Decision Equity

Piyush Kumar

• Publisher: Business Expert Press
• Year: 2011
• Print ISBN: 9781606491935
• eBook ISBN: 9781606491942
• Topic: Business
• Subtopic: Marketing
• Index: Business

In this book, we introduce Decision Equity as a breakthrough, overarching metric that can help free marketing managers from their existing mental models, and provide avenues to efficiently convert the vast amounts of data at their disposal into powerful information to make informed long-term and short-term decisions about products, customers, markets, and overall business health. There are four key objectives that we wish to achieve through this book. First, we wish to introduce the underpinnings of a new flow-based measurement and management process that can promote truly unshackled thinking both within the marketing function as well as at its interface with other functional areas. Second, we want to demonstrate how firms can win by learning to conceptualize and discover the linkages between marketing and related non-marketing actions and their ultimate marketplace and financial consequences. Third, we aim to provide guidance for how marketing managers and CMOs can significantly improve the outcomes of their decisions by following a verification-based linkage discovery process to connect marketing actions to profits. And fourth, we wish to provide evidence for the efficacy of verification-based thinking centered on the principles of Decision Equity from successful marketing interventions and implementations in well-known organizations.

Part I

Competing by Verification

Chapter 1

Harnessing the Power of Data

Let us begin with a few actual stories to set the tone for the book. The first one is about a leading consumer brand whose managers were perpetually enamored by technological improvements they were making to the products in their portfolio. Not surprisingly, top management had also committed to substantial funds for improving the technology across the product line, a strategy that was well in line with the firm’s long-term vision established a few years earlier. Along the way, however, the marketplace underwent dramatic changes. The most significant of these was that the product category in which the company competed was itself increasingly replaced by a better, faster, and cheaper alternative. Despite the writing on the wall, this firm continued to invest millions of dollars in enhancing its existing product portfolio and made no effort to invest in the new technology that was attracting an increasing number of customers. As a result, while the ongoing investments in technology did produce a substantially “improved” product, ironically the product class itself became “irrelevant.”

Over time, the market share and financial performance of the firm deteriorated significantly, and its misfortunes continue even to this date. While management did see the train wreck coming its way, nobody stepped up to challenge product policy decisions that were made earlier. There was widespread organizational belief in the strength of the firm’s existing product portfolio as well as its customer base. The belief created a flawed assumption among managers that the technology-driven product strategy that had served them in the past would continue to work equally well in the future. The marketing problem was exacerbated by the fact that senior management was constrained to planning from quarter to quarter, and long-term investments in an emerging product category seemed too stretched out to be explained to the investor community. Now this is not an isolated example, and we have come across several similar instances including video rental companies investing in redesigning their stores just as customers were migrating to ordering movies from the comfort of their living rooms. Similarly, there are examples of others who invested in superior quality on music CDs even as the market was moving toward downloading music directly from online sources.

Our second story is a very different scenario, which again, in our experience, is quite representative of a wide range of industries. In this case, the firm received thousands of inbound calls from its customers every month across its support centers spread throughout the state it operated in. Following industry practice, each incoming call was routed to the next available support representative, who was trained to make certain inquiries and then take appropriate actions. The call routing system, however, was antiquated and incapable of differentiating among calls from high-value versus low-value customers. To make matters worse, the representatives were not given any training or advice on how to handle the two groups of customers differentially. Instead, the focus was on cost containment and productivity—which led to the representatives attempting to maximize the number of calls handled per hour. They were also instructed to be strict about reneging late payment and other similar fines—a big reason for customer calls in the first place.

Senior management treated these call centers strictly as cost centers. It was insistent on keeping costs down through higher employee productivity and minimal cancellation of penalties and fees. Therefore, when a call came into the call center, the representatives treated the high- as well as the low-value customer groups in an identical fashion for all issues ranging from late payment to bill correction. A customer who had paid sizeable bills on time for the last several years but missed one payment because of a vacation was treated identically to another with a recurring record of missed or late payments. Not surprisingly, the system resulted in an exodus of a large number of the high-value customers who were always being solicited by competitors. Over time, the firm was left with a substantially less profitable customer base.

