Article - Page 2 of 7 - Marketbridge

Redefining GTM one word at a time

Posted on August 4, 2025August 1, 2025 by Steven Lewis

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!

Why your GTM Strategy needs a unified data backbone (and it’s not just a CDP)

Posted on July 28, 2025July 28, 2025 by Thea DeBell

You’ve heard it…the promise of a “360-degree view” of customers and prospects. It’s a north star that’s both commonly referenced and frustratingly out of reach for many marketing leaders. It’s even landed in the “trough of disillusionment” on Gartner’s Hype Cycle, the place where overhyped tech goes after reality sets in.

While a Customer Data Platform (CDP) certainly has benefits, like enabling personalized campaigns and orchestrating cross-channel journeys, it is often limited by its out-of-the-box focus on marketing activation, rather than comprehensive strategic insight.

Where we often see CDPs fall short:

They’re primarily built for activation, not strategic planning.
CDPs excel at delivering personalization at scale (like deciding which creative to show based on customer attributes). But they’re often not architected to support the kind of complex questions CMOs face, for instance measuring true marketing contribution to revenue, or forecasting ROI by channel and segment.
They depend on other systems to prepare and pipe in broader GTM data.
While CDPs can receive sales, finance, and LTV data, that information typically needs to be modeled elsewhere, limiting the CDP’s role in end-to-end GTM analysis and decision-making.
They can lock teams into a single vendor ecosystem.
Many CDPs are sold by marketing cloud platforms whose primary goal is stickiness. This means future needs could be constrained by their plans vs. yours.

In short, while a CDP can improve campaign execution, it rarely gives CMOs the full GTM picture needed to steer investment decisions, defend budgets, or adjust strategy mid-quarter.

What A Unified GTM Data Backbone Looks Like

A true go-to-market data backbone, what we’ve named a Go-to-Market Data Lake (GTMDL), can change the game. A GTMDL is an independent, GTM-specific database that serves as the single source of truth for your go-to-market efforts across marketing and sales, with the flexibility to incorporate other enterprise data like product usage, finance and servicing.

Not clear on the difference? Here are some comparisons:

CDP	GTMDL (Go-to-Market Data Lake)
Optimized for campaign activation	Built for strategic planning and execution
Focuses on marketing touchpoints	Integrates marketing, sales, CX, financial outcomes
Lives inside MarTech vendor stacks	Independent, supports any MarTech or CRM system
Limited advanced analytics	Designed for machine learning, AI, deep analysis
Good for personalization rules	Powers comprehensive GTM income statements + ROI
Pay-per-record pricing can limit scalability	Flexible storage and compute; scale linearly as needs grow

What a GTMDL has potential to mean for your marketing organization:

Support GTM income statements that tie marketing and sales activity to customer acquisition costs (CAC), lifetime value (CLV), and profitability by segment.
Get a defensible line of sight from spend to revenue. No more debates over marketing’s impact or which team gets credit.
Sharpen segmentation and targeting. Build more precise ICPs and buyer segments by running models across combined sales, marketing and product usage data, enabling deeper insight than simple rule-based segmentation.
Align sales and marketing plays and support account-based strategies. Design campaigns and outbound motions around the same accounts and signals, mitigating handoff gaps.
Quickly analyze what’s working across channels, audiences, and offers by running attribution models directly within the GTMDL, allowing you to more quickly pivot your strategy when needed.

Some might think, “that sounds just as frustratingly out of reach as CDPs often feel.” But it’s entirely achievable with the right marketing leader to shape the vision and data architect to bring it to life.

Two places to start:

Start with your use cases:
We often say any investment, whether it’s a research study, an AI tool, or a data platform, should be purpose-driven. That means starting with clear priorities and use cases, not technology for technology’s sake.

The first step for marketing leaders is to partner with sales, customer experience and revenue operations leaders and together:

a. Document the critical “jobs to be done” that run the business.
b. Create a wish list of what would make those jobs easier, smarter, or faster.

From there, identify and prioritize use cases, this will form your roadmap. High-priority use cases become your north star for strategic planning and any future tool evaluation.
Partner with a data architect:
With your high-priority use cases in hand, the next step is finding the right technical partner to architect a solution around them. You’ll need a data architect that understands modern cloud data platforms and has GTM domain knowledge to ensure that the technical design is sound and that it supports the unique operating dynamics of marketing and sales data.

A data architect will help you evaluate:

• How your GTMDL should integrate with your marketing tech stack
• How to design flexibility into the operating model to handle the dynamic nature of marketing data while remaining compliant
• Where gaps exist that only a more unified GTM data layer can close
• How to phase your roadmap so you can start realizing value quickly, without massive disruption

When you have a partner who’s aligned to your strategic vision, not just technical requirements, moving to a true GTM control center is absolutely within reach.

Learn more about the Marketbridge GTMDL model here: Beyond the CDP: Building a composable go-to-market data stack.

Don’t chase the elusive promise of a 360-degree customer view only to land in the “trough of disillusionment.” Make the vision real. Join the growing trend of leading marketing organizations that are turning their data strategy toward a modern data platform, such as a GTMDL. It starts by mapping out your critical use cases, aligning cross-functional priorities, and then partnering to explore what’s feasible and how to get there.

If you want to learn more about how a GTMDL could work for your organization, let’s talk.

Bridging the growth gap

Posted on July 28, 2025July 30, 2025 by John Shomaker

With the inclusion of ‘growth’ in many new senior leadership role titles and the relatively recent invention of the CRO position, it feels like ambitious organizations are pivoting hard towards strategies and efforts to fuel growth. But there’s an inherent problem to be resolved before this new intention becomes more than a hollow corporate mantra or sentiment to impress investors.

