concretely, that some individual at a buying group filled out a form, and was then “nurtured” until ready for handoff to sales as a “sales qualified lead.” In some cases, sales has to accept the lead for it to “count”—as a “sales accepted lead.”

There are three problems with this way of looking at marketing value. First, it assumes that marketing generated all of the “value” of the lead that it generated. This overstates marketing’s impact. However, this isn’t the biggest problem: All of the other value that marketing creates “under the water” is missed, because it’s not a part of the marketing software / CRM software that has largely come to define the B2B marketing organization. Finally, once a lead is “handed off,” marketing’s role is cut off, leading to both double-counting (marketing and sales both want credit for the deal), and a somewhat toxic “what have you done for me lately” adversarial stance between marketing and sales.

These dysfunctions have real negative impacts. Marketing’s insistence on taking full credit for leads—understandable given its typical fight to show value—drives a bias towards lower funnel behaviors that might not optimize long-run growth. The inability or unwillingness to understand how marketing drives value for all opportunities—known or unknown—makes assessing true ROI impossible. Finally, the “hand-off” concept itself creates an us-them duality that is nonsensical to a customer, and, once again, does not accurately capture marketing’s role in driving value.

Conclusion

Marketing and sales have a common goal: to drive revenue. Yet the most common marketing, sales and CRM tools today pit marketing and sales against one another to claim holistic credit for each sale. True B2B marketing ROI is achievable with the right measurement approach.

Stay tuned for Part 2: How to measure B2B marketing ROI and subscribe to our monthly Consulting newsletter so you don’t miss an insight.

The post The problem with B2B measurement appeared first on Marketbridge.

Economic predictions for a mixed year of nominal growth largely held true, supported by injections of tariff uncertainty and massive AI investments, leading to an unbalanced market that’s basically the magnificent seven vs. the rest of the economy, 2025 was a wild ride. Overall, AI dominated the GTM landscape, broadening the market understanding beyond generative AI to agentic integration across the GTM stack.

What we failed to anticipate was the stark, almost painful, misalignment between the pace of tech innovation and the inertia of the enterprise. Providers delivered tools at lightning speed, but customers couldn’t keep up. Much like the economy in general, AI predictions at least fared okay, and in most cases, pretty good!

Making the grade

To assess the veracity and reality of these shifts, I looked at consensus predictions against in-market performance and commentary from 2025, assigning grade based on proximity to how close predicted outcomes came to reality. To simplify, I bucketed the analysis on three dimensions that determined revenue success:

• GTM model alignment: Traditional GTM models will be replaced by blended Hybrid GTM approaches
• Data-driven profitability: GTM must transition to data-driven, Intelligent Pricing strategies
• AI workflow challenge: AI will successfully automate seller administrative tasks at scale

GTM model alignment

Blended GTM models were not fully adopted, but the economic climate necessitated the collapse of GTM silos and the adoption of more agile growth models, driven by predictions focused on unity and efficiency (Forrester, McKinsey). The core prediction here, the transition to Hybrid GTM Models, was a strategic success of the year, while implementation struggled.

Blending GTM models earned an A, signifying market adoption, but the underlying goal of Organizational Alignment (under the RevOps umbrella) fell short with a C+.

GTM successfully moved toward a hybrid operating model but underestimated the difficulty of achieving true organizational unity and structural alignment required to execute it efficiently.

Data-driven profitability

Maximizing margin and improving sales economics were paramount, requiring innovative intelligence-based GTM levers (Bain & Company). The single greatest failure of 2025: the inability to capitalize on advanced profit levers due to data deficiencies.

The most ambitious prediction, Intelligent and Dynamic Pricing, fell short with a C grade, directly contrasting the success of the foundational prediction: Data and RevOps as the Foundation, which earned an A+.

The ambitious revenue-driving predictions were entirely contingent on the fundamental work of RevOps and data quality, reinforcing that basic technical integrity is the prerequisite for innovation. While the assumptions are correct and the direction clear, much like organizational adoption, Data has a long way to go to achieve its profitability promises.

AI workflow challenge

The most compelling prediction for 2025 was the transformative impact of AI (Deloitte, Gartner). The critical question was whether organizations could translate this promise into measurable, scaled success. The results here were split: AI Co-Pilots earned a resounding A, while the mandate to Scale AI Across the Enterprise lagged with a C.

2025 proved AI’s effectiveness as an augmentation tool (Sales Support), but it revealed significant bottlenecks in process management and change adoption necessary for enterprise-wide transformation, especially as it comes up against entrenched teams, processes and vendors.

Final evaluation

A clear narrative coming at the end of 2025 is that while investors and technology providers move forward with AI-abandon, and talks of a bubble have dissipated, GTM leaders are taking more cautious approaches and investing strategically. The year demonstrated that while AI and market shifts are accelerating, successful transformation is ultimately limited by an organization’s willingness to address difficult, systemic, and people-centric challenges (Scale, Pricing, CX).

GTM organizations are entering 2026 leaner and smarter, having successfully prioritized operational efficiency and technology consolidation. However, the clarity gained from 2025 confirmed that the biggest blockers aren’t technological advancements—they are systemic and people-centric.

The success for your 2026 growth roadmap hinges on closing the adoption gap, turning C grades into A grades. This means tackling the fundamental human challenges. As you navigate this next phase of GTM transformation, we’d love to connect to help bridge the gap between technology potential and revenue reality.

The post Report card: Grading 2025 GTM predictions appeared first on Marketbridge.

Quality can differentiate brands, drive loyalty and increase revenue. We’ve been talking about this for a while on LinkedIn, on our blog, and at events.

Last week we co-hosted with the Association of National Advertisers a one-day conference that gathered marketing leaders and practitioners to discuss why quality matters and how to move toward quality marketing and analytics. Below are 5 key takeaways for marketing leaders to evaluate and discuss internally.

Build audiences offline to better control who you’re targeting

“One of our biggest dangers is thinking about people as datasets,” said Chief Analytics Officer Andy Hasselwander during his quality marketing analytics session, but multiple sessions discussed why thinking about your target audience as big numbers (and not individuals) is a problem.

