Any marketer will tell you that brand-building takes significant time, concerted effort, and major resources. Measuring that work, however, can feel like an altogether separate beast. Quantifying the impact of upper funnel efforts on eventual sales is an increasingly high priority for effective marketing teams—those seeking to avoid the measurement trap and balance out their marketing investments.
While Media Mix Models (MMM) are often the best tool for evaluating marketing effectiveness and avoiding last-touch bias, these advanced tools can struggle with the long-term and slowly compounding nature of brand-building. Inevitably, we reach a key question: “How do we take a multi-stage and data science-backed (read: econometric) approach to modeling a brand?”
Figure 1: Generalized multi-stage model
“Brand” as a Collection of Latent Factors
Let’s start with what we know: there is no single “brand equity” KPI that we can observe and track. There are certainly observable indicatorsof brand strength, things like awareness (be it aided or unaided), NPS, product ratings, repeat customer rates … the list goes on. And, on the other side of the equation, there are observable driversof brand equity—this is anything that pushes the brand forward (or backward), things like earned media, competitor spend, celebrity partnerships, paid marketing campaigns … again, the list gets long in a hurry.
Once we have collected the relevant data and built a comprehensive list of our drivers and indicators, we can leverage an econometric technique called structural equation modeling to estimate the factor (or factors) in the “middle”—the unobservable thing that is driven by the drivers and indicated by the indicators—a latent factor. With a bit of data science wizardry, we can load these observable metrics against factors and review the strength of those relationships.
Choosing Brand Latent Factors
Again and again, we find that collecting indicators and drivers to load on a single factor (call it “brand equity”) simply doesn’t work. We’ll often generate strange coefficients with low confidence in our modeling outputs when using a single factor. There is no one-size-fits-all approach here, but with modeling iterations, we can start to think about a collection of brand factors that have distinct sets of drivers and indicators. And this is where things get interesting.
When aligning drivers and indicators to factors and selecting how many factors to model, we start a conversation between quantitative data science and qualitative marketing strategy. We collect a comprehensive data inventory, build a multifaceted “story of the brand” using key events and trends over time, and finally let the data tell us which aspects of our story seem to be resonating econometrically. This give and take will help crystallize our mental model and generate a set of latent factors to model on.
Here are some unobservable latent brand factors that might underpin our brand model:
Brand Awareness
Category Awareness
Brand Affinity
Brand Salience or Fame
Perceived Quality
Brand Loyalty
Figure 2: Illustrative map of SEM results for brand latent factors
What Can We Learn from Modeling Brand?
Once we have a model that (a) loads econometrically with high confidence and (b) lines up with our intuition (our “priors”), we can chart these brand factors over time and begin to investigate causal relationships or key moments in a brand’s timeline.
Figure 3: Illustrative brand journey—not all factors move together
As we incorporate brand factors into a holistic marketing effectiveness model by layering on other components (i.e., modeling sales response, controlling for seasonality and macro factors, etc.), we can start to ask the juicy questions that brought us to this exercise in the first place, things like:
Which brand factor(s) align with the sales trend?
Which marketing channels drive which brand factors?
How much of a time lag is there between movements in brand factors and the resulting sales impact?
How much of a halo effect do our brand factors have on the effectiveness of lower funnel channels, like paid search?
How do strategic initiatives impact our brand factors, and are we moving the ones that matter?
In sum, what should we—as a marketing organization—be prioritizing?
Delivering Brand Success
A brand is a tricky concept to put a number on, but doing so is exceptionally powerful in a multi-stage model of marketing effectiveness. Using structural equation modeling with the right drivers, indicators, and latent factors, we can take a data science-backed approach and incorporate a cohesive brand story into a holistic effectiveness model.
Download our framework, “Measuring the Impact of Brand Marketing on Business Growth”
Quantifying the direct impact of upper-funnel branding on tangible business growth is a challenge. Many marketers inadvertently fall into the “Brand Measurement Trap,” overestimating direct marketing’s role at the cost of long-term brand-building. This framework introduces an integrated approach, combining time series-based brand health metrics (awareness, consideration, and affinity) with a multi-stage modeling system, giving businesses the tools to quantify how brand marketing drives positive consumer behavior and generates long-lasting growth.
In the rapidly evolving landscape of digital marketing, the healthcare industry has witnessed a surge in the demand for affiliate marketing, with aggregators playing a central role. Over the past decade, insurers, brokers, and senior care companies have increasingly turned to aggregators, paying a percentage of the product sold or a flat rate for a lead or referral. The rise of digital marketing has propelled these aggregators, who leverage sophisticated online comparison platforms, digital marketing expertise, and superior online customer experiences.
The insurance aggregator market in North America stands at an impressive $7 billion, according to data from Allied Market Research, showcasing the substantial influence of aggregators in the healthcare industry.
Lead Generation Aggregators vs. Product Comparison Aggregators
Understanding the nuances between lead generation and product comparison aggregators is essential for companies navigating the affiliate marketing landscape.
Lead Generation Aggregators
Lead generation aggregators employ their own no-brand-name marketing strategies to create demand, capture consumer interest, and subsequently sell leads to end-users such as insurers, brokers, or senior care companies. This can take various forms, including customer lists, leads, and introductions.
Exclusivity: In addition to determining the depth of lead generation, executives must weigh the importance of exclusivity in their partnerships with aggregators. Many aggregators sell the same leads to multiple partners unless the end user pays a premium for exclusive leads or referrals.
Figure 1: Higher and lower cost and exclusivity in partnerships with aggregators
Product Comparison Aggregators
Product comparison aggregators sell directly to consumers by collecting branded digital marketing from competing companies on a proprietary online platform. This enables consumers to comparison shop in an unbiased manner. These aggregators often partner with trusted retail, pharmacy, and wellness brands to create co-branded marketing, offering consumers a convenient one-stop-shopping experience.
Aligning Marketing Goals to the Right Affiliate Marketing Options
Leveraging affiliates for acquisition can be a lucrative endeavor if approached thoughtfully and executed with care. A clear understanding of the quality of memberaffiliate marketing can be crucial.Oftentimes, there are tradeoffs between lower acquisition costs and potentially lower customer lifetime value.
