A clever discount scheme, ostensibly announced in 2018 by the Mexican flag carrier airline Aeromexico, took the idea of price promotions to an unprecedented and intimately personal level. Travelers from the southern United States could fly to Mexico for a price discounted by the percentage of Mexican DNA they had.
The two-minute video with the tagline “Innerdiscounts: There are no borders within us,” showed surprised residents of the Texan town of Wharton learning that they could visit Mexico at, say, 15 percent or 18 percent off, after a DNA test revealed the extent of their Mexican ancestry.1 This campaign offered the airline a way to educate potential customers about their particular connection to Mexico and perhaps encourage them to travel there and explore. The advertisement, released by the airline’s ad agency Ogilvy2 became a viral hit in early 2019 during the U.S. government shutdown, which was caused in part by differences on funding for a proposed border wall between the United States and Mexico.3
Apparently, however, the offer wasn’t real.4 The ad was “an experiment to see what would happen” and languished on YouTube with no budget until some self-promotion from the agency and the controversy around the border wall caused the video to go viral.5
Nonetheless, its existence raises important and delicate issues about data collection. How much information can and should organizations collect about their customers? What stake do customers have in making their data available? What guidelines should govern how the “gatherers” of data use and safeguard that information? In this chapter, we will look at what customers stand to gain and what they stand to lose when they willingly reveal details about themselves and their consumption behaviors. Understanding the stakes starts with realizing how these “impact data” differ from everything else organizations have been researching about customers in the past.
When we think about the information organizations may be interested in about their customers, we distinguish among three types. The first type helps companies understand what their customers truly need and want. This has been the lifeblood of marketing departments and research and development teams for decades. The seminal writings of Peter Drucker, Theodore Levitt, and several other scholars spurred a more intense and nuanced use of market research tools such as focus groups and quantitative surveys designed to get inside the customers’ heads. Scientific and technological advancements have not only improved these methods, but also superseded them in specific cases. Today, approaches including direct observation and experiential interviews are increasingly popular due to their ability to tap into the subconscious thoughts and motivations of respondents.
The second type of data collected by organizations is relatively more recent and comprises information on the different steps that customers take to seek out and select solutions that supposedly satisfy their needs and wants. The original representations of these decision-making “journeys” were linear, with customers following a rather predicable path or “funnel” from awareness and interest to an actual purchase. However, the advent and growth of e-commerce both exposed and compounded the limitations of such a simple map of the purchase process. The decision journeys of twenty-first-century customers are anything but linear. They tend to unfold across multiple touchpoints and multiple channels, and organizations use this information to engineer rich experiences and forge stronger relationships with their target audience.
The third type of data is the impact data we defined in chapter 2. While collecting information on customers’ needs, wants, and decision journeys is typically not an intrusive or invasive exercise, collecting impact data is by nature a deeply personal task, as they reveal what customers do with products and services after they gain access to them, as well as how well these offerings actually perform. The monitoring and tracking that underlies impact data can reveal facts that an individual or business customer purposely kept hidden. The data and subsequent analyses can also reveal patterns, tendencies, and behaviors that neither the organization nor its customers anticipated. For these reasons, most attempts to collect impact data require customers to opt in, on the assumption that the organization harvesting the information will protect it and use it primarily in the customers’ interests.
Impact data enable organizations to take customer focus full circle and define a more efficient revenue model. Without impact data, in combination with traditional information on needs, wants, and journeys, there is no Ends Game because firms have no reliable means to identify and eliminate waste—they have no means to hold themselves truly accountable. Specifically, firms cannot switch to a revenue model that improves access and shifts the risks associated with consumption and performance from customers to themselves without knowing when and how its products are used, the context, extent, and effects of that use, and the resulting outcomes. The big question—probably the trillion-dollar question, if we look across all forms of access, consumption, and performance waste in an economy—is the extent to which customers are willing to share their information with firms and fuel the Ends Game.
