Article - Marketbridge

The analytical marketer

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

The Excel Effect

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

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

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

Craft versus Skill

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

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

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

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

Down with Guilds

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

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

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

Marketer, Heal Thyself

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

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

Of course, there will always be newer technologies that require specialized skills. Econometric modeling, for example, the method behind mixed media modeling (MMM,) demands statistical expertise and experience. However, writing SQL queries to derive dataframes that get to the bottom of why a set of customers are defecting at higher rates is not, as of 2025, something that requires a special department.
The same goes for ad hoc analysis using Python and R. AI copilots have made code writing pretty straightforward. Marketers who understand the basics of objects, functions, and loops should be able to quickly piece together analysis notebooks themselves—and doing so will make them much more intimate with their data and their customers.

Federated Data

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

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

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

An Analytical Marketing Team

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

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

Guiding Principles

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

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

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

AI-driven marketing use cases and watchouts

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

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

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

Data as the foundation

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

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

Insights AI

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

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

Generative AI

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

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

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

Decisioning AI

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

Key watchouts

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

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

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

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

Conclusion

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

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

The hidden costs of siloed ecosystem

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

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

The persistent margin leaks

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

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

Mitigating inefficiencies in the benefits ecosystem

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

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

Innovating towards a consolidated future

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

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

Invest in your brand to drive demand

Posted on May 27, 2025June 12, 2025 by Meredith Fuller

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Intentional AI use for employee benefits marketers

Posted on May 27, 2025May 27, 2025 by Tyler Leinan

Artificial intelligence is a tool that can unlock immense efficiencies within the employee benefits space. In this blog post, we discuss and provide examples for 3 areas of AI utilization within the employee benefits go-to-market space:

AI for acceleration: using AI as an ad-hoc tool for employee productivity
AI for insights: using AI to extract summary statistics from large sets of unstructured data
AI for workflows: integrating AI directly as features of the product or service

However, despite the promises of productivity and scalability for employee benefits organizations, users of AI should not replace their human intuition or creativity with AI automation. Hallucinations still pose a large barrier to full-scale automation of workflows, while AI-generated content lacks the “human empathy” needed to generate trust and connect with human audiences. Therefore, the core use-case for AI today is as a tool to enhance one’s own productivity, but not as a replacement for creative minds and product builders.

AI for acceleration

Content and product creation is the core example of using gen AI to accelerate the go-to-market process.

Streamlining Data Transfer: AI agents can automatically scan benefits enrollment and claims data and insert relevant qualitative details into each employee’s profile. We recommend the first step towards this implementation include efforts for building thorough and clean data for employee, lead, and account profiles which can be leveraged towards these AI-driven campaigns.
Personalization at Scale: In the B2B go-to-market space, gen AI is being used to produce emails with hyper-targeted value propositions based on a lead’s personalized profile; AI models can ingest past email interactions with a sales lead and draft a personalized email for relevant benefit plans based on these past interactions.

Fundamentally, AI’s principal utility is the ability to offload the burden of rote repetitive tasks onto the computer, allowing the user to focus on optimizing marketing and sales efforts through creativity and insights.

AI for insights

Large language models (LLMs), and more specifically Retrieval Augmentation Generation (RAG) models, can automate the finding, ingestion, and summarization of large volumes of texts. Some examples include:

Document Summaries: If desktop research has uncovered a 20-page document (e.g. quarterly financial reports), AI can now summarize those findings. A metric like employee headcount can be critical in estimating the growth of an account or value of a prospective client; and is easily summarized for each company through AI.
Extraction From Unstructured Data: AI can also quickly generate summary statistics from a large database of first-party unstructured data. A RAG model can be used to answer questions like, “how often do our leads mention some form of retirement benefits in emails during the sales journey?” or “what are the most common questions asked during member service calls?”

The ability to generate such summaries and statistics allows analysts to focus on interpreting the numbers, instead of the manual labor of compiling them.

