The Spanish government once risked throwing the country’s local live entertainment industry into a tailspin by raising the tax on tickets to theater performances. When the tax rate skyrocketed from 8 percent to 21 percent in 2013, the country’s small theaters—never blessed with stellar finances anyway—scrambled for ways to stay afloat.
To keep revenue flowing, Teatreneu, a popular comedy theater in Barcelona, Spain, decided to offer spectators a novel proposition. The logic behind this proposition was simple: if Teatreneu is in the comedy business, then it should sell comedy, not tickets. Accordingly, Teatreneu introduced a groundbreaking scheme that became so effective and so popular that it not only boosted attendance and average revenue per spectator, but also saw its app and advertising campaign garner international attention and earn major global marketing awards.1
The system was called Pay per Laugh.2 Spectators entered the theater free of charge. A facial recognition system mounted on the back of the seat in front of them registered each time they laughed during the performance. Each laugh was priced at 30 Euro cents. Teatreneu set the maximum charge at 24 Euros per show, or 80 laughs, so that “no one would need to cry because they laughed more than they could afford.”3 This decision effectively created a spectrum of enjoyment from “no fun at all” to “non-stop fun.” The unlucky spectator who hated the performance—or at least never laughed—paid nothing. Conversely, the lucky spectator who had non-stop fun paid the maximum amount, which was still reasonable. In between those two extremes, the number of laughs provided a clear, consistent, and quantifiable—albeit still imperfect—way to measure entertainment.
By bringing its revenue model into better alignment with the satisfaction of spectators, Teatreneu virtually eliminated what we label performance waste. Performance waste occurs when a product or service in the market is accessed by customers, it is used or experienced, but it doesn’t deliver the value expected from it. Perhaps the most prominent feature of revenue models that tackle performance waste is that they shift all the responsibility for a successful exchange from the shoulders of customers to those of the organization. This is certainly the case with Pay per Laugh. The quality of the show matters because it determines the financial return to Teatreneu. Yet an extremely poor performance doesn’t cost anything to the audience, except perhaps the time wasted in the theater.
At the same time, an organization that uses a performance model lives and dies by the “quality” of the metric it adopts. Some people may enjoy the show immensely but laugh very little, while others may attempt to stifle laughter in order to save some money. These concerns are always going to exist unless the metric is a perfect, tamper-proof proxy of the actual value derived by customers. Finally, the right technology is essential to make pay-by-outcome work. The practice of selling entry tickets to shows is centuries old and could hardly be less sophisticated. The Globe Theatre charged admission to performances of Shakespeare’s plays in the 1600s, and even the Colosseum in Ancient Rome had a seating chart.4 Teatreneu’s Pay per Laugh would be little more than wishful thinking without facial recognition applications, the hardware to deploy them across the theater, and the software programs that tally the laughs and allow spectators to share their experience on social media. Subjective outcomes such as one’s enjoyment are difficult to measure, but companies are increasingly finding novel solutions thanks to technologies that were underdeveloped or even nonexistent as recently as ten years ago.
Value is the ultimate outcome. If a firm could charge its customers based directly and precisely on the tangible and intangible satisfactions they derive in an exchange, then there would be no need for an intermediate measure to calibrate the exchange and access, consumption, and performance waste are minimized. Value establishes the natural equilibrium between You get what you pay for and You pay for what you get.
But finding a way to capture value consistently and reliably is often an insurmountable challenge. The practical alternative is to settle on a proxy: an outcome that can be quantified and verified and, importantly, is an accurate representation of value. “Enjoyment” is a good example of value that, at first sight, defies consistent quantification. The standard pay-to-own revenue model of theater companies is clearly inefficient because ticket prices are set before a show and fixed for all spectators. To reduce access and consumption waste, one could replace the ticket with a pay-per-time model, similar to the earlier example of Ziferblat. Yet comedy is ultimately a matter of taste, and metering here implies that spectators who stay in their seats for the duration of the show pay the same, regardless of whether they actually enjoyed the performance or not. To pay less, disappointed spectators would need to stand up and leave early. Which brings us to this question: How can a business register someone’s “good time” with confidence? Better yet: How can that business come up with a system that charges customers based on the amount of good time they had? Teatreneu’s solution was to use laughter as the proxy for enjoyment.