The bank robber was the folklore anti-hero in the nineteenth and twentieth centuries. Bank robber Willie Sutton purportedly said that he raided banks because “that’s where the money is.”6 In the twenty-first century, the bank robber’s counterpart is the hacker. According to one report, there were more than 3,800 data breaches in the first half of 2019.7 In the health-care sector alone, there were 503 data breaches in 2018, three times more than in 2017.8 Bank robberies in the United States, by the way, totaled a mere 2,975 incidences in 2018 according to the Federal Bureau of Investigation.9
The torch has been passed. The “money,” in the form of consumption and performance data, is now stored on servers rather than in safes. In this context, any data-driven quest for a better revenue model may feel like theft to customers unless organizations take serious steps to protect their privacy and foster a sentiment of trust. Protecting privacy implies putting the appropriate safeguards in place to keep data confidential. This is a technological and regulatory challenge that lies beyond the scope of this book. Building trust, on the other hand, implies reassuring customers that the organization collects and uses impact data for purposes that ultimately are also in their interests.
A lack of trust is costly. In its thirteenth annual Global Consumer Pulse research, published in late 2017, Accenture stressed: “Poor personalization and lack of trust cost U.S. organizations $756 billion last year, as 41 percent of consumers switched companies. Without deeper customer insight, companies cannot deliver the experiences they crave.”10 Similarly, the New York Times underscored the importance of trust, and the unease about its absence, in an article describing the blatantly commercial motives of some companies that collected behavioral data at the individual level: “In recent years, data companies have harnessed new technology to immediately identify what people are watching on Internet-connected TVs, then using that information to send targeted advertisements to other devices in their homes.”11
Finally, the software giant salesforce.com took this view one step further by using the term “crisis of trust.” Importantly, the organization sees transparency and accountability as the basis for a solution, a way for customers to accept and even encourage personalization. In a 2018 report titled The State of the Connected Consumer, the company explained: “Delivering personalized experiences requires a data-driven, 360-degree view—but more than half of respondents are uncomfortable with how their data [are] used. Customers say companies can earn their trust by taking certain steps, such as giving them control over how their data are applied, and being transparent about how they are used. Eighty-six percent of customers are more likely to trust companies with their relevant information if they explain how it provides a better experience.”12
Organizations face the trust challenge in a climate where customers are increasingly sensitive to the information that is gathered about them and their behaviors. In his book TAP: Unlocking the Mobile Economy, Anindya Ghose fittingly asks: Will the ability to collect and use personal data turn an organization into a concierge or a stalker?13 Clearly, there is potential for a company to become a creepy stalker, especially seeing that data collection, driven by the breakthroughs in information technology we described in chapter 2, seems to have become more invasive and in some cases surreptitious.
Yet the reality is that sharing one’s data has never really been a riskless proposition. Companies must be aware of the risk customers face and their sensitivity to it. They must do everything in their power to alleviate this concern. At the same time, however, companies must be able to communicate that sharing one’s data has never been a more valuable investment for customers as it is today. The challenge for organizations playing the Ends Game, therefore, is to ensure that the balance between risk and reward ultimately favors the latter. It remains an educational and ethical challenge for organizations as they seek to adopt a revenue model that makes the exchange more efficient. Adopting a better revenue model eliminates waste, converting market potential into actual value. Colloquially speaking, this change grows the “pie” shared by the organization and its customers. Customers know that they play a critical role in making the pie bigger by allowing the organization to track consumption and performance. As such, customers deserve to share in the incremental benefit.
Lean commerce is shaped by important changes in the type of data that companies need, collect, and use. It is this information-dense “fuel” that powers the initiatives of the companies featured in this book including, for example, Winterhalter (chapter 5) and Orica (chapter 6). Successful organizations have immersed themselves fully into impact data in order to measure with the highest precision not only the consumption patterns of their discerning customers, but also the real benefits that they derive from products and services. The active and passive tracking of customers provides them with insights they could never have imagined ten or twenty years ago. Moreover, when customers know firsthand that an organization can use these data to deliver the outcomes they desire, it puts both parties in the exchange in an enviable position.
The critical ingredient that makes these relationships endure—the bond that keeps the data flowing, so to speak—is accountability. Customers demand that organizations live up to their promises. They accept to share their data with organizations because this will allow them to prove their worth. But one problem is that customers potentially generate streams of data that are more voluminous and intricate than a company can reasonably capture and process. Companies, therefore, must now sort the rapidly flowing and growing influx of data to find what it needs to create and, importantly, demonstrate value for their customers and themselves. Companies that succeed in channeling and filtering these streams of data seize the enormous opportunity to eliminate waste. They can encourage and even incentivize efficient consumption, improve the likelihood of more and better outcomes, and understand how they co-create value together with customers.