Leveraging Aggregators for Acquisition
Acquiring members via affiliates is more than a “fire and forget” exercise. To maximize effectiveness, carriers can take concrete steps:
Capture Comparison Shoppers: Identify and capture consumers who are likely to comparison shop, ensuring a strong online presence on well-known comparison-shopping websites.
Increase Awareness for a Halo Effect: Elevate product and brand awareness to create a halo effect. Even when shopping around, consumers want to feel confident in their choices.
Broaden Reach: Aggregators, with their marketing efforts, have additional reach. Target audience segments that companies may not have otherwise captured, particularly in areas or demographics with lower penetration.
Optimizing Member Retention
Retaining members acquired via aggregators presents unique challenges. To enhance retention, marketing executives should focus on four key factors: good data, early engagement, strong aggregator partnerships, and holistic analyses of aggregator-captured members.
Good Data: Tracking member cohorts based on sales channels and applying predictive indices for likelihood to switch is essential for identifying high-risk members.
Early Engagement: Members captured indirectly need early engagement strategies to foster a connection. Promote downloading the app, selecting a primary care provider, and completing the member profile.
Strong Aggregator Partnerships: Collaborative communication plans between aggregators and insurers can improve retention among converted consumers, avoiding confusion and reinforcing the plan selection.
Holistic Analyses: Regular analyses of aggregator-captured members are necessary to assess goal achievement and optimize aggregator utilization for future strategies.
Navigating the Future of Healthcare Marketing
In conclusion, aggregators are an integral part of the health insurance landscape, offering companies the choice to actively capitalize on their strengths or passively receive sales. A scientific view of indirect channel tradeoffs, leveraging data to determine the future role of aggregators in a marketing and sales channel strategy, is key. To delve deeper into the intricacies of sales disruptions and recalibrated selling motions prompted by the pandemic, we invite you to download our whitepaper for invaluable insights.
Download our whitepaper, “Navigating 5 Fundamental Shifts in Healthcare Marketing and Sales Channels”
For a more in-depth exploration of the the role of aggregators, and four other disruptions, download our whitepaper.
Every organization should have marketing effectiveness measurement tools at its disposal. Marketers can’t optimize on last-touch attribution alone, and educating internal stakeholders on how marketing channels work together has long-term benefits.
We’ve outlined four situations when marketing effectiveness tools, such as MMM and MTA, can be particularly helpful.
When the focus is on expense control, lean staffing models, and profitable revenue growth, marketing is frequently viewed as a cost center. CFOs often push marketers to employ media channels with the lowest cost per acquisition (typically demand capture channels such as paid search and affiliate), which may have a negative long-term impact. MMM and MTA can help finance-led organizations understand what portion of sales are driven by marketing without the risk of overattributing to paid search and affiliates.
Additionally, aligning on marketing-driven cost per acquisition can support (or start) conversations around the level of investment needed to generate additional sales to hit targets.
If finance sets a cost-per-acquisition limit at $100, Display could support an average of $34K daily spend, while CTV could only support an average of $3K.
Or, looking at it another way, if the budget increased by 15%, how many more sales would convert, and how would the overall cost per acquisition change?
Finance may be persuaded that the additional $3.6M in budget is worthwhile to generate an additional 13,000 sales.
2. Diminishing Demand Environments
When an organization has over-rotated toward lower funnel activities, sales demand decreases over time, and cost-pers increase. Marketing effectiveness measurement can help identify opportunities to increase upper funnel spend (to generate demand) through response curves. Response curves show where the next dollar should be allocated to maximize return and when a media will see less return on additional spend.
Once marketing decides to shift investment up the funnel, MMM and MTA can validate that this was the correct move, help track cost-pers by channel over time, and refresh spend curves at higher (or lower) levels to continue to optimize spend.
3. Tightening Budget Environments
No organization has an unlimited budget, and marketers often default to last-touch attribution to determine where to cut when budgets are reduced. Unfortunately, this is short-sighted; last touch significantly underweights less trackable activities, such as TV, online video, and other awareness marketing.
Looking only at last touch in the above example, one would likely cut TV spend. However, the actual marketing ROI of TV is significantly better than what can be measured with last touch. Marketing effectiveness measurement helps make marketers smarter with more information about the performance of marketing channels.
4. Challenging Brand Environments
When organizations are struggling with brand—such as a PR incident, long-run customer service issues, a popular new category entrant, or lack of brand awareness activities—it can be challenging to stay the course on brand spend. Brand-building activities take a long time to pay off, and organizational stakeholders (finance or the board, for example) may want to see proof that investments are working.
MMM can measure the impact of marketing on brand health and, consequently, brand health’s impact on sales. While moving brand metrics significantly takes a long time, modeling can confirm and quantify the link between brand health and sales.
An equation on “responsiveness” and “stickiness” can quantify how much brand spend is required to move the brand metric. “Responsiveness” measures how the brand metric responds to marketing (i.e., how quickly marketing can move the metric). “Stickiness” measures how long the change marketing drove lasts (i.e., how quickly the metric returns to previous levels).
Combining these two elements quantifies how much spend is required to improve a particular brand metric and how long that change will last. Example: One week of $1M brand spend will yield a cumulative increase of 1.7 percentage points in Awareness over eight weeks.
Looking at historical sales performance during periods when brand metrics were higher helps quantify the sales impact of increasing a brand metric calculated in the formula above.
Together, these can help stakeholders outside of marketing understand the sales impact of these activities over a shorter period and buy marketing time for the flywheel of long-run brand improvement to take effect.
Conclusion
Usually, we are brought in when things aren’t going well, and frequently, the issue is one of the four situations covered above. All organizations could benefit from a more holistic approach to marketing measurement, though.
Download our framework, “Measuring the Impact of Brand Marketing on Business Growth”
For a comprehensive exploration of brand marketing’s impact and our multi-stage modeling approach, download our detailed framework.
Marketing and sales teams rely heavily on data to make informed decisions, drive strategies, and target their efforts effectively. In today’s world, zip code-based geographic and demographic data offer several invaluable insights that can supercharge their endeavors. Different regions can exhibit widely varying consumer behaviors, preferences, and needs. What works in one area may fall flat in another due to cultural, economic, or even regulatory differences.
