Defining Characteristics of Decision Equity

The aforementioned case of the credit card issuer illustrates some of the defining characteristics of decision equity. While the concept has the familiar computational underpinnings in the present value of future cash flows resulting from a decision, it has numerous distinguishing features that make it a powerful aid for making strategic marketing choices.

Paradigm Free

Perhaps the most critical difference between decision equity and other marketing measurement systems is that it is paradigm free. It is not bound either to any strategic prescription or to the sequence of consequences that follow from a strategic action. It is also not tied to the advancement of any specific metric in order to achieve a broader financial or strategic goal. Instead, it crosses functional boundaries and helps organizations draw linkages that go much beyond their silo-based paradigms. For example, depending on the context, a decision equity–based system may develop linkages spanning from innovation to human resource policies and from technology upgrades to price changes. As one can imagine, the measurement and implementation system in each case would end up focusing on a vastly different set of metrics and intermetric relationships.

How does this compare with traditional paradigm-driven approaches in marketing? Well, there are several important differences that result directly from abandoning a paradigm-based approach. For example, consider the customer satisfaction–based management paradigm. Those who follow it believe satisfaction to be the central, core metric and make it the fulcrum of critical decision making. The mental model that drives this thinking typically leads to a set of three or four initiatives. First, it almost automatically leads to the institutionalization of a customer satisfaction measurement system, typically using a series of surveys that capture changes in the level of this apparently critical metric. This tracking system is used to keep watch on satisfaction levels and judge the health of the business, at least in part, on the basis of changes in this metric. The second step involves initiatives dedicated to an understanding of the antecedents or drivers of this key metric. This involves enhancing the satisfaction measurement system and adding other variables that are believed to be the drivers of satisfaction. The third step is to identify the consequences of satisfaction to assess whether changes in the metric are resulting in the expected changes in downstream consequences, such as levels of loyalty, repeat purchase behaviors, price sensitivity, or word-of-mouth activity. And more recently, there is a move to connect changes in the level of the metric to financial consequences such as long-term profits, firm valuation, stock prices, or risk. While there is nothing fundamentally wrong in the argument that firms should strive to increase their customers’ satisfaction and expect returns from customer-based investments, the paradigm may close strategic options that might be more effective than merely embracing the paradigm

Similarly, consider the brand equity–based paradigm. Once again, those who follow it treat brand equity as the central metric of interest. Much like in the satisfaction-based paradigm, investments are made in equity tracking systems followed by others to unearth its antecedents and then its downstream consequences in terms of market share gains or pricing gains. In case measures of brand equity breach preset thresholds, actions are taken to raise them again to restore the health of the brand. Once again, while brands really are valuable and watching over their health is important, if firms embrace them too closely, they are likely to close strategic pathways that may lead to long-term profitability even though they might involve bypassing the brand equity route completely.

Spans Across Multiple Silos

The second characteristic of decision equity, which stems partly from it being a paradigm-free concept, is that its span crosses functional boundaries and organizational silos. The goal of pursuing decision equity is to connect actions and decisions in any domain at one end to their ultimate consequences, which typically are in the financial or stakeholder domains, at the other. Between these two bookends, a wide variety of intermediate effects could result from the widely different possible actions.

For example, consider a nonmarketing decision such as the outsourcing of the customer service operations to an independent outside entity. In many instances, these actions are driven by strategic objectives that relate to efficiency and cost saving. Therefore, the decision itself may originate in the finance department of the organization. However, the sequence of effects that might follow the decision typically span across several functional silos. For example, on the marketing front, customer perceptions of the quality of customer service might change, which, in turn, might influence their likelihood of buying from the firm over a period. This behavioral change would influence the revenue and margin stream of the firm. On the other hand, on the human resources front, there might be cost savings accruing from a dismantling of the internal customer service operations and a reduction in the headcount and the required office space. Similarly, on the accounting front, there would be cost savings from changes in the accounting practices necessary to manage an external vendor rather than an internally staffed operation. And finally, there would be changes on the information technology front because the control systems necessary to monitor an external vendor may be significantly different from those required for an in-house operation.