Multiple sources say 252,000 websites are created daily, and the number of viewable impressions and IP addresses vastly outnumber human beings on earth. According to Truth{set}, any two given data providers agree on what IP address matches a postal address at most only 14% of the time. Privacy is not an excuse for bad data, but marketers are getting duped thinking they’re targeting one person but reaching another. Marketing needs to start policing itself on quality—potentially by bringing the identity spine into the open rather than relying on blackbox, outsourced providers.

If you want better results, leverage data and PII to build offline audiences and “stay in the PII as long as possible,” according to Mark Pilipczuk from The Industrial Arts. Segment your ICP within your own PII data, and leverage a vendor’s database to build lookalike models for your target audience, hashed audience is uploaded to the publisher or ad platform (audiences drops due to match rates), and then the files are delivered with partner cookies.

Image credit: Mark Pilipczuk / The Industrial Arts LLC, © 2025 — used with permission.

This offline, more targeted audience almost certainly will outperform the third-party interest and intent categories available in DSPs and ad platforms for customer lifetime value (CLV) and return on ad spend (ROAS).

One lever advertisers can pull to improve impression quality is to ask their DSPs and ad platforms to provide data on refresh rates (higher is worse), sites with multiple advertisers in the same consumer view, and sites with high ad-to-content ratios.

Marketing and analytics should embrace uncertainty

Understanding what we know and what’s still uncertain supports good decision making. Yet many in both marketing and analytics are hesitant to state when we don’t have a definitive answer.

In statistics and modeling, error bars show the variability of data. When MMM reports out a cost per acquisition (CPA) or ROAS, typically only the mean or median value is reported. But that’s where you get into trouble.

In the image below, the estimate for TikTok’s CPA is €41. If Instagram Reels’ estimate CPA is €60, the marketing team may decide to shift budget to TikTok. But then in the next readout (and with more data), TikTok’s CPA is €80. Now marketing’s mad and doesn’t trust the MMM. But in reality, TikTok’s CPA is still within the confidence interval—error bars would’ve helped marketing make a more informed decision.

Analytics teams must help educate their stakeholders about error bars and confidence intervals so the organization can make better decisions.

Delivering ROAS or CPA without error bars doesn’t breed confidence, it breeds distrust.

Cultivate curiosity and come with a solution mindset

Humans are wired to collect data, but creating knowledge, driving insight and providing wisdom don’t happen automatically. So what makes a great analyst? Curiosity, clarity and capability are the core skills of a great analyst, according to Sravanthi Konduri from Navy Federal Credit Union. Often we equate degrees with skill, but building experience and knowledge is needed to drive insight (and eventually wisdom).

Cultivating curiosity within the organization is another matter. Organizations with a growth mindset support an analytical environment and aren’t scared of data and learning. Analytics teams can fail because of analysis paralysis and wanting to have the perfect answer, rather than collaborating with internal stakeholders.

A solution mindset is key for marketing analytics teams to partner internally and offer alternative approaches and solutions. Analysts should think like the GM of a business unit—understand the problems, where the question fits in and who would care, and delivering an answer in the context of why it matters.

How to combat too high and too low ad frequency

Often marketers worry about capping frequency for individual views to prevent waste. This has long been an issue, especially if you’re running a campaign across multiple channels and platforms. David Riva from The Trade Desk pushes for unified frequency control—rather than capping each individual placement, DSPs should support capping frequency across channels.

Another area of waste, according to Ray Van Iterson from the United States Postal Service, is the large group of people who see 1, 2 or 3 fewer impressions than needed to achieve the goal.

And the key question is: do you even know who those people are? Can you identify and target them differently or with additional inventory?

Understanding consumer journeys is essential and MTA isn’t dead

The announcement of cookie deprecation was overblown and yet many organizations stopped trying to understand individual consumer journeys. But reporting focused on campaigns, channels or business units is inherently biased.

Marketers should know what combinations of channels and which sequences lead to the best outcomes. This can be done for known, trackable touches as well as likely touches using probabilistic mapping. Adding up the small probability of seeing an ad on a given day in a specific DMA across an entire campaign can give a better picture of how channels work together.

That individual journey data also can identify if someone is less engaged than we expect. Marketers can then deploy a higher impact channel to achieve the goal.

Understanding the consumer journey at the most granular level of data possible arms marketers with an understanding of the impact of particular platforms or partners within a channel. Identifying the high engagement or high attention platforms and partners can provide optimization opportunities and drive better outcomes.

Want more insights?

If focusing on quality in marketing and analytics feels like a challenge, let’s talk. We’d love to hear about the roadblocks, share best practices and brainstorm solutions.

Complete the form below and we’ll connect to schedule time.

The post 5 key takeaways on quality in go-to-market appeared first on Marketbridge.

At the center of the issue is a recognition that, in theory, AI has potential to reshape every aspect of the go-to-market (GTM) organization ― from prospecting and pipeline management to customer support and pricing. In practice, however, leaders confront pressing realities: budgets for technology and transformation are finite, and teams already face limits on how much change they can absorb.

Instead of spreading resources thinly across the latest AI trends, success demands a disciplined focus on the highest impact opportunities, and constant attention to downstream organizational implications to turn AI investments into measurable results.

From possibility to impact: The critical importance of focus

For sales leaders, translating AI’s wide-ranging potential into practical tangible outcomes starts with identifying the right problem to solve. The key is not in asking, “where could we apply AI?” but rather, “where should we apply it first?”

The answer lies in framing AI opportunities through a clear set of guiding questions that connect business priorities, process pain points, and organizational readiness such as:

1. What are the strategic GTM priorities over the next year (new logo, cross-sell, churn reduction, etc.)?
2. Where in the sales process do reps or managers lose the most time?
3. Which parts of our sales model create the most drag on performance today?
4. Where would enhanced insight or foresight most change seller and buyer behavior?
5. Do we have the data and organizational readiness to act here?

High-performing teams use these questions to cut through the noise and target a handful of use cases where AI can truly change the game. Instead of scattering bets across pilots, they invest in focused applications that drive measurable business value.

