CNN Money once published a list of “America’s biggest rip-offs.” That eclectic collection of the most egregiously priced products and services included movie-theater popcorn, hotel mini-bars, and wine at restaurants. It also included college textbooks, whose prices allegedly reflect the wishes of “greedy publishers.”1 Between 1977 and 2015, prices of textbooks increased by 1,041 percent, or at three times the rate of inflation, according to an analysis of data from the United States Bureau of Labor Statistics.2
It is easy to scrutinize such lists, and the accompanying allegations of price gouging and profiteering, and ask several questions. In the case of textbooks, why are the prices so high? What should the “right” price be? What alternatives do students have, especially when second-hand books or library books may be hard to source?
In the spirit of the Ends Game, we feel that the solution to this particular “rip-off” should start with exposing and eliminating inefficiencies. Framed that way, the discussion leads to another set of interesting questions: how many students are denied access to educational materials because publishers insist on earning their revenue from the products themselves? How much waste occurs when students purchase textbooks but read only one or two chapters for a course? How much waste occurs when students feel that the textbooks they own contributed little to their learning objectives, be it a better course grade, the thrill of learning, or a specific job prospect? In short, do textbooks and other educational materials do the job that publishers intend them to do and teachers and students demand?
Knowing what truly drives educational outcomes—and what doesn’t—would have far-reaching consequences for everyone who participates in education; from the school systems to their suppliers, and from teachers to students and their families. Inspired by such questions, United Kingdom–based Pearson—the self-described “world’s learning company” and the leading publisher of educational materials—embarked on what it calls a “path to efficacy.” In other words, Pearson decided to tackle the problem of high prices, and the intensive backlash to them, by first reviewing the very nature of the exchange it has with customers.
In 2012, Pearson published The Learning Curve, a report aimed at helping “policymakers, school leaders and academics identify the key factors that drive improved educational outcomes.”3 Then, in March 2013, two Pearson executive directors and one adviser published a report through the United Kingdom’s Institute for Public Policy Research (IPPR) called An Avalanche Is Coming: Higher Education and the Revolution Ahead in which they described the “warped logic that has locked price and quality together” in higher education. “The price charged to students, even once the cost base is accounted for, is not always responsive to the classic relationship of supply and demand,” they wrote. “Indeed, thanks to the inadequacy of outcome measures for universities … input measures tend to be seen as proxies for quality.”4 They argued that this logic has locked price together with a cost-based proxy for quality, and that this link “needs to be broken.”
In 2013, Pearson made a full commitment to breaking this link when it decided to shift the focus of its business from selling educational materials to selling learning outcomes. It described the quest in literal terms in a regulatory filing with the U.S. Securities Exchange Commission. Pearson claimed its “path to efficacy” means it is “publicly committing to efficacy and improving learning outcomes. We will judge ourselves not by the products we make, but by their impact on learners. It will change how we decide which companies to acquire, where and how we invest, which products we get behind and which we retire. It changes how we recruit, train and reward each person in the company. This change will take time, and is why we talk about a ‘path to efficacy’ that we are on, and it is why we have committed to providing audited learning outcomes data for all our products and services by 2018.”5
Since beginning the journey, Pearson has refined its aspiration as the pursuit of “greater impact on learner outcomes and learners’ lives.” Moreover, Pearson developed an “efficacy framework,” which intends to focus the company’s efforts on four key questions: What outcomes are we trying to achieve? What evidence do we have that we will achieve them? What plans and governance do we have in place to achieve them? And what capacity do we have to achieve them?6
In 2018, Pearson provided the first detailed reports and data on audited learning outcomes, under independent review by the auditing and accounting firm PwC. Pearson learned, for example, that its product suite for mathematics yielded only mixed results for students at universities. Overall, it noted that “increased attempts in quizzes and tests, increased average scores on quizzes and tests, and mastering a higher number of learning objectives were associated with statistically significant higher course grades.”7 Breaking the results down further, though, Pearson said: “Usage of MyLab Math and performance in quizzes and tests were significantly and positively associated with the two course outcomes: course grades and completion. However, use and performance on QuizMe was negatively associated with course grades.” Pearson speculated that the discrepancy could be due to the fact that students work to reach a specific score threshold on QuizMe rather than to actually master the material.
Currently, Pearson charges students a yearly fee for access to MyLab materials, with the price and license period varying by subject matter and by the duration of a given course. That is, Pearson earns revenue on a pay-per-time scheme. The company collects impact data at the individual level from more than eleven million student users annually. The company claims that MyLab now “reacts to how students are actually performing, offering data-driven guidance that helps them better absorb course material and understand difficult concepts.”8 Importantly, as Pearson continues to gather impact data, it will gain sufficient evidence and confidence to make the basis of exchange with students some function of the improvements achieved. In other words, Pearson will have the opportunity to hold itself accountable for learning outcomes.
