We’re pleased to share that Forrester recognized Marketbridge in its report, “The Marketing Measurement and Optimization Services Landscape, Q3 2025.” The three extended business scenarios that we selected as top focus areas for the report are attribution modeling, data quality diagnostics, and owned/earned media measurement.
Our inclusion in the overview report is exciting because it validates our efforts to deliver excellence for our clients through custom, white box MMM and MTA solutions, which we’ve been doing for years. In fact, we wrote the original Measuring Marketing’s Effectiveness whitepaper way back in 2021 based on our learnings from client projects.
From all the model iterations and readouts we’ve conducted, we’ve learned a great deal and refined our approach over time. We’ve also had countless conversations with customers dissatisfied with others’ solutions that failed to account for difficult-to-measure channels, didn’t include long-run brand effects, or took three months to get to results—and built our solution to solve these problems.
Our unique approach
Marketbridge takes a consultative approach to building MMMs, MTAs, and “UMMs”—which integrate the functionality of both. We start with our extensive econometric, inference, and optimization libraries, and then build a bespoke solution for each client. But across projects, our core marketing effectiveness principles remain the same: the open-source measurement consultancy, complex measurement specialists, and actionable brand measurement.
The Open-Source Measurement Consultancy
Built in your infrastructure: We build inside your domain, keeping data first-party and your measurement code in your version control. This also means that data engineering pipelines are native. API calls come from your environment direct to platform, publisher, and Martech sources.
Radically whitebox: Both custom elements and Marketbridge libraries are viewable and modifiable at the source code level in Github, ensuring auditability and reproducibility.
Near real-time data connectivity: Direct APIs wherever possible allow rapid updating of source data and re-estimation of model coefficients on a daily basis.
Complex measurement specialists
Bespoke complex builds: Over and above simple business use cases, we model the most complex go-to-market activities across considered purchases, financial services and subscription businesses.
B2C to B2B flexibility: Our methods handle small-n, long transaction cycle businesses as well as high-n consumer brands.
Right-size performance marketing: We use systems of equations to avoid over-attributing value to branded paid search and affiliate “capture” channels, and then redistribute value to driving channels.
Actionable brand measurement
Quantify your brand’s long-run impact: We model advertising’s impact on brand strength, and brand strength’s corresponding impact on sales—insuring accurate ROAS up- and down-funnel. The common question “should I be optimizing on ROAS because it doesn’t take brand into account” is now obsolete.
Considers both paid and earned media: With our strong heritage in PR, we weigh investments in syndication, influencer marketing, and media relations. This will be increasingly important in the era of LLM discoverability.
Measures the right brand attributes: Identify the upper-funnel KPI that does matter to drive true long-run growth.
Learn more
We’d love to meet with you to share some case studies, learn about your organization’s current stage on its measurement journey, and discuss potential pitfalls for MMM and MTA. Contact us to get in touch.
Footnote:Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here .
Why quality matters: Building trust in a distrustful society
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:
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.
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
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.
Platform lock-in can occur in any industry that is dependent on other companies to go-to-market. As platforms achieve more and more market power, amassing control over eyeballs (in the case of advertising) and customers (in the case of commerce), advertisers and sellers are forced to give up more economic rent (in raw dollars as channel discounts and advertising fees, or as informational control in the form of loss of data.)
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.
A common problem with media mix models (MMMs) is over attribution of down-funnel, demand capture channels, such as paid search and affiliate. These channels have become, in some cases, distribution channels rather than marketing channels. Said another way, many actors have naturally sought to extract economic rent by inserting themselves in a buyer’s discovery and purchase process.
Importantly, there are two different kinds of down-funnel rent seekers: Duplicative and Distribution Taxing. They are defined by what would happen if they weren’t in place, in other words, the counterfactual case.
If Duplicative channels were removed in the counterfactual case, nothing would happen to sales.
If Distribution Tax channels were removed in the counterfactual case, sales attributed to these channels last touch would disappear.
Google search is perhaps the best example of distribution taxing. Given Google’s ubiquity, it is essentially impossible for the average consumer to get around the search interface, and in a competitive market, money must be paid via a search bid to be in the list of considered companies. Hence, removal of the bid term will likely result in lost sales. It is generally impossible to disintermediate well established distribution taxers, but it is important to understand where they sit and the amount of economic rent they demand, typically best understand as a percentage of revenue—just like the margin discount a manufacturer provides to a retailer.
Accretive
The channel created new demand, or captured demand that would have gone unfulfilled
Demand Capture
Distribution Tax
The demand was driven upstream (usually by mid-funnel tactics), but down-funnel “marketing” channels will drive it to someone else—unless you pay the toll
Duplicative
You would have gotten the sales somewhere else—usually somewhere cheaper
Figure 1: Three basic types of down-funnel channel; two are truly “demand capture”
Certain Affiliates, on the other hand, are often duplicative. In the case of a toolbar like Honey, the Affiliate is essentially cherry picking, waiting until the consumer is ready to buy to take credit. In these cases, removal of the bid to these platforms will likely have little effect.
The third type of channel, accretive, generates demand that would otherwise not have happened. This is often taken into account in a taxonomy determining the channel’s objective funnel position—usually called demand generation—but any channel can be accretive. For example, a demand capture channel that nudges an on-the-fence buyer to purchase could be partially accretive.
A helpful way to think about the degree of duplication is downside elasticity. Using our counterfactual example, this is simply the quantity of sales lost divided by what we would have predicted the loss would be based on the channel’s assumed ROAS—usually reported on a last touch basis. For example, say that an affiliate channel reports a ROAS based on last touch of 5 (an investment of $1M drives $5M of sales, e.g.) In the counterfactual case of no investment in the channel, say we only lose $1M of sales. The downside elasticity would be 0.2—not a great result, implying that the true ROAS is about 20% of that reported.