Consider five proven examples:

• Dynamic lead scoring: Equipping teams to identify and act on accounts most likely to convert, streamlining prospecting for greater efficiency and increasing qualified pipeline coverage.
• On-demand sales intelligence: Providing real-time access to relevant product, technical, industry, and client information, enabling sellers to navigate even the most complex conversations without pulling in additional specialist resources.
• AI-enabled sales coaching: Leveraging analytics platforms and conversational intelligence to provide real-time, personalized coaching to reps—guiding call strategies, recommending best practices, and helping sales managers tailor development to each team member’s strengths and opportunities.
• AI agents for inside sales: Deploying conversational AI avatars to qualify leads, book appointments, and handle routine inquiries before seamlessly passing high-potential prospects to human reps.
• Pricing optimization: Adapting pricing in real time based on client behavior and market conditions, helping teams close deals faster and at better margins.

New capabilities, new operating models

Done correctly, successful AI deployment should not simply tweak workflows; rather, it should help to inform the future shape of the sales organization itself. As automation handles more data analysis and tactical decisions, the burden of manual, repetitive tasks shrink. Account executives shift toward relationship-building and strategic thinking. Operations and enablement teams shift from report builders and content archivers to stewards of data quality and insight. In aggregate, these shifts enable GTM organizations to deploy fully empowered teams designed for agility and impact.

For example:

• AI-enabled account executives: At a global SaaS company, account executives use AI assistants embedded in their CRM. Rather than depending on a separate team of product specialists, they instantly access up-to-date case studies, technical specs, and dynamic pricing proposals ― strengthening credibility and accelerating sales cycles.
• Operations as a strategic center of excellence: An industrial manufacturer consolidates its sales operations and analytics into a single “insights” team. This group goes well beyond reporting; they continually curate and upgrade the data that AI models rely on, so field reps always act on the clearest possible view of client needs.
• AI agents for inside sales: A technology firm deploys conversational AI avatars to manage the initial stages of prospecting ― qualifying leads, booking appointments, and handling routine inquiries before seamlessly passing high-potential prospects to a human touch. This reallocation of effort allows business development reps to focus on high-value client engagement and strategic nurturing, while machines efficiently scale outreach and qualification.

These shifts let people do what machines can’t: listen, collaborate, and build trust ― faster and with more precision than ever before.

Common pitfalls

Even well-intentioned AI programs stumble when the basics aren’t in place. Two pitfalls in particular tend to limit momentum before value is ever realized.

1. Underestimating the data lift
   AI doesn’t run on hope ― it runs on clean, connected data. Too many sales teams launch pilots only to discover their CRM is riddled with duplicates, gaps, and outdated records. Without sustained investment in data quality, governance, and integration, even the most advanced AI deployments stall.
   
   Key imperatives:
   • Treat data stewardship as a core enablement function, not an afterthought.
   • Establish clear ownership for data quality across sales, marketing, and operations.
   • Start with one or two critical data domains (e.g., accounts, opportunities) before scaling.
     
2. Treating technology as the strategy
   AI can sharpen decisions and automate repetitive tasks, but it cannot replace judgment, creativity, or trust-building. Leaders who treat AI as a silver bullet risk weakening customer relationships and demotivating teams. Technology should enable—not dictate—the sales strategy.
   
   Key imperatives:
   • Position AI as a strategic enabler providing guidance and augmentation, not replacement.
   • Reinforce the uniquely human strengths GTM teams bring: teamwork, empathy, negotiation, creativity.
   • Set adoption expectations early and broadcast success stories throughout the change management cycle.

Principles for sales organizations in 2025

• Prioritize ruthlessly: Anchor every initiative in business value, rather than novelty or hype.
• Redesign deliberately: Let structure follow strategy, adapting roles to maximize new capabilities.
• Invest in data: Treat data quality and integration as non-negotiables.
• Retain a human core: Encourage teams to use AI as a catalyst for insight and creativity, not a substitute for them.

The future of B2B sales will be shaped by leaders prepared to invest with discipline, reimagine their structures, and blend technological horsepower with human-led strategy and ingenuity.

If you want help evaluating if your organization is ready for AI or which use cases to implement first, get in touch.

The post Sharpening the edge: How focused AI integration is transforming B2B sales organizations appeared first on Marketbridge.

If AI uses your brand’s content, you have more influence on what buyers do next. If it doesn’t, consumers may rely on competitors or old information. This matters most when people are researching and deciding which options they prefer.

Recent research found that even older adults are using AI. In a study across several countries, people aged 55 and older were most likely to use AI tools when learning about health insurance (Cognizant). This challenges old assumptions about digital habits and shows why healthcare payers need to make their content easy for AI to find.

Generative Engine Optimization, or GEO, helps your content appear and be trusted in AI-powered searches. For health insurance marketers, GEO is more than a technical detail. It’s a strategic imperative.

This blog explores how generative AI is changing the way people shop for health insurance, from ACA marketplace enrollees to Medicare Advantage members. You’ll also learn how marketers can use GEO to help their content get noticed and trusted in AI-driven conversations.

Inside the New AI-Driven Shopping Journey

People of all ages are now using generative AI tools to research and choose health insurance. This change is especially noticeable in two groups:

ACA Shoppers

First-time ACA shoppers often feel overwhelmed by the complexity of choosing and enrolling in a plan. Many begin with digital research, searching Google, browsing Reddit, and scanning social media for basic information on subsidies and how the ACA works. With new federal changes from the One Big Beautiful Bill Act and anticipated rate increases, more consumers will seek reliable ACA information wherever they can find it. As of the writing of this piece, Google AI (Gemini), Meta AI, and Claude are identified as the most frequently used platforms for ACA-related searches. Among the top ten most-mentioned web pages for ACA-related AI inquiries, only three are from payer websites.

After learning the basics, consumers start comparing specific plans. This step can be challenging. Most marketplace or payer websites require a zip code before showing local plan options. As a result, most generative AI platforms cannot access live health insurance plan databases or search by zip code unless connected to a backend system. This makes it harder for consumers to use AI tools to compare plans and can cause payers to lose visibility when shoppers look elsewhere for plan comparison.*

Medicare Shoppers

As mentioned earlier, research shows that consumers aged 55 and older—including those eligible for Medicare—are increasingly turning to AI tools when researching health insurance. Why? Because they understand how complex plan choices can be, and they value AI’s ability to simplify and personalize the experience.

In our recent CX study (conducted before the rise of GEO), the top reason Medicare Advantage enrollees sought help during enrollment was to ensure they were selecting the right plan for their needs.