Most industries are at the early stages of a process that will unfold over the next few years, with improving technology making performance models not only feasible, but also practical and profitable. The primary concern for organizations in the meantime is understanding the true source of the value they create for customers—often with the collaboration of partners and customers themselves. If value itself cannot be measured, the choice of outcome is critical. There may be factors that contribute to an outcome that the organization cannot observe, measure, or control. To the extent that there are significant differences in the value customers derive from a product or service, then the chosen outcome measure must be “personal” enough to reflect this.
In the remainder of this chapter, we will look at how other sectors or individual firms define and employ outcome measures in their performance models. In some cases, the proxy is already established in the industry. In other cases, the debate is ongoing.
How much do people enjoy the music they listen to? Does their appreciation for particular songs peak and then diminish over time, or does it persist regardless of the number of times a given track is played? Importantly, can one’s choice of music influence downstream behaviors and create value by enabling or preventing certain outcomes?
One group of academics has examined how existing technologies can generate music recommendations for drivers based on their mood. They argued that “considering the high correlation between music, mood, and driving comfort and safety, it makes sense to use appropriate and intelligent music recommendations based on the mood of drivers in the context of car driving.”5 In the spirit of the Ends Game, such a statement begs the question of what is the right outcome measure. One official at Ford believed that the proper proxy is the minimization of “driver distraction and stress.”6 As far back as 2013, Ford was working with software and hardware developers to explore and capitalize on “unheard-of access to vehicle data, entirely new application categories and hardware modules” that can promote “safety, energy efficiency, sharing, and health.”7 The other interesting questions are how to measure the driver’s mood, which is a critical ingredient, and what type of performance model can turn these impact data into revenue. One idea could be that a music streaming service “re-imagines itself and its relationship with consumers,” as the market becomes both saturated and more competitive.8 For example, drivers could pay based on the pleasure they derive from listening to music. These data could in turn influence insurance premiums, prices at highway tolls, and other driving-related costs. Drivers could be rewarded if there is indeed a correlation between listening to music and safer driving.
Specifically, automakers and their tech partners are exploring three sources of information to understand mood behind the wheel: external factors such as weather, traffic, and road conditions; telematics data from the car itself; and biometric data collected directly from drivers. In the future, cars could use a combination of biometrics such as facial recognition and vital signs such as heart rate, breathing rate, and even sweat to measure the stress level of drivers. One company is even experimenting with a gel applied to the steering wheel that can serve as a biometric sensor.9 When drivers get into stop-and-go traffic, an intelligent radio can shift to a more mellow station or playlist because it “knows your braking and acceleration patterns.”10 The idea behind these initiatives is that the change in music might not only provide entertainment, but also lower the risk of road rage and, hence, lower the risk of accidents. More than 90 percent of automobile accidents involve human error, therefore knowing and “adjusting” the mood of drivers may be a way of limiting problems with severe consequences.11
The field of advertising has long faced the challenge of defining meaningful outcomes. Prior to the Internet, it was difficult, if not impossible, to say with any certainty whether someone in the target audience saw or heard an advertisement, never mind engaged with it or acted on it.