Consider the Medicare insurance market. In this industry, healthcare policy terms, conditions, and coverages change at the local (zip code) and state levels. Not only do sales and marketing teams face restricted distribution and data collection regulations, but plans and markets are highly competitive. For marketing and sales professionals, the capability to segment by location ensures marketing messages on the benefits of plans are accurate and relevant to the demographic.
Optimizing Marketing’s Impact Using Geographic Data
Many of Marketbridge’s clients are in the thick of geographic-based data every day. The granularity and precision offered by such data can be a game-changer in the competitive landscape, ensuring that efforts are data-driven, precise, and, most importantly, effective. For example, a marketing campaign that resonates strongly in an urban setting may not have the same impact in a rural area. Similarly, a product that sells well in a high-income neighborhood may not perform as well in a less affluent one. By breaking down performance data geographically, organizations can tailor their approaches to align with local conditions, enabling them to allocate resources more effectively and improve ROI.
Predicting Customer Changes
Additionally, a geographic lens on performance data can provide valuable insights into emerging market trends and opportunities that are not immediately obvious when looking at aggregate data. For instance, a sudden spike in sales in a specific region could signal an unmet need or untapped market potential, providing a first-mover advantage to companies that act quickly. Alternatively, a decline in a particular area could be an early warning sign of market saturation, increased competition, or shifting consumer sentiments. Understanding these geographic nuances allows businesses to adapt and pivot their strategies proactively, ensuring they stay ahead in meeting customer needs and expectations.
Examples of Zip Code-Based Use Cases for Modern Go-to-Market
Targeted Marketing Campaigns: Zip code demographics allow marketers to tailor campaigns, ensuring better audience resonance and higher conversion rates.
Resource Allocation: Sales teams use zip code data to pinpoint high-potential areas, optimizing resource distribution.
Predictive Analysis: Geographic and demographic insights help companies anticipate product or service demand.
Product Development: Demographics guide product features, pricing, and branding for potential launch areas.
Optimizing Distribution: Zip code data identifies ideal locations for distribution hubs or outlets.
Competitive Analysis: Understanding area demographics helps marketers assess competitor footholds and adjust tactics.
Risk Management: Businesses gauge regional risks using socio-economic data, influencing decisions like insurance premium settings.
Enhanced Customer Segmentation: Zip code data refines customer targeting, from luxury promotions in affluent areas to budget offerings elsewhere.
Compliance and Regulation: Zip code demographics help sectors like finance maintain local regulatory compliance.
The Challenges of Geospatial Data Analysis Using Zip Codes
While the significance of geographic data for sales and marketing teams is clear, transforming raw data—whether latitude/longitude, address, zip code, or census block—into actionable insights requires expertise. In the past, businesses leaned heavily on U.S. postal service data for geographic information, often outdated (by over a decade) and plagued with technical inaccuracies. Data associated with zip codes that traverse state boundaries presented additional challenges. U.S. Census Data provides a more accurate alternative data source for mapping, but one-off analysis is time-consuming.
The Roles and Toolsets Needed
Zip code analysis requires the right combination of talent and automated technology. At Marketbridge, “data detectives”—analysts with a deep data science toolkit who explore diverse data sets to understand why events/trends have happened—play a critical role in this endeavor. They use open-source, fully documented code connected to source systems, vendor files, and public geospatial data to standardize analysis and modeling of geographic data changes over time. The solutions these analysts create are document-based with code embedded in workbooks and are easily knittable to reports mapping changes and trends in consumer behavior for sales and marketing teams.
Analysts at Marketbridge use RStudio and the extendable RMarkdown/Quarto framework for this use case. Unlike Jupyter Notebooks, Quarto documents are entirely text-based, making version control easy via Git/Github, and are then convertible into HTML reports, PowerPoint slide decks, or Word documents.
Building an Open-Source Solution for Zip Code Data
Frustrated by the limitations of R mapping when using Census data to overlay demographics on zip codes, I built Choroplethr, an open-source package for the R language, as a solution. Choroplethr seamlessly connects to U.S. Census Data for mapping, providing more accurate geospatial data and automatically handling visual mapping tasks.
Choroplethr is open-source and available to the public; to use it from R, simply type:
install.packages(“choroplethr”)
Choroplethr works very well with a fairly new R package, zctaCrosswalk. This package contains the U.S. Census Bureau’s 2020 ZCTA to County Relationship File, as well as convenience functions to translate between states, counties, and zip code tabulation areas (ZCTAs). You can install the package like this: install.packages(“zctaCrosswalk”).
Mastering Data for Strategic Growth
In today’s data-driven landscape, the nuanced understanding of geographic and demographic data, down to the zip code, is pivotal for marketing and sales strategy. Through tools like open-source software (R studio), professionals can access refined insights that were previously difficult to harness or done via a one-off analysis. These tools not only aid in crafting precise campaigns but also in understanding market dynamics at a granular level. This journey underscores the importance of evolving technology and innovation in making sense of vast data sets.
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Download our marketing analytics benchmark report to learn more about 1) Direct-from-the-source challenges and priorities heard from marketing analytics leaders, 2) Key insights on how organizations are maintaining, running, and growing their analytic functions, and 3) Go-forward actions for marketing analytics teams to improve processes and advance analytics.
Today, 39% of health insurance consumers purchase online, and this percentage is on the rise. However, it’s important to note that 61% of consumers still rely on face-to-face or telephonic agents for their insurance needs.1 Therefore, insurers must navigate the right balance between self-service-led experiences and agent-led experiences effectively.
3 Key Steps to Balancing Self-Service and Agent-Led Models
Fortunately, finding the right balance between self-service and agent-led can be undertaken systematically:
Embrace the notion that channels don’t choose customers; customers choose channels. It’s a foundational principle for any go-to-market strategy. Regular buyer research is crucial for understanding why buyers prefer one channel over another.
A Self-Service Digital-Led Channel is defined as customers independently navigating digital platforms for transactions. When customers engage with agents—either virtually or in person—for assistance, guidance, or to finalize complex transactions, that is considered an Agent-Led Channel. As you can see in Figure 1 below, both Channels include owned (direct channels owned by an insurer) and rented channels (such as brokers, aggregators, etc.).