Conversational AI can provide users with a personalized Q&A experience, but only if it uses reliable and well-organized information. As with ACA plans, it is challenging to utilize AI models like ChatGPT or Gemini to compare options because plan-level data typically requires a specific zip code. However, some lead aggregators have found a way around this by creating standalone landing pages filled with local plan information that frequently appear in Gen AI recommendations. This is not surprising, since our cursory audit found these pages are easy to read, display STAR ratings, and allow AI tools to access plan information quickly.

What Is Generative Engine Optimization (GEO)?

GEO is about making your content easy for AI models like Meta AI, Gemini, or Claude to find, trust, and use in their answers. While SEO is about ranking high in search results, GEO focuses on being included in AI-generated responses that are contextually relevant.

SEO vs. GEO: A Quick Comparison

In health insurance, GEO means making content that clearly and confidently answers real consumer questions. For example, rather than just using the keyword “ACA subsidy qualifications,” your content should explain the rules, mention trusted sources like Healthcare.gov, and be organized so AI can easily use it.

GEO Best Practices for Payer Marketing Teams

Here’s how to make your content AI-ready:

1. Publish Authoritative, Well-Cited Content
   
   • Cite reputable websites and studies from reputable sources such as CMS and Healthcare.gov.
   • Identify topics with few authoritative sources and create well-structured content to fill those gaps.
   • Attribute published content to a named author and link to an author bio page (Google Search Central).
   • Include statistics and expert quotes.
   • Distribute press releases through wire services since they are ingested into LLM training and retrieval pipelines (Mynewsdesk).
   • Blogs are still important for GEO. Even if organic traffic drops, blogs can help demonstrate your expertise, especially when they are part of a broader strategy that includes media coverage, guest posts, and mentions on other sites.

“GEO isn’t just about search. It’s a window into the effectiveness of your entire marketing ecosystem,” Niall Moran, Director of Technology PR, said during the Marketbridge GEO webinar.

2. Use Q&A and Conversational Formats
   • Structure content around real questions.
   • Example: “What does Medicare Part C cover?” followed by a clear answer.
   • AI works best with FAQ-style formats because question-answer pairs are the exact format AI systems are designed to retrieve (Lisa Lee, Salesforce).
     
3. Optimize for Snippets and Quick Answers
   • Use bullet points, tables, and summaries.
   • Example: A 5-step ACA enrollment guide or a Medicare comparison table.
     
4. Keep Information Up-to-Date and Monitor AI for Accuracy
   • Update content as needed with each policy change and enrollment season.
   • Monitor AI outputs for accuracy about your brand.
   • AI can sometimes generate inaccurate or misleading information, which can hurt your reputation. Being aware of this is the first step to managing the risk.
     
5. Expand Your Digital Footprint
   • Build “citation loops”—ensure your content is referenced across multiple high-authority platforms.
   • Publish thought leadership on your site.
   • Contribute to Reddit or Quora threads.
   • Appear in podcasts and share video content.
     
6. Make Your Website AI-Crawlable
   • Use schema markup and clean HTML.
   • Provide context on each page (e.g., “This guide is provided by a licensed expert…”).
     
7. Leverage AI Tools Internally
   • Use AI for keyword research, content drafting, measuring visibility, and testing how AI presents your info.
   • Deploy AI chatbots on your site for personalized guidance.

Embracing GEO in the Consumer Shopping Journey

Generative AI is reshaping the way people shop for health insurance. Whether it’s a 28-year-old exploring ACA options or a 68-year-old comparing Medicare plans, Gen AI is often the first stop. GEO ensures your brand is part of that conversation.

Publishing clear, reliable content and structuring it so AI can easily understand and cite it helps position your brand as a go-to source. Payers that do this well will stand out by offering accurate information and guiding consumers toward informed decisions.

By delivering helpful content at key moments of the buyer journey, you ensure consumers receive the guidance they need when selecting a health plan.

Remember, generative AI is changing fast. Like SEO, GEO best practices will keep evolving. Still, consumers always look for reliable, high-quality information when choosing health insurance. GEO can help you deliver on that. Because AI reflects the values of its creators, your commitment to clarity, accuracy, and consumer empowerment can extend your brand’s reach and impact.

Need help to get started? Reach out.

* 9/10/25 data from LLM Refs, ACA searches conducted YTD 2025

The post The case for GEO: A strategic imperative for ACA and Medicare marketing appeared first on Marketbridge.

Marketing is defined as all customer-facing functions of the enterprise. Decisions about brands are made by people; acquisition, retention, and advocacy are all human choices. Despite speculation that autonomous AI agents will be the customers of the future, the current reality is that people drive growth.

However, modern marketing is increasingly mediated by technology. Analysts and marketers interact with data, processes, and colleagues through screens, often reducing customers to digital avatars. This trend risks losing the human element in marketing decision-making.

Marketing analytics has followed suit, becoming technical and quantitative. This has enabled precision and accountability, allowing marketing to better communicate with finance, justifying and explaining marketing’s contributions. This is a good thing.

Marketing analytics is the de facto quality department of the go-to-market function. The analytics team—or the analytical resources in the marketing team—are ultimately responsible for identifying data problems, revealing invalid inferences, and uncovering inefficient programs.

However, quality-focused analytics team must beware of disembodiment. Our data represent people, and people are very complex. Our representations of them and their actions are crude indicators of their latent properties. Losing empathy is a road to poor quality, and poor quality leads to long term brand decline and de-growth.

What the Toyota Production System teaches us

In the 1950 and 60s, Detroit dominated the U.S. auto market. Their cars were big and beautiful. The factories of the big three automakers were high-tech—management science was born in Detroit, drafting after the mass production successes of WWII. However, they soon learned that data, technology, and automation do not necessarily mean quality. In fact, layers of abstraction can hide defects. By the mid-1960s, Detroit was shipping lemons, and buyers were quietly getting fed up.

Enter the Japanese importers, led by Toyota. Taking advantage of labor arbitrage, Toyota delivered less expensive cars. They also filled an unmet niche need for fuel efficiency. However, their value proposition didn’t stop there: Buyers soon discovered that their cars were also better built, had fewer defects, lasted longer, and consequently, kept their value over time.

