Over the past several years, the growth seen by Medicare Advantage (MA) carriers and brokers has begun to stagnate. One of the forces driving this stagnation is the decline of Member Lifetime Value (MLV). MLV is a member’s inbound cash flow, minus outbound cash flow, over a member’s tenure with a single insurer; essentially, the expected net profit from a member for the time they are expected to remain with the carrier, discounted in future years by a carrier’s weighted average cost of capital (WACC). Over the past four years, three primary forces have concurrently exerted downward pressure on average Medicare Advantage MLVs:
Declining member tenure
Stagnant CMS payments
Increasing claims costs
1. Declining member tenure
With a plateau in age-in Medicare Advantage penetration beginning in roughly 2022, an increasing number of new members are “switchers” from other carriers. Having already switched carriers at least once, these members may be more likely to switch again, regardless of actions taken by the carrier. With members remaining with a single carrier for a shorter period, the carrier has fewer years to extract value from that member.
2. Stagnant CMS payments
Over the past two years, carriers have seen lower reimbursements from CMS to pay for their members’ claims.In 2024, CMS payments under the Physician Fee Schedule adjusted for inflation began declining by 1.25%–in 2025, CMS payments will decline an additional 2.93%. However, with the policies in the CY 2026 Rate Announcement released by CMS last week projected to result in an increase of 5.06% in MA payments, this factor is expected to moderate for plan year 2026.
3. Increasing claims costs
Possibly the largest driver of declining MLVs, claims costs have been increasing for the past few years. During COVID, most members chose to delay screenings, tests, and non-emergency procedures (Journal of Clinical Oncology). From 2022 onward, the repercussions of these delays emerged, resulting in more serious—and costlier—diagnoses once members returned to the doctor (UC Davis Health). Members in the past few years have also been receiving the elective procedures that they delayed during the pandemic. It is likely that, over the next several years, these impacts of the pandemic will subside as members return to normal rates of screenings, tests, and non-emergency procedures.
Additionally, product decisions made during the 2020-2022 Plan Years to attract customers often resulted in coverage for expensive services not previously included, such as vision, hearing, and in-home support services. Finally, CMS temporarily expanded the definition of dually eligible members during the pandemic, but once these members began to be dropped in June 2023, carriers had fewer profitable DSNP members—and those that remained were less healthy and more expensive to service.
Toward higher Member Lifetime Value
Business leaders in the Medicare Advantage industry must adapt to the changes to Member Lifetime Value that have been observed over the past four years:
Better audience targeting can allow a carrier to isolate and target audiences by their estimated mean lifetime value and to only invest in acquisition marketing and sales up to the point where the marginal acquisition cost is estimated to be equal to the present value of future cash flows.
Adoption of vertical integration can result in tighter communication and better information from the carrier, leading to longer member tenure and higher lifetime values.
Digital and digitally assisted application technologies can improve speed, accuracy, and applicant satisfaction, reducing buyers’ remorse and the resulting switching behavior that decreases member tenure—with longer tenure, a higher MLV is possible.
For more information on how to succeed in the face of declining Member Lifetime Value, and other challenges in the MA industry, read our executive whitepaper: “The next decade of Medicare Advantage: 2025 and beyond.”
Download our whitepaper, “The next decade of Medicare Advantage: 2025 and beyond”
Learn how the next decade will reward Medicare Advantage leaders who embrace agility, analytics, and a member-first approach.
BioCatch is a world-renowned leader in financial crime prevention powered by behavior biometric intelligence, which uses advanced analysis of a user’s physical and cognitive behavior to help banks protect consumers and their assets from fraud and cyberattacks.
BioCatch’s marketing team faced a familiar challenge: a lack of actionable data. This made it difficult to effectively connect with their ideal audience using personalized, relevant messaging.
“We didn’t want to be on an ad platform where we were wasting even a penny showing ads to people who didn’t care or were not within our ICP,” said Jonathan Daley, CMO of BioCatch.
Past campaigns leaned on more traditional marketing tactics, often generating leads that didn’t align with their ideal customer profile (ICP). Without a way to clearly understand buying signals and real-time intent, resources were being drained without measurable ROI.
Solution: implementing 6sense
To address this, we helped BioCatch implement 6sense and build out an ABX strategy to use this data.
Our team designed a series of one-to-few and one-to-many campaigns, integrated a multi-touch framework, and established a robust reporting framework for tracking full-funnel performance.
We began by refining their ICPs and deploying 6sense’s Predictive Analytics to continuously optimize messaging based on customer behaviors and buying signals. This AI-driven capability provided visibility into where accounts were in their journey, enabling BioCatch to prioritize high-potential prospects.
6sense’s Intent Scoring added another layer of precision, giving the team the data they needed to focus efforts on the accounts most likely to convert based on prior engagement trends.
Outcome: a wildly successful pilot campaign
We rolled out a pilot initiative with a bold target: engage 553 global banks that had shown little to no previous interest, and move at least 60 into the active sales pipeline—all through an Account-Based Experience (ABX) strategy.