These remaining customers had a shorter tenure and smaller lifetime value, and the firm faced greater uncertainty in the cash flows expected from this pool. To make matters worse, given the high level of service required by these customers, the firm continues to spend a lot on servicing their needs. Suggestions to update the call center infrastructure to link a customer’s value and payment history to the incoming call identification or the account number of the customer have been ignored for years. Now in an environment of belt tightening, these investments, or “costs” as senior managers often call them, are even more difficult to justify to investors, and the status quo is maintained. Management continues to treat the call centers as mere cost centers and gauges their performance solely on metrics of productivity and cost containment. New investments in these locations contradict management belief, while the firm continues to see an exodus of its high-value customers. From an external observer’s perspective, even a cursory look at the data makes it painfully obvious that customer exodus often follows an unsatisfactory call center interaction, but the faith and belief in a well-established, productivity-driven model continues to drive decisions even in the face of compelling evidence to the contrary.

Finally, in a somewhat similar but generalized example, we often find that retail firms emphasize productivity in their individual stores. These productivity improvements are believed to have a strong positive impact on the financial performance of the firm. However, a recent engagement with one such retail firm suggests that caution should be exercised when boarding the “productivity wagon.” Productivity improvements, especially beyond a critical point, often lead to compromises in the level of service quality experienced by customers. For example, customers experience great resentment when they find fewer employees available to help them in these so-called productive stores and fewer stock-keeping units to choose from within a “rationalized” product assortment. While such adverse customer experiences are often not obvious in the short term, they lead to an erosion of customer loyalty in the long term. Extensive work, done by the American Customer Satisfaction Index (ACSI)¹ research team, also supports our observation, where they find that service companies, such as airlines, often score below manufacturing organizations in the ACSI report card. This poor performance can often be attributed to attempts made by these firms to boost their productivity levels. Interestingly, these strategic choices are often not made in isolation but are the result of boarding the benchmarking bandwagon. Management feels the pressure to match its industry peers on select metrics, including those related to productivity, without giving deep thought to the ultimate consequences of adopting common industry practices and metrics. The underlying assumption, which we find often seriously flawed, is that not everyone within the industry can be wrong, especially when the short-term financial merit of emulating them can be observed relatively quickly. For example, we often read statistics about the “instantaneous” extra revenue airlines make because of new baggage fees, food for sale on board, and the removal of pillows and blankets from their aircrafts. However, we seldom hear about the potential adverse long-term consequences of such choices.

Our interactions with thousands of managers, consultants, management students, and academic thought leaders suggest that such stories can go on endlessly. In fact, we find that every day, a large number of managers make decisions based on intuition, entrenched mental beliefs, or knee-jerk reactions to competitive actions, without pausing to seek empirical support or validation. While observing the inner workings of big and small businesses, we have been a regular witness to the execution of beliefs-based decisions that rely on untested and unvalidated assumptions. Even as we write this book, we can find many senior leaders who continue to place extreme levels of confidence in the benefits of their unwavering beliefs in a variety of performance drivers, including innovation, cost control, productivity, benchmarking, and many more. In addition, the long-term effects of decisions that generate a positive short-term return, such as higher customer fees and lower levels of customer service, are seldom tested or validated.

The corporate world seems to have little time to pause and think and plan for fact-based decision making, for fear that nervous investors are ever so willing to abandon the ship and invest in alternatives. Such short and finite periods also correspond with the finite tenure of top management within most organizations. For example, in 2005, about 6 in 10 chief executive officers (CEOs) of Standard and Poor’s (S&P) 500 firms had less than 6 years in their jobs as CEO.² In addition, as is well known, these leaders are evaluated and remunerated based on financial results produced during their tenure. This short-term orientation exacerbates the problem and the vision, and decision making at the top remains myopic. In such an environment, rapid fire, beliefs-based decision making continues to thrive at the expense of fact-based and data-driven decisions that possibly require longer periods of incubation and an alternative strategic mind-set.