Toyota did not accomplish this via technology. Toyota’s approach emphasized quality through human-centric principles:

• Andon cord: Stop processes when defects are found
• Hansei: Learn from mistakes
• Just-in-Time: Request or provide only when needed
• Kaizen: Empower the workforce
• Genchi Genbutsu: Direct observation of problems
• Nemawashi: Open information sharing
• Genba: Visibility on the factory floor

Quality improvement in marketing analytics requires a similar focus on people and process transparency.

Five Principles for Quality in Marketing Analytics

These five principles have emerged over the past decade at the Marketbridge team. They are best practices, proven through countless hours of focusing on delivering the best quality. They share many of the same values of the Toyota Production System:

Truth Seeking (Hansei)

The word “analysis” comes to use from Greek: Ana (untie) and Leuin (knot). Said differently, working to find all of the holes and loops in something complex, and getting to truth. For marketing, this means analysts should challenge strategies, not support them. There are many ways that this value can be practiced in real life. Some common use cases include:

• True up CPA: Demand capture channels’ last touch CPAs are usually much too low. Truth seekers can decompose lower funnel channels’ effects into accretive, distribution tax, and duplicative, and then report on CPAs taking each of these effects into account.
• Seek Outliers: The most common explanation for an unexpected result is a data issue. For example, if a tactic shows an extremely low CPA—say, two standard deviations below the mean—in almost all cases there will be some error in data collection. Of course, sometimes these are real results, but a good rule of thumb is that 9 out of 10 two sigma outliers aren’t real (unsurprisingly.)
• Eliminate Double Counting: It is rare that the marketing contribution attained from summing reports from individual channel owners adds up to less than total revenue, but very common that it is more. This is understandable; managers want to claim as much credit as possible. It is up to truth-seeking analysts to ensure that totals foot and sum. This will cause hurt feelings temporarily, but this is the cost of truth seeking, and in the long run, trust will be established.

Humanization / Embodiment

Customers are humans; we use data to represent them. A high-quality team should flip their default view, and consider what each customer receives, not the aggregate marketing mix going out the door. Humanization of prospects and customers is a consistent challenge for analysts who are almost always a few levels of abstraction away from people. However, technology also provides us with tools to humanize—if we choose and use them carefully.

• Center Data on People: A Longitudinal Human Record (LHR) aggregates all known and probabilistic data about prospects and customers, enabling empathy at scale. It also powers multi-touch attribution, MMM, and UMM, along with Customer 360 dashboards and reports.
• Go Small: The typical data science workbook flow is to chain data transformation steps together to get to an end result—typically a data frame or a model. However, this approach can miss detail. The best “data detectives” examine individual customer records and follow their journyieys. For example, following a journey from pre-sale to sale to customer—with all of the touchpoints in between—can unearth insight or quality issues that would otherwise be missed. When these findings are scaled up, they can yield big results.
• Build Audiences from Real People and Events: Too much abstraction can quickly yield muddled pictures. While advertising technology is amazingly sophisticated, real signal can be hard to find. Fortunately, addresses are still real, and so are purchasing events. Building audiences from addresses and events is grounding, and yields better results than models built upon models.

Transparency (Nemawashi, Genba)

Reproducibility is a term we borrow from scientific research. When a researcher reports a finding, it is important that other researchers can come back to the work, and at a minimum, follow the steps from raw data to results. Generally, transparency drives quality because more people can see “inside the box” and find problems.

Technology is often seen as impenetrable, but this does not have to be the case. Choiceful adoption of transparent tools and methods can make data simpler, not more complex.

• Use Version Control Tools like Git and Github: Code (R, Python, SQL) and documentation (markdown, readmes) are not just stored and available, but are change logged. In other words, the code base—a manifestation of an organization’s IP—can we seen evolving over time, and nothing will be lost in anyone’s desktop folder.
• Use Workbooks and Notebooks: Workbooks (for example, Databricks) and Notebooks (for example, RMarkdown and Quarto) are visual mash-ups of code, narrative, and results outputs. They facilitate inspection and intuitive understanding.
• Adopt a Medallion Architecture: A medallion architecture is a loose framework that acknowledges three states for analytical data. First, unstructured “dirty” data sits at the Bronze level. Second, taxonomized and QA’s data that is used commonly sits at the Silver level. Finally, use case-ready data sits in scrubbed data frames. supports both formal and innovative use cases, with ad hoc analysis (Bronze), centralized clean and governed data (Silver), and use case-ready data frames (Gold). The key is that all data end up in the data lake—even one offs (in the Bronze layer.)

Ownership (Kaizen, Andon)

There is a temptation to think that technology, data, and automation require less human ownership—indeed, this is the primary idea behind technology driving productivity. Toyota realized that people owning technology drove quality, and that hasn’t changed. In a marketing team, this means that everyone should understand data, have access to it, and be able to perform analysis.

• Own (at least partially) Martech: Marketers and marketing analytics are deeply dependent on Martech. Concretely, most data problems originate in source systems, and, by corollary, are easier to fix there than to band aid later. The people who use the data generated by these systems should at least have a large seat at the table for Martech implementation and customization—and should ideally own the systems altogether.
• Advanced COE: Analytics centers of excellence (COEs) should avoid the guild mentality, and continually focus on only the most advanced use cases—yielding simpler analysis to the marketers themselves. In other words, they should be an innovation lab, not a walled off set of protected jobs. Marketers owning their own data and analytics is a good thing.
• Celebrate QA: Data mistakes are scary. There is a human tendency to want to hide problems, particularly when they were your fault. World class organizations know that admitting failure and then correcting it should actually be celebrated. This is the basis of Toyota’s Andon Cord. The best marketing analytics teams have targets of “bugs found”—and give shout outs to those who find them (even if they created them.)

Probabilistic Communication

Because we are working with people, we can’t know everything about them. This is one of the things that makes marketing fun. When technology really started taking off in the early 2000s, some thought we would start knowing everything about audiences and customers. If anything, the opposite has happened. There is so much noise in the system that we know less, not more. However, leaders want precision—whether in terms of ROAS (return on advertising spend) or CPA (cost per acquisition.) It is up to analytics leaders to not given them exact answers—but rather to train them that everything in marketing is about probability and confidence, and to help them make probabilistic decisions.