AI for workflows

The ability to embed AI agents in a network of connected software systems allows such AI agents themselves to become directly embedded into workflows of a product or service.

Account-Based Marketing: AI agents can produce recommendations to optimize account-based marketing (ABM) campaigns. By scanning the entire ecosystem of accounts, leads, contacts, and opportunities, AI agents can rank accounts by likelihood to convert, renew, or churn. AI agents also can recommend messaging sequencing
Concierge Services: AI agents can field and reply, in real-time, to questions asked by employees regarding benefits and aid in the selection of insurance plans and retirement contribution amounts. As well, AI agents can help members make enrollment changes during qualifying events, and even answer questions about what qualifies as a life event.

Implementations of such core features could become table stakes for employee benefits organizations in the near future.

Limitations of AI

Despite the vast potential advantages that AI holds, there are still potential weaknesses which must be understood and considered when utilizing AI’s services, including:

Non-existent Sources: AI agents can sometimes cite non-existent and completely fabricated sources that sound like they should exist. This often happens when users ask leading questions to AI agents: “Why does Singapore have a larger GDP than the United States?” Our recommendation is to use AI to summarize information and then double-check the relevant sources.
Plan Fabrication: If an enrollee is comparing benefits plans, an AI agent could fabricate plan details. For example, an enrollee might ask: “If I make $X each month, why is Plan A better than Plan B?”. In this instance, the AI agent might claim that: “Plan A is better because it is cheaper than Plan B” when Plan A is actually more expensive than Plan B. LLMs developed by mature AI organizations have found huge success in mitigating these kinds of logical reasoning errors, so our recommendation is to use a mature product offering from an industry leader, rather than a homegrown solution.
Lack Of Human Empathy: AI-generated content (both text and images) often come with an “uncanny valley” effect; the content feels sterile, generic, and disconnected from a human audience. In fact, 91% of organizations with over $50 million in revenue do not feel prepared to implement AI with the necessary safety and responsibility (Mckinsey). Our recommendation is to use AI as a jumping off point, and have smart marketers customize the copy to make it real, human and effective.

Ultimately, AI agents generate what they believe the user wants to hear and not what is necessarily factually correct. AI’s untrustworthiness leaves it incapable of owning specific insight generation and workflows without oversight. Therefore, it is still imperative that users of AI do not rely on it for items which require critical thinking, though the employee benefits space can leverage this powerful tool for acceleration, workflow and the early stages of insights generation.

What other bold moves should benefits leaders make to compete in the coming decade?

Agentic AI: Key PR takeaways from RSAC 2025

Posted on May 19, 2025May 19, 2025 by Nina Korfias

The cybersecurity world witnessed a paradigm shift at RSAC 2025, where agentic AI dominated both conversations and the expo floor. With nearly 44,000 attendees and over 650 exhibitors converging in San Francisco, the message was unmistakable: no longer theoretical, autonomous AI agents are reshaping how we approach security operations.

As someone who helps innovative B2B tech brands across all stages, I observed firsthand how this shift towards agentic AI is creating both challenges and opportunities for emerging companies seeking to stand out in a crowded market.

Key PR takeaways from RSAC 2025

From a PR perspective, the companies that came to win at RSAC weren’t just showcasing technology. They were telling compelling stories about real business impact. With nearly half of conference sessions focusing on AI, the winners differentiated through clear messaging about measurable outcomes: reduced alert fatigue, faster threat response, and tangible ROI.

The lesson here is to ditch the speeds and feeds. Your stakeholders (customers, partners, investors) want to know what’s in it for them. How will your product or service make their lives better, faster, stronger?

Three proven PR messaging strategies to consider:

Focus on operational impact, not just innovation hype.
Investors and enterprise buyers are increasingly demanding proof that solutions deliver concrete results. The most successful pitches highlight metrics rather than technical capabilities alone.
Position your identity story.
With machine-to-machine communications now outnumbering human interactions, startups addressing machine identity management are capturing outsized attention—especially those articulating how their approach reduces breach risk.
Communicate governance frameworks.
The companies gaining traction aren’t just showcasing AI capabilities but demonstrating thoughtful approaches to responsible deployment and risk management.