Using 6sense, we developed over 200 unique audience segments and ran personalized one-to-one, one-to-few, and one-to-many campaigns.
Over the course of six months, we launched highly tailored landing pages, ran full-funnel, multi-channel campaigns across 6sense Display Ads, LinkedIn, and Google, and synced our messaging to match where each account was in the buying cycle.
In total, we created over 450 creative assets and built over 10 landing pages. And after six months, the results were:
5x increase in accounts in active pipeline stage
6% of the full target account list moved into the pipeline stage since March
63% increase in accounts in active engagement stage
This initiative marked a turning point for BioCatch’s marketing strategy—transforming their approach from broad and traditional to data-driven and precision-targeted. By leveraging the power of 6sense and a deeply segmented ABX framework, BioCatch was able to focus its efforts where it mattered most, align closely with buyer intent, and drive measurable pipeline impact at scale. The success of this pilot not only proved the value of intent data and predictive insights but also laid a strong foundation for future growth.
Current Medicare demographic trends are rooted in the Baby Boom. Post-war prosperity and GIs returning home in the 1950s and 60s led to inflated numbers of Medicare-eligible seniors entering the market from 2010 to 2025. Healthcare payers have been able to ride that demographic wave and observe year-over-year growth during this period with over four million new seniors becoming Medicare-eligible each year.
However, moving forward, we will likely never see so many people turning sixty-five. The number of incoming Medicare-eligible seniors has peaked and will begin to decline in the coming years. This will translate directly to fewer net-new Medicare Advantage enrollments each year.
Impact of the demographic cliff on customer acquisition
Smaller incoming applicant pools mean carriers will have to fight much harder for a limited supply of prospective Medicare Advantage members. These carriers will be competing in a highly saturated market as higher numbers of carriers have further increased competition. Increased competition also means more difficult marketing.
Marketing challenges:
The amount of mail, television ads, and other marketing that individuals receive has increased threefold over the past decade, decreasing the effectiveness of marketing.
Media costs continue to rise above the rate of inflation. In other words, carriers are spending more on less efficient marketing.
The road ahead for Medicare Advantage
The road ahead looks difficult for the Medicare Advantage market. Applicant pools are shrinking, competition has increased, marketing is more expensive yet less efficient, and decreasing member tenure combined with stagnant CMS payments and increasing claims costs is driving down member lifetime value (MLV).
Those who will win in the Medicare Advantage market throughout the next decade will need to turn to go-to-market, product attractiveness, and clinical maturity as the leading drivers of growth. For more information on how to succeed in the face of these challenges, read our executive whitepaper The next decade of Medicare Advantage: 2025 and beyond.
Download our whitepaper, “The next decade of Medicare Advantage: 2025 and beyond”
Learn how the next decade will reward Medicare Advantage leaders who embrace agility, analytics, and a member-first approach.
Almost three-quarters (71%!) of B2B buyers are Millennials or Gen Z (Forrester).
Seems like only yesterday that pundits were yakking about the rise of millennials and how it would affect business culture. Those Millennials are now well into their careers and rapidly entering middle age. (I’m sorry, Millennials, but it’s true. You can switch to wearing taller socks but time marches on regardless.)
People born in or near the 2000s are the new kids in town, and this Gen Z wave is changing the game for B2B marketers once again.
The buying group is even bigger than you think.
Forrester predicts, “As the Millennial and Generation Z buyer cohorts increasingly drive purchases, they will rely on external sources — including their value network — to help make their decisions.”
A few related stats to mull:
6sense reports that nearly three-quarters (72%) of buying teams now hire consultants or analysts to help with purchasing decisions.
Among younger buyers who responded to Forrester’s Buyers’ Journey Survey, 2024, 30% indicated that 10 or more people outside their organization are involved in purchase decisions.
Not surprisingly, word-of-mouth recommendations still carry the highest weight, with 73% of buyers ranking it as their most trusted source (Wynter).
So, what does that mean for B2B marketers?
Just as we’ve gotten our heads around using account-targeted campaign and media strategy to reach multiple members of the buying group, we must expand our understanding of the audience. We need to reach more broadly to influencers outside of the target organization –– without becoming scattershot.
And where do you start?
1. Continue to invest in your social presence
Social media has become a top source of information across B2B buyers regardless of age (PR News). As more and more “social media natives” get into decision-making roles, its influence will only grow. My LinkedIn scroll is already replete with memes and personal stories, and yours probably is too. The divide between personal and professional social media is getting thin (LinkedIn). You may want to consider expanding your brand’s presence on social channels that have traditionally been thought of as more personal if you have the resources, savvy and determination to support them.
Even if you’re not actively publishing widely, you should be listening widely. Keep digital ears open across social platforms, online communities and industry forums. Conversations are happening in these channels and consideration sets are being formed –– whether you’re part of them or not.
2. Influence the influencers
“Influencers” are not just for aspirational lifestyle brands. They’re part of the value network for B2B buyers too. Identify who has credibility and clout, engage them, and look for opportunities to partner with them.
