Hello Marketers, and welcome to another episode of Funnel Lab Fridays!
Funnel Lab Fridays is your go-to weekly LinkedIn Live session where we unravel the complex use cases and challenges faced by data-savvy marketers. I’m your host, Eric Westerkamp, CEO of CaliberMind, and today, we have an exciting discussion lined up for you.
Joining me are two industry experts who bring a wealth of knowledge and experience to the table:
- Tracy Earles, Senior Director of Marketing Analytics at NetApp. Tracy has spent years at the forefront of marketing analytics, helping large enterprises like NetApp make sense of vast amounts of marketing data. His expertise lies in uncovering actionable insights that drive decision-making and business growth, particularly in the context of complex sales cycles and high-value transactions.
- Misha Salkinder, Director of Customer Data Strategy at CaliberMind, is no stranger to Funnel Lab Fridays. With a strong background in customer data strategy, Misha has been instrumental in helping organizations, including NetApp and CaliberMind, develop robust ROI models that tie marketing efforts directly to business outcomes.
Today, Tracy, Misha, and I will delve into a topic that’s on every marketer’s mind: the underlying equation for marketing ROI. We’ll explore the complexities of measuring ROI in large enterprises, the challenges of pipeline attribution, and the strategies that can help you optimize your marketing investments. Let’s dive in!
Defining the Marketing ROI Equation
Marketing Return on Investment (ROI) is a metric that has long been the cornerstone of evaluating marketing effectiveness. However, as businesses grow and their marketing efforts become more complex, so too does the challenge of accurately measuring ROI. Tracy Earles from NetApp kicks off the discussion by recounting a pivotal moment when his team was asked a seemingly simple question: “If I have $5 million more to spend, should I invest it in marketing or hire more salespeople?”
This question underscores the complexity of defining marketing ROI, especially in large enterprises with extensive sales cycles and high-value transactions. Tracy explains that for NetApp, the focus is not solely on revenue but on pipeline – the opportunities generated that are likely to convert into sales in the future. This shift in focus is critical for organizations with long sales cycles, where the immediate impact of marketing efforts on revenue may not be visible for several months or even years.
The journey to answer this question led Tracy’s team to develop a sophisticated equation that considers both the pipeline generated by marketing efforts (the numerator) and the overall marketing investment (the denominator). But as Tracy quickly points out, this is far from a straightforward calculation.
Pipeline vs. Revenue: A Strategic Shift
In the world of high-value, long-sales-cycle industries like NetApp, the traditional focus on revenue as a metric of marketing success doesn’t always provide the most actionable insights. Instead, Tracy and his team shifted their focus to pipeline – a measure of potential revenue that has been sourced by marketing but not yet realized.
This strategic shift is crucial in environments where sales processes can take a year or more to complete. By focusing on pipeline, NetApp can gauge the effectiveness of its marketing efforts much earlier in the sales process, allowing for more timely adjustments and optimizations.
Tracy describes how his team developed a pipeline measurement system that not only identifies marketing-sourced opportunities but also attributes them accurately to the appropriate marketing activities. This system was met with enthusiasm by NetApp’s leadership, particularly by the company’s president, who saw it as a vital tool for understanding the value of marketing investments.
However, as Tracy notes, the president’s excitement quickly led to a new challenge: if the pipeline is growing, what does that say about marketing ROI? And more importantly, how can this information be used to make strategic decisions about future investments?
Attribution Complexity: Navigating the Numerator and Denominator
The challenge of accurately attributing pipeline to specific marketing activities is one that many organizations face. For NetApp, this challenge is amplified by the sheer scale of their marketing operations. With 250 to 300 marketers generating thousands of campaign codes annually, the data involved is vast and varied.
Tracy explains that the numerator in the ROI equation – the marketing-sourced pipeline – is not a simple figure. It requires careful consideration of which marketing activities truly contributed to generating pipeline and to what extent. This involves sophisticated data modeling and collaboration with NetApp’s data science team to ensure that the attribution is as accurate as possible.
On the other side of the equation is the denominator: the marketing investment. Here, too, the complexity is significant. Not all marketing expenditures are aimed directly at generating demand. Some investments are focused on building brand awareness, supporting sales teams, or even investor relations. Each of these expenditures must be evaluated to determine whether it should be included in the ROI calculation.
Tracy highlights the meticulous process his team undergoes to categorize each line item in their budget. For example, events often involve substantial overhead costs, such as manufacturing and transporting booths, which may not be directly attributable to a single campaign. Yet, these costs must be accounted for in the overall marketing investment.
