Article - Page 6 of 6 - Marketbridge

6 most common marketing analytics pitfalls

Posted on January 25, 2023January 29, 2025 by Andy Hasselwander

Data-driven marketing has become the go-to standard, as marketers increasingly rely on empiricism to understand customer behavior, preferences, and needs. However, promises of better results have often been missed by marketing analytics teams.

Over the past decade, hundreds of billions of dollars have been poured into marketing technology, data assets, and talent. But, the party may be ending: Gartner predicts that CFOs will slash marketing analytics teams by 60% in 2023, and a likely recession bites. While macroeconomic forces are partly to blame, marketing analytics teams’ performance over the past decade is also responsible; Gartner also found that analytics teams influence only about half (53%) of marketing decisions.

However, there are actions that marketing analytics teams can take to increase their effectiveness—and sweat the assets that they already have. Over the past decade, while working with over 50 marketing analytics teams, we have identified six common issues that hinder effectiveness. By understanding these pitfalls upfront, marketing analytics teams can operate in a more agile, scientific way.

Technology Often Fails to Cure Marketing Ailments
Poor Reproducibility Leading to Ongoing Maintenance Problems
Marketing Evolves Quickly
Marketers Are Overly Dependent on Engineers
No Reporting Standards
Fire Drills Trump Long-term Vision

1. Technology Often Fails to Cure Marketing Ailments

Marketing technology software has been a huge business for 20 years. CRM, marketing automation, digital asset management, and digital marketing platforms are all major investments for large enterprises. In many cases, upwards of $20M can be spent on a single martech platform migration and installation process, employing armies of consultants, and taking years.

These projects are undertaken based on promises that are too often wildly unrealistic. A marketing automation platform might promise end-to-end lead tracking, easy campaign configuration, and seamless integration with CRM—but after years, the same data problems persist, and in some cases are even worse.

Furthermore, after installation, we found 50% to 80% of the functionality of these so-called magic bullets goes unused, particularly since much of it is force-fitted in the first place.

Instead of relying on out-of-the-box solutions for analytics, use marketing software for what it was designed for: contacting customers. Build analytics and data architecture internally. By keeping your marketing data warehouse/data lake as internally built and maintained resources—and ensuring that data flows into these systems from each new marketing technology platform—data discontinuities can be avoided.

2. Poor Reproducibility Leading to Ongoing Maintenance Problems

Extraction, transformation, and loading (ETL) are the processes that move data from system to system, or from source systems to data warehouses. In the short run, it’s easier to build these flows using graphical tools or to manually create “hacked” processes. Many marketing organizations’ data pipelines are a hodge-podge of varying batch, manual, and streaming processes that are poorly documented.

To avoid this, marketing analytics departments should demand the use of text-based (SQL, Python, Apache Airflow) ETL or ELT processes, coordinated via a version control system like GitHub. All pipelines will then be transparent and traceable. If data engineering capabilities become a bottleneck, marketing analytics departments should train this capability broadly, making data pipeline creation and maintenance a core capability.

3. Marketing Evolves Quickly

Like other industries, marketing changes and progresses in the blink of an eye. Every year, new terms, systems, and strategies must be learned. Usually, these channels and technologies are designed without data structure standards.

By establishing a standard taxonomy of marketing channels, customer segments, and products—and ensuring that new marketing technology matches this technology—data continuity can be established through disruption. This is sometimes the rule of the data governance leader, but it is incumbent upon marketing analytics to advocate for metadata consistency.

4. Marketers Are Overly Dependent on Engineers

Most marketers are creative, analytical, and organized, but they tend to be avoid coding. As such, they tend to depend on others to design, develop, and integrate tech and data. This dependence on parties outside of the marketing organization can lead to bottlenecks, delays, and poorly configured hard-to-maintain systems.

Largely due to this inefficiency, we foresee that by 2030, 50% of marketing jobs will require coding-type technology skills. These individuals will also be required to understand different data structures and access procedures that marketing data uses. Think JSON, XML, and .csv files, along with batch access (FTPs) and APIs. What seems unlikely today will likely become table stakes for marketers going forward.