Downside elasticity: The total revenue lost when deinvesting in a channel (all other things being equal) divided by predicted lost sales based on assumed (usually last touch) ROAS
Channel
Assumed ROAS
Deinvestment Amount
Lost Sales
Assumed Lost Sales
Downside Elasticity
Branded Paid Search
5
$1M
$1M
$5M
20%
Paid Social
4
$1M
$4M
$4M
100%
Figure 2: Downside elasticity is a helpful way to think about duplication vs. incrementality.
Measurement
In reality, all channels have some mix of duplication, distribution taxing, and accretive behavior. It is the job of the marketing analyst to estimate this ratio and keep it fresh. There are two basic ways to do this: econometrically and via testing.
In marketing, econometric estimation of cause and effect is called MMM (media mix modeling or mixed media modeling, depending on who you ask.) In this approach, stimuli (marketing promotions and earned media) are used to explain response (sales or some proxy). In the case of down-funnel channels, it is critical to model these as an intermediary between demand generation and sales. In other words, the modeler must create multi-stage models to allow a channel like search both “receive credit” from other channels, and “create demand.” This can be a challenge for the modeler, as multiple “second stage” channels are generally required. This ends up looking like a system of equations, and visuals are extremely helpful to remember what is going on.
Figure 3: A system of equations is helpful to visualize what’s going on with channels in an MMM.
After the modeling is complete, it is possible to list each channel’s CPA (cost per acquisition) and / or ROAS (return on advertising spend, essentially the inverse) in three ways: last-touch (what Google or the Affiliate will take credit for); one-way (the causal impact of the channel not taking other contributing channels into account); and multi-stage (removing credit and reassigning it to contributing channels.) Seeing these three metrics side-by-side allows marketers to understand the trade-offs between channels, and to better interpret the often misleading results reported by platforms and agencies.
We sometimes call this a “systems expansion” view of marketing contribution. In the figure below, which for simplification’s sake does not include last touch data, each channel’s single-level regression spend, return, and ROAS are listed in the upper-left table. In the top-right table, each “upper funnel” channel’s contribution to paid search and affiliate are added to its last-touch contribution, and then subtracted for paid search and affiliate. Once ROAS are adjusted, an “MTA effect” (in the bottom left table) is calculated—essentially the degree to which each channel is taking credit from or giving credit to other channels.
“Single Level” MMM Return and ROAS:
spend
return
roas
dm
65,102,400
60,860,154
0.93
online_video
5,349,912
40,573,436
7.58
ooh
7,892,882
58,606,074
7.43
social
17,629,144
60,860,154
3.45
paid_search
43,024,004
160,791,024
3.74
affiliate
12,402,081
113,455,349
9.15
intercept
–
256,213,734
total_receiver
151,400,423
751,359,924
4.96
Multi-Level MMM Return and ROI:
spend
paid_search
affiliate
total-driver
roas
dm
65,102,400
22,671,534
3,630,571
87,162,259
1.34
online_video
5,349,912
19,455,714
6,126,589
66,155,739
12.37
ooh
7,892,882
5,145,313
8,849,517
72,600,904
9.20
social
17,629,144
16,239,893
9,189,883
86,289,930
4.89
paid_search
43,024,004
49,041,262
49,041,262
1.14
affiliate
12,402,081
36,872,988
36,872,988
2.97
intercept
353,236,841
total_receiver
151,400,423
160,791,024
113,455,349
751,359,924
4.96
“MTA Effect”:
dm
43%
online_video
63%
ooh
24%
social
42%
paid_search
-70%
affiliate
-68%
Figure 4: The MTA effect can be calculated by dividing a channel’s true “driver” contribution by its last touch or single-level MMM contribution. A positive effect means the channel is more accretive than it appears, and a negative effect means it is over-crediting on true incrementally.
Of course, testing is the gold standard way to calculate a channel’s incrementality or downside elasticity. There are generally two options: Geo-based holdouts or time-based reductions.
Geo-based holdouts using synthetic controls have become common in modern marketing. In this approach, several test markets are chosen for a treatment—either a positive (upside) treatment or a negative (downside) one. At the same time, a synthetic control—essentially a weighted grouping of the remaining markets—is set aside to run at “standard” levels. Then, a causal inference Bayesian analysis is performed to understand the difference between the experiment and the control-the counterfactual.
The challenge with geographic tests is that they can be difficult to execute. In many cases, it is simply impossible to persuade an affiliate to shut off bids geographically (for obvious reasons—they don’t want to be tested.) In other cases, algorithmic optimizations interfere with test purity. In these cases, a whole market reduction in spend can be used. In this case, a channel can be “dimmed” by, say, 50% to understand reduction in sales. This is not as statistically easy to read—there is no same-time-period counterfactual—but they provide the natural variability that can be read in an econometric time series (MMM) model.
Key Takeaways
It is important to think about demand capture channels as distribution taxing, purely duplicative, and accretive
Distribution taxing channels cannot be avoided, but should be understood strategically to potentially disintermediate with long-run go-to-market strategy changes / routes-to-market
Analysts can be model duplicative and distribution-taxing channels via a multi-stage econometric modeling approach inside of an MMM
Last-touch, single-level, and multi-level CPA and ROAS should be reported side-by-side in output
The gold standard to understand downside elasticity is a geo-based holdout
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:
Net Organic Growth: New 65+ eligibles entering the system
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:
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.
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.
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.
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:
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.
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.
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.
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.
Download the whitepaper, “Building a composable go-to-market data stack”
Rethink your data foundation and lead the next era of AI-ready, insight-driven marketing.
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.
Download the whitepaper, “Building a composable go-to-market data stack”
Rethink your data foundation and lead the next era of AI-ready, insight-driven marketing.