More and more of our clients are getting serious about their influencer strategy, and it’s about time. Chevron Lubricants has been effectively working with influencers for years, most recently with Bryan Furnace, a heavy equipment operator, content creator and the host of Equipment World’s weekly video show, The Dirt. He’s got the expertise, experience and street cred (worksite cred?) to discuss oil technology claims and benefits with authority. (Chevron’s work in this area recently won them a 2025 B2BMX Killer Content Award for “Best Influencer Marketing”. You can see their award-winning video series with Bryan here.)
3. Authenticity still matters
Consider how you might enable and encourage customers to share honest reviews about your services or solutions. It may feel risky, but it’s a strategy that pays off in increased visibility and credibility.
Reviews help you get found. Great reviews are social proof that speaks for itself. Not-so-great reviews give you the opportunity to authentically engage and repair. How you show up in moments of challenge has enormous influence on the perception of your brand. The “Service Recovery Paradox” has been observed for decades – that is, brands that respond to challenges transparently, quickly and with meaningful action may be perceived more favorably than if no problem had occurred in the first place (Wikipedia).
4. Be sharable
While the idea of a B2B campaign going viral may sound unlikely – at least before Workday’s delightful “Rock Star” spots – it’s a worthwhile ambition. Especially when you use “viral” to mean “gets shared among target audiences.” Sure, you could take a cheeky, entertaining (and costly!) approach like Workday did, but there are other ways to create experiences that are worthy of being shared amongst value networks and by influencers.
What is your brand expert on? What do you care deeply about? What causes or ideas do you want to be associated with? Answer the same questions about your target audiences. Draw your Venn diagram and start in the areas of overlap as a jumping off point for ideation. Maybe there’s content you can create, a learning opportunity you could sponsor, or a contest or event or handy-dandy calculator or tool.
5. Learn about – and from – your audience
Look around your organization. I bet there are at least a few Gen Zs, and I know it’s bursting with Millennials. Tap into your own team for insight. How they make significant purchase decisions in their personal lives may reflect how they’d want to approach business buying. Extensive online research, reaching out to friend and family networks for opinions – almost certainly ducking the salesperson until they have already decided to buy. Ask: how can you reduce friction from your processes and get ahead of theirs?
In B2B marketing, strengthening your brand and accelerating demand go hand-in-hand. (Yikes. Do I leave the corny rhyme? Yes, I do.) They should be thought of as deeply interconnected marketing motions serving the same ultimate goals – build interest, build trust, build results.
There you go. One new generation, three major shifts in the landscape, and five things B2B marketers should be thinking about now.
Five forces are converging to stall growth of the Medicare Advantage industry over the next decade—halting momentum enjoyed since the 1990s. While inflation and immutable demographic trends are recurring characters in this story, trends in trust and product quality reveal that carrier actions also contribute to the five stagnation forces:
Increased member acquisition costs
Fewer age-ins
Declining member lifetime values
Declining innovation on both product and member services
A rapidly changing go-to-market landscape
1 – Increased Member Acquisition Costs: Marketing and Sales
Since Covid, inflation has left its mark across the marketing funnel. Competition for cost per click is ensuring the Paid Search dollar doesn’t go as far, especially during the Annual Enrollment period peak. Postage rates make key Direct Mail campaigns costlier at 120% the rate of core inflation. Meanwhile in upper funnel, cost-per-thousand (CPM) was increasing by 5% per year shortly before 2024, leading to costlier brand marketing.
On the sales front, expenses are climbing too. On-target earnings (OTEs) for captive field agents have risen 5-10% per year since Covid. Agent turnover rate has also increased, lowering sales productivity, as newer agents need more training and “at bats” to sell more.
The takeaway: Costs are not going down, but better audience targeting over the next decade can make acquisition budgets drive a greater impact.
2 – Fewer age-ins
All Baby Boomers will have turned 65 by 2030, spelling an end to the era of rapid growth in newly eligibles. Between 2022 and 2027, an estimated 4.2-4.3 million individuals turn 65 per year. By 2040, the annual age-in population will be only 70% of that, around 3.1 million.
Medicare Advantage penetration of the eligible population also seems to be approaching a plateau around 55%. The sum effect is that annual net new enrollments in Medicare Advantage will trend downward in the decade to come. (American Community Survey, NCHS birth data, CMS.gov data)
The takeaway: In order to sustain growth, the industry will have to nurture product attractiveness and clinical maturity. In other words, insurers will have to work harder to grow.
3 – Declining Member Lifetime Values
Member Lifetime Value (MLV) is a member’s inbound cash flow minus outbound cash flow over a member’s tenure with a single insurer. All three parts of the equation are driving down MLV: members are disenrolling more frequently in the face of declining benefits, stagnant CMS payments and mounting claims costs. These claim costs can trace to Medicaid eligibility changes affecting Dual-eligible Special Needs Plan (DSNP) members, added dental and vision benefits, and forgone preventive care during Covid.