Then along came the Internet and, with it, a pioneering company that wanted to inject accountability in the process of monetizing advertisements. “The whole point of Internet advertising, I thought, was accountability,” said one of the founders. “You could measure it, unlike with print ads. But here was everyone still selling ads the old way: buy a bunch of impressions, cross your fingers, and hope it turns out well.”12 Accordingly, this newcomer ranked search results not by where and how often keywords appeared, but by how much advertisers were willing to pay for them. The company “posts the per-word pricing in an open auction, allowing Web sites to continually bid for higher placement on a given topic.”13
You would be correct in thinking that this innovation resembles what Google does today with its Google Ads platform. In fact, this concept first appeared in a New York Times article in March 1998, about six months before Google even came into being. The pioneering company was GoTo.com (which in 2001 renamed itself Overture Services and in 2003 was acquired by Yahoo!), and the founder quoted above is Bill Gross, not Larry Page or Sergey Brin. Google first launched its own auction-based, pay-per-click search-advertising product in 2002.14
Today, Google claims that its customers “only pay for results, like clicks to your website or calls to your business.”15 More specifically, Google offers “advertising on a cost-per-click basis, which means that an advertiser pays us only when a user clicks on an ad on Google properties or Google Network Members’ properties or when a user views certain YouTube engagement ads.”16 This is another quintessential example of lean commerce, and one that fulfills the desire for accountability that Gross expressed. Advertisers pay only for a specific, desired outcome of advertising rather than purchasing advertisements and hoping that the audience engages with the content. Google’s model has become so successful that its parent company Alphabet’s revenue from advertising totaled $116.3 billion in 2018.17
Looking ahead, one may question whether clicks is the best proxy of actual value. Performance waste is a matter of degree, and while the ability of an advertisement to drive online traffic is important, traffic is not the same thing as purchases. To the extent that advertisers ultimately care about actual transactions on their websites, a better performance model today may be “pay-per-conversion,” where advertisers pays Google or one of its competitors only when users click on an advertisement and then shop. Whether pay-per-conversion ultimately replaces pay-per-click depends in great part on the development of measurement technology that can establish the causal relationship between exposure to an advertisement and purchase behavior accurately and transparently.
Despite years of almost constant experimentation, the use of outcome-based agreements in the health-care sector is still in its infancy. The examples that follow demonstrate just how difficult it is within the sector to represent “health” in a manner that is consistent, reliable, and, above all, accurate. The challenge in the United States is particularly acute, with spending seemingly out of control. For instance, a 2017 article in the Washington, DC, policy and politics journal The Hill claims that, while around 90 percent of all drugs on the market are low-cost generics, “roughly 5 percent of patients take so-called ‘specialty’ drugs to treat serious or life-threatening diseases. These drugs represent one-third of all drug spending, and this trend is expected to continue with the discovery of new treatments for rare diseases and other highly personalized medicines.”18
One existing and relatively popular measure is the quality-adjusted life year (QALY), which is calculated by estimating the years of life remaining for a patient after a particular treatment or intervention, weighted by a quality-of-life score on a scale from 0 to 1.19 Several countries, including Canada, the United Kingdom, Ireland, and the Netherlands use QALYs as the starting point to calculate the value of a specific drug or treatment and to judge what the burden on public funds should be.20 But the adoption of QALYs is more controversial in the United States and explicitly forbidden under some circumstances by the Affordable Care Act (ACA).21 One justification for the ban is the clear moral dilemma that QALYs expose: “This is America not wanting to put a value on the price of a life.”22
Roche, a Swiss multinational, is developing Personal Reimbursement Models (PRMs), which consider that the effects of medications typically vary by indication (a patient’s specific condition), combination (with other medications), and response. This approach clearly departs from the tradition of charging for a pill or treatment—the legacy pay-to-own model in the industry. Under a PRM, the price is “driven by the value the therapy delivers to patients, and one product can have different prices.”23 Drug utilization data help provide the necessary insights into efficacy. Roche believes that PRMs “will accelerate patient access to innovation and reduce financial pressure on prescribing by enabling the benefit of a medicine to be better reflected in its price.”24
In 2007, Johnson & Johnson proposed a pay-by-outcome model for an oncology treatment in the United Kingdom, under which the company would refund any money spent on patients whose tumors did not remiss. That same year, Cigna, a major insurer in the United States, suggested that makers of cholesterol treatments (statins) pay the medical expenses of patients who suffered heart attacks, even if they had complied steadfastly with the treatment regimen.25 Finally, in 2017 the drug maker Amgen and health insurer Harvard Pilgrim reached an agreement similar to the one Cigna suggested. The contract provided Harvard Pilgrim with a rebate if an eligible patient has a heart attack or stroke while on Repatha, a treatment intended to reduce the risk of heart attack or stroke by lowering LDL (bad) cholesterol.26
While these arrangements are often referred to as “pay-for-performance” or “value-based” schemes, fundamentally they couple a traditional ownership model with a money-back guarantee. As we mentioned at the outset of the chapter, a prominent feature of pay-by-outcome models is that they shift the responsibility for a successful exchange from the shoulders of customers to those of the organization. This rebalancing of risk is one of the primary mechanisms for reducing or eliminating performance waste. But the timing of payment matters. Even if patients are promised a refund in situations where the desired outcome does not materialize, they still face the upfront expense and some uncertainty as to whether they will actually get their money back, which together reduce the efficiency of the exchange.