5. No Reporting Standards

Whereas finance departments are required to produce quarterly and annual income statements and balance sheets, marketing doesn’t have required standard reporting. CMOs and other marketing managers are left to build a patchwork of dashboards, or purchase software promising to piece together the puzzle.

To catch up to finance, marketing should focus on simplicity first. “How much, how many” reporting simply counts spending, stimulus, leads, and other key data by standard groupings (the taxonomy mentioned in three above). Once standard reports are established and rigorously QA’d, marketing analytics can move on to more advanced reporting, like multi-touch attribution.

6. Fire Drills Trump Long-term Vision

When attempting to understand marketing performance, unfortunately, short-termism dominates long-term thinking. Immediate fixes are always simpler than putting in the effort to develop solid data pipelines, universal taxonomies, and organized data frames.

To remedy this, marketing analytics teams should maintain product roadmaps. These roadmaps aren’t software roadmaps—they should outline the capabilities and use cases that will be supported on a quarter-by-quarter basis, looking out at least three years. These roadmaps should be tied to manager compensation, to ensure that they are actioned.

These Challenges Are Repairable

While these six issues are real for marketing analytics teams, they can be fixed. The fix starts with establishing a baseline of current capabilities.

4 lead generation mistakes CMOs (and their teams) make

Posted on June 6, 2022January 29, 2025 by Sarah Renner

Assessing Lead Generation Mistakes for Long-Term Success

A CMO’s to-do list continues to expand. Generate leads and sales, navigate the ever-evolving digital landscape, maximize share of voice, optimize media, and continue to innovate to beat the competition, all while delivering a great experience online and offline by leveraging the latest technology (while also ensuring it integrates with existing MarTech).

In addition to that ever-expanding purview is increased competition everywhere. So it’s no surprise to see that CMO tenure remains at its lowest level in more than a decade, according to the Wall Street Journal. Success isn’t a given and CMOs face an uphill battle to justify budget and prove results.

Where do many CMOs turn when they need to demonstrate marketing’s effectiveness? Lead generation campaigns. Due to an inherent focus on measurability and ROI, lead-gen is a natural fit. However, that hyper-focus can lead to short-term gains at the expense of long-term results.

We’ve identified four common mistakes busy CMOs and their teams make that hinder lead gen performance. Is your team suffering from these problems?

Can’t See the Forest for the Trees
Over Segmentation
Not Testing Enough
Forgetting to Review Lead Generation Campaigns Holistically

Mistake #1: Can’t See the Forest for the Trees

Marketing teams can get so focused on tactics (A/B testing and tactic-level optimizations especially) that cross-media performance becomes an afterthought. Often marketing develops expertise by media channel and those individuals focus on that media alone—but who is reviewing how the media are working together? Are those individuals talking to each other regularly to identify cross-media trends (such as messaging that resonates with a particular audience)?

In extremely competitive industries, creative and messaging often become similar across all competitors. Rather than breaking through the clutter, each new creative test brings more sameness: same CTA, same value proposition, same visuals. When did you last audit your creative and messaging and compare value propositions and CTAs to your competitors?

Or maybe your organization is one that has historically focused on marketing activity. The number of content pieces developed, social media followers gained, or emails sent is not important if you aren’t able to tie that to marketing and business outcomes. Helping team members shift their mindset to be more strategic and focused on business outcomes can be a full-time job.

Mistake #2: Over Segmentation

How many segments are you targeting today? Is it the same across all media? Often audience segments sub-divide for a specific marketing tactic or campaign, and then these new sub-segments become the norm. Or sometimes segments are developed for a specific use case. However running an ever-expanding list of segments across marketing is inefficient, when really, most segments are based on the same factors.

In lead generation campaigns, creating messages specific to increasingly niche audience segments adds complexity across the buyer journey and narrows the funnel. You’ll be missing potential leads who don’t respond to the niche messaging while also driving smaller audience sub-segments that may not convert at the same rate as the larger established segments. The consumer experience can be personalized and adapted without over-segmenting and sacrificing the strategy.