Keeping an ongoing 360-degree view of how customers buy is critical to deeply understanding why buyers within each line of insurance are choosing one channel over another. These varying customer intentions and motivations by channel will provide insight into current and potential future buyer segments, and ultimately, the path to designing a more relevant CX.
Figure 1: Health insurance channel landscape
Step Two: Adjust for Channel Economics
Channel economics are the efficiencies (or lack thereof) to acquire new customers. Focus on three key performance indicators (KPIs) to adjust appropriately:
Selling expense-to-revenue ratio (E/R) – Measuring E/R by channel ensures that cost-of-sale economics are affordable in the context of overall business profitability targets.
Customer Acquisition Cost (CAC) – Understanding the cost to acquire a member provides insight into the upfront channel cost, which is essential to forecasting and projections.
Member Lifetime Value (LTV) – While some channels may be more expensive in the short-term, they may yield high lifetime value customers for greater long-term results.
Step Three: Design Routes-to-Market Resources
Conduct thorough resource planning for each channel, covering infrastructure, training, and marketing. Ensure clear documentation of resources allocated to each channel, including expenses, and review this at least quarterly to align with channel performance and buyer feedback.
Update Infrastructure: For Self-Service Digital-Led channels, prioritize a best-in-class online experience. For Agent-Led channels, secure or renew partnerships with brokers and aggregators.
Ongoing Training: Train technologists to troubleshoot issues via Self-Service Digital-Led channels and train agents how to use sales platforms and understand the nuances of plan benefits in local areas.
Marketing Support: Deploy demand capture campaigns for Self-Service Digital-Led channels to drive awareness and traffic. For Agent-led channels, deploy awareness marketing to drive consumer awareness and consideration, as well as provide agents with audience-specific materials to aid in the sales experience.
Planning for Tomorrow’s NextGen Distribution Model
Health insurers are facing a dynamic landscape where the balance between self-service and agent-led channels is pivotal. Adapting to evolving customer expectations and optimizing business outcomes requires a strategic approach. The three steps detailed in this blog should be addressed in the context of long-term strategy. Utilizing a “clean sheet” technique, it’s important to envision a distribution model five years ahead to foster innovative test-and-learn opportunities.
Adapting to digital and omnichannel experiences is essential for success. Doing so while adjusting for channel economics, and investing in effective routes-to-markets, insurers can not only thrive in the present but actively prepare for the uncertainties of tomorrow.
Download our whitepaper, “Navigating 5 Fundamental Shifts in Healthcare Marketing and Sales Channels”
For a more in-depth exploration of the changing balance between self-service and agent-led channels, and four other disruptions, download our whitepaper.
I have spent most of my career measuring go-to-market effectiveness across many different industries. I find myself increasingly thinking about what I’ve learned—not about the techniques of measurement, but about the right investment decisions in various scenarios. Because I’ve been inside at least 100 brands’ data, I have quite a few heuristic learnings about what to do, and when. I thought I’d put them down on paper.
The Discriminatory Dimensions that Matter
Like any good consultant, I look for organizing frameworks to help make decisions. I came up with five dimensions that seem to drive choice in marketing mix. This is a blog post so they may change, but I believe them to be mostly exclusive, and maybe 80% exhaustive. So, mEcE?
Transaction Complexity: How many forms, sign-offs, and configuration steps are required to purchase the product? In other words, how much “buying friction” is involved? Examples of complex transactions include buying a car, configuring a new software license, or opening a bank account. Examples of simple transactions include buying a can of soda or a media subscription. I originally had “criticality” as a separate dimension—whether the purchase could potentially drive a lot of buyer’s remorse, but upon further reflection, I think it’s almost perfectly correlated with transaction complexity.
Competitive Landscape: How many competitors are there, what is their market share, and what are they spending on marketing? A highly competitive market tends to be well-defined and attractive, whereas less competitive markets are more niche, or have high barriers to entry. Examples of competitive markets include airlines, consumer package goods, and consumer banking. Less competitive markets include niche B2B or software plays and essential monopolies like utilities. New entrants to competitive markets generally have a very steep hill to climb when it comes to marketing investment.
Distribution Strategy: Is the product being sold directly, through third parties, or both? Companies selling their products directly have an information advantage: They know their customers and can see them progress through the shopping process—whether via e-commerce or via sales reps entering information in a CRM system. Selling via partners or retail trades distribution scale for information and margin.
Wanted vs. Needed: Is the product a desired purchase or a necessity? Just like the Rolling Stones song, keeping oneself fed, clothed, and housed are needs for pretty much everyone. These critical needs can be driven by emotion, but quality, price, and circumstances tend to trump. Luxury goods and impulse purchases are far more about scratching an itch or signaling to others and require very different marketing approaches.
Product Lifecycle Stage: Is this a new category, or established? New categories require education and evangelism. There’s evidence that “First Mover Advantage” is a myth; the first movers soften up the market for later entrants who don’t need to spend as much on go-to-market.
Figure 1: The five product dimensions that matter when choosing a marketing mix.
The Decisions that Matter
Just like there are discriminatory dimensions, there are dimensions of decision-making around marketing. Oftentimes, marketers will use the term “channel” as a catch-all, particularly in media mix modeling. However, this is doing the richness of media planning a disservice. Each channel is really a collection of attributes. While channels certainly have their own unique dynamics, how to employ multi-channel strategies with these other marketing dimensions is where organizations can differentiate themselves.
Funnel Position: Upper-funnel advertising drives awareness or affinity; mid-funnel advertising typically serves to educate; and lower-funnel advertising converts ready-to-buy customers. Upper-funnel advertising can be thought of as a “TAM (Total Addressable Market) Booster.”
Emotional Content: Emotional messaging (sometimes called “System One”-type messaging, after Daniel Kahneman’s Thinking Fast, Thinking Slow framework) drives brand equity—that ephemeral store of goodwill in consumers’ brains that takes years to build—and can last for decades. Functional messaging (System Two) acts quickly, typically reminding customers of a product with rational price-value proposition and a buying opportunity.
