Competing Against Luck: The Story of Innovation and Customer Choice

by Clayton M. Christensen

Among the common mistakes he’s identified? “The tendency to treat facts as insights and leap directly from data to action,” Zaltman recently wrote in the Journal of Advertising Research. “It is common when research is used t...

There’s an even more fundamental problem with data. Many people view numerical data as more trustworthy than qualitative data. But where does “objective” data come from? The data used in many research projects comes from companies’ financial statements, for example. Is this objective? H. Thomas Johnson and Robert S. Kaplan showed quite convincingly that the numbers representing revenues, costs, and profits in financial statements are the result of processes of estimation, negotiation, debate, and politics in allocating overhead costs that can produce grossly inaccurate reflections of true cost and profit.9

The healthiest mindset for innovation is that nearly all data—whether presented in the form of a large quantitative data set on one extreme, or an ethnographic description of behavior on the other—is built upon human bias and judgment.

Data is not the phenomena. The primary function of data is to represent the phenomena—to create a simulation of reality. But there is a misconception about data that is so prevalent it’s tacitly embedded in many organizations—the idea that only quantitative data is objective. There’s a pervasive belief that there is some set of ideal data that can, together, yield the perfect insights about customers. It’s just a matter of figuring what the right data is. In short, we can know “truth” if we just gather the right data in quantitative form, the kind of information that can be fed into a spreadsheet or regression analysis. How many? What? Where? Who? When? By contrast, qualitative data—observations and insights that don’t fit neatly into a spreadsheet to be sliced and diced—is not as reliable as quantitative, because there’s no single “truth” at the core. Quantitative data, the thinking goes, is somehow better.

Chapter Takeaways The origin story of most companies typically involves an entrepreneur identifying an important job that does not have an existing satisfactory solution, and developing a creative way to solve it. As a company grows up, however, it’s very common for it to lose focus on the job that sparked its existence in the first place. Despite the best intentions and a century of marketing wisdom, companies start to act as if their business is defined by the products and services they sell (“quarter-inch drills”) instead of the jobs that they solve (“quarter-inch holes”). While there are many drivers of this drift away from the true north of the customer’s job, foremost among them is the tendency of managers to fall prey to the Three Fallacies of Innovation Data: The Fallacy of Active Data Versus Passive Data: Instead of staying cognizant of and focused on the type of data that characterizes the rich complexity of the job (passive data), growing companies start to generate operations-related data (active data), which can seduce managers with its apparent objectivity and rigor but which tends to organize itself around products and customer characteristics, rather than Jobs to Be Done. The Fallacy of Surface Growth: As companies make big investments in customer relationships, they focus their energies on driving growth through selling additional products to those customers or solving a broader set of their jobs, what we call surface growth—as opposed to staying focused o...

Chapter 9 The Jobs-Focused Organization The Big Idea Many companies have lofty mission statements with a variety of intentions from motivating workers to informing strategies to attracting investors, but almost as many companies struggle to translate these mission statements into everyday behaviors. However, when the job has a voice in an organization, individual work streams have meaning and employees understand why their work matters. A well-articulated job provides a kind of “commander’s intent,” obviating the need for micromanagement because employees at all levels understand and are motivated by how the work they do fits into a larger process to help customers get their jobs done.

Not long ago, Intuit founder Scott Cook led a brainstorming session devoted to improving one of Intuit’s flagship products, TurboTax. For years the team has focused on how to improve the “interview” built into TurboTax that asks customers to answer questions and fill in data to generate an accurate tax return. Every year the team would debate how to improve that interview tool, polishing and perfecting and adding specificity that led to the most accurate possible results.

Measuring What Matters “What gets measured, gets done.” It’s generally used in the positive sense of urging managers to measure for benchmarking efficiency and improvements. But the data we use to measure efficiency is double-edged. Yes, it enables measurement and management, but data also creates a model of the external world. Managers inside a corporation—especially large ones—rarely know their customers directly. They know the customer only through data—the models and spreadsheets that slice, dice, and reconstruct real people into “segments” of similarly attributed phenomena. When companies organize themselves into business units with responsibilities for products of certain characteristics or units with responsibilities for certain customer groups, data is gathered through those filters creating models that rarely map to customer jobs. “Turns out in the modern world, there’s so much you can easily measure: screens, traffic, conversion rates, frequency, screens per use. . . . There were so many things to measure that our people got full of measuring all the stuff that was easy to measure because it rolled off our servers,” says Intuit founder Scott Cook. But in spite of the volume of data that Intuit had on every click its customers made, something fundamental was missing. “We weren’t measuring what was most important to our customers. Because it’s hard to measure. But it matters profoundly. We were not measuring whether we were improving customers’ lives.” “Improving c...