Mistake #3: Not Testing Enough

Marketing needs to take risks to stay ahead of the competition and continue to drive strong results. Is your organization following the 70/20/10 rule?

Seventy percent of the budget focused on proven results, 20% focused on new promising areas, and 10% on brand new ideas.

Unfortunately, many Marketing teams focus too much on tactical tests: A/B testing minute changes on messaging, media, or segment. These optimizations are lower risk and easier to measure, but without some larger risks, how will your organization find areas of growth? Top teams test new audiences, new media, and completely new innovations, all while partnering closely with analytics to ensure tests are measurable and reproducible. What big bets has your organization made recently?

Mistake #4: Forgetting to Review Lead Generation Campaigns Holistically

The team is conducting daily media optimizations and testing regularly, but ROI is getting worse. Even if performance is flat, nearly one-third of marketers say generating more leads is a top priority (Hubspot). With pressure to improve performance, short-term gains are prioritized, which can lead to worse long-term performance.

Even if you’re doing everything right, an organization’s maniacal focus on measurability and ROI can lead to a bias for lead generation investment and other lower-funnel activities.

Top Marketing teams conduct holistic campaign assessments every few years to understand overall performance trends, identify where strategy may have unintentionally drifted and diagnose issues driving performance decline.

How to Fix Common Lead Generation Mistakes

If your organization is struggling with declining ROI or worse lead generation performance, you’re not alone. Often campaigns reach a point where optimizations have little impact and returns continue to decline.

That’s why we’ve created a 4-step approach to help Marketing teams evaluate lead generation campaigns for long-term success.

Health insurance leaders: How to assess your underperforming markets

Posted on April 6, 2022February 7, 2025 by Melanie Russo

Today the health insurance industry is as crowded as it’s ever been, making the race to the top a steep climb. Incumbents are expanding plans, new entrants are bringing innovative new business models, and let’s not forget the lead aggregators angling for the attention of the same consumer group as every individual health insurer.

What magnifies this dynamic is the local nature of health insurance markets. Plans are designed and distributed within ZIP Code ranges. As a result, most insurers have a focus on market-level budgets, market-level membership goals, and even market-level advertising. However, conditions often vary from market to market which can make market-level planning and preparedness in this crowded environment a bit of a quagmire without a clear assessment.

Market Assessment for Health Insurance Leaders

After working with some of the nation’s largest health insurance companies, we’ve found there are nine imperatives that when jointly implemented are the building blocks for market success. We’ve organized these nine imperatives into three tiers:

Foundational Tier
Table Stakes Tier
Advantage Tier

Addressing the Foundational tier first is essential, as without doing so, the remaining tiers will be significantly less effective.

For anyone in the insurance industry, these imperatives will be familiar. The organization of the imperatives in this assessment was developed by our Marketbridge team when asked to diagnose the root cause of why specific markets for a particular insurer were underperforming. After countless discovery interviews and performance analysis across marketing and sales, we learned these nine imperatives—when activated collectively and effectively—were the key to achieving membership goals at the market level.

Not surprisingly, during our initial analysis for the insurer, it was common to find gaps across the nine imperatives. It was also common to see a disjointed focus on where to invest the most time, resources, and budget given the context of those gaps. This assessment served as a tool to quickly convey how to view all nine imperatives, where to start to address gaps, and in what priority order.

1) Foundational Tier—Why will consumers consider your brand and plans?

As you’ll see foundational imperatives are the highest priority as they are what’s needed to compete in the market. These imperatives answer the question, “Why will consumers consider your brand and plans?”

Benefits Package: Offer plans with competitive benefits and competitive prices.
Provider Network: Include notable local hospitals and a wide network of healthcare providers.
Member Experience: Take a member-centric approach to caring for members.

Benefits Package/Provider Network

Research has shown the cost of benefits and lack of access to providers are reasons why consumers consider buying or switching plans, so it’s no surprise to see those among the first two imperatives Benefits Package and Provider Network.