The real challenge is that to genuinely enable and drive growth across your organization, you require levels of collaboration and internal go-to-market clarity well beyond the current status quo. You must enact structural and behavioral change. Most organizations believe that they have their teams, regions and channels aligned, but here’s the reality: in the process of execution, the divide begins and the gap continually widens.

A true orientation to growth requires all the fundamental functions within an organization to ruthlessly follow a single vision, to universally understand and adopt the same strategy. Unfortunately, this rarely happens. Executives share their vision, sales start to implement a commercially focused revenue strategy and marketing works to build a powerful brand. Everyone believes that they are connected, they all attempt to align but, in reality, they’re each acting in isolation. Somewhere in the routine day-to-day translation from strategy to execution, the signal is lost in the race to deliver against their independent goals.

The next generation of (B2B) professional services providers has emerged to address this embedded issue. There is now a world where business consulting and creative agency services are being integrated to ensure that an organization can achieve a unified growth ambition across its entirety, regardless of global footprint, channel complexity or the reality of internal power plays. In this new era, the big-picture corporate vision drives a powerfully aligned go-to-market strategy, which is delivered through an aligned sales model, branding strategy and in-market activation program, all of which are underpinned by a cohesive, integrated data/technology platform.

It sounds so simple, but it takes a new breed of professional services partner to support a business in transitioning from a growth ambition to a growth outcome, in removing the disconnects and the silos, in delivering a single strategy without a moment of signal loss.

Some firms have built their new capabilities by adding creative skills to a business consultancy; others have taken creative agencies and bolted on business consulting skills, but both models are challenged commercially and culturally. The alternative is a new type of professional services partner that is being built from the ground up, part agency and part consultancy but one company– like our firm, Marketbridge – with one connected team, seamlessly reinventing growth globally.

It’s time to start bridging the gap. It’s time to meet Marketbridge.

This article originally appeared in B2B Marketing’s 2025 U.S. B2B Agency Benchmarking Report.

Five ways a CDP can help financial services marketers

Posted on June 23, 2025June 30, 2025 by Carrie Donahue

Marketing leaders in financial services are navigating a long list of expectations: personalizing communication, improving acquisition performance, retaining customers, and demonstrating returns. And yet, for all the investment in technology and talk of “data-driven” strategies, many marketers still struggle to access the data they need to do the job well.

Customer Data Platforms (CDPs) were introduced to address the need for the comprehensive, multi-channel data necessary for modern marketing. For many organizations, these solutions provide helpful structure around audience segmentation and campaign targeting. But traditional CDPs are built with fixed logic. They assume a degree of centralization and integration that most financial institutions simply do not have – and often require marketers to adapt to the software, rather than the other way around.

That’s why I believe composable CDPs (sometimes referred to as go-to-market data lakes) are a better fit. They allow marketing, analytics, and technology teams to assemble a flexible data foundation that works across existing systems. Instead of being forced into someone else’s box, you get to design the system around your own business needs. And in an industry with complex products, legacy infrastructure, and heightened regulatory expectations, flexibility matters.

Here are five ways a composable CDP can help:

1) Align Marketing, Analytics and Tech Around Shared Goals

One of the biggest challenges I see in financial services is that marketing, analytics, and tech teams operate in their own ecosystems and still speak different languages. They’re doing good work, but often on different timelines, with different priorities and different definitions of success. Marketing focuses on strategy and outcomes, analytics is buried in reporting and data engineering, and tech is managing capabilities and infrastructure. When those groups aren’t working from a shared roadmap, priorities get misaligned quickly.

A composable architecture helps bring those teams together. When you organize around specific use cases – like onboarding new customers or identifying upsell opportunities in the advisor channel – it’s easier to stay aligned. Everyone understands what they’re building and why. That cuts down on back-and-forth, reduces wasted effort, and improves speed to market.

A composable architecture also helps reduce cost. Anyone who’s worked through multiple rounds of rework knows how expensive it can be to get it wrong. This approach minimizes that risk.

2) Tame Data Complexity from Mergers and Legacy Systems

Most financial institutions aren’t starting from a clean slate. They’ve grown through acquisitions. They manage multiple product lines and deliver through multiple distribution channels. And they often rely on infrastructure built over decades – which means customer data lives across dozens of antiquated systems, none of which were designed to talk to each other. Add in a wide range of state- or account-level variations and compliance requirements, and you’ve got a perfect storm.

Trying to shoehorn all of that into a single CDP can be painful and expensive. Composable CDPs work differently. They allow you to connect the systems you already have, extract what matters, and standardize the data just enough to activate it. You don’t have to rebuild everything. You can move forward with what’s useful and gradually evolve from there.

This is particularly helpful when you’re trying to deliver consistent experiences across business lines or channels that weren’t originally designed to coordinate. A composable approach makes that achievable.

3) Protect Customer Trust While Meeting Regulatory Demands

Another big reason this matters in financial services? Regulation. Privacy and compliance are non-negotiable and a marketing data strategy that doesn’t fully account for them will eventually fail – if not operationally, then reputationally.

A composable CDP can help on both fronts. It provides structure for managing consent preferences, documenting data lineage, and making sure sensitive data isn’t used out of context. It gives compliance teams the transparency they need, while still giving marketers the ability to move with speed.

You don’t have to choose between responsible data practices and effective marketing. With the right setup, you can do both.

4) Move Beyond Guesswork and Test Like Scientists

Many marketing teams want to build a culture of experimentation; however, in financial services, it can be a struggle to run tests that meet both business and regulatory standards. Whether you’re optimizing retirement planning campaigns or fine-tuning service reminders for lapsed policyholders, experimentation can feel risky without the right controls.