For those of us with tenure in the world of cyber PR, we know the narrative is always evolving. What’s hot today might not be this time next year. It’s why fast-moving cyber brands that want to be seen (and heard) invest in PR/comms partners who understand both technical innovation and enterprise buying priorities in this new landscape.

Have an early-stage company navigating the agentic AI revolution? Let’s connect about crafting your story for maximum impact.

And for everyone else, see you at Black Hat USA later this summer.

Building brand trust in the crowded benefits market

Posted on May 19, 2025May 20, 2025 by Nolan Barry

The group benefits ecosystem —insurance and retirement products —is a growing market in the United States. Employees now have more options than ever, whether enrolling family members or adding auxiliary products to their plan. Unfortunately, as the market grows, competition gets fiercer. Group insurance providers can no longer rely on traditional marketing models, as consumers are growing less trustful of the industry.

Why indirect models weaken brand connection

Historically, insurers’ marketing model focused on the middleman, the broker. This B2B2C model limits the direct interaction the employee has with the brand itself. No matter the strength of the product, this model can still create customer mistrust. To combat eroding trust, benefits firms must create broader emotional appeals to the consumer through strong brand messaging.

Why build a strong brand?

Strong brand salience can help consumers differentiate between benefits products in this increasingly crowded industry. When employees trust a brand, they’re more likely to opt for higher-value plans—transforming a basic individual policy into full family coverage. And that matters: in 2023, the average American employee spent over $8,000 on single coverage premiums and nearly three times that for family coverage.. These expenditures have been increasing around 5% annually over the last 5 years (KFF). Furthermore, companies are evolving to offer more auxiliary insurance options, such as pet coverage. When employees trust a brand, they’re more likely to add extras—while unfamiliarity or lack of connection can cost the company those valuable add-ons.

By increasing the consumer’s affinity for their brand, an insurance company can set themselves apart from their competitors, enhance the impact of their typical marketing tactics, and plant the seeds for the long-term trust not always generated by demand harvesting tactics.

Building brand salience

Most benefits companies aren’t investing in large-scale, emotional brand marketing—and it shows. Here’s why that needs to change:

Lack of comparison shopping. Buyers aren’t choosing based on premiums or investment performance. The products all feel similar—and that’s the problem. To build brand affinity and stand out from the competition, companies must be bold and begin long-term emotional marketing.
Companies have more audience data than ever—and should use it to their advantage when targeting consumers. Tell human stories that someone can relate to – don’t lean on functional benefits that only marginally differentiate from the competition.
Still think full-funnel. Investing in brand-building does not mean abandoning mid- and lower-funnel marketing, as closing the deal remains key, but rather it means adapting to continue evolving with the marketplace while standing out from the competition.

Measuring what matters

Despite the clear value of brand in driving trust, loyalty, and higher-value coverage selections, many benefits marketers hesitate to invest in brand-building—largely because it’s hard to measure. The impact isn’t always immediate, and without the right data science approach, it can be difficult to prove ROI. But avoiding brand investment because it’s “hard to quantify” is a missed opportunity.

If you’re wondering how to make brand more measurable and defensible, don’t miss our this blog, “Delivering brand success backed by data science.” We break down how to leverage data/latent factors to optimize brand building and strategy.

The age of the non-technical benefits marketer is over

Posted on May 13, 2025May 13, 2025 by Katrina Gross

The modern benefit admin ecosystem is a sprawling system defined by fragmented channels, complex integration paths, and deeply regulated products. For the proactive benefits marketer, staying on top of this rapidly evolving playing field requires a toolkit of integrated analytic and technical capacities. Those who fail to adapt will quickly find themselves falling behind. Winning in this space requires marketers to adopt three approaches that enable smarter, data-driven execution.