Member Experience

The third imperative, Member Experience, is linked to keeping existing customers, as any marketer knows the cost of acquiring a new customer is higher than retaining one. Of course, avoiding an exodus of members also helps bolster membership goals. This is something a handful of Medicare aggregators and carriers struggled with this past AEP as they lost members, resulting in significant drops in their company stock prices.

One other note on this imperative is the emphasis on being member-centric. Customer experience in a commoditized industry like insurance can be a powerful differentiator. Unfortunately, 60% of insurance executives agree that their organization is lacking in CX strategy (Forrester). So, a word of caution; beware the urge to ignore Member Experience as a foundational imperative. Its placement as a priority in this assessment is significant.

2) Table Stakes Tier—How will consumers hear about your brand and plans?

Table Stakes imperatives are what’s needed to win in the market. These imperatives answer the question, “How will consumers hear about your brand and plans?” and consist of the core Marketing, Sales, and Provider channels.

Marketing Channel: Deploy a multi-channel marketing plan to drive awareness and generate inbound leads.
Sales Channel: Equip and enable sales channels consumers want (field teams, call centers, websites, etc.).
Provider Channel: Engage with providers to build spheres of influence to drive awareness and consideration.

Marketing Channel

When it comes to Marketing, consumer shopping behaviors are shifting. We’re seeing more consumers shopping and purchasing online; either across the full purchase journey or navigating across online and offline channels throughout the journey. While that’s not news to health insurance marketers, it can be a challenge when it comes to giving consumers an omnichannel, frictionless experience. Too many carriers are still working with siloed media teams focused on a limited section of the experience. They are also working with disparate technology systems—some old, some new—but none seamlessly integrated.

Another challenge within the Marketing Channel is determining the media mix. As the marketplace has become increasingly crowded, marketing cost-pers have increased. And with budget allocations often tight, marketing teams must spend each dollar as efficiently as possible. In fact, Gartner has predicted that this is the year profitability will overtake customer experience (CX) as a top strategic priority. Unfortunately, the answer to “what’s really working and what’s not” is not simple or easy, but there are solutions that can help. See our whitepaper, “Measuring Marketing’s Effectiveness.”

Sales Channel

Now, once a consumer is ready to purchase a health plan there’s a natural segue to the Sales Channel imperative. Over the years, the health insurance industry has seen significant investment in building direct-to-consumer online purchase paths. While some consumers are taking advantage, most are using digital channels to compare plans and better educate themselves, but often still choosing to speak with a licensed agent when it comes time to purchase. Therefore, equipping and enabling agents to conduct a best-in-class buying experience is crucial; from the plan information, they share with consumers to the platforms they use for enrollment.

Of course, there are many types of agents, from telesales to field with carrier-exclusive contracts to multi-carrier contracts which introduce unique dynamics such as compliance and incentives that may impact how you implement across the Sales Channel.

Provider Channel

The last Table Stakes imperative is the Provider Channel. The critical role of providers in the healthcare value chain positions them as key influencers and partners in local communities. Admittedly, the relationship between provider and insurer can be a complex one. Providers want to remain carrier-neutral but desire to help build their patient population and appreciate the advertising and on-site event support carriers can offer. Bottom line: Insurers who have support from key providers are often winners within their market.

3) Advantage Tier—What will consumers remember about your brand/plans vs. competitors?

Advantage imperatives are what’s needed to stand out in the market. These imperatives answer the question, “What will consumers remember about your brand and plans vs. competitors?” Standing out when you are offering a commoditized product like health insurance isn’t easy, especially during demand capture seasons like Open Enrollment (OE). Here are the three imperatives in the Advantage Tier:

Specific Benefits: Provide consumers with high-demand benefits, perks, and key differentiators.
Personal Service: Ensure and deliver personal and timely service via all distribution and service channels.
Relatability: Show consumers you deeply understand who they are and what they value.

Can you win without these three imperatives? Yes, it’s possible. That’s why these are not included in the Table Stakes section.