A composable CDP changes the game. It gives you access to real-time data across systems, supports test design, and makes it easier to track and optimize performance in a way that stands up to internal scrutiny. This doesn’t just improve outcomes – it improves credibility and trust with the rest of the business. When marketing shows up with results instead of opinions, it becomes easier to justify budget, ask for resources, and lead with confidence.

5) Scale Personalization That’s Actually Useful

Personalization is important, but only if it’s meaningful. Sending someone their first name in a subject line doesn’t move the needle. However, a needs-based approach that allows you to recognize that a young family is saving for college, or that a retiree is reevaluating their drawdown strategy, actually might.

A composable CDP helps you make that leap. It enables you to respond to intent-based behaviors, engagements, signals, and life events—so that you can serve the right message at the right time. And because it’s connected across systems, you’re not guessing. You’re making decisions based on what people are doing, not just who you think they are.

Done right, this builds trust. Customers begin to expect, and appreciate, that your outreach makes sense given their situation.

Getting a handle on this is 100% doable

I’ve worked in financial services long enough to know how hard all of this can be. The systems are fragmented. The expectations are high. And the time to show results is always shorter than anyone would like.

But I’ve also seen what’s possible when marketing, data and tech teams come together around a common strategy. Composable CDPs don’t eliminate the complexity, but they make it manageable. They provide the architecture to move faster, plan smarter and execute with greater clarity.

At Marketbridge, we help financial services organization build these kinds of systems. We’ve got both the technical and industry expertise to help connect strategy to architecture, marketing to analytics, and data to decisions.

If you’re navigating disjointed infrastructure, dealing with legacy or disparate systems, exploring how AI fits into your stack, or just trying to modernize the way your team operates, we’d be glad to share what we’ve learned. Let’s talk.

Healthcare marketing needs a data strategy reset

Posted on June 23, 2025June 23, 2025 by Melanie Russo

In today’s healthcare marketing landscape, data is everywhere, but insight is elusive. From campaign performance and broker interactions to claims and clinical records, the sheer volume of data should be a competitive advantage. Instead, fragmented systems, siloed reports, and disconnected teams often result in more confusion than clarity.

Whether you’re focused on B2B or B2C, this challenge is widely understood. Most marketers don’t lack awareness; they’re already making moves to fix it. Unfortunately, that’s where many are encountering their next problem.

In an effort to unify customer data and power smarter campaigns, many teams invested in Customer Data Platforms (CDPs), viewing them as the silver bullet. But the promise hasn’t matched the reality.

CDPs were built to activate, not to analyze. Most struggle to provide true cross-channel visibility, AI-ready insights, or the depth of performance tracking required by modern go-to-market teams and c-suite stakeholders. We’ll explore these needs in detail in just a moment, but first, it’s worth understanding why the CDP model is breaking down.

Traditional CDPs promised a single source of truth, but delivered it in a rigid, vendor-controlled box. They’re often expensive, difficult to adapt, and optimized for short-term execution rather than long-term agility. As marketing stacks evolve, these singular platforms become bottlenecks, limiting integration, innovation, and insight.

Introducing Composability: A Smarter, More Strategic Path Forward

Composability offers a fundamentally different approach. Instead of relying on one vendor or platform to do it all, composable data architecture lets marketers build and evolve their own stack—piece by piece—based on changing needs.

Think of it like LEGO bricks. Marketers can connect best-in-class tools for campaigns, analytics, and CX while maintaining a centralized, AI-ready data layer. Composability empowers marketers to innovate without ripping and replacing systems, while still ensuring their data is unified, structured, and usable across teams.

At Marketbridge, we call this centralized foundation the Go-to-Market Data Lake (GTMDL): a composable environment that integrates marketing, sales, CX, operations and even clinical data to fuel attribution, personalization, and growth.

And while the architecture matters, strategy matters more. That’s why we help healthcare marketers not only build GTMDLs but also design the right data strategy to make them valuable.

Three Essential Components of a Modern Go-to-Market Data Strategy

To unlock the full potential of a composable architecture, you need more than the right tools, you need a strong strategic foundation. These three elements are the building blocks of a future-proof data strategy for healthcare marketers:

1. Marketing Attribution Models

Attribution remains one of the most challenging aspects of healthcare marketing. At its core, it’s about understanding what’s working, what’s not, and why. In today’s complex, multi-touch, multi-channel customer journeys, that clarity is hard to come by.

Every interaction matters. From paid search to sales outreach to out-of-home campaigns, each touchpoint influences the decision-making process. Yet most marketers still can’t answer critical questions like:

Which campaigns are actually driving conversions?
Are we over-investing in one channel and ignoring others?
What is the true ROI of our brand or media spend?
Which messages resonate with specific audiences?

The absence of visibility leads to misallocated budgets, inconsistent performance, and decisions based on intuition rather than data. Although improved dashboards can assist, they are insufficient on their own.
This issue becomes even more critical when marketing leaders must demonstrate their impact on the C-suite. Executives primarily focus on business outcomes rather than specific channel metrics.

Without attribution models that connect marketing investments to clinical engagement, member retention, or revenue growth, marketing efforts are often viewed as a cost center rather than a driver of growth. The pressure to prove ROI in measurable, financial terms has never been higher.

Solving attribution requires a unified data foundation and models that reflect real-world behaviors, not just last-touch conversions. Tools alone cannot connect every part of the customer journey or account for external variables like competition, compliance, or seasonality. That is why advanced models, such as Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA), are essential to a modern marketing strategy. They move attribution from reactive reporting to proactive planning and provide the kind of defensible, business-aligned insights that resonate with senior leadership.