The takeaway: CMS payments are not keeping pace with the costs of servicing the neediest members, and a more productive partnership with CMS will be key to remedying this shift.
4 – Declining Innovation on Product and Member Services
Product innovation – adding fitness programs or dental benefits to health plan benefits for example – characterized recent years of Medicare Advantage. Inflation, once again, acts as a headwind here by discouraging maintenance of characteristic benefits and by reducing the value of food cards. But abandoned benefits stem from other sources too—in-home nurses’ visits, for example, received negative publicity with accusations of abundant fraud. Consequently, they became deemphasized.
Member service innovation might temper member disillusionment, but it occurs in a narrow domain. Government regulations require many communications, driving members to largely ignore most insurer messages. Another potential communication channel, insurer mobile apps, suffer low adoption rates due to competition with separate provider apps and limited data portability and functionality.
The takeaway: Innovation can happen under constraints, but a return to quality can light the path forward through greater efficiency across acquisition, member retention, and care delivery.
5 – Rapidly Changing Go-to-Market Landscape
Unit economics and member preferences comprise serious threats to the traditional channels for reaching the Medicare population. Linear TV is less captivating for seniors, direct mail is being hit with inflation, and effective paid search now requires commitment to bidding wars.
Insurers are rethinking their distribution channels, too. Third-party call centers and aggregators rose to importance around 2010, as Medicare Advantage welcomed age-ins at unprecedented volumes. However, aggregators have shown to generate customer confusion while attracting low lifetime value members, and third-party call centers may have eroded trust by their less than honest call tactics.
The takeaway: Winning over the next decade will look a few different ways. We present more data and additional considerations in our whitepaper, which you can download below.
Download our whitepaper, “The next decade of Medicare Advantage: 2025 and beyond”
Learn how the next decade will reward Medicare Advantage leaders who embrace agility, analytics, and a member-first approach.
As a consulting firm based outside of D.C., we are acutely attuned to politics, even though we don’t do any political or governmental work. Big political and governmental moves ripple across the economy to impact businesses across sectors. I haven’t seen much advice or discussion of what advertisers should consider given the declining consumer financial outlook and the potentially rising consumer activism, so I put together this post to distill my thoughts on how advertisers should prepare.
Declining Consumer Financial Outlook
For the first time in nearly two years, U.S. consumer spending in January fell. That doesn’t yet fully account for the agency staff cuts in February, or the forewarned mass layoffs coming in March. However, those changes and upcoming tariffs likely contributed to the drop in consumer confidence seen in February. As I write this, Target and Best Buy are warning that prices will increase soon.
Consider cuts in lower funnel if demand is soft or non-existent. As consumer confidence wanes, fewer consumers will be shopping. For demand-based channels (such as search and affiliate) advertisers should closely watch efficiency and set gates for when to reduce budget because of lower results.
Don’t throw out your marketing playbook or completely reinvent your mix. Work with marketing analytics and agencies to size the impact and reforecast what marketing can achieve. Develop tests and performance gates to re-evaluate metrics and KPIs.
Focus on impression and ad view quality. Rather than quantity of impressions, advertisers should prioritize quality ad views for prioritized segments. This may mean significantly limiting where your ads are placed and rigorous testing and analysis to ensure you prioritize high customer lifetime value audiences; however, learnings will continue to pay dividends long past the recession.
Consumers may shop earlier to avoid boycotts. The retail shopping calendar may be out the window if consumer boycotts gain additional traction. Products sold through big box stores and Amazon in particular should evaluate how to drive conversions in the absence of historical sales holiday periods.
Consumers may want to shop directly. If you’re already set up to sell directly to consumers, ensure that experience is optimized. Plan and prepare to pivot budget and efforts between sell-through and sell-to channels.
Monitor competition on boycott and blackout dates. Competition for consumers who are shopping will heat up. Expect cost pers to increase and advertisers should look at whether these consumers are incremental and high value.
Preventing reactionary decisions to stay ahead
Though the governmental upheaval is unprecedented, seasoned advertisers have weathered low consumer confidence and poor financial outlooks before. To prevent reactionary decision making, advertisers should prepare and set expectations in advance, and develop stage gates or guardrails around performance. Monitoring competitor activity and continuing to test and learn what is working also is essential.
If you’re unsure how to get started, get in touch! Simply fill out the form (put “develop performance stage gates” in the anything else we should know box) or send me an email: srenner@marketbridge.com.
Download our resource, “Accelerating growth through test-and-learn marketing culture”
For an in-depth look at full-funnel marketing strategy, marketing imperatives, and key testing levers, download our 20-page paper.
AI is no longer a distant future—it’s here, transforming the way Sales and Marketing teams operate. From hyper-personalized customer interactions to intelligent automation and predictive insights, AI is accelerating efficiency and driving smarter decision-making at an unprecedented pace.