However, given the shifting market dynamics such as aggregators outspending insurers in advertising and insurers partnering with retailers to foster familiarity and brand preference, those companies that don’t focus on these imperatives will continue to fade into the background.

Keep in mind that this isn’t just an acquisition challenge, it is also one for retention. Customers who have a poor experience, or don’t feel connected with their insurer, will be more readily coaxed over to a hungry competitor.

Specific Benefits

Specific Benefits are a way to spotlight a particular benefit consumers want. An example may be a Healthy Foods Card, something Anthem leaned into when it announced co-branded Medicare plans with supermarket chain Kroger.

Personal Service

Personal Service is how to put the human back into the experience of shopping and utilizing health insurance. Your health is personal. Companies helping you care for your health should show they understand that. Arguing on the phone over a claim, after having been hospitalized, is an experience that will sour a consumer’s attitude. A recent LinkedIn post I saw showed a Humana Medicare Advantage member who had been recently hospitalized coming home to a “Mom’s Meals” package from the insurer. This made sure she didn’t have to worry about shopping or cooking food for the week. That’s Personal Service done well.

Relatability

Finally, the last imperative is Relatability, which means showing your consumers you deeply understand who they are and what they value. United Healthcare embraced this concept with its Dual Complete television ad that shows those eligible for Medicare and Medicaid. However, often there are Relatability gaps in markets. For example, multicultural populations may not feel represented or heard by insurers. Insurers that embrace the Relatability imperative will have the advantage of better connecting with consumers in the market—a true advantage in today’s crowded health insurance marketplace.

Achieve Your Market-Level Goals

When activated collectively and effectively, we’ve found these imperatives are the key to achieving membership goals at the market level.

The problem with multiple customer segmentations

Posted on April 4, 2022January 30, 2025 by Andy Hasselwander

Segmentation, targeting, and positioning are critical for marketers. Tailoring messaging, media, and products for specific customers can drive better responses and higher-quality customers. For marketers or business owners owning a specific piece of a larger business, it can be tempting to create “the perfect customer segmentations” to optimize that specific product, use case, or channel. However, this over-segmentation impulse is almost always a mistake.

To understand why this is the case, it’s necessary to look at three separate topics:

Factors and Dimension Reduction
Knowability and Assignment
Stability Over Time

1) Factors and Dimension Reduction

A core principle of social science generally and psychology in particular—of which marketing is essentially a sub-discipline—is that there are a limited number of orthogonal underlying factors that reliably determine behaviors. Personality, for example, can be reliably explained by five factors, known as the “Big Five”—Openness, Conscientiousness, Agreeableness, Extroversion, and Neuroticism. Further breaking these factors down—or finding other dimensions of personality that are truly different from these—has rapidly diminishing returns and can be counterproductive.

The same theme holds for marketing. Different customer segmentations can be dressed up to sound different, but ultimately, a few factors will repeat themselves over again and again, across products, channels, and use cases. Inside an enterprise, different customer segmentations might sound necessary, but often, these repetitions are different clusters of the same factors. For example, one business unit might define five segments with clever names such as “Nervous Nellies”, “Confident Carls”, “Emotive Emilies”, “Bargain Seeking Brads”, and “Overwhelmed Omars.” Another unit might have five other segments with different names. However, in all likelihood, the same underlying factors—such as price sensitivity, savviness, and sensitivity—are driving both segments. They are only different because slightly different clustering techniques or assumptions were used in each case.

Dimensions can be more flexible and “MECE” than clustered segments

Furthermore, it’s not even necessary to create segments if underlying factors are well defined. Instead of giving segments clever names and marketing to one group, organizations should instead train marketers to think of the underlying factors—conscientiousness, openness to marketing, price sensitivity, etc.—that consistently drive behavior, and market to “high” or “low” intersections of these—as few as possible.