2. Taxonomy

Taxonomy may not be flashy, but it’s the quiet engine behind every successful data strategy.

Simply put, taxonomy is the consistent naming, tagging, and classification of data across systems. Without it, even the most sophisticated models and tools fail. In healthcare, where data flows from CRMs, marketing automation platforms, claims databases, and EMRs, inconsistency is the enemy. The same channel might be labeled “DM,” “Mailer,” or “Offline Touch” making measurement, automation, and analytics nearly impossible.

A clean, governed taxonomy enables:

More Accurate Attribution: Consistent tags let you connect touchpoints to outcomes with confidence.
Stronger AI Models: Clean, labeled data is essential for machine learning and predictive analytics.
Better Collaboration: When Sales, Marketing, CX, and IT speak the same data language, you eliminate misalignment and confusion.

Ultimately, taxonomy turns raw data into structured, queryable insights. Without it, your composable stack cannot function effectively, no matter how advanced the architecture.

3. Data Integration and Accessibility

Even with strong models and a clean taxonomy, your strategy stalls without seamless integration and access to data.

In healthcare, valuable insights are often buried in disconnected systems. A claims system captures utilization, a marketing platform tracks member engagement, a CRM tracks sales outreach, and none of it talks to each other. The result? Manual workarounds, data gaps, and missed opportunities.

Composable architecture solves this by connecting systems through APIs and microservices. It allows you to:

Centralize and Normalize Data in Real Time: Build a unified view without replacing every tool in your stack.
Act on Data Immediately: Marketers can analyze and optimize campaigns without waiting weeks for reports.
Personalize at Scale: Trigger outreach based on real-time behavior, clinical activity, or enrollment milestones.
Minimize IT Bottlenecks: Provide governed access to marketers and analysts while maintaining compliance and security.

Integration is the connective tissue of a data strategy. Without it, even the best models and insights stay locked inside silos.

The Next Era of Healthcare Marketing Starts with Data Strategy

Healthcare marketers are navigating one of the most complex data environments in any industry. The stakes are high, and so is the pressure to deliver measurable outcomes. But as tempting as it is to chase the next big platform, lasting success comes from something deeper: a modern data strategy that aligns teams, tools, and tactics around shared goals.

A composable approach gives you the flexibility to evolve as your business grows, the clarity to connect action to outcome, and the control to move fast without breaking compliance or collaboration. But the real power lies in how you use it. In healthcare, the ROI of better data isn’t just improved marketing and sales performance; it’s healthier members, reduced churn, and stronger care engagement. A strategic approach to data is what turns fragmented insights into meaningful action that drives both clinical and business impact.

At Marketbridge, we help healthcare organizations move beyond disconnected tools to build integrated, insight-driven systems that support real business outcomes. From attribution models and clean taxonomy to full data integration, we bring strategy, structure, and execution together—so marketers can finally do what they’ve always wanted: make smarter decisions with confidence.

Because in the end, it’s not about collecting more data. It’s about putting it to work.

The rise of composability in marketing stacks

Posted on June 23, 2025July 28, 2025 by Steve Erbentraut

Marketing leaders are demanding more flexibility around selection, implementation, and licensing for their Martech stack―and the industry’s answer is “Composability.” The term gained prominence in Martech circles in recent years and follows the microservices pattern popularized with cloud-based software architecture, where the components of a software system are broken down into smaller, discrete parts. Martech vendors are now orienting their platforms around this idea to provide more flexible deployment and licensing options.

This idea originated from customer frustrations with bloated Marketing software platforms as major vendors acquired dozens of point solutions over the past decade. Implementations became notoriously complex, and modules lacked strong integration or simply underperformed compared to best-of-breed alternatives. Composability shifted more control back to the client and focused on creating building blocks and better integrations. The approach has been welcomed across the industry, and many major vendors have modernized their offerings around this concept.

Evolution of Customer Data Platforms

The demand for composability becomes particularly evident when examining how customer data platforms have evolved and where they’ve fallen short. Marketing platforms have been embedding customer databases since the ’90s. The functionality started as storing basic customer lists and has grown to include a sophisticated set of attributes including preferences, behavioral signals, order history, and more. This was coined in 2013 as the “Customer Data Platform” (CDP) and the vision promised a 360-degree view of the customer that could be built inside marketing platforms without the help of IT. This vision resonated because customers were already using these platforms to orchestrate across an array of digital channels, and enriched data enabled additional use cases like personalization, cross-selling, and retention.

However, several limitations emerged with CDPs. First, they required pre-cleaned data, necessitating that transformations and cleansing be handled before import. Additionally, CDPs were not designed for advanced modeling and analytics, requiring data exports to dedicated data warehouse systems for complex analysis. Cost management also posed challenges, as most CDP pricing models were based on the number of stored records, which could inflate costs quickly. While CDPs are a great tool for digital activation and campaign execution, they fall short as comprehensive solutions for end-to-end go-to-market data management.

As organizations recognized these constraints, the prominence of CDPs has started to shift in the marketplace. The State of Martech 2025 survey revealed that CDPs fell from 27% to 17% and Cloud Data Warehouse ticked up from 21% to 24% as the center of the Martech Stack amongst B2C and B2B organizations. This trend indicates that a growing number of organizations are demanding more from their marketing data infrastructure.

Data as a First-Class Citizen

The solution lies in treating data as a foundational infrastructure layer rather than an embedded software feature. We call this solution the go-to-market data lake (GTMDL)—a strategy that places customer data in a modern cloud data warehouse such as Databricks or Snowflake. This provides greater flexibility, cost management, and analytical capabilities compared to using embedded databases within CDPs.

Importantly, the scoping and prioritization of a GTMDL should be guided by marketing use cases, not IT milestones.