1) Making go-to-market a data-driven discipline

In the modern financial services environment, a successful marketer is part analyst, part growth hacker, and part systems architect. Cutting-edge marketing strategy aims to measure incrementality, test and re-test creative performance, gauge audience potential, and understand channel-specific ROI. All of this must be continuously optimized. In the Ben Admin space, this means tracking the entire benefits choice lifecycle and working closely with sales teams to segment targets, pursue account-based sales strategies, and bring the right content to the right buyer at the right time. At Marketbridge, we take a use-case based approach to simplify this process into a series of “jobs to be done” for a quantitatively robust go-to-market strategy (Figure 1).

2) Micro-segmenting groups

It is now possible to build segmented activation strategies not just by employer size and geography, but also by industry vertical, renewal cycle, benefit portfolio, and even internal HRIS configuration. Strategies utilizing machine learning techniques can create targeted activation based on factors such as account size, tenure, industry and policy mix. In the example below, these factors were used in a random forest model to score groups for marketing activation during the open enrollment period, and half of all converters were accurately predicted by the top 3 deciles. This allowed for more targeted and efficient marketing activation and conversion strategies across the funnel (Figure 2).

3) Remixing digital channels for real enrollment lift

Employee benefits marketing must now account for the complete activation and retention funnel, requiring fluency across multiple digital channels and the ability to test channel mix optimizations in all stages of the buying cycle. Techniques like MMM (media mix modeling) and MTA (multi-touch attribution) can determine which tactics and channels drive groups and employees towards decisions, and powerful open-source data science libraries make these methods accessible to anyone with data. In Figure 3 below, the output from a typical MMM shows how three different channels reach the same CAC (customer acquisition cost) at very different spend levels—implying an optimal mix for maximum effectiveness and efficiency.

Why this matters

The benefits market is still highly competitive, but this won’t last forever. Carriers and brokers still operating with a traditional marketing mindset will find themselves increasingly left out of bids, while those willing to modernize their marketing and sales teams will rise to the top. The next generation employee benefits marketer won’t be “digital” in the superficial sense—they’ll be technical, data-fluent, and operationally embedded across the entire go-to-market stack.

B2Be less boring. How to bring the funny to B2B creative.

Posted on May 13, 2025May 14, 2025 by Michael Palmer

Just four years ago, in their 2021 report, “Cashing In On Creativity: How Better Ads Deliver Bigger Profits,” LinkedIn’s B2B Institute showed the connection between powerful, emotional B2B creative and long-term business growth. The report was a rallying cry for B2B marketers—and their agencies—to elevate their creative game and deliver more memorable, impactful ads.

Since then, we’ve witnessed a B2B creative awakening of sorts (or so it feels). Even the Cannes Lions Awards recently launched a new B2B Creative category. But how far have we really come? Are most B2B ads pushing the creative limits further? Or are the widely celebrated recent spots from the likes of Workday, Amazon and Salesforce just isolated examples of highly creative (and big budget) advertising in B2B?

Unfortunately, follow-up new research from LinkedIn suggests we might not be as far along as we’d hoped.

In their latest 2024 study, “The B2B Renaissance,” LinkedIn reported that the majority of business decision makers remain underwhelmed by the B2B ads they encounter.

Despite stating that more creative ads would drive their interest and action, 64% of respondents said they rarely saw B2B ads with emotional appeal or humor. Similarly, 60% said ads lacked characters they could connect with, and 59% said ads failed to offer a unique perspective. Yikes!

So, B2B buyers are unimpressed by your ads. The cure? Be less boring. Be more memorable.