Media Channel: Media channels are incredibly diverse, and getting richer. Thirty years ago, we would talk about print, TV, out-of-home, and mail. Today, we have social, streaming video, display, search, affiliates, influencer, gaming, and mobile—to name only a few. To make it more complicated, each publisher within a channel has their own unique blend of audience, medium, content, addressability, and measurability. All this being said, “Linear TV,” for example, does have immutable traits that are different from streaming video, and publishers within media channels tend to have much in common—including transaction economics.
Content Granularity: Content can be customized to finer and finer degrees, to match audience affinity, test concepts, or both. The term for this used to be “one-to-one marketing”; in B2B settings we call it “account-based marketing.” The promise of purpose-built content for specific audiences is obvious; if we know exactly what Jane wants to see or hear, and can provide that, we’ll perform better, all else being equal. However, while tempting, content “small ball” can be difficult to execute and measure.
Figure 2: The four marketing mix decisions that matter.
What to Do, All Else Being Equal, for Each Dimension
Every marketing mix decision is complex, but there are some established “laws of physics” that apply in each discriminatory dimension. It’s surprising how often these are ignored by marketers.
Transaction Complexity
High transaction complexity products require education and longer sales cycles. Because they tend to be “high consequence”—said another way, if the buyer makes the wrong choice, they will have a lot of remorse—buyers need to be reassured emotionally and functionally.
Full-funnel marketing is certainly required, but upper-funnel activities should focus on quality and eliminating barriers to purchase. Self-service educational content should be easy to find, with links from upper-funnel digital media to that content. Pushing buyers to buy too quickly can be counterproductive.
Content should generally be less emotional for high-complexity products. Because complex products require “thinking slow”—rational decision-making—focusing too much on pathos can be off-putting for consumers. Creating a feeling of safety should be the goal—ensuring that buyers will be taken care of post-purchase.
High complexity products can also take advantage of more content granularity, both because higher transaction sizes make customization economically feasible, and because there tend to be very real differences between buying use cases. That being said, more content does not always equal better outcomes. In A/B tests over the years, we have consistently found it difficult to beat a “generic” best message with tailored content, perhaps because targeting the right audience for content remains challenging.
For simpler products, the opposite strategies tend to apply. Simpler products still require full-funnel marketing, but upper-funnel tactics should focus on emotional resonance (System 1-type thinking.) Content should also be less granular, and focused on simple, universal messages.
Competitive Landscape
Competitive product landscapes require competing on share-of-voice. This has been well established, in studies such as Binet and Field’s The Long and Short of It. The essential message is that brands that want to grow market share need to advertise above their “fair share”—a higher share of voice than market share. As shown in Figure 3 below, a ten-point extra share of voice position (e.g., 50% SOV vs. 40% market share) will drive about 1.5 percentage points of share growth per year, if sustained.
Share-of-voice, in this case, means upper funnel, emotional (System One) advertising. This is perhaps the hardest thing for CMOs to do, however, as this type of advertising is the least measurable. This mismatch between the right strategy and measurability might be the number one factor driving CMOs’ notoriously short job tenure.
Figure 3: All things being equal, consumer brands in competitive markets need to spend above their fair share to grow. This spending should be emotional (System One.) Promotions, price, and functional education don’t help much.
This extra share-of-voice can be achieved across many media channels, but video and visual media are better. These types of channels typically meet consumers when they are leaning back and relaxed.
Less competitive, niche markets do not necessarily require competing on share-of-voice and can get by without much upper funnel advertising. Less competitive markets can rotate to lower funnel activities, focusing more on the functional benefits of their product or solution (System Two). However, the total addressable market can be stunted without education outside of prime prospects, so it’s always worth testing additional tactics. Increasing cost-pers and slowing sales are also indicators that the top of the funnel needs to be filled—but don’t wait too long, or you could be fighting an uphill battle
Distribution Strategy
Consumer package goods (CPG) companies have traditionally relied on retailers to transact with consumers. While this model has been challenged over the past decade—think Dollar Shave Club—retailers are still king for smaller transaction-size, simpler products.
This means that CPG companies have always been forced to compete with broad-reach advertising. Affiliate marketing just isn’t attractive for Tide detergent or Coca-Cola; neither is paid search. Instead, brand managers at P&G are trained to think from the consumer’s perspective, building marketing strategies using pro formas, with feedback from econometric (MMM) models. We’ve often said that CPG’s bane (being unable to know their customers directly) is actually a benefit; being information-poor requires better marketing and better thinking.
Direct-to-consumer (DTC) companies have far better data about their prospects and their customers. They invest in software to track them through the buying cycle, and buy (down-funnel) performance media to bring them to their site. This isn’t the wrong strategy; companies with direct relationships should spend far more on down-funnel media. They should also be able to customize in a far more granular way, as they know more about their audiences. However, over-rotating too much towards performance media is a danger.
This devolves, particularly in competitive markets, into companies chasing low value switching customers into each other’s arms—all the while lining the pockets of late-stage goalkeeping media companies, particularly affiliates and paid search.
Wanted vs. Needed
Needed products—water, milk, toilet paper, housing—aren’t particularly sexy on their face. It’s tempting to therefore assume that a more functional marketing approach would be more appropriate. However, this is almost precisely the opposite of what to do.
It’s right there in the name—wanted products are desired. In a sense, the marketing is built into the product; the marketer just needs to provide distribution. Needed products need help to stand out.
Wanted products tend to perform better in down funnel-heavy strategies. Because they are desired, marketing’s goal should be to surface it to as many people as possible and “let it sell itself.”
Needed products depend hugely on brand identity. Building brand identity takes decades of persistence. Upper funnel marketing focused on core brand (emotional) attributes is a key part of building the brand, which will ultimately translate into lower demand price elasticity (pricing power), better shelf placement at retail, and higher, longer-term sales.
Product Lifecycle Stage
Truly new products are unique cases. On the one hand, good product-market fit can create a flywheel that takes off on social media, creating a firestorm of demand. These “lightning in a bottle” scenarios do happen—but they are very hard to predict. It’s more common that new products require evangelism: Spreading the news before competitors catch up.
This is why a good amount of venture capital funding goes towards go-to-market. High initial CPAs (cost per acquisition) are worth it if those users start telling others about the product, and if they stick around for a long time (high customer lifetime value, or CLV).