The existential question for any company playing the Ends Game is: What are we asking customers to pay for? The truth about how organizations earn revenue lies in how they answer this question, and not in the promises made in advertisements, on websites, or face to face by sales people. Indeed, claims about offering superior value to customers are “cheap talk” unless organizations back up those claims by delivering the solutions customers seek to their needs and wants, and by adopting a revenue model that aligns its success with that of customers. The choice of revenue model is what drives accountability. If a company generates revenue on any basis other than the actual solutions it brings to the market, then that company is shortchanging customers—and ultimately itself—by possibility creating waste in the exchange.
Shifting to a relentless focus on outcomes—and preferably value itself—also makes an organization’s innovation efforts more accountable. The curse of so many innovators is that they focus on making the product “better,” only to realize that the tinkering has no link to actual customer benefits. If the product or service is superior to the offering of a competitor, but customers themselves are not interested this difference, then the innovator has wasted precious resources. But when the firm aligns its business goals with the satisfactions of customers via the choice of revenue model, the firm has the strongest possible incentive to focus research and development on the performance measure that the two parties have in common.
Pursuing this level of accountability would have been fantasy over a decade ago. The technological barriers to collecting and using impact data were too high. But these barriers are disappearing, in some industries much faster than others, allowing (and at times pushing) organizations to finally “put their money where their mouth is.” As we will show in chapters 8–11, implementing a change from a less efficient revenue model, probably one that is still based on the transfer of ownership of the product or service from the organization to customers, to a revenue model based on desired outcomes is neither swift nor linear nor certain. It presents a range of challenges, from the ability to collect and analyze impact data without abusing the privilege, to ensuring that customers are active and positive participants in the creation of quality outcomes, and to the organization’s own mindset, skills, commitment, and resources to make the transition.
The starting point, clearly, is defining “outcome” in the organization. From our perspective, there are four conditions that jointly determine whether a given outcome is suitable as the basis for a revenue model. First, the outcome must be meaningful—and therefore valuable—to customers. This point is obvious, yet many businesses still fall into the tempting trap of focusing on product or service attributes that they have an inherent interest or competitive advantage in, yet these attributes matter little to those who buy. Claims about meaningful outcomes—which are the cornerstones of a firm’s value proposition—could be objective or highly subjective, such as “enjoyment” in the case of Teatreneu.
Second, the outcome must be measurable using one or more parameters that are understood and accepted by the organization and its customers. The organization must be able to quantify and express its performance claims in a manner that can form the basis of the exchange with customers. Customers must be able to verify the performance claims. Without these inputs, customers are exposed to possible access, consumption, and performance waste. In business markets, for example, perhaps the most basic outcome is that a particular product or service improves the profitability of customers, either by lowering their costs, increasing their revenue, or a combination of the two. But if profitability cannot be measured directly, then organizations must search for a parameter that can be observed. Orica adopted “broken rock” as a proxy for the impact of its offerings on mining operations.
Third, the measurement of the outcome must be robust, in the sense that the parameter is a faithful representation of the underlying outcome that interests the organization. Obviously, a low correlation between parameter and outcome challenges this requirement. In the worst cases, the correlation between the two could be zero or even negative, as Pearson discovered in the initial efficacy studies involving QuizMe. In the best cases, the company is able to define a functional relationship between parameter and outcome. Taking Pearson again as an example, its dedication to understanding efficacy ultimately led the organization to the point where it established a solid relationship between the use of MyLab by learners and outcomes such as course completion and course grades.
Finally, the measurement must be reliable, in the sense that neither customers nor a third party can tamper with it. That is, customers should not have the means to “fake” a performance level that is not accurate in order to derive a benefit. Some customers may try to outsmart a tracking system, especially when the measured variable has a direct effect on a price, a discount, or the opportunity to win a prize. One scenario is when customers attempt to underreport performance to reduce payment. The flipside is attempting to overreport performance to reach a specific reward, such as what happens with “fitness fraud.” When people have a financial incentive to walk—such as a lower health insurance premium or discounts on fitness products—they can trick a step counter through any number of creative hacks, from metronomes and remote-control race cars to simply moving one’s wrist while working or watching television.9
The most meaningful outcome, of course, is value itself. But measuring value can be difficult, despite all the technical improvements we described in chapter 2. For example, in consumer markets research, technology has not progressed to the point where a company can identify and measure (in a manner that is cost effective and scalable) the changes in brain activity that signal the pleasure individuals derive from everyday products and services. Even if this point could be reached, social norms may stop the company from collecting and using such intimate impressions, no matter how advantageous this could be for customers once a measure for “pleasure” is reflected in the revenue model. Accordingly, even in the most promising scenarios, firms typically have to select an outcome other than value itself that satisfies the four conditions above.