2) Knowability and Assignment

Just because a factor or segment is real, it doesn’t mean it’s findable in-market. This is less of an issue for a large consumer advertiser reaching huge audiences with big television buys; they can define their prime prospect attitudinally, and then blast a tailored message to a large audience, knowing that a good portion will be receptive to it. However, for direct marketers, or marketers seeking to use segmentation to create different messages for specific different individuals, this is impossible. It’s thus necessary to create models that find a segment using knowable data—also called “assignment.”

Assignment is very hard and sometimes is basically impossible. This is probably the number one reason that customer segmentations fail; they simply cannot be acted upon. There are several ways around this, but the simplest is to keep segmentation simple. If one underlying factor is something around price sensitivity, then use readily available income data to define the factor, rather than an over-complex multivariate solution that might have a bit more signal, but that is impossible to find in databases.

Even good machine learning classifiers struggle to bridge survey-based and behavioral data. Once in the real world, models suffer more, and get worse over time.

Another approach is skipping the attitudinal part altogether, and instead simply using readily available demographic data to find “maximum difference pockets.” This wouldn’t pass muster in a doctoral program, but for marketers, there’s a lot to like. For example, say we have reason to believe that income and net worth are top predictors of affinity for a certain product. There are other things that probably matter too, like education, and we might have found out in interviews that it’s not the money that drives affinity, but a stylish or luxury mindset. However, a perfect solution—doing factor analysis to find the perfect orthogonal vector that describes affinity—might not scale across all the use cases a marketer needs. It will be easier and more realistic to use an “A-“ proxy than to spend thousands of hours looking for a perfect solution that will have major implementation issues.

3) Stability Over Time

Machine learning models suffer from a very common problem that frustrates marketers—overfitting. For the analyst, getting a more accurate model is the goal. However, adding more features to a model to get an extra point or two of accuracy comes with serious risk. Overfitting is not a mistake, per se—it just means that the model will be “over-tuned” for the specific data set being used. Overfitting is like turning an amplifier to its highest level to tune to a signal. When the signal is precisely tuned, it will sound great. However, even the slightest change in the environment will cause feedback and loud static.

This problem is particularly acute for segmentation. A segmentation created via a survey instrument is measured at a point in time with precise questions. When assigning out to the larger universe, even more uncertainty is added, because analysts are forced to rely upon modeled data with high error rates. The result can be a segmentation that looks good on its surface, but is brittle over time. A key test for this problem is re-running segmentation assignment algorithms over months to measure the percentage of individuals who move from segment to segment. Movements of more than a few percentage points are a big warning sign.

The Solution: Keep it Simple

For most organizations, one segmentation is enough. It is true that it won’t be the most precise solution for all use cases, but one segmentation—or set of factors—will help marketers and business leaders streamline their processes, and will prevent needless waste. A single, simple segmentation should:

Be stable over time
Be assignable and knowable for all (or most) use cases
Use knowable, simple data wherever possible
Be simple and easily explainable
Potentially be made up of 2-3 clearly defined and knowable factors, instead of a clustering solution

3 “must-have” foundational marketing assets for success

Posted on August 27, 2021January 29, 2025 by Thea DeBell

Common Challenges of Rushing to Market

Businesses and product teams are often eager and quick to ‘get to market’ with their latest solutions and marketing campaigns. With competition looming and customers yearning for the latest and greatest, we get the haste. Yet, while speed-to-market is critical, skipping the creation of foundational marketing assets—like defining your audiences, researching their purchase preferences and behaviors, and establishing consistent messaging and positioning—can set a company up for challenges down the line.