In 2018, the year after it reached the deal with Harvard Pilgrim, Amgen reduced the list price of Repatha by 60 percent to $5,850 exactly in response to these contingencies.27 Amgen chairman and CEO Robert A. Bradway explained the move by saying: “Concerns over out-of-pocket costs have proven to be a barrier to its use for too many patients. We want to make sure that every patient who needs Repatha gets Repatha.”28
Some twenty-first century businesses have an air of science fiction to them. They are an intricate combination of information technology and intimate customer focus. Using sensors, digital platforms, cloud computing, and machine learning they understand the varying circumstances of customers, then draw on their experience and technological expertise to create superior solutions to their specific needs and wants.
One might think that this description is more likely to fit a clean, “sophisticated” industry such as robotics or autonomous vehicles than the grubby type you may encounter on the television series Dirty Jobs. Yet the Australian company Orica, the world’s largest provider of commercial explosives and blasting systems to mining companies, exemplifies how a smart choice of revenue model is critical to sustained success.
Instead of selling explosives and blast-related services, Orica makes a living based on the quality of the “broken rock” it delivers. This outcome-based model, which they refer to as Rock-on-Ground contracts, has become a defining characteristic of how Orica manages the business, both internally in terms of innovation and product development, and externally in terms of its ongoing relationships with customers and positioning in the market. The size of the broken rock that results from a blast appears to correlate well with the value mining companies derives from using explosives, as smaller rocks are easier and cheaper to dispose of.
Think back to chapter 3, where we cited Theodore Levitt’s insight that customers “don’t want to buy a quarter-inch drill, they want a quarter-inch hole.”29 Continuing this example, instead of selling customers the equipment to blast a “hole” on the mine surface, Orica ultimately focuses on the complete act of creating that hole, from the planning stage through to the aftermath. Citing academic research, Orica claims “the downstream impact of variable and poorly controlled blast outcomes today can impact as much as 80 percent of the total mine processing costs” and adds that “this presents significant opportunity for the industry.”30 In other words, managing a blast can play a critical role in the productivity and profitability of a mine—the value mines derives from Orica’s products and services. The better the mine manages the blast and controls the output, that is, the quality of the broken rock, the more money it can save. That is the niche Orica has defined for itself.
If Orica were to sell its explosives, advisory services, and other blasting materials under a traditional ownership model, or even under a subscription model, it would risk generating all forms of waste. The prices in a pay-to-own scheme might restrict access to the products and services by some mines, depending on the size of their upcoming jobs, the amount of explosives needed, and their financial wherewithal to buy what they need. Both pay-to-own and pay-per-time arrangements might generate consumption waste, unless customers can estimate with precision just how much product they need. Finally, these models might create performance waste if mines, for whatever reason, cannot generate quality blasts from the use of Orica’s products. In this scenario, the financial consequences of a poor outcome rests solely on the shoulder of the mines, assuming of course that there are no defects in Orica’s explosives themselves.
Orica reduced all three forms of waste by putting itself in the shoes of customers. For a success-dependent fee, Orica takes care of the necessary planning, provides the appropriate materials, and manages the blast. Just as important is Orica’s steadfast focus on adopting new technologies to continually optimize how it integrates and performs these tasks. In late 2018, the company released the next generation of its digital platform, BlastIQ, which integrates data and insights from digitally connected technologies across the drill and blast process. Orica claims that the solutions powered by BlastIQ “can deliver predictable and sustainable improvements that can reduce the overall cost of drill and blast operations, improve productivity and safety, and facilitate regulatory compliance.”31 In other words, BlastIQ “will enable our customers to make better decisions, more rapidly and deliver improved blast outcomes across their operations.”32 It does that by providing “near real-time, hole by hole, blast-related data visually to the multiple users across the drill and blast process.”33
Orica has reconfigured blast operations in mining around the idea of lean commerce. The adoption of Rock-on-Ground contracts creates opportunities for optimization that are inconceivable under the local, manual, highly variable ownership models that prevailed to that point. The difference is that the quality of the outcomes is now not only more quantifiable and verifiable, but also more predictable. Mining companies can make better decisions on how to conduct any given project, save a considerable amount of time and money, and capitalize on opportunities that might have otherwise been uneconomical without Orica’s proposition.