• Always Include Error Bars: Error bars show stakeholders that the mean value, while the most likely result, is not precise. They can then make decisions that account for the chance that a result is higher or lower than reported.
• Forecasts that Get More Uncertain: Weather forecasts end at 10 days, but generally don’t include a range around a chance of rain or temperature. Marketing forecasts should not make this mistake; confidence definitionally falls further into the future, and confidence bands should follow suit.
• Beware Extrapolation: Diminishing response curves—which are typical in marketing—are sometimes fit with a small range of stimulus (x) data. The curves look sharp all the way out on the right, but in truth we have no information to predict those points. So, if we are recommending spending a lot more, we have to be honest about what is likely to happen. Flipped on its head, marketers should seek to add variation to spend levels to “soft test” on the outer parts of curves.

Quality Pays

Most readers will nod their heads to the points made above, but might wonder if they are worth doing. Companies, after all, care about profit—and focusing on quality might sound like an expensive nice-to-have. Well, Ford adopted its “Quality is Job One” tagline after they got their clock cleaned by Japan, and if you went back in time and asked their executives, they would have told you that not focusing on quality probably cost them hundreds of billions of dollars over several decades.

But what about marketing? Marketbridge has been collecting data on quality and precision among its clients since 1997. Specifically, we have been interested in its impact on four key metrics: customer lifetime value (CLV); net CPA; long-run growth; and the percentage of go-to-market dollars that are “working” vs. “non-working.” In all four cases, quality-focused organizations have achieved superior results.

• CLV is particularly affected; on an industry-by-industry basis, CLV in quality-focused marketing organizations averages about 10% higher—mainly result of a scientifically-determined, less sugary acquisition diet
• High-quality digital targeting yields around 20% better 6-month retention than cookie-based, spray-and-pray approaches
• Organizations that adopt high-quality approaches have better working / non-working dollar ratios than those that do not, a result of better efficiency and less waste that can be translated into higher media spend
• Overall, high-quality marketing organizations grow at healthier long-run rates than those that are more reactive

A focus on quality isn’t sexy or flashy—it is a cultural shift, and a long-term commitment. However, it might just be the highest ROI marketing mix decision an organization can make.

Wondering how to ensure your organization is focusing on quality in marketing and measurement? Reach out!

The post Quality marketing analytics appeared first on Marketbridge.

In today’s environment – where skepticism towards healthcare institutions is at an all-time high – the gap between consumers and the health care providers feels wider than ever (John Hopkins Carey Business School, Edelman). This mistrust doesn’t just affect perceptions of a given brand or provider; it can directly impact health outcomes; when trust is lacking, patients may delay care, avoid preventative services, or disregard treatment altogether.

While not necessarily its primary job, marketing should be used as a tool to bridge the gap and rebuild trust. Effective techniques do not just promote services; they build credibility, demonstrate transparency, and convince consumers that their needs and values come first. Moving to a quality-driven, trust-building marketing approach benefits all parties by fostering connections, driving engagement, and ultimately, improving outcomes for both the consumer and the provider.

Defining quality: What to say, and how to say it

In the context of marketing, “quality” means more than grammatical correctness or an aesthetic design. It’s about delivering a specific message to a specific audience in specific places at specific times, with each touch curated to resonate and to either drive a specific action or build trust and brand equity. This requires understanding the target audience, and crafting each communication to be honest, accessible, and most importantly, personal.

Consider John, a rural Virgina resident who hasn’t seen a doctor in years. A generic email about low-cost scheduling might be cheaper to send, but it won’t necessarily convince him to act. A tailored direct mail piece highlighting proximity of care or a two-way text encouraging conversation with an agent might be the experience that would work better with John. Send both and explain how a doctor’s visit is likely to improve John’s quality of life, and you’ve transformed a generic outreach into a personalized invitation—one that feels relevant and actionable.

Now consider the type of insurance shoppers that would likely respond to a TV ad promoting a “$0 Premium” “No Risks” plan versus members that would be more swayed by a billboard stating, “Insurer A has been serving the Charlotte community for 15 years. We are committed to bringing the highest level of care to our members today and for years to come.” Will the TV ad outperform the billboard on applications? Probably. But will the quality or value of the members that enroll from the TV ad be as high as the members that enroll from the billboard? Probably not. And which ad goes further to rebuilding trust between the provider and the consumer?

The need for measurement: Quality marketing requires quality data and KPIs

High-quality data is a pre-requisite to running personalized marketing campaigns. In the John example, the team wouldn’t have been able to deliver the right message if they hadn’t known he 1) hadn’t been to a doctor in years and 2) lived in a rural area. And how did they know that? Data. Or how could the team even determine that the tailored direct mail plus 2-way messaging drove a response, while the generic email did not? Again, they had the tracking in place to tie marketing touch points AND desired outcomes back to John.

However, what if the team noticed that, in aggregate, the generic email campaign drove a greater number of responses than the tailored direct mail + 2-way SMS? What would they deem the better campaign? Just like the gimmicky “$0 Premium” TV ad versus the Local billboard, to say which is “better,” an objective or goal metric must be in place. What was the goal of the “get to your doctor” campaign? Appointments scheduled? Appointments kept? Repeat visits to the doctor? For the health insurance ad, was the goal to just drive as many new members as possible, to build brand equity, or drive high future tenure enrollments? It is critical that marketers set a campaign objective prior to launching, and that the campaign objective aligns with the overall business strategy.

Conclusion

Marketers in the health care industry face real headwinds in an environment where individuals are increasingly skeptical. Those who seek to foster trust through the messages and creatives they deploy should consider this simple list:

1. Determine what business goals a campaign is meant to align to and set KPIs accordingly (i.e. value vs. volume)
   • For acquisition marketing, “quality KPIs” could include total lifetime value (LTV) of new member sales, specific membership profile target, or even a longer term KPI of 12-month retention of new members.
     
2. Use messaging that is personalized to the consumer and/or that gives the consumer a reason to trust their company
   • Steer clear of relying solely on quick-win tactics that are effective at eliciting an immediate response from customers, and shift more into creatives that are meant to build credibility and consideration of the brand and product over time
     
3. Track the right metrics (defined in #1) to determine if campaigns have the desired effect
   • Remember, the desired effect will almost always take longer to observe within a value paradigm vs. a volume paradigm. When value is the goal, it typically also means that measurement will be over a longer time horizon than if measurement only required counting appointments, clicks, applications, etc. Be patient.