In a first-mover situation, spending mostly down funnel on performance marketing can make sense—obviously, if there is a solid product-market fit. In this case, down-funnel performance marketing can be very simple; you have a need; we have a product; it’s new. This isn’t emotional or trying to create brand love; it’s informational.
In a competitive new product category, things get squirrely. A common occurrence is that a first mover innovates, and for the first year or few years, thinks things are easy. Profits are high, CPAs are low, and it seems like smooth sailing. However, lucrative markets attract competitors. It can take quarters or years for some first movers to realize that they’re losing share, buried in the noise of weekly lead or sales reports. In these cases, it’s essential to get ahead of competitors and begin upper-funnel advertising before competitors get a foothold. In other words, it’s all about timing.
New product launches also benefit greatly from multi-channel approaches. Testing across different channels will yield a plethora of insights that can be quickly turned around into strategies. New product marketers will learn something new every month—provided they experiment.
The same can be said for content granularity. Most older product categories have settled on the content that works; it’s hard for creatives to break through. Brand-new products can try crazy messaging, and it can work.
Figure 4: “Teddy told me that the most important idea in advertising… is new. Creates an itch…” Maybe so, but you have to watch for the copycats on your heels.1
Summary Cheat Sheet
With all that being said, here’s an attempt at a cheat sheet matching the discriminatory scenario dimensions with marketing decisions. Of course, these are rules of thumb and should be validated via both econometric modeling and continuous testing or either.
Figure 5: All things being equal, start with these strategies depending on the dimensions (blue) of your business problem.
In the hyper-connected digital era, where immediate results and short-term metrics often dominate the marketing narrative, communicating the long-term benefits of brand investment to finance-focused decision-makers can be challenging. Using a seminal study that bridges the gap between theory and practice, this article explores the common challenges faced by marketers and provides actionable best practices.
Insightful Revelations from the Binet Study
In 2010, Les Binet and Peter Field unveiled groundbreaking findings about the significance of “extra share-of-voice” (ESOV) for predicting long-term market share growth among UK consumer brands. They found that brands with higher ESOV experienced growth, while those underinvesting in brand marketing saw a decrease.
Key takeaways from the study include:
A brand maintaining a 10-point advantage in share-of-voice (“extra” share-of-voice) could anticipate a yearly market share growth of about 1.5 points.
Brands using emotion-rich “System 1”* messaging built stronger connections, increasing the chances of long-term market share growth.
* System 1 messaging relies on emotional and intuitive appeals to evoke immediate, unconscious consumer reactions.
The study’s findings highlight the importance of brand building and competing for share-of-voice to achieve lasting market share gains. By investing in advertising and creating emotional connections with their audience, companies can build a strong brand identity that drives sustainable growth.
Challenges to Measuring the Impact of Brand
When CMOs advocate for brand investments, they face the tough task of quantifying benefits to CFOs. How do you measure the essence of something intangible yet undeniably powerful? Why is it so hard to measure the impact of brand marketing?
Lack of Direct Causality: Unlike performance marketing, where ad spend directly correlates with sales, brand marketing slowly molds consumer behavior requiring longer-term tests.
Persistency of Brand Effects: Brand investments have long-lasting effects that accumulate over time, making it harder to pinpoint the impact of individual campaigns or strategies.
Data Quality and Access: Accurate brand impact measurement requires diverse data sources, including consumer surveys and sales data. This can be challenging, particularly for new or resource-limited businesses.
As a result of these complexities, companies often fall into the “Brand Measurement Trap,” prioritizing short-term, easily measurable tactics over long-term brand building. Without the tools to properly assess brand marketing impact, businesses could miss avenues for sustained growth.
Best Practices for Measuring the Impact of Brand
Using the following five best practices, CMOs can deliver measurable data showing the value of brand investment.
1. Embrace a Long-Term Vision
Re-define brand marketing not as a mere expenditure but as a strategic long-term investment. Upper funnel investments take longer to manifest than down funnel performance marketing tactics. Harness ad stocking—a technique applying campaign stimulus over a defined period post-launch. By correctly using ad stocks—and staying patient—the full temporal impact of the campaign will be captured.
2. Prioritize Attitudes Before Behaviors
Remember that “System 1” messaging primarily influences attitudes rather than immediate behaviors. These attitudes, in turn, shape long-term actions. Therefore, an effective two-stage model first measures how advertising affects attitudes and then evaluates how these attitudes impact metrics like leads, sales, or revenue. This involves tracking awareness, affinity, and comprehension before, during, and after a campaign.
Changing attitudes offer a significant advantage; after one positive impression, the “halo effect” can lead to a long-lasting positive connotation of the brand name. There’s a reason you still remember jingles for brands you watched on television as a kid but don’t remember the junk mail you receive every day in your mailbox.
3. Adopt a Multi-Stage Modeling Approach
Employ a modeling approach that considers the potential impact of upper funnel marketing on brand health metrics. To determine the total ROI of these efforts, link three models together: (1) Brand Marketing Impact on Sales, (2) Brand Marketing Impact on Brand Health, and (3) Brand Health Impact on Sales.
4. Invest in High-Quality Data Sources
Prioritize high-quality data sources like consumer panels, social media listening tools, and third-party research studies. While these sources may incur higher costs, they yield valuable insights to enhance decision-making.
To gauge relative brand share-of-voice without substantial survey expenses, leverage existing data from Google and social media platforms. Companies like Honest Marketing and Cashcow utilize Google’s Search Console and Google Analytics to gather information on brand-related impressions, clicks, and mentions. Others rely on social media reach as a proxy for brand awareness, analyzing post-performance to refine future brand strategies.
5. Test Big or Don’t Bother
Conduct tests with sufficient scale. Unlike direct mail, upper funnel ads don’t yield immediate returns with small investments. Instead, they follow an “S-curve” pattern, with modest impact initially, exponential growth at scale, and eventual saturation. Continuous testing and refinement are key for an effective brand measurement strategy.
Unlocking Growth and Measurement Tools
Companies that successfully reframe brand investment as a long-term strategy, attain extra “share-of-voice” through sustained upper funnel marketing campaigns, and engage the five best practices outlined in this article will not only achieve significant growth over time but will also have the tools for measuring their efforts.