There are INTERNAL organizational challenges…

Inconsistent positioning across the organization—which means your sales and marketing teams might see and position the solution in a different light
Wasted sales efforts from reaching the wrong buyers through the wrong channels
Wasted advertising budget reaching the wrong audiences with the wrong messages
Lost deals and missed opportunities from not positioning solutions in the most compelling manner

...that translate into EXTERNAL challenges customers face with your solution and brand…

Disconnected customer experiences—which leads to customer confusion and inconsistent experiences with your brand
Missed awareness—because customers don’t see your solution on the channels they prefer to engage (this goes back to our saying, “customers choose channels, channels don’t choose customers”)
Irrelevant marketing messages with no audience resonance
Customer attrition or slow revenue gains—because frankly, it looks to the buyer like your competition offers a better and more personalized solution

3 “Must-Have” Foundational Marketing Assets for Business Success

For any new product launch, rebrand, or campaign, it’s easy for marketing and sales teams to get ahead of themselves. Typically these teams jump right into customer-facing content building and that’s understood. Putting tangible, explanatory assets in front of potential customers is critical. Even so, before pressing go on a new and shiny campaign or dialing for dollars, there are three internal assets every business should have. These assets are core to building a robust go-to-market strategy and matching customers with relevant content. These assets are also shared between both marketing and sales, connecting the divide in sometimes uncoordinated efforts.

So what are these foundational marketing assets, and why are they important?

(1) Buyer Personas

What are they:
Buyer personas are characters created to personify your target customers. They help you paint a picture of prototypical customer demographics like age range and occupation. Additionally, they help you understand psychographics such as pain points, solution priorities, and how they make purchase decisions. Start with one or two core buyer personas; however, you may grow that into five or six later down the line. While there is no magic ‘maximum’ number of personas you should have, personas are only valuable if you use them. Don’t create more personas than your organization can act on.

Why are they important:
By defining your target customers and sourcing data on their unique characteristics and buying behaviors, you can inform highly relevant and more effective sales and marketing efforts. Defining and documenting personas also helps ensure all sales and marketing team members are aligned on the target audiences and how to approach different buyers in a more personalized way. Furthermore, your marketing and messaging can resonate with prospective customers; making them more likely to purchase.

(2) Buyer Journey Maps

What are they:
Buyer journey maps document a persona’s typical path-to-purchase, including the steps they typically take in their purchase journey, as well as their pain points, needs, preferences, and sentiments along the way. While some journey maps stop there, we recommend journey maps that also explain how to effectively activate sales and marketing efforts along the customer’s journey. The customer journey is really just the best channels, supporting content, and messaging for each stage. Journey maps should be built using primary research collected from existing and/or target customers. Ideally, each persona should have its own buyer journey since different audiences likely have different purchase preferences and behaviors.

Why are they important:
Journey maps, that are data-driven and actionable, help sales and marketing teams identify how to best reach different audiences with the right message, through the right channel and/or content, at the right time.

Learn how to develop a buyer journey map.

(3) Messaging Architectures

What are they:
Messaging architectures define a set of consistent messages that should be used across sales and marketing communications. This also involves assets that convey the value of a company, product, or solution. Organizations may have multiple messaging frameworks. For example, an overarching messaging framework might describe the organization or integrated solution-set as a whole. But one might also want individual messaging architectures for each product line. In addition, organizations may create variations of each messaging architecture for different personas.

Messaging architectures typically include an overarching message, 3-5 main value propositions, supporting messages, and proof points. Overarching messages are similar to slogans and value propositions are the core benefits you want to communicate. Proof points provide evidence and credibility to your messaging architectures.

Why are they important:
Strong messaging architectures are data-driven and equip companies with compelling messaging that will resonate highly with target audiences. You can achieve data-driven messaging architectures by following quantitative and qualitative research. Messaging architectures also ensure that sales and marketing resources are using consistent, approved messaging to represent the brand or product. They also ensure sales and marketing are using the best available messaging every time.

Learn how to develop a messaging architecture.

It’s Not Too Late to Reset Your Go-to-Market

While it’s valuable to develop personas, buyer journey maps, and messaging before launching new products or solutions, it’s never too late to create these foundational marketing assets. The benefit of building and continuously refining these assets can be tremendous. In a competitive landscape, resetting who your buyers are (personas), their unique journeys (which may have drastically changed in the pandemic landscape), and unique differentiating messages, can set your business above the rest. Allow your marketing, sales, and product teams to better reach the right audiences, the right way, with the right messages to close more deals by developing actionable segments and personas, a buyer journey map, and a messaging architecture.