In business markets, the availability and use of impact data can lead to not only the measurement of outcomes, but also their prediction. For instance, the firm Syncron,14 which claims to help companies “maximize product uptime,” lists several valuable applications for data streams, such as predicting the remaining useful life of an aircraft engine, predicting the failure of electric submersible pumps used to extract crude in the oil and gas industry, and forecasting energy demand in small communities to predict the overload situations of energy grids.15
Consumer markets similarly offer opportunities for organizations to use customer data as a means to eliminate waste and enhance the customer experience. As we mentioned in chapter 8, reaching a destination via autonomous driving is a complex outcome. A flow of customer data is absolutely necessary to make it not only feasible, but also lucrative. A study by A.T. Kearney estimated that autonomous vehicles could save $1.3 trillion annually in the United States alone by reducing traffic accidents, cutting energy consumption, and lowering maintenance and service costs.16 The operation of such vehicles depends on the interchange of massive amounts of data among passengers, vehicles, and central management and guidance systems. Autonomous vehicles also offer an important complex outcome: mobility. Put one way, the general goal of mobility is to “[s]eamlessly and intuitively assist passengers with where they are going, how they get there, and what they do along the way.”17 Accordingly, the exchange of data can have a direct influence on the revenue model, depending on how the provider of this complex outcome defines it. As the authors of the A.T. Kearney report write: “Driverless cars from Google are not only passenger transportation, but an ingenious data collection system. If passengers feel comfortable exchanging rich data—telemetry, pictures, and video—in exchange for a ride, Google can significantly lower the barriers to access. There is no reason, however, why the incumbent original equipment manufacturers cannot either pursue a similar strategy or neutralize Google’s.”18
Finally, the exchange of data can open up opportunities for organizations and customers to enhance their relationships even in something as seemingly simple as light bulbs. In an article on the Internet of Things (IoT), the Boston Consulting Group described the opportunity: “Smart bulbs transform consumers from occasional, anonymous buyers of light bulbs into consumers of connected lighting. If consumers opt in to the network, their usage data can yield insights that are valuable not only to the light bulb provider but also to other firms within the broader network, which could include utilities, interior designers, and consumer electronics firms. The challenge is to figure out how valuable the IoT data is to each of these potential customers.”19 The same thinking applies to large commercial installations as well. Signify (originally Philips Lighting) doesn’t sell lamp installations (luminaires) to the Schiphol Airport in Amsterdam. It sells light, or more specifically, light as a service. One driving force for Signify is a commitment to a circular economy, which it describes as using “resources more effectively by creating rather than wasting, using rather than owning, and reusing rather than disposing.”20
The most progressive organizations—especially startups that don’t have to undo an entrenched, less efficient revenue model—promise customers something that most incumbents never seriously considered before: to profit only when customers do. As such, the more progressive organizations are collecting and exploiting new types of data to form a better picture of when customers use their offerings and how they perform. Success hinges on how well the company manages and capitalizes on this newfound transparency about its customers. This is where accountability and trust come into play, so that organizations and their customers work together to their mutual benefit.
1. “DNA Discount Advertisement for ‘AeroMexico Airlines,’” YouTube, May 25, 2018, https://www.youtube.com/watch?v=2sCeMTB5P6U&feature=youtube, accessed April 13, 2020.
2. L. Marcus, “AeroMexico’s New ‘DNA Discount’ Ad Goes Viral,” CNN, January 18, 2019, https://www.cnn.com/travel/article/aeromexico-dna-discount-travel-ad-video/index.html.
3. B. Keveney, “Aeromexico Ad Campaign Trolls Anti-Mexican Sentiment in US with DNA Discounts,” USA Today, January 19, 2019, https://www.usatoday.com/story/travel/2019/01/17/aeromexico-tweaks-u-s-dna-discount-campaign/2609363002/; S. Van Sant, “How the Partial Government Shutdown Could Affect You,” NPR Morning Edition, December 23, 2018, https://www.npr.org/2018/12/23/679652640/how-the-partial-government-shutdown-could-impact-you.