Download our framework, “Measuring the Impact of Brand Marketing on Business Growth”
For a comprehensive exploration of brand marketing’s impact and our multi-stage modeling approach, download our detailed framework.
With the tech economic slowdown likely extending into 2024, businesses must adapt their resources, budgets, products, and partners to the new selling environment. As buyers delay large transactions, subscription services that are cost-efficient or vital to daily operations (like SaaS for payroll) will gain prominence. Given tech firms’ reliance on channel partners to seize market opportunities and deliver value-added services, companies must ensure their partners can effectively develop and sell new Anything-as-a-Service (XaaS) solutions.
Our direct client feedback and survey responses revealed that partners often struggle to adapt customer experience and solutions to embrace new XaaS-driven revenue streams. In the pursuit of XaaS success, tech leaders face three prevalent channel partner challenges:
Swift pivots to XaaS models can result in inconsistent and confusing partner strategies and programs. Whether due to a lack of strategic vision or a lack of partner communication, it’s not uncommon to find a company trying to run two partner program “flavors” at the same time—one for traditional on-premises sales (which might be a large legacy revenue stream) and another for XaaS selling (which has different channel partner program characteristics). Businesses that haven’t thought through where XaaS revenues will come from and which partners are best positioned will struggle to grow.
Partner sales channels deliver 3x more XaaS revenue than direct sales channels (Figure 1), but not all partners are created equal when it comes to XaaS suitability. Managed Service Providers (MSPs), Cloud Solution Providers (CSPs), and Independent Software Vendors (ISVs) are best positioned to deliver clear recurring value to line-of-business decision-makers because they are embedded in their operations, delivering always-on outcomes with the potential for “moment of truth” experiences. Traditional Value-Added Resellers (VARs) and System Integrators (SIs) are more transactional, with client interaction on an “as needed” basis with extended time between purchases. During economic slowdowns, VAR and SI partners are far more likely to experience delays in client purchases, hurting a company’s potential to drive XaaS revenues.
The Fix: Align Objectives and Deliver on New Goals
To remedy misaligned go-to-market objectives, there are two primary areas of focus. First, redefine and quantify where new as-a-service revenue will come from (in other words, prioritize your growth pathways). Second, once the market opportunity has been refreshed, make the necessary partner network adjustments to your channel strategy, capture new revenue, and focus on partners with stickier relationships with clients.
2) Unmet Service and Delivery Expectations
Partners’ struggles to meet service and delivery expectations have become more complex with the introduction of XaaS offerings. The days of simply selling and installing a solution are over. To maintain a subscription contract and potentially expand it with add-on services, partners need a broader set of skills for ongoing support.
While many traditional partners have tried to enhance their customer service abilities, this transition has been slower and more challenging than businesses initially anticipated. As a result, partner performance has suffered, leading to dissatisfaction among tech firms with their partners’ capabilities throughout the sales process.
The Fix: Enable and Train “XaaS-Ready Channel Partners”
To tackle this challenge, companies should enhance partner skills in new delivery models and recruit and enable top XaaS-ready partners. Expectations also need to be clearly defined for each partner type. Along with training partners, setting thresholds of competencies/certifications, tracking performance against those expectations, and intervening on an ongoing basis can course-correct each partner and raise the performance bar for each and all.
3) Lack of Data-Driven Engagement Models
XaaS solutions generate an abundance of data, yet many partners lack the capabilities to use these new datasets to deliver value for customers. Businesses must aid partners in harnessing data, conducting analysis, and prescribing better experiences. With only 30% of respondents “very satisfied” with how XaaS partners service their customers with great experiences, honing in on the data and using it to build a better experience is a first start.
The Fix: Build an Always-On Insight Engine
With subscription and hardware-as-a-service solutions, businesses have access to individual and aggregated customer usage data that was previously unavailable. Companies can use these insights to prescribe to partners precisely what message to deliver to customers based on usage behavior, but they must first invest in building the data analytics solutions to extract that data. We call this an “always-on insight engine,” one that feeds actionable intelligence to specific partners in a conveyor belt fashion. A constant stream of actionable insights (“Hey, partner. Here’s what you can do now and why.”) will not only strengthen the vendor-partner relationship but drive incremental sales.
Overcome Challenges in the Race to XaaS Success
CROs must refresh channel strategies to outperform the competition. To deliver the predictable business outcomes and customer experiences needed for a winning subscription model, companies must focus on finding the right mix of partners, enhancing those partners’ skill sets, and leveraging customer usage data.
Download our report, “A CRO’s Guide to Transforming the Channel for XaaS Success.”
In this report, built from quantitative benchmarks and interviews with industry leaders, we dive deeper into the challenges presented in this blog and provide a four-step path to move forward.
In the dynamic landscape of B2B sales and marketing, the concept of coverage design stands as a strategic linchpin, weaving together the threads of revenue generation, customer experience, and cost-effectiveness.
Coverage Design—What Is It?
Coverage design is a strategic approach to revenue generation. It involves outlining the specific channels and roles that will execute each task in the customer journey. This systematic organization of revenue motions, termed a ‘coverage model’, is key to providing a top-tier customer experience while ensuring cost-effectiveness, typically measured as the Expense-to-Revenue (E/R) ratio.
Coverage models map essential customer touchpoints to the resources required to service them. This includes both strategic and tactical levels of interaction. At the strategic level, channel models align with market segments, whereas, at the tactical level, coverage extends inside accounts and across the sales pipeline.
Two Coverage Design Types: Tactical and Strategic
There are two levels of coverage design typically approached in business operations: 1) Strategic Coverage Design and 2) Tactical Coverage Design.
1. Strategic Coverage Design
Strategic coverage design involves long-term decisions about the selection of channels used to engage targeted markets. At the highest level, this means choosing between direct and indirect coverage.
Direct coverage leverages a company’s internal resources (say internal sales) to drive revenue. It gives more control over the customer relationship, which, over time, is a considerable advantage, especially in owning customer data.
Indirect coverage, on the other hand, involves outsourcing go-to-market activities to an agent. Indirect coverage, often referred to as “the channel” in B2B tech go-to-market strategies, is attractive for several reasons. It can be quickly activated, offers full-funnel solutions, and can bundle your product with other complementary products, creating a more attractive value proposition. However, it comes with a literal cost, the channel discount or commission.