This graphic illustrates key use cases the GTMDL supports.

The power of the GTMDL can be illustrated with a common direct-to-consumer (D2C) acquisition use case. The first step is to acquire a prospect list from a data provider and import it into the GTMDL. Next, data pipelines need to be created to augment, clean, and enrich the data with additional sources.

Once the data is prepared, a propensity model is created by training on historical customer sales data. This model identifies the optimal attributes to target when finding “look-alike” prospects in the new list. Each record on the prospect list is scored against the trained model, assigning a probability between 0-100%, indicating the likelihood that the prospect would convert as a customer.

The scored list is then segmented into testing groups (typically deciles) and exported to the activation platforms before being pushed out to the market. As the offers run in the market, performance data including clicks, impressions, engagement, and sales flows back into the GTMDL. This drives real-time campaign performance reports showing metrics like return on ad spend (ROAS) and enabling advanced measurement like multi-touch attribution models.

This entire workflow can be handled within the GTMDL, demonstrating why this approach is the preferred solution for marketing measurement and activation. Storage is cheap, and millions or even billions of rows can be imported without concern. Multiple languages such as Python, SQL, and R can be run in the platform to perform advanced data transformations and modeling. Compute resources can be provisioned on-demand, with costs incurred only during active processing. Finally, activation platforms integrate seamlessly to streamline audience deployment and engagement data collection―and you have full control of the data layer, which provides resilience from perpetual vendor upgrades and platform changes. Leading vendors like Databricks, Snowflake, and AWS Redshift all provide robust capabilities.

Conclusion

Marketers need the ability to both measure and activate their customer data, and the GTMDL makes this possible within a single platform. This solution enables measurement capabilities that CDPs have failed to deliver and that are essential for proving marketing ROI. The composable approach establishes GTMDL as the central hub, proving superior to an embedded CDP database.

The transition to this approach is more accessible than many organizations realize. Unlike the lengthy, resource-intensive CDP implementations that can drag on for months or years, GTMDL projects start with a use-case driven approach. Organizations can begin by focusing on their highest value measurement or activation challenge, such as multi-touch attribution or retention segmentation to demonstrate clear business value. This approach allows marketing leaders to build confidence while minimizing risk and resource commitments, ultimately enabling them to prove their ROI with real-time results and accurate measurement that drives budget allocation and business credibility.

For go-to-market leaders, the choice is becoming binary: evolve toward a data-centric, composable data architecture or risk being outmaneuvered by competitors who can measure, test, and optimize at greater speed and scale. In today’s marketplace, data-driven marketing is no longer a competitive advantage; it’s table stakes for survival.

Download our GTMDL whitepaper for a comprehensive guide to implementing this approach.

The analytical marketer

Posted on June 16, 2025August 26, 2025 by Andy Hasselwander

The Excel Effect

In the 1980s, when they were first introduced, spreadsheets were a novelty. VisiCalc pioneered the flexible row-and-column software category first, but they were quickly eclipsed by the still dominant Microsoft Excel. Spreadsheets could solve practically any problem faced by financial analysts in a fraction of the time that was required using paper, pencil, and an HP 12C calculator.

Imagine an alternative history where, instead of finance departments using Excel all day to solve all kinds of problems, they instead worked with partners who were Excel experts. It’s not as crazy as it sounds; I remember my father—an executive raised on pen and paper—working with a consultant whose superpower was building Excel models. It seems likely that in this world, financial analysis would be slow and frustrating, and that analysts wouldn’t understand their work as well.

Today, practically everyone in the enterprise uses Excel. Some use it better than others, but everyone can open a spreadsheet, write a formula, and do basic analysis. This has been true for decades.

Craft versus Skill

However, the data that employees have to deal with today has gotten much bigger—and more complex— in the roughly 30 years since the widespread adoption of Excel. Software has automated practically all of an enterprise’s functions, which has in turn generated orders of magnitude more data. Perhaps the biggest source of new data has been the vast digital marketing ecosystem, which generates billions of rows of interactions per day at large companies. This means that more and more information is now stored in countless complex databases, warehouses, and lakes, and has made spreadsheets an increasingly unsuitable tool for analysis.

Fortunately, the data analysis toolkit has gotten far more robust to meet the data exhaust challenge. Open-source data science libraries provide elegant solutions to seemingly any statistical or data visualization problem, while cloud-based compute and storage have reached the scale and speed levels required to cost cost-effectively deal with billion row data frames. It is now possible for a “citizen data scientist”—a pejorative term I’ve never liked for reasons I will elucidate below in the Guild section—to basically do anything they want to.

However, the Excel effect—where employees adopt new tools themselves to superpower their work—has not really happened with these new tools. Instead, “analytics” is mostly outsourced by craft experts to skill experts, whether inside or outside of a team. A craft expert is someone who fully understands how their part of the business works, while a skill expert is someone who can operate a particular piece of technology. This siloing has had the unfortunate effect of creating a lot of lousy output—slow, inaccurate, and in some cases not even answering the right questions.

However, there is evidence that we are getting to another “Excel revolution” tipping point. New integrated cloud solutions like Databricks integrate storage, compute, development environments, and source control in one place, while AI copilots and massively open online courses (MOOCs) have made learning Python, SQL, and R straightforward.

Down with Guilds

So what is stopping teams from superpowering themselves and owning and analyzing their own data? Certainly, there is a lagging skill issue. Excel’s successors are more complicated and require more expertise to run, and many employees are simply unwilling or unable to be reskilled. However, I think that the main reason business teams remain reluctant to become analytical is internal political pressure from entrenched centers of excellence exhibiting guild behavior.