High-Volume, Low-Value vs. Low-Volume, High-Value Touchpoints
A key insight from the discussion is the distinction between high-volume, low-value touchpoints and low-volume, high-value touchpoints. Tracy notes that in a data-rich environment like NetApp’s, it’s easy for the sheer volume of certain activities, such as email campaigns, to overwhelm more impactful but less frequent touchpoints, such as executive briefings.
To address this, Tracy’s team works closely with Misha and the CaliberMind team to develop models that can accurately identify and weigh these different types of touchpoints. The goal is to ensure that the ROI equation accurately reflects the true value of each marketing activity, rather than simply measuring volume.
For example, while email campaigns might generate a large number of touchpoints, their individual impact on pipeline might be relatively low. Conversely, an executive briefing might occur less frequently but have a significant impact on moving a high-value deal forward. Understanding this distinction is crucial for making informed decisions about where to allocate future marketing resources.
The Role of Assumptions in ROI Models
As Misha points out, one of the key challenges in developing a robust ROI model is the need to make assumptions, especially in the early stages. These assumptions are necessary to fill gaps in data and to account for the inherent uncertainties in marketing activities.
Misha explains that at CaliberMind, they often start with broad assumptions and refine them over time as more data becomes available. This iterative approach allows organizations to develop a more nuanced understanding of their marketing ROI, even if the initial models are somewhat rudimentary.
One example Misha provides is the use of time cohorts to align marketing investments with the pipeline they generate. While it’s true that marketing investments made in one quarter may not produce measurable results until several quarters later, this doesn’t mean that the initial data isn’t valuable. By acknowledging the assumptions and refining them over time, organizations can still extract actionable insights from their ROI models.
Optimizing Channel and Campaign-Level ROI
As the discussion progresses, the focus shifts to optimizing ROI at the channel and campaign levels. Tracy shares his insights on the importance of understanding the elasticity of different marketing channels. In other words, just because a channel is currently delivering a high ROI doesn’t mean it can scale indefinitely.
For example, email campaigns may have a high ROI, but there’s a limit to how many emails can be sent before diminishing returns set in. At that point, additional investments in email marketing might not yield the same results, and it may be more effective to explore other channels.
Tracy also emphasizes the need to evaluate campaigns based on their operational metrics rather than relying solely on ROI. For instance, while ROI can provide a high-level view of a campaign’s effectiveness, operational metrics like engagement rates, conversion rates, and lead quality can offer more detailed insights into what’s working and what isn’t.
Misha adds that this level of granularity is particularly important when dealing with large-scale marketing operations. By breaking down the data to the campaign level, organizations can identify specific areas for improvement and make more informed decisions about where to allocate resources.
Global Perspectives: ROI Across Regions
The conversation also touches on the geographic variation in marketing ROI, with a particular focus on differences between regions like North America, EMEA, and Asia-Pacific. Tracy presents a geographic view of NetApp’s marketing data, which reveals some surprising insights.
For example, while Asia-Pacific appears to be underperforming in terms of access to pipeline, it has the highest ROI of any region. This raises important questions about whether there is more pipeline available in that region and whether additional investments could yield even higher returns.
Tracy also notes the unexpected cost discrepancies between regions. Despite the added costs of language translation and localization in non-English speaking markets, the costs of marketing in these regions are not as high as one might expect. This data is new to Tracy and his team, and they are still exploring the reasons behind these trends.
The key takeaway here is that ROI can vary significantly by region, and organizations need to consider these differences when developing their global marketing strategies. What works in one region may not be as effective in another, and understanding these nuances is critical for maximizing the impact of marketing investments.
Conclusion: Achieving Usable ROI Insights
The insights shared by Tracy and Misha during this episode of Funnel Lab Fridays underscore the importance of developing robust, yet flexible, ROI models. While perfect ROI analysis may be impossible, Tracy emphasizes that usable ROI analysis is certainly achievable. By making informed assumptions, refining models over time, and understanding the nuances of different channels and regions, organizations can gain valuable insights into the effectiveness of their marketing efforts.
As you apply these strategies in your own organization, remember that the goal is not to achieve perfect accuracy but to make better, more informed decisions about where to invest your marketing resources. Whether you’re optimizing individual campaigns, exploring new channels, or expanding into new regions, the principles discussed in this episode can help you navigate the complexities of marketing ROI and drive meaningful business outcomes.
For more content on B2B marketing trends, see the full Funnel Lab Fridays episode at the top of the article or on CaliberMind’s Event Page on LinkedIn.