This chapter and the previous two have shown how many companies in many industries around the world are using impact data, skills, experience, and creativity to shift their revenue models away from traditional, inefficient ownership schemes and toward agreements that come closer to reflecting the underlying value that customers derive in an exchange. Some of the models we described tackle access waste alone, while others aim higher by addressing consumption waste. Finally, the models discussed in this chapter attempt to quantify and track meaningful outcomes, if not value itself, and therefore focus on performance waste.
Part III of the book is all about action. Across five chapters, we will discuss the steps a company needs to take to develop and implement new revenue models successfully, and the challenges they are likely to face along the way.
1. “Glassworks Project ‘Pay Per Laugh’ Wins 8 Lions at Cannes,” Little Black Book, 2014, https://lbbonline.com/news/glassworks-project-pay-per-laugh-wins-8-lions-at-cannes/; “Teatreneu/Pay Per Laugh,” Clio Awards, 2015, https://clios.com/awards/winner/public-relations/teatreneu/pay-per-laugh-1214.
2. “Teatreneu—‘Pay Per Laugh,’” Adforum, https://www.adforum.com/creative-work/ad/player/34498880/pay-per-laugh/teatreneu, accessed April 13, 2020.
3. D. Basulto, “An Innovative New Payment Model That’s No Laughing Matter,” Washington Post, October 14, 2014, https://www.washingtonpost.com/news/innovations/wp/2014/10/14/an-innovative-new-payment-model-thats-no-laughing-matter/.
4. “Fact Sheet: Audiences,” Shakespeare’s Globe Theatre, https://teach.shakespearesglobe.com/fact-sheet-audiences; L. Clark, “Evidence of a Seating Plan Discovered at the Colosseum,” Smithsonian, January 26, 2015, https://www.smithsonianmag.com/smart-news/please-find-your-seats-evidence-seating-plan-discovered-colosseum-180954023/.
5. E. Çano, R. Coppola, E. Gargiulo, M. Marengo, and M. Morisio, “Mood-Based On-Car Music Recommendations,” Lecture Notes of the Institute for Computer Sciences 188 (2016): 154–163.
6. J. White, “A Car That Takes Your Pulse,” Wall Street Journal, November 28, 2012, https://www.wsj.com/articles/SB10001424127887324352004578131083891595840.
7. K. Fitchard, “Forget Apps, Ford’s OpenXC Project Will Produce Open-Source Car Hardware,” GigaOm (blog), January 10, 2013, https://gigaom.com/2013/01/10/forget-apps-fords-openxc-project-will-produce-open-source-car-hardware/.
8. G. Peoples, “The Growing Cost of Music’s Monthly $9.99 Price Tag,” Billboard, September 20, 2019, https://www.billboard.com/articles/business/8530616/music-streaming-prices-competition-subscribers.
9. “Driver Health Monitoring,” MIT Technology Review, March 1, 2018, https://insights.techreview.com/7-breakthrough-car-technologies-to-watch-driver-health-monitoring/.
10. K. Fitchard, “How Gracenote Is Building a Car Stereo That Senses Your Driving Mood,” GigaOm, February 19, 2013, https://gigaom.com/2013/02/19/how-gracenote-is-building-a-car-stereo-that-senses-your-driving-mood/.
11. G. Topham, “The End of Road Rage? A Car Which Detects Emotions,” Guardian, January 23, 2018, https://www.theguardian.com/business/2018/jan/23/a-car-which-detects-emotions-how-driving-one-made-us-feel.
12. W. Oremus, “Google’s Big Break,” Slate.com, October 13, 2013, https://slate.com/business/2013/10/googles-big-break-how-bill-gross-goto-com-inspired-the-adwords-business-model.html.