Channel (indirect) vs. Direct economics. The channel discount–13 cents per dollar in this example—essentially pays for go-to-market other than brand advertising.
Therefore, strategic coverage design involves deciding which route-to-market to choose for a given product-focus segment intersection.
2. Tactical Coverage Design
Tactical coverage design takes a deep dive into the learn-shop-buy process to map each interaction from the customer’s perspective. There are three primary models of tactical coverage in use today:
Single Point of Contact approach: One resource handles the entirety of the sales process, from prospecting to closing, and ensuring post-sales customer success.
Hunter-Farmer approach: This model bifurcates the sales team into acquisition-focused and retention/success-focused resources.
Hybrid approach: This approach is the most specialized, dividing tasks into lead generation, sales, and account management roles.
Designing an optimal tactical coverage model is a balance of three interrelated factors: customer-channel preference, seller and role capability, and economic efficiency. By considering these factors, businesses can ensure that all customer touchpoints are covered and the appropriate resources are allocated to each stage of the sales process.
The Power of Coverage Design in Optimizing Business Performance
To summarize, coverage models play a pivotal role in enabling organizations to drive profitable revenue growth while providing a superior customer experience. Strategic and tactical coverage designs form the backbone of these models, with strategic coverage choosing the ideal channels for market engagement, and tactical coverage optimizing the allocation of resources across the customer journey.
By striking a balance between channel preferences, role capabilities, and economic efficiency, an optimized coverage model can be developed, leading to enhanced organizational performance and customer satisfaction.
For a more comprehensive understanding of designing optimal B2B go-to-market strategies, download our whitepaper “A Roadmap for Modern B2B Go-to-Market—Part 1: Growth Design.” This guide offers detailed insights and practical tips to help you streamline your go-to-market operations and achieve your business objectives.
Download our whitepaper, “A Roadmap for Modern B2B Go-to-Market: Part 1 – Growth Design”
Learn what it takes to find and maintain predictable revenue growth in our essential 49-page whitepaper.
In business-to-business (B2B) operations, the term “revenue motion” refers to the strategic method of entering and expanding within client accounts. This term evolves from the more traditional “sales motion” to reflect the shift towards more customer-centric, account-based marketing, and product-led marketing strategies. Revenue motions essentially describe how customers within accounts can best identify, try, and purchase a product or solution. It is the translation of within-account targeting into a growth thesis.
Revenue motions are not just a tactical approach but serve as a blueprint for all customer-facing aspects of a business. As such, they require close collaboration between marketing, sales, and service departments. By understanding how accounts and contacts try, buy, and use the product, these departments can work in concert, using the account as the focus for their strategies.
Driving Growth with Revenue Motions
B2B organizations are increasingly recognizing the importance of revenue motions. According to Forrester Research, companies that excel at lead nurturing generate 50% more sales-ready leads at 33% lower cost. Moreover, nurtured leads make 47% larger purchases than non-nurtured leads, underlining the value of a well-planned revenue motion.
Three Primary Types of Revenue Motions
There are three primary archetypes of revenue motions: Top-Down, Bottom-Up, and Middle-Out. Each archetype has its own set of advantages and constraints that revenue leaders must evaluate prior to selection. Revenue leaders can determine which model to choose by considering three key factors: the adoption method, value transfer, and price.
Adoption method describes how potential customers find, try, and purchase a given solution
Value transfer describes who seeks to benefit from the implementation and utilization of a given solution
Price describes the affordability of the solution for a given set of stakeholders
1. Top-Down Revenue Motion
In a Top-Down motion, sales teams build and leverage relationships with high-level stakeholders to drive product choice and rely on those stakeholders to drive implementation across the organization.
Adoption Method: This motion relies heavily on senior leaders to drive standardization throughout the organization. This strategy requires direct access to key decision-makers, with marketing playing a secondary role.
Value Transfer: The value is realized by the company as a whole, with individual contributors possibly feeling detached from the product or solution.
Price: These solutions command significantly higher prices and realized revenue than bottom-up or middle-out motions, often exceeding $1,000,000 annually.
2. Bottom-Up Revenue Motion
A Bottom-Up motion is geared towards end users, only elevating to more senior levels once a critical mass of users has adopted the product.
Adoption Method: In this scenario, marketing plays a lead role in helping potential customers find, understand, and adopt the product.
Value Transfer: The product does most of the selling, relying on low barriers to entry, quick time-to-value, and broad appeal.
Price: In a Bottom-Up revenue motion, adoption is the primary objective, with revenue coming later. Thus, prices tend to be significantly lower compared to other motions.
3. Middle-Out Revenue Motion
A Middle-Out motion strikes a balance between the Top-Down and Bottom-Up methodologies, targeting both end users and C-suite level stakeholders, with a sweet spot typically at the manager or director level.
Adoption Method: A Middle-Out motion is sometimes termed “land and expand,” starting with one business unit at a customer organization and sequentially moving to new buying centers until standardization becomes a viable option.
Value Transfer: The value transfer is applicable to both individual users and their management teams.
Price: Pricing for Middle-Out motions can vary greatly due to the nature of the products and focus of the revenue motion (e.g., land and expand).
The Importance of Understanding Revenue Motions
The modern B2B landscape is evolving at a rapid pace. As such, it’s critical for organizations to adopt a more customer-centric approach to their go-to-market strategies. Understanding and employing the right revenue motion for your organization can dramatically increase the effectiveness of your marketing, sales, and service departments, leading to higher customer satisfaction, increased lead conversion, and ultimately, a healthier bottom line.
We’ve just skimmed the surface of this vast subject. To delve deeper into the intricate details of B2B revenue motions, including best practices, pitfalls, and proven strategies, download our whitepaper, “A Roadmap for Modern B2B Go-to-Market—Part 1: Growth Design.” This comprehensive guide will equip you with all the knowledge you need to craft a revenue motion strategy that fits your business like a glove. Happy reading!
Download our whitepaper, “A Roadmap for Modern B2B Go-to-Market: Part 1 – Growth Design”
Learn what it takes to find and maintain predictable revenue growth in our essential 49-page whitepaper.