But while its impact is undeniable, adoption is still in its early stages. The question is: will you take the lead or risk falling behind? In 2025, leaders must move beyond exploration and take decisive action. What to do in 2025? Take action to explore and embrace AI’s potential to help Sales & Marketing be more efficient and effective.
Identify Areas of Emerging Growth. The increased demand for AI-enabled solutions creates opportunities for new revenue streams. It’s critical for GTM leaders to identify areas with the most potential for growth and invest accordingly.
Recognize Changing Buyer Needs. As buyer behavior shifts, closely track changes in that behavior to guide strategies around customer targeting, promotion timing, support tactics and more.
Reinvent New Routes to Market. Disruptive technology like AI will create new expectations from customers about how they want to engage with vendors. Organizations will need to rethink their strategies to meet those expectations and optimize their distribution channels.
Reimagine the Jobs AI Won’t Do. With AI handling routine tasks, teams can refocus on higher-value activities that drive growth, encouraging a more strategic use of human resources. Successful companies will identify jobs with the highest potential and redesign AI-enabled workflows to support them.
Take a Unique Approach to AI Solutions. It’s not enough to offer innovative AI solutions. To stand out in a crowded marketplace, companies must match their unique value propositions with AI, making it clear how they differ from competitors. That enables crisp positioning that makes the value clear to your audiences.
Activate New High-Performing Sales Motions. Effective activation is the key to driving value from new AI strategies. Marketing and sales leaders will need to work together on creating demand generation campaigns, account-based marketing (ABM) programs and sales motions to build the pipeline.
As AI continues to reshape sales and marketing, now is the time for leaders to take proactive steps toward adoption. The opportunity to drive efficiency, enhance personalization, and unlock new revenue streams is too significant to ignore. Organizations that embrace AI’s potential will gain a competitive edge by identifying growth areas, adapting to evolving buyer behaviors, and reimagining go-to-market strategies.
As highlighted in our latest research, success will depend on integrating AI in ways that enhance—not replace—human expertise. The future of Sales and Marketing is AI-powered, and those who act now will be best positioned to lead the way.
Download our report, “The impact of AI on Go-to-Market strategies, programs, and investments”
It’s well known that GenAI is transforming go-to-market strategies. “From content creation and product development to improving employee productivity, its use as a tool in sales and marketing to automate manual processes and personalize customer interactions is beginning to emerge.” (Forbes, 2024).
But AI isn’t just driving a seismic shift in how marketers and sellers get things done. It’s also fundamentally shifting how B2B buyers get answers to key buying questions, find and consider potential providers and conduct research on them faster.
Now, you might be thinking this adoption trend might just be for younger B2B decision makers (see trend #4). But you’d be wrong. Buyers’ shift to generative AI (GenAI) over standard web search engines is fast becoming universal across all B2B buyers. Get this: Since ChatGPT was first introduced just a few years ago, 89% of B2B buyers now use GenAI as one of the top sources of self-guided information in every phase of their buying process (source: Forrester, 2024 B2B Buyers Journey Survey).
The question is, do your marketing efforts reflect this shift? Do you know how tools like ChatGPT, Claude, Perplexity and Gemini represent your brand in relevant results generated? Are you taking steps to ensure your brand is being found and is showing up in the right way?
What to do in 2025? Don’t get caught off guard—time to integrate Generative Engine Optimization (GEO) into your SEO strategy.
This change in buyer behavior is moving fast, so put simply, it’s (past) time to start getting more proactive when it comes to managing your brand for AI-generated search results. To do so, consider these five tips:
Generative Engine Optimization (GEO) focuses on clear, direct answers within comprehensive and context-rich content to address user queries. Format your on-page content accordingly.
When stuck, just ask AI. Utilize LLMs to review and critique your on-page optimizations as well as test or simulate user query response.
While Search AI results can be tough to track, Google AI Overviews (via tools like SEMRush) can provide insight into how well other LLMs are indexing your work.
Ensure GEO and AI search strategies work in tandem with brand building campaigns, as GEO relies on strong, authoritative brands, backlinks, and user engagement, just as much as traditional SEO.
Review your existing organic strategy. Ask your agency, is GEO part of it and how are you optimizing towards it?
The rise of generative AI is transforming how businesses connect with and influence their audiences. As buyer behavior evolves, so must our strategies, ensuring we adapt to new technologies and meet buyers where they are. Success in this new landscape requires proactive engagement, thoughtful innovation, and a commitment to staying ahead of the curve.
Over the past two years, we have been working on building a business intelligence, scenario planning, and optimization SaaS platform for marketers. MEP provides decision-makers with a single place to understand multi-channel performance, and perform “what-if” analysis of spend by channel.
A tremendous amount of care has been put into building MEP to be more than just a shiny app (no pun intended.) Each company (or business unit) has its own unique marketing mix and architecture, and each element of that architecture—channels, time granularity, cross-section (segmentation), upper- versus lower-funnel—has been parameterized in model objects. Model objects must validate using a JSON metadata file before they are displayed in the platform—and this provides real scalability.