Guild behavior is understandable; people have spent years training to acquire an arcane skill do not want to see their market flooded with competition, and so they tend to jealously guard their profession, through certification or simple bullying. Guilds exist all over today’s economy—including in medicine and law. However, they shouldn’t exist inside enterprises. Leaders should be very wary of centers of excellence that claim that what they do can “only be done by us.”

Specialized departments that are not core to the business should exist as enablers—not as blockers. Today, many functional departments are being held hostage by experts who have forgotten their mandate to serve and instead seem to focus endlessly on all the wrong things. I’m not saying that these departments shouldn’t exist—but they need to remember their mandate.

Marketer, Heal Thyself

Marketing might be the most egregious example of craft versus skill siloing in the entire enterprise. Marketing has become technical over the past two decades. Digital data, martech software, attribution modeling, customer and audience segmentation and insights, and propensity to buy are not nice-to-have; they these are marketing’s primary use cases.

And yet, marketing departments remain siloed, in some cases with marketing analytics separate from “execution” groups, and in an even more extreme model, outsourcing anything technical to general “analytics centers of excellence.” It is time to start transitioning away from these models, and to make marketing itself analytical. Concretely, marketing teams should own their data, tools, and analysis, in the same way that accounting teams own the general ledger and finance teams own forecasts and cap tables. Most specifically, if marketing is truly the customer customer-facing aspect of the enterprise, then they should own customer data.

Of course, there will always be newer technologies that require specialized skills. Econometric modeling, for example, the method behind mixed media modeling (MMM,) demands statistical expertise and experience. However, writing SQL queries to derive dataframes that get to the bottom of why a set of customers are defecting at higher rates is not, as of 2025, something that requires a special department.

The same goes for ad hoc analysis using Python and R. AI copilots have made code writing pretty straightforward. Marketers who understand the basics of objects, functions, and loops should be able to quickly piece together analysis notebooks themselves—and doing so will make them much more intimate with their data and their customers.

Federated Data

Marketing data remains a more complex issue than analytical tooling; we will never get to a place where data are fully democratized, nor do we want to. Without steady ground, no one will ever agree on the truth. However, too much focus on control leads to sclerosis and “paved cow paths” around formal data flows.

Figure 1: In the old model, IT controlled data, and while it was accurate, it failed for many use cases, leading to paved cow paths. A more modern medallion approach brings raw data together in one place; standardizes “truth” in the middle, and lets business experts create their own “gold” dataframes (or views.)

Fortunately, the medallion data model provides a helpful roadmap for balancing centralization with agility. The model, shown in Figure 1, can be adapted to many use cases, but the basic idea is that raw data can be ingested easily with little control; silver data is processed and provides a “truth substrate” that multiple teams can work from; and gold data are processed maximally for specific use cases—for example, in the case of the MMM example used above, into an econometric time series data frame.
In this model, IT or the Data Team still play two critical roles: They provision the storage, compute, and development environment for the business users; and they coordinate the metadata and taxonomy standardization required to do the categorical analysis that marketers do day in and day out —for example, naming channels consistently, or calling customer segments the same things. However, IT also recognizes that they need to step back when it comes to marketers adding the bronze (raw) data they need for ad hoc purposes, and in creation of the gold data frames that will power the expert use cases that make the business run.

An Analytical Marketing Team

The analytical marketing team should be made up of swiss army knife types—employees who don’t just understand but can work with the technology of marketing. Concretely, this means a grasp of source systems (Martech); the data itself; data science tools; and, of course, the typical marketing superpowers of segmentation, testing, channel strategy, and creative storytelling.

This means changes to hiring and training. Many marketing majors lack analytical tooling, so these employees will need major technical upskilling. Another approach is teaching more technical individuals marketing fundamentals. Either can work.

Guiding Principles

A marketing leader who wants to merge craft and skill can start with a few guiding principles:

Environment
a. Data, compute, and development should all happen in one place…
b. …but this means that the team provisioning the environment needs to focus on responsiveness
c. Martech should be owned by marketing, and marketers should be the experts in these systems
Data
a. It should be easy to add new data sources as Bronze assets…
b. …but taxonomy, metadata, and quality should be centralized for Silver assets…
c. …and experts should drive development of Gold assets that are fit-for-purpose
People
Marketers should increasingly be analytical , and marketing data and analytics teams should be increasingly integrated into marketing generally
The Work
All marketing data and analytics projects should be “use-case first”: what will this accomplish? (In Agile lingo, “user stories”)

These all boil down to a single principle: combining craft with skill to create a team of generalists. This might sound counterintuitive; “generalist” can be seen as a demeaning term. However, flip this on its head to the optimistic version of the word—polymath or renaissance man (person.) This describes the ideal marketer to a tee—someone who is both a jack of all trades, and a master of many.

AI-driven marketing use cases and watchouts

Posted on June 9, 2025June 9, 2025 by Brad Pierce

AI is transforming marketing, likely in ways we haven’t realized yet. From rapid content generation to programmatic advertising, marketers have obtained a new level of automation and insight that allows them to shift their focus to analysis and strategy. However, AI requires clean and comprehensive data at the foundation. One use case for the go-to-market data lake (GTMDL), the single source of truth between sales and marketing, is to power current and future AI use cases.

But what are the key building blocks for an AI-driven Go-to-Market Data Lake (GTMDL)? How are businesses leveraging cloud data warehouses and AI tooling to level up their marketing operations? What are the pitfalls? Let’s look at a few ways tech-led organizations are rethinking and rearchitecting their data, in view of the AI revolution.