Easier said than done, I know. In an effort to lift the burden, here’s how you might raise the bar in your B2B ads (without necessarily breaking the budget):

Humor is surprisingly powerful in B2B advertising—even though B2B is often seen as all serious suits and spreadsheets. Here’s why humor works so well in that space:

Cuts Through the Noise
B2B audiences are bombarded with dry, technical, jargon-filled content. A well-placed joke or clever twist stands out and grabs attention.
Humanizes the Brand
Businesses don’t buy things—people do. Humor shows there’s a real, relatable human behind the brand, which builds trust and emotional connection.
Boosts Memorability
Funny ads are easier to remember. If you make someone laugh, they’re more likely to recall your brand later when they actually need your product or service.
Encourages Sharing
Humorous content gets shared more—even in professional circles. This can amplify reach without extra budget.
Makes Complex Ideas Digestible
B2B products can be complex or dry. Humor can simplify and make boring stuff fun, helping audiences understand your value proposition more easily.
Differentiates in a Serious Market
When competitors are all saying the same things in the same tone, humor makes your brand distinct and more likable.

So, how might you add a touch of funny to your ads? Great question—here’s how you might pull it off:

Start With Empathy
Know your audience’s day-to-day struggles. Humor that taps into real pain points (“Ugh, another 43-tab spreadsheet.”) is gold. Think: “We know your procurement software feels like it was coded in 1996… because it was.”

Use Smart, Situational Humor
Avoid slapstick or over-the-top silliness. Instead, go for wit, irony or exaggeration based on real business life. Examples:
- The endless Zoom meetings
- Office buzzwords (“Let’s circle back.”)
- “Mission-critical” tasks that are actually just moving files

Play With Format
You don’t have to write just funny copy. Try:
- Funny charts with absurdly obvious insights
- Mock testimonials (“This changed my life—my inbox now has only 912 unread emails.”)
- Parody ads styled like something your audience already knows
- Video! Stats show video content helps drive better brand engagement.

Personify the Problem (or the Product)
Give your tech or service a voice. Or make a dramatic villain out of the problem you solve.
- “Meet Tom. Tom is the spreadsheet that’s been running your operations since 2010. Tom has feelings. Unfortunately, ‘efficiency’ isn’t one of them.”

Tone It Right
Balance is key. You can be playful without being unprofessional. Think of your brand voice like a smart, funny coworker—the one who makes meetings bearable but also knows their stuff.

Avoid
- Inside jokes that are too niche (unless you’re 100% sure your audience gets it)
- Humor that could offend or feel like a punch-down
- Overuse—it should support your message, not overshadow it

OK, you’ve got the big idea—but how do you pull it off in a time when budgets (and timelines) are shrinking? Well, we’ve leveraged AI in a few key ways to be our secret weapon for scaling big ideas without “Mad Men” budgets.

Everything from brainstorming and concept development to visual and video production and testing and optimization, AI has helped us scale effectively and efficiently.

But is that actually doable? Hell yeah! Check out our recent award-winning campaign for Chevron, where we brought humor and AI together and achieved some pretty amazing results.

Or there’s this campaign we created for BioCatch, which recently won a 2025 Communicator Award for AI Creative Integration—where we leveraged generative AI imagery and video to deliver outsized creative impact while keeping production nimble.

The line between B2B and B2C hasn’t just been blurred; it’s disappearing. By leveraging some of the tips above, you might just find your ads stand out from the crowd, help move the needle for your business and get some laughs along the way.

The case for embedded marketing analytics: why internal teams can’t do it alone

Posted on May 5, 2025May 6, 2025 by Jared Lampal

With ever-growing and increasingly complex data, multichannel processes, and varied measurement mechanisms, Marketing leaders often have difficulty answering analytics questions with one-off, traditional professional services engagements. Fully in-house marketing analytics are often constrained by budget and headcount, while fully outsourced models can lack oversight, context, and alignment to real business challenges.

That’s why a hybrid approach — embedding third-party experts directly alongside internal teams — becomes essential. Embedded teams help shoulder the heavy lift: tackling the painstaking work, building new processes, and driving adoption of best practices. By working inside complex systems and closely with your teams, they combine an outside-in perspective with deep, day-to-day understanding. This enables faster, more relevant insights and the ability to adapt in real time to shifting macroeconomic factors, priorities, and strategies.