4. A. Garcia, “Is Aeroméxico Offering ‘DNA Discounts’ for People Traveling to Mexico?,” Snopes, January 21, 2019, https://www.snopes.com/fact-check/aeromexico-dna-discount/.
5. D. Griner, “A Viral Hit, 7 Months Delayed: Aeroméxico’s DNA Ad Shows the Unpredictability of Organic Video,” AdWeek, January 22, 2019, https://www.adweek.com/creativity/a-viral-hit-7-months-delayed-aeromexicos-dna-ad-shows-the-unpredictability-of-organic-video/.
6. The quote makes a valid point, even though Sutton denies he ever said it: https://www.snopes.com/fact-check/willie-sutton/, accessed April 13, 2020.
7. P. Ausick, “Billions of Records Exposed: 2019 On Track to Be Worst Year Ever for Data Breaches,” USA Today, August 19, 2019, https://www.usatoday.com/story/money/2019/08/18/2019-on-track-to-become-worst-year-ever-for-data-breaches/39963021/.
8. J. Davis, “The 10 Biggest Healthcare Data Breaches of 2019, So Far,” Health IT Security, July 23, 2019, https://healthitsecurity.com/news/the-10-biggest-healthcare-data-breaches-of-2019-so-far.
9. FBI, “Bank Crime Statistics 2018,” Documents, https://www.fbi.gov/file-repository/bank-crime-statistics-2018.pdf/view, accessed April 13, 2020.
10. Accenture, “U.S. Consumers Turn Off Personal Data Tap as Companies Struggle to Deliver the Experiences They Crave,” news release, December 5, 2017, https://newsroom.accenture.com/news/us-consumers-turn-off-personal-data-tap-as-companies-struggle-to-deliver-the-experiences-they-crave-accenture-study-finds.htm.
11. S. Maheshwari, “How Smart TVs in Millions of U.S. Homes Track More Than What’s on Tonight,” New York Times, July 5, 2018, https://www.nytimes.com/2018/07/05/business/media/tv-viewer-tracking.html.
12. Salesforce, “The State of the Connected Consumer 2nd Edition,” 2018, 5, https://www.salesforce.com/ap/form/conf/state-of-the-connected-customer-2nd-edition/, accessed July 6, 2016.
13. A. Ghose, TAP: Unlocking the Mobile Economy (Cambridge, MA: MIT Press, 2016).
14. Syncron, https://www.syncron.com/company/, accessed April 13, 2020.
15. L. Bell, “Uptime: The Most Disruptive Change in Pricing History,” Syncron, May 4, 2018, https://www.syncron.com/uptime-the-most-disruptive-change-in-pricing-history/.
16. C. Weiss, S. Gaenzle, and M. Römer, “How Automakers Can Survive the Self-Driving Era,” A.T. Kearney, 2015, 4, https://www.kearney.com/documents/20152/434078/How%2BAutomakers%2BCan%2BSurvive%2Bthe%2BSelf-Driving%2BEra%2B%25282%2529.pdf/3025b1a0-4d71-e24d-51e0-2cc1f290447c?t=1493941955625.
17. S. Corwin, N. Jameson, D. Pankratz, and P. Willigmann, “The Future of Mobility: What’s Next?,” Insights, Deloitte, September 14, 2016, https://www2.deloitte.com/us/en/insights/focus/future-of-mobility/roadmap-for-future-of-urban-mobility.html?icid=dcom_promo_standard|us;en.
18. Weiss, Gaenzle, and Römer, “How Automakers Can Survive the Self-Driving Era,” 19.
19. D. Langkamp, J. Schürmann, T. Schollmeyer, R. Kilian, A. Petzke, J. Pineda, and Jean-Manuel Izaret, “How the Internet of Things Will Change the Pricing of Things,” BCG, December 7, 2017, https://www.bcg.com/publications/2017/how-internet-of-things-change-pricing-of-things.aspx.
20. Philips, “Circular Lighting at Schiphol Airport,” global professional lighting website, https://www.lighting.philips.com/main/cases/cases/airports/schiphol-airport, accessed April 13, 2020.