For many, this may be a significant shift in strategy that will require alignment from multiple parts of the organization and therefore take some time. But committing to a plan like this will pay off in higher brand equity, more satisfied customers, and almost certainly, more sustainable growth.

The post From quantity to quality: Rethinking healthcare marketing for long-term value appeared first on Marketbridge.

Platform lock-in creates significant go-to-market challenges for manufacturers. Over-dependency on monopsonistic advertising and distribution platforms results in diminished control over pricing, branding, and customer relationships, as these platforms leverage their monopsony power to dictate terms. The consequences are far-reaching: manufacturers face intense competition within a crowded marketplace, struggle to differentiate their products, and often incur high costs for visibility through advertising or premium placements.

Strategies to Combat Platform Lock-in

Fighting expansive and accelerating market power is not a novel problem. Previous generations have had their own behemoths to deal with, both in advertising and distribution. The best practices that marketers adopted in the ages of company stores, television media dominance, and Wal-Mart can all be applied today, with slight digital tweaks.

Diversify Distribution Channels

Treat large marketplaces like traditional retail channels, evaluating them based on economics (e.g., margin giveaways), control over branding, and customer preferences. Manufacturers can explore smaller, niche retail retailers or platforms, or even direct-to-consumer (D2C) models that offer greater control over pricing and presentation. For example, partnering with specialized retailers or leveraging platforms like Shopify can enhance brand visibility while maintaining higher margins. Scarcity can also drive perceived value, so focusing on exclusive or curated distribution channels can differentiate a brand from the oversaturated marketplace.

Prioritize First-Party Advertising and Retention

Shift focus to first-party advertising strategies that strengthen direct customer relationships. Invest in retention programs, upsell opportunities, and word-of-mouth campaigns to build a loyal customer base. Email marketing, loyalty programs, and personalized content on owned channels (e.g., a brand’s website or app) can reduce reliance on third-party platforms. By owning the customer journey, manufacturers can capture valuable data and tailor experiences without intermediaries extracting rent.

Approach Third-Party Advertising with Skepticism

When using third-party advertising on platforms like Amazon or Google, manufacturers should critically evaluate algorithmic recommendations and performance metrics. Relying solely on platform-provided analytics can obscure true performance and lead to over-optimization for platform goals rather than business objectives. Instead, use independent analytics tools to measure campaign effectiveness, track customer acquisition costs, and assess return on ad spend (ROAS). This ensures advertising efforts align with broader strategic priorities.

Invest in Owned Logistics and Infrastructure

Where feasible, manufacturers should explore building or partnering for their own logistics capabilities to reduce dependency on platform-controlled shipping. While replicating Amazon’s logistics network is impossible, collaborating with regional carriers or third-party logistics providers (3PLs) can offer competitive delivery times and cost structures. This approach allows manufacturers to maintain flexibility and avoid being locked into a single platform’s ecosystem.

Educate Consumers and Build Brand Trust

Finally, shifting Combat the noise of low-quality products by investing in consumer education and transparent branding. Highlight product quality, certifications, or unique value propositions through content marketing, social media, and influencer partnerships. By building trust directly with consumers, manufacturers can reduce the impact of platform-driven competition and create demand outside of dominant marketplaces.

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For the past two decades, Medicare Advantage (MA) growth was fueled by a steady flow of age-ins thanks to the Baby Boomer generation. From 1946 to 1964, Americans reversed a steady decline in birthrates and had a lot more children—and these children of the post-war years have been turning 65 since 2011. However, the peak of the baby boom turning 65 is already in the rear-view mirror. To make things even rosier for MA payers, this demographic boom coincided with favorable policy and benefit enhancements which drove a growth in preference for MA over traditional Medicare. However, demographics are destiny, and the political winds have changed—maybe for good.

As we discussed in our July 8 webinar and explored in our recent whitepaper, the MA market is approaching a critical turning point. The combination of the current Boomer demographic plateau and imminent drop, tightening policies, and rising cost pressures means that future growth will depend less on who’s aging in, and more on how well plans compete for remaining preference. In other words, competition will be fierce.

The Baby Boom effect: A growth engine slowing down

The Baby Boomer generation — those born between 1946 and 1964 — has been the primary driver of Medicare enrollment growth. But we’ve now reached the peak of that wave.

• In 2024, the U.S. hit a high of 4.3 million net new Medicare eligibles.
• By 2040, that number is projected to decline to 3.1 million — a 28% drop.

This shift marks the end the “demographic party.” The steep climb in new enrollees is flattening, and soon, it will begin to decline.

From organic growth to net preference

Historically, MA growth came from two sources:

1. Net Organic Growth: New 65+ eligibles entering the system
2. Net Preference Growth: Beneficiaries switching from traditional Medicare to MA

As the pool of new eligibles shrinks, net preference becomes the dominant — and eventually the only — growth lever.

In 2024, over 55% of MA enrollment growth came from net preference. That number will only increase as organic growth slows. This means plans must now compete more aggressively for members already in the system, and do so in a more constrained, cost-sensitive environment.

What’s driving preference?

Historically, preference for MA has been driven by:

• Enhanced benefits (e.g., dental, vision, transportation)
• Lower out-of-pocket costs
• Simplified care coordination

But as the “One Big, Beautiful Bill” tightens funding and limits supplemental benefits, these differentiators may weaken. Plans will need to find new ways to stand out, and that means focusing on trust, experience, and long-term value.

Strategic implications: Competing in a shrinking pool with smart growth

To succeed in this new era, MA plans must rethink their growth strategies, moving from “growth at all costs” to smart growth.

1. Focus on member lifetime value

Plans must focus on member lifetime value (LTV). Critically, lifetime value must factor in to both acquisition and retention strategies. In the past, most new members acquired during the Annual Enrollment Period (AEP) have been chronic switchers—members who never stick around long enough to get into the groove of quality preventative care. This outcome is bad for everyone—payers, providers, and patients. It’s up to carriers to not encourage switching behavior among likely future defectors; if their current plan is degraded, they will find another—without a nudge by acquisition marketing.