From a practical standpoint, outcomes have two primary dimensions: breadth and depth. The breadth of outcomes is a function of the heterogeneity of customer needs and wants. Conceptually, outcomes can vary by individual customer, by individual occasion, and across specific moments in time. Think of the automotive sector, where cars can serve a wide range of purposes. For example, some people seek the peace of mind of having a dedicated, 24/7 option to move around without delay or unwanted surprise. Others have specific transportation requirements (hauling equipment or animals, transporting children, etc.) that require a vehicle they can modify and customize as they see fit. A third segment of customers seeks flexibility because the objective changes constantly—commuting, business, grocery shopping, an off-road adventure, and so forth. A fourth group has a limited range of transportation alternatives, and therefore are “stuck” into a specific arrangement. This is especially true in rural areas. One manager at J.D. Power pointed out that the majority of Americans still live in a rural setting and need a car to get to work, a situation that means that “Uber is not an affordable alternative.”10Finally, there are people who want a car simply because they derive satisfaction from knowing they own an exclusive object. It has nothing to do with actually using it!
On the other hand, the depth of outcomes is a function of complexity. Outcomes tend to be less complex when they depend only on the organization that develops the product or service and its customers, and when they can be broken down into a small set of clear, controllable steps. Conversely, outcomes tend to be more complex when they depend also on intermediaries and third parties, and when the underlying process is unclear or difficult to control. Ultimately, complexity is an issue of how many “moving parts” the organization has to keep track of and coordinate. The number of contributors is particularly important because, if a market evolves to the point where customers pay according to some measure of performance, then the “team” responsible for delivering that performance needs to agree on how to share the added value generated. For instance, this is likely to become an issue as autonomous vehicles grow in presence and acceptance. Transporting passengers or cargo efficiently from one point to another in an autonomous vehicle is clearly a complex outcome comprising the work of several stakeholders. The business that built the vehicle itself, the one that supplied its intelligence, the one that manages the onboard entertainment, and the one that coordinates traffic flows and navigation can all claim that they make a significant contribution to the quality of the outcome. The challenge facing these players is to settle on a revenue model that keeps waste to a minimum. We raise this as an exercise that requires thorough analysis and planning, not as an impediment.
Despite the emphasis of this chapter on the importance of embracing outcomes, the proper conclusion to draw is not that every organization can and should implement a performance model in the shortest possible time. For a variety of valid reasons, a company might not be able to reach the final destination of the Ends Game in the foreseeable future.
Instead, the proper conclusion to draw is that, today, revenue models anchored on the ownership of a product or service are patently inferior, and that making the transition to a revenue model anchored on time or use is certainly within reach of most businesses. What is important is to start the quest. A company needs to stretch and push itself to think in terms of outcomes, and thus focus on ends rather than means. If the jump turns out to be unrealistic or impractical, then the organization can still fall back on a revenue model that reduces access and consumption waste by following the lead of the companies we highlighted in chapter 5, or one that reduces only access waste, such as the examples featured in chapter 4.
1. J. Pepitone, “America’s Biggest Ripoffs,” CNN Money, February 2, 2010, https://money.cnn.com/galleries/2010/news/1001/gallery.americas_biggest_ripoffs/6.html.
2. B. Popkin, “College Textbook Prices Have Risen 1,041 Percent Since 1977,” NBC News, August 6, 2015, https://www.nbcnews.com/feature/freshman-year/college-textbook-prices-have-risen-812-percent-1978-n399926.
3. Pearson Company, “Pearson Launches the Learning Curve,” news release, November 27, 2012, https://www.pearson.com/corporate/news/media/news-announcements/2012/11/pearson-launches-the-learning-curve.html#.
4. M. Barber, K. Donnelly, and S. Rizvi, “An Avalanche Is Coming: Higher Education and the Revolution Ahead,” IPPR (March 2013): 14, 55.
5. Pearson 20-F filing with the US Securities and Exchange Commission, from the fiscal year ended Dec. 31, 2013, https://www.sec.gov/Archives/edgar/data/938323/000119312514117876/d693538d20f.htm.
6. Pearson promotional video, https://
7. Pearson, MyLab Math for Developmental Math: Efficacy Research Report (April 3, 2018), 13.
8. Pearson, MyLab: Math, https://www.pearsonmylabandmastering.com/northamerica/mymathlab/, accessed April 13, 2020.
9. “Seen at 11: Fitness Tracker Users Turn to Creative Hacks to Increase Step Counts,” CBSN New York, August 22, 2016, https://newyork.cbslocal.com/2016/08/22/fitness-tracker-hacks/; J. Brown, “Fitness Fraud? How to Fool Your Fitness Tracker,” Fox19Now.com, February 25, 2017, https://www.fox19.com/story/34601621/fitness-fraud-how-to-fool-your-fitness-tracker/.
10. J. Charniga, “Debt-Saddled Buyers Lean on Mom, Dad,” Automotive News, March 10, 2019, https://www.autonews.com/sales/debt-saddled-buyers-lean-mom-dad.