The Data Deluge

Yes, we could give these decision makers some benefit of the doubt. We could possible argue that, even if they wanted to, these managers might not have ready access to data to test and verify their hypotheses. Alternately, we could reason that the data are actually available to test management beliefs and their mental models but are not deployed effectively while making key strategic and tactical decisions. While until a few years ago, it might have been possible to make a case for the absence of good quality data, a number of recently published studies suggest that high-quality data are now widely available. Most organizations today live in an extremely data-rich environment. Recently reported statistics³ suggest that over 160 exabytes (1 exabyte = 10¹⁸ bytes) of digital data were generated worldwide in 2006. This is the equivalent to 36 billion digital movies, 43 trillion digital songs, or 1 million digital copies of every book in the Library of Congress. In 2006, these books would represent about 6 tons of books for every man, woman, and child on earth—approximately the weight of a large adult elephant. By the end of 2010, this number was expected to grow to about 1,000 exabytes at a whopping 57% compounded annual growth rate and was projected to outpace the capacity to store such data. Another way to think of this volume of data is that in 2006, the digital universe was the equivalent of 12 stacks of books extending from the earth to the sun, or one stack of books twice around the earth’s orbit. By the end of 2010, the stack of books could reach from the Sun to Pluto and back!⁴

While a lot of the information, such as digital entertainment, is for consumer consumption, it is also generated by various for-profit and nonprofit organizations through customer relationship management (CRM) systems, internal metrics and processes, as well as external organizational measures of sales and competitive activities. In 2006, about 25% of the worldwide digital data were generated in the workplace—approximately 40 exabytes. By the end of 2010, this volume is expected to rise to about 30%—about 300 exabytes. Wal-Mart’s gargantuan database, for instance, has grown from 110 terabytes in 2000 (1 terabyte = 10¹² bytes) to half a petabyte in 2004 (1 petabyte = 10²¹ bytes). These data are generated primarily to support internal decisions and provide information to other partners, such as suppliers in the value chain. The point is that an absence of adequate and readily available data no longer seems to be a justifiable reason for the pattern of intuition-based decision making that is rampant among various layers of management today. Instead, it is perhaps time for them to leverage the vast amounts of well-structured data at their disposal and to hone their skills to make more effective decisions.

Critical Leadership Skill

We believe that in this environment of data sufficiency and perhaps excess, coupled with the impatience of the investor community, a key managerial skill is the ability to sift through piles of data and hone in on those pieces of information that are most critical to organizational success. This requires decision makers to be quick and articulate in summarizing the situations they face, to convert them into sets of formal or informal hypotheses, to identify the data requirements to test the hypotheses, and then to make strategic calls at high speed.

For example, let us say that you are the country manager for a quick-serve restaurant in an emerging market. At the current juncture in the organizational evolution, you are faced with the task of increasing your market penetration. One morning, you hear unexpected and potentially game-changing news that a key competitor has big plans of entering the marketplace! Being from the trade, you recognize the competitor’s likely entry strategy, but at best, it is an intelligent guess. What would you do? Would you make changes to your prices to attract more customers? How would you assess if you could afford it? Alternatively, would you seek greater retail outlet penetration? How would you know if you can find good sites at a fast pace? Would you spend more on advertising? Would you make changes to your menu items? Or would you do some combination of these possible reactions, or do nothing? Can all the learning from brand equity, segmentation, category and brand awareness, and pricing studies aid you? Think fast—time is running out!

How can this be done? While we will go into the details of the answer to this question, we will briefly illustrate the importance of this critical skill using a simple example. In this case, a large and financially distressed firm appointed a new president. He was an outsider to the industry, but had a proven record of successfully turning around companies. Most of the employees in the firm looked forward to his arrival and the excitement was palpable. Recent market research had indicated that the firm was losing customers at a much faster pace than usual and that its brand was losing its equity. In order to quickly hone in on the problem, the new appointee immediately offered to spend a few days with the consumer insights group to identify key areas of focus that could help the firm deliver superior customer experiences and rebuild the brand. After all, the insights group had spent considerable time and money on various studies and was most likely to have an answer. The group presented multiple studies to him and he listened to each one of them patiently. At the conclusion of each presentation, there were recommendations on potential areas of investment toward building a stronger marketplace presence for the brand.

