The wealth of data available today creates the potential for more targeted, granular and real-time metrics of marketing effectiveness but also raises the question of which metrics really matter—and to whom. The metrics managers use to allocate marketing resources are not ends in themselves but rather means to align marketers’ objectives with the bottom-line objectives of CFOs and CEOs.

Metrics that matter to CEOs (and Wall Street)

A CEO understandably cares about Wall Street’s assessment of her company as reflected in its stock price, but why should she care about marketing and brands? A meta-analyses of 114 academic studies, in MSI’s Empirical Generalizations about Marketing Impact finds that “the linear relationship between marketing capability and firm performance is positive (r = .35) and stronger than that for R&D (r =.28) or operations (r = .21) capabilities” (p. 7). One way to view these findings is that effective R&D and operations are both necessary but not by themselves sufficient conditions for business success. Even more than these assets, what differentiates more from less successful companies is superior marketing. In their 2005 study, "How Brand Attributes Drive Financial Performance," marketing scientists Natalie Mizik and Robert Jacobson further documented this impact, noting that a one-unit change in their calculated Brand Asset Index is associated with a 4% change in the market value of a firm. Importantly, only one-third of these effects are reflected in current-term earnings while two-thirds reflect information about future-term performance—demonstrating the long-term value of brand building.

There are different ways that marketing might matter to investors and thus affect a firm’s stock price. In "Value-Based Brand Exploitation Strategy to Grow Firm Value," Marc Fischer, Max Backhaus, and Tobias Hornig cite findings from another meta-analysis that improving brand equity by 10% can increase firm value by 3.3%. The researchers study three possible ways customer-based brand equity (CBBE) can create firm value. Analyzing data from 2005 to 2013 for 613 firms, they find that earnings growth and sustainability of excess returns are the most influential drivers, on average, for most industries and firms. This suggests that in many cases, investors see marketing and strong brands as a way to grow the business, taking share from current competitors or extending into new markets and categories, as well as to sustain a competitive advantage over a longer period of time, For media, information technology, and industrial and utilities, the return on invested capital by leveraging strong brands to increase prices, reduce the cost of providing services or price discriminate is more relevant. By aligning their branding strategy with the main value drivers for their category, companies can realize the full potential of the CBBE.

Metrics that matter to managers (resource allocation and accountability)

While financial metrics exist to demonstrate the bottom-line impact of marketing to senior management, marketing managers need metrics to allocate these resources, assess their effectiveness, and communicate results in ways that are intuitive and persuasive to both senior management and others who need act on these metrics. Rather than dictate what metrics marketing managers should use, it might help to first examine the metrics they do use—and why.

Marketing managers and senior management may be talking past one another.

In "Drowning in Metrics: How Managers Select and Trade-off Metrics for Making Marketing Budgetary Decisions," Ofer Mintz, Yakov Bart, Peter Lenk, and former MSI Executive Director David J. Reibstein note that the explosion of data today has many managers concerned about metric “overload.” Based on over 200 interviews with marketing professionals and a multi-disciplinary literature review, the authors develop a conceptual model of marketing managers’ metric preferences and trade-offs . In a conjoint-based survey, they asked 563 managers to design ideal metrics dashboards for (1) making marketing decisions and (2) for seeking approval of these decisions from senior management. Overall, their results suggest that marketing managers and senior management may be talking past one another: the former tend to prefer simple metrics such as satisfaction and total customers rather than more technical financial metrics that speak to CEOs and CFOs such as Tobin’s q (the ratio of a firm’s total market value to its total asset value). While marketing managers may want to retain intuitive metrics such as customer satisfaction for communicating with their peers, they should also be able to relate these to more financially-relevant metrics.

To examine the effectiveness of managers’ metrics, we can turn to the "Right Metric for the Right Decision: A Behavioral Model to Infer Metric Effectiveness in Managerial Marketing-Mix Decision-Making" by Ofer Mintz, Timothy J. Gilbride, Peter Lenk, and Imran S. Currim. The authors test the effectiveness of 24 metrics used by over 400 U.S. managers to make nearly 1,300 marketing mix decisions. Of the 24 metrics tested, awareness and willingness to recommend stand out as those most often associated with better performance across most marketing-mix decisions. Conversely, total customers, target volume and net present value are associated with worse performance for most decisions. Importantly, marketing metrics on average are more effective than financial metrics for managers making different marketing-mix decisions. This may explain their preference for simple marketing metrics noted earlier, but it also reinforces the sense that marketing managers may be talking past senior management. All things being equal, managers should consider using metrics associated with better outcomes such as awareness, willingness to recommend and customer loyalty and avoid using simple metrics such as total customers.

Marketing metrics on average are more effective than financial metrics for managers making different marketing-mix decisions.

Another way to assess the effectiveness of marketing metrics is to model their use under varying market conditions. In "Investigating the Performance of Budget Allocation Rules: A Monte Carlo Study," Marc Fischer, Nils Wagner and Sönke Albers compare the performance of four marketing budget allocation rules: (1) percentage-of-sales (allocation proportional to previous year’s sales); (2) naïve allocation (equal distribution across all products and activities); (3) an attractiveness heuristic (incorporating information on the profit improvement potential of allocating a higher budget to the unit); and (3) a numerical optimization (which generates optimal budgets for a specified problem). With profit gained as the dependent variable, the authors apply these allocation rules across a wide range of scenarios to determine their performance under various market conditions. Their results show that an “exact” method such as numerical optimization is not always the best choice unless managers are confident about their data. Under the more realistic assumption of unknown demand parameters the other “rule of thumb” approaches are often more feasible and may be just as good – that is, there’s no false sense of precision from modeling noisy data. Using the results of their simulations, marketing managers can assess which of these approaches to measuring marketing effectiveness are most feasible and reliable given the availability of specific types of data and market conditions.

Do mindset metrics (still) matter?

We have just seen that there may be reasons why marketing managers would prefer to be approximately right rather than precisely wrong. There are other reasons why allegedly “soft” metrics such as awareness, liking, and purchase consideration may still be useful and need not be displaced by “black box” quantitative models. In fact, the two approaches may be complementary. To answer the question: Do Mindset Metrics Explain Brand Sales?, Shuba Srinivasan, Marc Vanhuele and Koen Pauwels analyze a dataset with seven years of four-week measures of sales, marketing actions, and consumer mindset metrics for more than 60 brands of four fast-moving consumer goods. They find that including customer mindset metrics improves a sales response model of the short- and long-term effects of the marketing mix beyond the direct effects of advertising, price, distribution and promotions. Importantly, almost one-third of the total explained sales variance was attributed to mindset metrics. In addition to improving marketing mix and other models, mindset metrics can help managers explain their findings in intuitive ways that others can readily understand and act on. Even as they seek to use more financially-relevant metrics that can speak to senior management, marketing managers should not throw mindset metrics out with the proverbial bathwater!

Conclusion

Marketing metrics are means to an end, not ends in themselves. Specifically, as ways to align the activities of marketers, senior management and ultimately investors, they must meet the needs of each constituent while speaking to the others. CEOs and CFOs need to allocate resources across different functions of the organization, including marketing—which may be the key to reaping the full benefits of other investments such as R&D and operations. Marketing managers need mindset and other simple, intuitive metrics that their peers can understand and act on. To determine the full contribution of marketing to firm performance, however, managers need to relate these intermediate metrics to more financially-relevant metrics that senior management can use to compare returns from different types of investments. Achieving the right balance won’t be easy, but marketers today have more data sources and analytic methods than ever before to do this. In the end, the metrics that matter most will be those that attract investors to help companies creatively meet the needs of their customers.