For the first two years of its development, we were focused on critical infrastructure. We made steady progress below the water line, but there wasn’t a ton to show for it. Over that time, the team focused on integrating the front end with our big data back end (Databricks); user roles and permissions, to ensure that each user and client’s data were secure and private; building out our JSON metadata parameterization; adding support for all of the models and curve functions we use (Bayesian, GAM, etc.); and building out the critical tables and charts to understand marketing effectiveness.
Over the past three months, the ship has started to take shape over the water line, and it’s really impressive (it’s even more impressive knowing how robust the hull is—OK, I’ll stop torturing that analogy.)
Scenario Planning
We thought a lot about how to let managers visually plan different levels of marketing spend, and show what the results of these decisions would be. At first, we deployed a simple spreadsheet download/upload function. We thought this would be the most flexible option, but our users thought it was clunky (it was). So, we went back to the drawing board and came up with three different on-platform scenario planning options: Manual, Strategic, and Optimized.
Figure 1: Choosing a scenario type from a model. Note the richness of the metadata; this is evidence of the “underwater” work of the past two years.
Manual provides the user with ultimate power. In this approach, users interact directly with model dataframes in Databricks and then recalculate the scenario. This is particularly useful for our analysts, who are routinely running scenario after scenario with tiny changes in spend and mix in preparation for client deliverables.
Strategic is for business users who want to quickly get to “what if” answers. In the strategic pane, users can choose any input variable—spend, impressions, or controls—and change it, up or down, either by a percentage of a fixed amount, for any time period. The number of these changes have no upper limit, and if you make a mistake, you can delete it. Once you’re happy with a scenario, you save it, give it a name, and then send it back to the Databricks cluster to run.
Figure 2: What would happen if we only spent 25% as much on consideration-focused advertising, over the entire modeling period?
Optimized is just what it sounds like: A user can optimize for, say, total sales in a given period, and then add a series of constraints. Once they are satisfied, the scenario is sent back to Databricks for computation. This can take a while; these models aren’t simple linear regressions, so we can’t use matrix algebra to solve for an optimum. Instead, our awesome team (led by Sam Arrington) built a two-stage program that searches for a macro solution, and then hones in on a local minimum/maximum. When the optimization is done, the user gets an email and can see what the answer is.
When doing this work, we realized that the days of simple “linear program” (think Excel Solver) optimization for marketing are over. We’ve entered a new phase, where advanced machine learning techniques are required, not optional. I don’t like using “AI” flippantly, but we have some of that in here, and it’s the only way this works as fast as it does. More to come on that in coming quarters.
Model Comparison
When we started down the path of scenario creation, we knew we needed an easy way to compare two models or outcomes. We went a little further than just allowing a user to compare two scenarios, however. We built a more robust method that allows a user to compare two of anything. The comparison looks both at overlapping channels and those that are only present in one of the objects—a full outer join, if you will. This allows a lot of flexibility—if you want to know how two different models look, you can do that, too. It’s basically a Swiss Army Knife for marketing data comparison, and will support many future use cases for MTA, testing, and basic reporting.
Figure 3: Model comparison provides a clean ledger between base models and scenarios, or between two scenarios.
Multi-Stage Modeling
We spend a lot of time at Marketbridge making sure that upper-funnel tactics—like display, OOH, digital video, and social—get proper credit for their contributions to sales. To do this, we build multi-stage models, where upper-funnel tactics regress both on end sales and on so-called capture channels—typically branded paid search and affiliate.
To make this happen, models must be “aware” of other models—concretely, a dependent variable of one model is also an input (independent variable) of another model. Behind the scenes, this means that model objects have been built with metadata that attaches them to one another via variables.
At the same time, users should be able to visualize and link models together. In MEP, a user should be able to point a model’s output to another model’s input—potentially in an endless chain. We’ve added a neat visualization to make this happen.
Figure 4: Making a model “system” (for now, two stages only) is now visual.
Up Next: AI, APIs, MTA and Testing Integration, and Benchmarking
Our roadmap is really exciting for the rest of 2024 and into 2025. We’re working on a more integrated marketing measurement approach called ITMA (integrated testing, mix, and attribution) that takes the best elements of test-and-learn processes, econometric inference, and multi-touch attribution and integrates them into a single approach.
We are spending a lot of time building data connectors to the big publishers and platforms to get data into longitudinal human records (LHRs) and econometric data frames. Traditionally, the time to get data into a model has been the limiting factor for multi-channel attribution; our goal is to get this time down from months to hours. It’s a big job, with a lot of edge cases, but expect announcements on this in Q1.
AI is a big topic in effectiveness and attribution. Today, we use generative AI mainly in the code-building and model-construction phase. We have cut the time to write a function or method by around 80% using various AI copilots. The next big step will be integrating AI as “agentic search agents” looking for optimal fits and unexpected relationships.