4 signs of stagnant marketing analytics

Posted on June 8, 2021January 30, 2025 by Andy Hasselwander

Marketing analytics organizations are under incredible pressure from business stakeholders. Business owners want everything—and they want it quickly. This insatiable appetite for analytics can lead to demoralized teams who get “C” grades on all their assignments, at best. The result: no one is happy. At Marketbridge, we try to help our clients transform into Agile Marketing Analytics teams. For us, this is more than a catchphrase, it’s the way we do business, and it’s how we help our clients achieve measurably better results on everything from top-of-funnel brand advertising to last-mile lead conversion. Before defining Agile Marketing Analytics, let’s look at the typical baseline state of marketing analytics.

Diagnosing A Stagnant Marketing Analytics Function

When we come in to try to diagnose demoralized, stagnant marketing analytics function, we look for four key, telltale signs of trouble.

First, we look at how the team works. Is the team using a lot of Excel workbooks to answer questions? Is a version control system like Github being used? Are analysts and data scientists constantly reinventing the work that colleagues did before? Stagnant marketing analytics teams do not deploy scalable methods to solve problems.
Next, we look at how quickly a team is able to move. If the business asks for an answer, how long does it take to design and field a test—days, weeks, months, or quarters? If a simple descriptive statistic is needed, how long does it take to write the code to get the answer? Minutes, hours, days, or weeks? Teams that take a long time to do simple (or more advanced) tasks are hobbled by seen or unseen snares.
We then look at assets. Does the team rely heavily on proprietary software to answer questions, or is data and code more open and owned? In other words, how much of the analytics is being done inside black boxes, and how much is actually understood by the team?
Finally, we examine the culture. Agile marketing analytics organizations are learning organizations. Analysts, data engineers, and data scientists grow quarter over quarter and year over year, tackling new and exciting problems, and adding to their craft. They stay at the organization because they’re happy.

Organizations that struggle in more than one of these areas have room for improvement. Some organizations need more than just improvement—the warning flags are waving across all four, indicating the need for a total overhaul.

How to Improve Analytics Function with 5 Principles

Once an analytics organization has been identified as needing help, there are five principles that can be implemented to improve scalability, flexibility, ownership, and culture.

All Activity Needs Clear Objectives
All too often, a desire for analytics—any analytics—drives teams to build models “just because,” leading to a proliferation of “interesting things” that lack a coherent purpose. Eliminating model-palooza begins with clarity of purpose. Define the decision-making or actionability that each piece of work or model will enable or lead to.
Relentless Focus on Data Lineage and Asset Creation
Each objective—if defined clearly—implies a data structure or “endpoint” supporting it. The extraction-transformation-loading (ETL, data munging) required to get the data into a tidy shape should be job one, and this process itself should be repeatable and reproducible. By focusing on data endpoints, future agility is baked in.
Shorten the Distance Between Business and Analytics
This comes straight out of the agile manifesto; the closer engineers are to users, the better the product will be. The same goes for marketing analytics. Analytics informs business objectives and decision making, and the business informs analytics. A one-way requirement to analytics path is demoralizing and will lead to stale analyses. Instead, have frequent and direct conversations between business owners and analysts. These aren’t inefficient meetings—they’re critical in generating innovative insights.
Embrace Iteration and Imperfection
As long as you’re keeping track of your changes via version control. Never let the idea of a perfect model get in the way of productive results. Iterate frequently, always using Git. Don’t fall victim to rigid timelines, or the looking-for-significance trap. The work will be done when it is done.
Make Every Deliverable Reproducible
The final output—even if PowerPoint—should be reproducible across the company. Anyone hitting “execute” on the code should get the same results. This isn’t a pipe dream; it’s a scientific principle that ensures that business owners will act only on real results. When reproducibility and quality are glossed over, everything is newsworthy—meaning nothing is. The result is a muddled mess.

This is just the tip of the iceberg when it comes to agile marketing analytics. Transforming your organization fully is a years-long process, but these five principles—applied consistently—will start paying immediate dividends.