Ultimately, this model creates a more fluid dynamic across marketing organizations. It minimizes learning curves, preserves institutional knowledge, and brings the expertise of a consultancy without the limitations of static deliverables. Rather than fading in relevance after a single project cycle, embedded analytics relationships continue to build and evolve, delivering lasting value and continuously improving outputs over time.

Why embedded analytics teams create lasting value

1. Closeness to both the data and the specific business problems

Being directly embedded within your team allows third-party experts to understand nuances — from data structures to evolving priorities.Ramp up periods are significantly shortened when tackling new workstreams, causing the resulting output to be more impactful (in service of the intended goal) and relevant (to the specific needs of the stakeholders involved).

2. Expertise on emerging best practices

Blending the methodology- and industry-specific expertise of traditional consulting firms with a bias to action allows implementation of those concepts to specific, unique client applications. That team’s experience from a wide range of past work coupled with a bespoke understanding of how to effectively deliver in the existing ecosystem, allows seamless, continuous delivery of quality insights that are often unachievable with other engagement models.

3. Agility of high-impact resource deployment

Too often, novel questions go unanswered due to resource constraints; “run the business” work always takes precedence. This is especially true for marketing analytics teams. Embedded analytics elegantly solves this problem by infusing agile, high-impact resources to tackle newer problems. This agility allows this work to get done without de-prioritization of day-to-day activities.

4. Increased ability to focus on lifting and shifting the internal “analytics mindset”

As opposed to clunky, do-as-I-say attempts at enacting change (which are typically distrusted by staff), a Managed Analytics team drives innovation from within. The “teachers” start with intimate relationships with across the organization, allowing new approaches to be trusted and successful. This fosters an environment where a “lift and shift” of analytics is both well-founded and well-received.

5. Answers to broad or complex questions that may not have a clear-cut solution

Oftentimes, open-ended questions are hard to approach (and therefore hard to answer) with only internal perspectives. Having partners who understand both internal context and outside perspective creates a working relationship uniquely positioned to tackle the big questions that may not have a clear-cut solution.

The jobs of embedded analytics

So what does this look like in practice? The right third-party embedded analytics team can flex across nearly any marketing analytics need, typically in three categories:

Growth

Growth activities focus on answering broad questions that help support strategic goals.

These questions come from a variety of sources, including marketing stakeholders, executives, and analytics leadership. Growth-related questions tend to be related to larger scale changes, such as “how can we increase marketing’s profitability?”, “are we thinking about marketing investment in the right way?”, or “is the way we measure success properly tied to business goals?” Oftentimes, these high-level, open-ended questions are difficult to answer with internal resources, whether due to staffing and capacity constraints or a lack of external context. Managed Analytics teams provide the capacity, outside expertise, and institutional knowledge necessary to answer them.

Process

Process activities are directly concerned with improving the existing “analytics universe”.

This type of work includes finetuning existing practices, creating new ways of delivering insight or measuring success, and upskilling internal analytics teams. Frequently, in the hectic flurry of day-to-day activities there is not enough time in the day to consider how these activities can be improved.

Measurement

Measurement & Testing encompasses the ongoing quest to understand what’s working, what isn’t, and where to shift dollars to increase effectiveness.

This scope includes both ongoing measurement efforts (MMM or Media Mix Modeling; MTA or multi-touch attribution; and last-touch attribution), as well as in-market testing (A/B testing) and analysis. These approaches can be shifted, improved, and repeated over time by employing an agile approach to understanding and improving marketing effectiveness.

Why this matters now (and how Marketbridge can help)

As marketing grows more complex, the need for embedded analytics partners — ones who work with you, not just for you — has never been greater. Whether addressing strategic challenges, improving day-to-day processes, or navigating evolving measurement needs, embedded analytics relationships drive faster, more effective outcomes.

Connect with us to see how our analytics consulting team can help!