When the presentations ended, he congratulated the group for sharing the work and posed one simple question: While each presenter had provided good ideas, if all of them were intended to create more satisfied customers and a stronger brand, why was it that the recommendations had no synergy? Each study asked for investing in a completely different area of performance, and he was now even more confused than when he started. He had a pool of funds budgeted for strengthening the brand and the customer experience but was not sure of the best way to deploy them.

In this case, the leader demonstrated two of the critical leadership skills we are referring to. The first was a faith in information and data to solve strategic problems. The second was recognition of the limitations of a set of unrelated, albeit data-driven, solutions without a unifying synergistic framework. To help resolve the problem, the firm worked toward integrating the numerous pieces of existing feedback from the marketplace and building a unifying framework around them. The estimated impact of each proposed initiative was then linked to monetary value. It created a common metric to compare different investment alternatives. With the help of the overall framework to synthesize the information from the various studies, and the estimated bottom-line impact of each, the new president was able to compare alternate initiatives using common evaluative criteria. He could prioritize them and draw linkages among them. For example, he could see how clearer communication during the presale and the sales processes could minimize customer confusion and reduce the volume of expensive inbound calls. He could draw similar linkages between the quality of the billing process and the rate of customer defection. And most important, he could append estimates of returns on investment for each potential area of improvement. As a result, he was ultimately in a position to make informed, coherent, and data-driven decisions after accounting for their firm-wide strategic and financial impact.

Rising Cost of Errors

In these opening remarks, we would also like to allude to the viral impact of failed decisions in today’s information-rich and interconnected environment. Corporate errors are instantly visible in a world of global communication and are increasingly less pardonable. Consumers, advocates, activists, and citizens at large get information at their fingertips almost instantly, which in turn can have serious repercussions for organizational survival. For example, a quick Google search for “Toyota brake problems” generated about 4 million links on the web, while the search for “BP oil spill” generated 387 million links.⁵ Before the Deepwater Horizon rig exploded and sank on April 20, 2010, BP was Britain’s biggest company with a stock market value of 122 billion pound sterling, and by June 10, 2010, the oil spill wiped out 47%, or roughly 50 billion pound sterling, of its value. And this may not completely reflect the erosion of customer goodwill, loss of human lives, cleanup effort costs, and countless payments and retributions that may follow. While the exact cause of the spill is still unknown, someone somewhere perhaps made a decision that eroded half the value of the company in a matter of days.

While the consequences of the BP oil disaster are somewhat immediate, the erosion of a firm’s equity in the marketplace, in many cases, could be slow and silent. Consumers vent on the web and on social media sites, and the epidemic can travel rapidly to other current and potential consumers. According to a study published by Forrester Research and Intelliseek in 2006, recommendations from other consumers, through consumer-generated media, were the most trusted form of advertising vis-à-vis other popular sources of advertising. More than 4 in 5 respondents claimed trusting the recommendations that other consumers made on various products and services. And then there are numerous case studies of consumers venting their frustration on the web, which went viral in a matter of hours. All it takes for such information to be created and widespread is an unpleasant moment of truth, or one wrong decision made by an individual employee. In reality though, some of it is unavoidable—a chain is only as strong as the weakest link. And that weakest link could be a bad decision made with good intent—an employee who thought he was being helpful to the customer but was perceived otherwise or somebody somewhere refusing to accede to a seemingly unjustified customer demand that then blew out of proportion.

The purpose of the subsequent chapters in this book is not to propose a method to avoid all such errors. An error-free organizational performance is perhaps not a realistic or attainable goal. Further, sometimes the criticality of a quick decision leads to an expedited decision-making process where little time might be available to gather and process the necessary information. However, where possible, managers and frontline workers should tap into vast amounts of data that are available to them to provide j...

Table of contents

• Title Page
• Copyright
• Contents
• Prologue
• Part I: Competing by Verification
• Part II: Decision Equity
• Part III: Pursuing Excellence Through Decision Equity
• Epilogue
• Appendix: The “Analytical Tools” of Linkage Analysis
• Notes
• References