This simple example illustrates a few important issues. First, even though the decision might originate in the finance area, a focus on decision equity will require coordination across several departments to realize the anticipated cost savings and revenue gains. Second, the information necessary to compute the anticipated decision equity prior to the decision as well as the actual realized equity after the decision is made again requires coordination across functional silos. Finally, the likely sequence of effects emanating from a decision will all end up being very diverse across the various functional silos.

Now if we generalize across decision domains, we can see that decision equity, because of its cross-functional underpinnings, will necessitate, maybe even force, interdepartmental coordination both at the time the decision is under consideration and later when its consequences are being evaluated. To that extent, we expect a focus on decision equity to have a very different organizational impact than more silo-centered mental models such as those that focus on customers or brands or employees. Decision equity is likely to promote the breakdown of silos and encourage a sharing of responsibilities, credit, and blame across functional boundaries, and the construction of cross-functional models of cause and effect.

Multiple Pathways and Linkages

As the previous examples show, there are many pathways from actions to profits or other terminal consequences of interest. And in an increasingly cross-functional business world, it is becoming evident that the chain of events that follow from a decision made in one domain tend to transcend functional boundaries and organizational silos. However, mental models of marketing strategy, for the most part, have not kept up with notional dissolution of intrafirm boundaries. Further, in part because they are paradigm bound, marketing practitioners do not necessarily embrace a culture of data-driven verification of linkages. Under the best-case scenario, they focus on a set of a few metrics that still operate within the confines of the functional silo. The only exception is the move to directly connect a within-silo metric with a financial outcome. An unfortunate consequence of the existing state of practice is that strategic pathways of cause and effect are constructed within functional domains and the opportunity to explore more powerful cross-functional options is foregone.

In contrast, decision equity encourages the exploration of all pathways to common ultimate goals. These pathways tend to have very different structures for the various alternatives under consideration at a point in time and also change differently across time. They also vary across firms within the same industry as well as across industrial sectors. Decision equity therefore promotes the exploration and verification of alternative linkage systems that collectively constitute a portfolio of multiple within-silo and intersilo pathways from the action to the ultimate outcome.

Focus on Actions and Terminal Metrics

Finally, a key characteristic of decision equity is a focus on the bookends, that is, on the decision at one end and the ultimate terminal metric of interest at the other. It is not tied to the promotion or optimization of intermediate metrics such as employee productivity, customer satisfaction, service quality perceptions, or price sensitivity. While in some instances, an increase in one or more of these intermediate metrics might be correlated with a rise in the terminal metric, in other instances it might not. For example, while in the case of one health care provider we observed a positive relationship between customer satisfaction and profitability, we observed the exact opposite for another. This contrast is easily accommodated within the decision equity framework because it relies on data to tell the story about the direction and strength of a link in the pathway between strategic actions and terminal outcomes.

Another key benefit of the decision equity approach is an avoidance of double counting while assessing the impact of strategic choices. If we recall the discussion in the prologue to this book, it is easy to see that some actions may have a multitude of consequences in a variety of functional domains. However, if we pursue a function-specific approach to estimating the impact of actions, it is possible that we will end up attributing the same successful outcome to multiple constituents and end up double counting the impact of the individual performance metrics. For example, as we saw, the pathway from a decision to the ultimate financial outcome might go through employees, brands, and customers. However, if we do not explore the entire pathway in one shot and do it selectively, we might be misled. We might infer that employees affect financial outcomes, as do brands and customers independently. However, in reality, they might all be the common beneficiaries of a single common action.

The decision equity framework also helps solve the metrics selection problem. As we noted in the case of the credit card issuer, in the absence of a unifying framework, constituents from each silo tend to focus on silo-specific metrics. Marketing managers, for example, may focus on changes in brand perception, call center managers might focus on the percentage of first-time resolutions, and human resource managers might focus on overall employee productivity. It is easy to see that managing these contrasting measurement systems is neither easy nor free from internal conflict. However, if the functional areas are under the common umbrella of decision equity, the silo-specific metrics have limited value and are useful only if they lie on the linkage pathway from decisions to the terminal metric. And even those that do are only valuable to the extent that they indicate the extent and direction of progress toward the ultimate financial outcome. For example, employee engagement levels are of value only for an upstream decision alternative for which the linkage pathway to the terminal metric goes through this metric.