Finally, benchmarking is a big ask from clients. What’s the typical CPA of branded paid search in healthcare? Is a ROAS of 120 good for a direct-to-consumer electronics company? What percentage of business should marketing be driving? Today, these answers are qualitative; we’ve done a lot of projects and “know” the answers, but we don’t have a quantitative database. The key to getting to this database is metadata and taxonomy. As I mentioned above, we’ve put a huge amount of effort into parameterization, so we will be starting a benchmarking service later in 2025 leveraging all of these data, at a channel and industry level.
That’s all for now on MEP. We’d love to talk to you about your marketing measurement and effectiveness challenges. Complete the form below to schedule a meeting!
Today’s marketing leaders are looking for instant, accurate, and complete insights from their analytics stack. Unfortunately, no single tool–whether Testing, MMM, or MTA–can be that golden bullet on its own. The solution is to combine all three approaches into one unified system. We call this ITMA, or Integrated Testing-Mix-Attribution.
In ITMA, we use each inferential method for what it is good for, in a partially automated, integrated data science environment:
(T)esting is good for precisely understanding incrementality
M(M)M is good for understanding long-run, non-marketing, and inter-channel effects
MT(A) is good for fast reads with unlimited granularity
Figure 1: Testing results are integrated into MMM, which then feeds insight to real-time MTA reporting.
This approach provides significant benefits to the marketing leader:
Immediate Results: Because results are built at a record level for each new sale, marketing leaders can understand channel, campaign, and audience attribution in real time via business intelligence dashboards.
Consistent Answers: Because stimulus, response, control, and audience data all sit in one data lake, consistency is baked in.
Confidence Estimates: Mean estimates are always shipped with upper and lower bounds, at any percentile. There is no limit to channel granularity; more channels mean confidence will decline but will re-narrow with time or testing.
Total View of Causality: Integration of upper-funnel brand-focused marketing—and its impact on attitudes—is built in. Every channel comes with its immediate (within 90 days) and long-term impact, forming a complete picture of return.
Marketing Data Lake: ITMA is built on a Spark delta lake (e.g. DataBricks) data lake that can serve multiple use cases, including reporting, ad hoc analytics, and activation. Because all of the data are pristine, marketers can most likely replace multiple existing systems with one unified ledger—a marketing income statement for the CMO.
The nitty gritty: how does it work
Marketbridge’s ITMA is built in Databricks, hosted at the cloud provider of your choice. This is not SaaS. Rather, it is a purpose-built, evolving service infrastructure that can be insourced as required.
Components include:
Databricks tables with common taxonomy and metadata
Data connectors to publishers, platforms, and marketing technologies
Reproducible data engineering workbooks
Version control and documentation in Github
The R-Shiny front-end MEP, which provides reporting, scenario analysis, and optimization
The R modeling library mbmmm, which provide econometric, longitudinal, and testing inference, optimization, and taxonomy standardization
Figure 2: Technical architecture of ITMA.
Marketing Data Lake
The ITMA rests on a marketing data lake: A complete view of marketing stimulus and response, along with associated audience and customer information. This data lake provides significant ancillary benefits beyond attribution and optimization; because it must undergo ongoing quality assurance (QA) testing and remediation, it can function as a marketing general ledger—a sorely missing component of many organizations.
Download our whitepaper, “The superpowered CDP: Building a go-to-market data lake”
For a comprehensive exploration of the technical and use case review of a marketing data lake, download our paper.
The basic table structure starts with a longitudinal human record (LHR): a record of each individual’s interactions both “in the wild” (third party) and on domain (first party). Where identity resolution is not available, a probability is attached to a record to provide a complete view of potential stimulus. This LHR is then enriched with aggregated data (for example, upper funnel advertising, brand tracking, or economic data). When customers convert, first party demographic data can be cross-walked, and third-party demographics can be appended via an identity resolution service of the client’s choosing (for example, Experian, Equifax, or LiveRamp).
Because Databricks uses distributed storage and compute, query times are shortened from hours to seconds. When compute is not being used, clusters can be shut off, keeping costs reasonable.
Rapid Data Connectors
Because speed to insights is a primary objective of ITMA, shortening the time between marketing execution and ingestion into the data lake is critical. To accomplish this, APIs and direct linkages to data providers via cloud providers are the preferred methods of data transfer. This is most feasible for programmatic and digital marketing.
Reproducible Data Engineering
Marketing effectiveness measurement most often fails in the data transformation phase, before any analysis takes place. The “garbage in, garbage out” mantra is operative—small errors in grouping, summing, counting, and joining multiply and drive large errors downstream.
No black-box code or spreadsheet math is used to drive results. All code—whether custom for a given installation, Marketbridge libraries, or open-source libraries and packages—is available to inspect. Changes to code are preserved in perpetuity, ensuring auditability.
Download our whitepaper, “A roadmap for modern marketing analytics”
Download our whitepaper to learn more about reproducible data engineering in the context of marketing analytics.