Data as the foundation

Quality data is paramount for the underpinning of an AI infrastructure. Unsurprisingly, flawed input data leads to flawed analysis and output data. Without the proper attention to data quality, AI will simply provide the wrong results at a much faster pace. Many organizations fall into this trap – the best product in the world, either a CDP or homegrown solution, will fail if data architecture is not at the forefront of the design.

We believe the GTMDL is the answer to this problem. A carefully designed data lakehouse, tailored to your customers’ needs, serves as the foundation. The AI platform, integrated with the GTMDL, serves as the hub of activities and is where a Large Language Model (LLM), Retrieval Augmentation Generation (RAG), and other AI modeling are built and executed. With this base, AI operations can be executed with ease, providing content, insights, and recommendations in a fraction of the time it would normally take a marketing team.

Insights AI

A centralized GTMDL provides the bedrock: growth and scaling for both storage and compute. AI bridges the gap between humans and systems by putting the data at our fingertips. There are many insights to be explored, and AI tools are being capitalized on to provide these insights for marketers.

Underlying data are now queryable by non-technical stakeholders, providing natural language search to SQL systems and removing the IT hurdle
Predictive analytics churns customer data, both real-time and historical, anticipating future behaviors and calculating CLV
Reporting agents do much of the legwork required to distill customer data into dashboards, allowing for more focus on the details

Generative AI

Generative AI, or simply GenAI, is likely one of the first areas of AI adopted by marketers and we’re seeing it significantly boost productivity. For example, consider the lift required to leverage an existing blog post or whitepaper as the source of a targeted ad campaign. GenAI can produce as many versions of the concept as needed and in the necessary format―whether that be text, image, or video―in a fraction of the time.

Using commercially available or free tools such as ChatGPT is a common route for most marketers looking to get started with AI and is great for many cases. However, AI coupled with the GTMDL is beneficial as an augmentation for many reasons:

Data stays within the governance of IT, enhancing privacy and security
Content is generated from internal sources and is more relevant
Models can be trained and customized for different uses

Decisioning AI

How might AI enhance our decision-making process for something as simple as A/B testing? At the most basic level, AI can identify patterns in data that humans may miss, providing recommendations that will lead to more successful results. But we still must conduct the test and wait for the results, right? Not necessarily. An AI-driven GTMDL can be leveraged to learn on the fly and adjust recommendations. A single A/B test that may take 8 weeks can be accomplished in much smaller periods, such as 2 weeks, increasing the number of experiments within the timeframe. Response data from the test is continually received and ingested back into the model, informing us of the success of the test cases. AI decisioning subsequently adjusts the recommendations and provides alternate tests that are then adjusted in downstream campaign platforms. As we continue to feed this data back into our GTMDL, along with all the other data points we are gathering, we gain a deeper understanding of our audience and create a system that is nimble and responsive to the market.

Key watchouts

We’ve covered a handful of great use cases for marketing teams to implement an AI-driven GTMDL and there are hundreds more. We also should pay close attention to a few factors that will impact your outcomes, such as governance, cost, and data quality.

Governing how AI is used within an organization is important to reduce risk, protect sensitive data, and establish guidelines for responsible use. With AI in its infancy, it’s important to establish as many guardrails as needed without hindering the exploration process. One failure, such as improperly handling PII, will be devastating.

Be mindful of cost―AI is storage and compute intensive. Carefully architecting AI systems is important to control runaway costs and stay within budget. Start with small data sets that don’t require immediate results and 24/7. Also, enabling spending limits where possible is a must.

Most importantly, as stated previously, the data quality is crucial. The term GIGO (Garbage In, Garbage Out) may seem trite, but it certainly still applies. Investing significant amounts of capital in AI without proper oversight of the data will result in less effective, if not useless, outcomes.

Conclusion

Current and future AI uses for marketing and sales will require clean data and strong taxonomies and metadata. Smart organizations are building a flexible and scalable go-to-market data architecture that can be AI driven—now or in the future. Granular data in an organized structure readable by AI can power current use cases like propensity modeling and segmentation and prepares for future use cases like real-time media optimization.

Check out the GTMDL whitepaper here to learn our vision and approach to AI, data, and Martech. Data is the key to your success, and we can help!

The hidden costs of siloed ecosystem

Posted on June 9, 2025June 9, 2025 by Lainey Kasian

The employee benefits market is highly complex and rife with inefficiencies. On the path between providers and customers lie brokers, software platforms, and HR departments, to name a few, each with their own priorities and costs. With every intermediary taking their cut–whether from commissions, service fees, or administrative overhead–over a third of the premium dollar may be lost to non-coverage-related spending.

The persistent margin leaks

Unfortunately, the inefficiencies within the benefits market are not a new problem–and innovation in the industry has been largely stagnant over the past decade. Perhaps due to the difficulties stemming from high complexity and regulation or the lack of sufficient data integration leading to silos, benefits have not seen the innovation that other financial services industries have.

But advancements are long overdue. Benefits companies must decide how best to streamline the ecosystem between carriers and beneficiaries without compromising overall effectiveness to lead in this industry in the coming decade.

Mitigating inefficiencies in the benefits ecosystem

Providers have a variety of avenues through which to streamline their go-to-market process and offerings:

Leveraging a team dedicated to the innovation of the benefits ecosystem will facilitate a more painless integration process, regardless of approach.

Innovating towards a consolidated future

In an industry bogged down in complexities and intermediation, heightened integration is key, and there hasn’t been a better time than now. Technology is more capable than ever, including more powerful AI and more complex software integrations. Companies that can establish streamlined processes that limit margin leaks without degrading consumer value will remain relevant in this highly competitive industry.

For more information on how providers can innovate their go-to-market strategy to be strong players in the employee benefits space in the coming decade, read our whitepaper: “A new golden age for employee benefits.”