13. L. Flynn, “With Goto.com’s Search Engine, the Highest Bidder Shall Be Ranked First,” New York Times, March 16, 1998, https://www.nytimes.com/1998/03/16/business/with-gotocom-s-search-engine-the-highest-bidder-shall-be-ranked-first.html.
14. Oremus, “Google’s Big Break.”
15. Google Ads, https://ads.google.com/home/, accessed April 13, 2020.
16. Alphabet Inc., Form 10-K, Annual Report 2018, United States Security and Exchange Commission, https://www.sec.gov/Archives/edgar/data/1652044/000165204419000004/goog10-kq42018.htm.
17. Alphabet Inc., Form 10-K, Annual Report 2018.
18. J. Greenwood, “The Future of Drug Pricing: Value over Volume,” The Hill, October 11, 2017, https://thehill-com.cdn.ampproject.org/c/s/thehill.com/opinion/healthcare/354913-the-future-of-drug-pricing-value-over-volume?amp.
19. National Institute for Health and Care Excellence glossary, https://www.nice.org.uk/glossary?letter=q, accessed April 13, 2020.
20. D. Roland, “Obscure Model Puts a Price on Good Health—and Drives Down Drug Costs,” Wall Street Journal, November 4, 2019, https://www.wsj.com/articles/obscure-model-puts-a-price-on-good-healthand-drives-down-drug-costs-11572885123.
21. J. Whalen, “What Is a QALY?,” Wall Street Journal, December 1, 2015, https://www.wsj.com/articles/what-is-a-qaly-1449007700.
22. Roland, “Obscure Model Puts a Price on Good Health—and Drives Down Drug Costs.”
23. A. Frappé, “The Call for Further Advancement of Indication-based Pricing,” PharmExec.com, February 1, 2018, http://www.pharmexec.com/call-further-advancement-indication-based-pricing.
24. Roche, “Innovative Pricing Solutions,” https://www.roche.com/sustainability/access-to-healthcare/innovative-pricing-solutions.htm, accessed April 13, 2020.
25. A. Pollack, “Pricing Pills by the Results,” New York Times, July 14, 2007, https://www.nytimes.com/2007/07/14/business/14drugprice.html.
26. Amgen Corporation, “Amgen and Harvard Pilgrim Agree to First Cardiovascular Outcomes-Based Refund Contract for Repatha® (Evolocumab),” news release, May 2, 2017, https://www.amgen.com/media/news-releases/2017/05/amgen-and-harvard-pilgrim-agree-to-first-cardiovascular-outcomesbased-refund-contract-for-repatha-evolocumab/; Repatha, https://www.repatha.com/.
27. Amgen Corporation, “Amgen Makes Repatha® (Evolocumab) Available in the US at a 60 Percent Reduced List Price,” news release, October 24, 2018, https://www.amgen.com/media/news-releases/2018/10/amgen-makes-repatha-evolocumab-available-in-the-us-at-a-60-percent-reduced-list-price/.
28. Amgen Corporation, “Amgen Makes Repatha® (Evolocumab) Available in the US at a 60 Percent Reduced List Price.”
29. A. Gallo, “A Refresher on Marketing Myopia,” Harvard Business Review, August 22, 2016, https://hbr.org/2016/08/a-refresher-on-marketing-myopia.
30. Orica Corporation, “Optimising Drill and Blast Operations with the Next Generation BlastIQ™ Digital Platform,” news release, October 1, 2018, https://www.orica.com/news---media/optimising-drill-and-blast-operations-with-the-next-generation-blastiq-digital-platform.
31. Orica Corporation, “Optimising Drill and Blast Operations with the Next Generation BlastIQTM Digital Platform.”
32. P. Moore, “Orica’s New BlastIQ™ Features Take Blast Digitalisation to the Next Level,” International Mining, April 11, 2019, https://im-mining.com/2019/04/11/oricas-new-blastiq-features-take-blast-digitalisation-next-level/.
33. Orica Corporation, “Optimising Drill and Blast Operations with the Next Generation BlastIQTM Digital Platform.”