Fortunately, the math allows carriers to make these decisions effectively and at scale. As media becomes more targetable, it’s increasingly possible to isolate audiences by projected LTV and align acquisition spend accordingly. The goal: invest only up to the point (or below) where the marginal acquisition cost equals the net present value of future cash flows. This approach helps reduce overspending on high churn “switchers,” who tend to have lower LTV from the outset.

By segmenting audiences based on profitability, which often correlates with loyalty, plans can:

• Align channels to acquire higher-value members
• Reduce media waste and froth, lowering overall costs for everyone
• Build separate CAC/LTV curves for key segments (e.g., switchers vs. loyalists)
• Prioritize quality acquisition over volume

2. Invest in brand equity

In a market where demographic growth is slowing and preference is the new battleground; brand equity becomes a critical—and often underleveraged—asset.

As discussed in our July 8 webinar, brand equity is a latent construct—not always visible in short-term metrics, but deeply influential in long-term performance. It shapes how members perceive your organization, how likely they are to choose your plan, and how long they stay.

Yet many plans underinvest in upper funnel, brand-building activities, because their impact is harder to measure than lower funnel demand generation tactics. This is what Marketbridge calls the measurement trap: The tendency to overvalue easily tracked lower-funnel tactics and undervalue upper-funnel brand-building efforts.

Fortunately, for most health carriers, brand equity—and trust—are local. In other words, most plans have very strong equity in a few states and metro areas, many mid-level, high-potential markets, and many more markets where breaking through is prohibitively expensive. By using this to their advantage, carriers can invest in brand building strategically, targeting markets using provider and local marketing tactics to turn “mid-level” markets into long-run winners.

To break this cycle, plans should:

• Track brand equity consistently across DMAs using awareness, affinity, and base lift metrics;
• Identify “elastic” markets where brand investment can shift preference;
• And, maintain consistent presence in strategic markets to allow equity to accrue over time.

3. Embrace digital go-to-market and experience innovation

Every year, new age-in MA members get more technologically savvy. Today’s age-ins were 40 on 9/11; remember what technology use looked like 25 years ago. It’s clear that successful plans must fully embrace digital go-to-market strategies and member experience platforms. Moving forward, each new cohort of age-ins will be even more digitally native, making e-commerce not just an acceptable alternative to call center or in-person enrollment, but a preferred one.

Digital video is a standout opportunity. Channels like YouTube, social reels, and connected TV offer hyper-targeted reach with faster deployment, a major advantage during AEP. These upper- and mid-funnel formats are already displacing traditional DRTV and are expected to dominate by 2030.

On the fulfillment side, digital and digitally assisted applications are improving speed, accuracy, and satisfaction, while reducing buyer’s remorse and OEP switching. This directly supports higher member lifetime value.

Finally, journey-based marketing and unified CX platforms — though still emerging — offer the potential to streamline communications across ANOCs, billing, clinical reminders, and more. Carriers that standardize and scale these systems will gain a long-term edge in retention and cost efficiency.

Conclusion: A new era of strategic growth

The rules of the Medicare Advantage game are changing. The days of easy growth are behind us. It’s time to focus on strategic growth: a more competitive, more disciplined, and more analytical decision-making paradigm for go-to-market leaders.

Plans that understand the demographic shift, embrace net preference as a core strategy, and invest in long-term value creation will be best positioned to lead in this new era.

Watch the peer insights webinar, “Navigating Medicare and Medicaid marketing, sales, & retention in a dynamic environment”​

Hear how leading organizations are adapting their strategies in the face of rising costs and shifting consumer behavior.

Watch

The post Medicare Advantage’s new reality: From demographic boom to preference battle appeared first on Marketbridge.

Ask five experts and you’ll get five different answers — six if one went to Harvard.

—Edgar Fiedler

That quote from Fiedler sums up a challenge we’ve seen for years: everyone’s talking GTM, but few are speaking the same language. While as consultants we strive for simple terms with plain speak definitions, we’re well-aware the language around GTM is squishy―prone to interpretation and misunderstanding. That ambiguity slows down teams, derails alignment, and undermines strategy. So we built something to help.

Working in go-to-market, there are so many terms we use with such regularly that it’s become its own jargon language. As part of the exploration we tried to find the right, perfect, ONE definition for the terms we frequently use. There’s no shortage of definitions. Some are academic. Some are consultant-speak. Never are they all found in one place.

• Is Go-to-Market strategy the execution of a business model, or marketing execution
• Are routes-to-market about sales structure or distribution strategy?
• Is demand generation the same as lead gen? Or does it encompass brand and awareness?

And, we kept finding slightly nuanced answers, as Fiedler pointed out. Not only that, amid the multitudes of definitions, we found inconsistencies in context and applicability.

Step 1 Alignment: Agree on Language

The nuance is where the misalignment hides. These differences aren’t hair-splitting, they’re real definitional differences that can confuse what’s being said and misguide audiences. We needed our own list, the Go-to-Market Glossary.

According to Gartner, 70% of B2B sales and marketing teams report misalignment on strategy and execution priorities. We all know, organizations with high alignment outperform those with low alignment by up to 15% in revenue growth, and 20% in customer retention. And McKinsey noted most failed GTM transformations aren’t due to quality but to coordination breakdowns, unclear roles, and inconsistent planning language.

Introducing the Go-to-Market® Glossary

You can’t align teams with fuzzy language. At Marketbridge, Go-to-Market isn’t just a phrase we use. It’s a practice we have built over 30 years—when we trademarked the term Go-to-Market®. Since then, we’ve worked with Fortune 500 companies, leaders and innovators to define and execute Go-to-Market strategies that drive growth and customer relevance. Across industries, we’ve learned is this: GTM isn’t static―it evolves with the market, the buyer, and the business model. We launched the GTM Glossary not to settle every debate, but to start a conversation―one grounded in practical experience, shared language, and strategic clarity.

It’s a dynamic resource that includes:

• Simple, usable definitions of core GTM terms in one place
• Related terms and concepts, to highlight how one definition influences another
• Resources to go deeper, for those looking to connect ideas to action
• And a feedback loop to continue the conversation

We didn’t build this to be definitive—we built it to be useful. The Go-to-Market Glossary is meant to evolve, just like GTM itself. We’ll be updating it regularly with feedback from practitioners, clients, and readers like you.

Check the Go-to-Market Glossary out and let’s discuss!

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