Invest in your brand to drive demand

Posted on May 27, 2025July 10, 2025 by Meredith Fuller

Finding the right marketing investment mix—one that meets both short- and long-term goals—is a balancing act that’s not easy to get right.

One of the (many) dimensions to consider is the allocation of dollars towards brand building vs. demand marketing investments. On the one hand, brand investment focuses on building awareness and reputation, while demand investments generate leads and drive sales. 

In our work with B2B enterprises, we typically see marketing budgets heavily weighted to demand. This is not surprising given the omnipresent pressure from the C-suite and sales for qualified leads and pipeline generation. Couple this expectation with the reality that it can be difficult to measure the effect and ROI of brand-focused investments, and demand marketing will win the budget bounty every time.

Despite these realities, what our marketing clients keenly understand—and a concept other organizational stakeholders need to grasp—is the unassailable continuum of brand TO demand. It is not brand OR demand, nor brand AND demand: It is that brand-focused investments and activities lead directly to the returns of demand-focused efforts.

How can marketers help convince the powers that be of the value of brand investments? By helping B2B leadership and sales better understand how their buyers buy. Consider this…

6sense recently surveyed 2,509 B2B buyers to analyze how and when purchase decisions are made and found:

Buyers enter their purchase journey with a ‘day 1 shortlist.’ It turns out that this shortlist contains four out of the five vendors that will ultimately be evaluated.
95% of the time, buyers have prior experience with at least one of the vendors they’ll evaluate.
Being on the day 1 shortlist matters with buyers ultimately choosing one of the first four vendors from the day 1 shortlist 85% of the time. (Or as Kerry Cunningham from 6sense puts it, “The truth is, all is not lost if you’re not on the day one short list… but 85% of all is lost!”)
Buyers reach out late in their journeys. B2B buyers are nearly 70% through their purchasing process before connecting with sales.
Buyers choose a winner early. In 81% of cases, buyers have chosen a preferred vendor before talking to sellers.

In addition, LinkedIn B2B Institute’s highly referenced statistic—that only 5% of your target market will be in an active purchase stage (or ‘in-market’) at any one point in time—all builds the case for marketing and, in particular, brand-building to ensure you’re on buyers’ ‘day 1 shortlists.’

Each of these stats underscores the critical importance of establishing your brand with buyers BEFORE targeting them with any demand marketing. Further, this data supports the fact that brand marketing is the direct on-ramp to successful demand generation.

What’s the ideal brand-to-demand budget balance?

While what constitutes ‘ideal’ does vary based on your organization’s unique situation, new ANA research previewed at a recent B2B event in NYC revealed most B2B leaders (75%) feel the ‘ideal’ Brand:Demand spend is an equal split, 50:50. Despite that, only 1 in 4 (23%) of marketers balance their budget in that way, with most (45%) running budgets that skew mostly towards demand.

While B2B marketers may still be challenged to put 50% of their budgets towards brand activities, how can they ensure their brand budgets work as hard as possible for them? Here are a few tips:

Brand doesn’t have to equal broad: Think ICP, not TAM, and employ targeted media to minimize waste.
Focus on creating brand affinity, not just awareness: This means your brand must connect with the buyers’ hearts and minds, a connection that starts with meaningful buyer insight.
A head-snapping creative concept with a smaller media budget will deliver more brand ROI than a wah-wah concept with a larger media budget. (For tips on how to elevate your B2B creative, read this post by Executive Creative Director Michael Palmer.)
Establish and nurture relationships with industry influencers to amplify your reach to relevant audiences. (For more on why influencing the influencers is particularly important for Millennial & Gen Z B2B buyers, read this post by SVP, Brand Strategy, Frances Ranger.)
Punch above your weight by pursuing and promoting industry award wins and earned media coverage.

Bottom line: Brand investments really are demand investments. Don’t limit marketing’s success by underinvesting in the all-important front end of the brand-to-demand continuum.

Looking for more on this topic? From storytelling to ROI: Bring brand full circle

Crafting a brand that resonates is only half the equation—proving its impact is the other. The most effective marketers are getting both right: defining a clear, differentiated brand and measuring how that brand drives real business results. Explore how our creative and analytics teams have helped teams on both sides—from sharpening their positioning to quantifying brand impact.

Step 1 Alignment: Agree on Language

Introducing the Go-to-Market® Glossary

What A Unified GTM Data Backbone Looks Like

1) Align Marketing, Analytics and Tech Around Shared Goals

2) Tame Data Complexity from Mergers and Legacy Systems

3) Protect Customer Trust While Meeting Regulatory Demands

4) Move Beyond Guesswork and Test Like Scientists

5) Scale Personalization That’s Actually Useful

Getting a handle on this is 100% doable

Introducing Composability: A Smarter, More Strategic Path Forward

Three Essential Components of a Modern Go-to-Market Data Strategy

1. Marketing Attribution Models

2. Taxonomy

3. Data Integration and Accessibility

The Next Era of Healthcare Marketing Starts with Data Strategy

Evolution of Customer Data Platforms

Data as a First-Class Citizen

Conclusion

The Excel Effect

Craft versus Skill

Down with Guilds

Marketer, Heal Thyself

Federated Data

An Analytical Marketing Team

Guiding Principles

Data as the foundation

Insights AI

Generative AI

Decisioning AI

Key watchouts

Conclusion

The persistent margin leaks

Mitigating inefficiencies in the benefits ecosystem

Innovating towards a consolidated future

How can marketers help convince the powers that be of the value of brand investments? By helping B2B leadership and sales better understand how their buyers buy. Consider this…

What’s the ideal brand-to-demand budget balance?

Locations