The Marketbridge Marketing Effectiveness Platform (MEP) is a web-based decision support interface that allows marketers to understand channel-by-channel return, run hypothetical scenarios, and optimize their marketing mix for different objectives. It runs on the same open-code framework, using the same data, all the way back to the longitudinal human record.
mbmmm
mbmmm comprises a set of libraries and packages that power statistical inference, model validation, metadata, and data structures. It is totally flexible and extensible, with no tight couplings that will limit future flexibility.
Case Study
A health insurance carrier was juggling multiple marketing measurement and effectiveness methods, tools, and data structures. Each provided different answers—sometimes dramatically different from system to system. This resulted in low trust in analytics and slow, unconfident marketing decision-making.
Marketbridge worked with the marketing analytics team to replace a black box MMM; a software-based MTA, and a fragmented testing approach with a single measurement and optimization process: ITMA. Over the course of nine months, technology, analytics, and change management workstreams were launched and ultimately integrated to provide marketing executives with a unified multi-channel performance system.
The core of the system was the Marketing Data Lake, built around each newly acquired customer. A complete graph of known and inferred touches prior to conversion allowed attribution, while crosswalks to first- and third-party data allowed almost unlimited audience profiling—critical in understanding how different kinds of customers made the journey from awareness to learning to shopping to submitting applications.
The data lake is fed into three core systems. First, an econometric model forecasting total applications and sales by day and by region was built. This model used the data lake as its main input, grouping and summing both stimulus and response to create a cross-sectional time-series data asset, updated daily. This econometric model—essentially an MMM (media mix model)—also estimated revenue, leads, and other KPIs, and included non-marketing variables like plan strength, the macroeconomy, seasonality, and pricing. Second, a testing “factory” was built and kicked off. Tests were planned in a systematic way, using a kanban board. Each test was appropriately scoped (with one learning objective); statistically powered for low-risk readouts; and scheduled and integrated with marketing execution teams.
Testing was championed at the highest level of leadership (CMO and Chief Commercial Officer) as an ongoing innovation driver; because of this, most short-run concerns about lost performance were overcome. Once tests concluded, standard readout templates allowed learning to be effectively catalogued and put into action. Finally, test results were fed back into the econometric model and the MTA as Bayesian priors.
Download our whitepaper, “Accelerate growth through test-and-learn marketing culture”
To learn more about the Marketbridge approach to test-and-learn marketing, download our whitepaper.
Finally, a multi-touch attribution (MTA) system used Markov Chain modeling to estimate how each upstream touch or interaction—whether known or inferred—contributed to the ultimate outcome. Priors from the econometric model (MMM) and testing were also fed back into the multi-touch model to provide better estimates for long-run and latent effects. This system powered a daily dashboard showing attribution for each channel, down to a “micro-channel” level (e.g., branded paid search, specific affiliate partners, Meta-social-reel, etc.) This dashboard was used by executives to tune campaigns quickly. As priors from MMM and testing were updated, inferences were likewise updated.
The system replaced a six-figure black-box MMM solution and several complex identity graph-based attribution technologies, saving around $2.5 million dollars per year, while adding near-real-time attribution, and reducing confusion from conflicting ROAS and CPA estimates. The marketing data lake quickly drove additional use cases, including audience profiling, customer experience, and media auditing. Within one year, overall marketing-touched applications increased at a higher than fair share rate, and early indications are that reinvestments in upper-funnel brand marketing is paying off in higher yield rates in previously weak markets.
What to expect
Embedded: We embed our world class marketing data science team inside your domain to build your system. No clean rooms, software licenses, or restrictive contracts. Because we act as our clients’ direct agents, there are no pass-through markups or side arrangements with other vendors or software providers.
Nine Months to Answers: The Marketbridge team sets up the ITMA system inside your domain in six months, and then spends three months in pilot mode, making sure everything works. Because we are consultants at heart, you get weekly updates from the start, where we work with your team to hook up data sources, instantiate tables, run models, and set up dashboards.
Don’t Lose Your Marketing Brain: Because the infrastructure we build is open source, you don’t run the risk of losing what’s been built. While the mbmmm and MEP packages are Marketbridge IP, your team can keep using them and extending upon them, whether we remain your provider or not, subject to a license that they stay inside your walls. This de-risks marketing measurement, future-proofing your team from unforeseen future technologies, marketing approaches,
We Stick Around to Keep the Innovation Going: Once ITMA is moved into production mode, the team shifts into “run” mode, providing weekly updates on marketing performance, making enhancements, and helping you move from good marketing performance to world-class.
New innovations are tackled using an agile approach. A backlog of tests, analytics features, and new data sources is maintained in a kanban board. We work with the client collaboratively to prioritize what to work on next. All new work is done using the same reproducible, white-box methods.
Learn more and get started
We would love to meet with you to understand the current state of marketing measurement and optimization at your company, and to plan an ITMA journey that will get you to better effectiveness in less than a year.
Complete the form below to schedule a meeting with our Chief Analytics Officer.
1 These more comprehensive econometric models are sometimes called “Commercial Mix Models” due to their larger scope. As the scope of explanatory statistical models increases, they become useful to other parts of the organization, like finance and sales.