SNHU has a similar focus. “Our success is defined by our students’ success,” President Paul LeBlanc says. While SNHU tracks reams of data at a micro level, LeBlanc and his leadership team keep one critical statistic front and center: Would graduates of SNHU do it all over again if they had a chance? In essence—did they hire the right “solution” to get their job done? As of early 2016, 95 percent of those surveyed said yes. As LeBlanc puts it: “We can measure lots of things. But what you measure matters.”

Gilbert was then-CEO of Deseret News Publishing Company,1 the organization that publishes Utah’s oldest daily newspaper, Deseret News. The

Gilbert used this insight to frame the challenge for his leadership team: find a job for which print media is still relevant and can be distinctive, and focus all your energy on nailing that job. “I used to say to people, ‘You’re going to have to pretend that everyone already knows the story you are writing about. It’s old news. That is the context for consumption of anything we might put out in print.’”

happened. To describe this job, they borrowed an acronym from the publishers of the Dallas Morning News, PICA: “Perspective, Insight, Context, and Analysis.” Readers were looking for all these things after an event had already happened and been reported on.

Functional oversight and efficiency is a requirement of competitive markets. However, efficiency is only value creating when it is in the performance of a process that is creating customer value by fulfilling a high-priority job. Successful organizations pursue operational efficiency without compromising the customer Job to Be Done.

Chapter Takeaways Understanding the most important jobs your company solves for customers can be translated into a rallying cry that aligns individuals across the organization behind a common purpose and functions as an enduring innovation North Star. In contrast to the usually generic nature of most companies’ mission statements, a well-crafted statement of the jobs a company exists to solve can be both inspiring and practical. An organization explicitly focused on a clearly defined job enjoys four key benefits: Distributed decision making: Employees throughout the organization are empowered to make good decisions that align with the job, and to be autonomous and innovative. Resource optimization: The jobs focus shines a light on which resources are aligned against what matters most and which are not, and enables them to be rebalanced accordingly. Inspiration: Solving a customer’s job is inherently inspiring to individuals in an organization, as it enables them to see how their work enables real people to make progress in their lives. Better measurement: With a focus on the job, people will naturally seek to measure and manage to more customer-centric metrics. Finding the right way to articulate the job your company is in business to solve—and driving this deeply into your culture—can be difficult and takes real work, but the benefits are worth it.

Chapter 10 Final Observations About the Theory of Jobs The Big Idea In this final chapter I have three hopes. First, I want to convey my enthusiasm for what the Theory of Jobs can accomplish for innovators, because it answers one of the most important questions that has bedeviled managers for decades: Is innovation inherently a question of luck? Our answer is decidedly “no!” Second, I want to convey the boundaries of the theory—what it can explain, and what it cannot. This is critical. If the theory is applied beyond what the theory was designed to explain, it will lose its clarity and predictability. Third, I want to cultivate your curiosity—by showing you through illustration the depth and breadth of questions we can explore through the lenses of Jobs Theory.

When you see numerical data, remember that it was created by people: individuals or groups of people who decide which elements of the phenomena they include in published data and which they overlook and destroy. Hence, data reflects bias. A wonderful book, Relevance Lost by H. Thomas Johnson and Robert S. Kaplan,1 shows that there is a complicated story behind every number. These stories are hidden when they are parsed and distilled into numbers. When the stories are told, they are rich in data. The insights from the right cases are deep. Numbers that were distilled from stories offer insights that are often shallow but broad.

We concluded that school is not a job that children are trying to do. School is one of the things that children might hire to do the job. But the job is that children need to feel successful—every day. And they need friends—every day. Sure, I could hire school to do these jobs. But I could drop out of school and hire a gang to feel successful and have friends. Or I could drop out of school, get a minimum wage job to earn some money, and buy a car—and cruise around the neighborhood with my friends.

Seems like Jobs are actually an end state. I do (method) something so that I feel (end state)

For example, the job of most people is that they want to be so healthy that they don’t even have to think about health. Yet, in systems where the providers of care are reimbursed for services they provide, they actually make money when the members of their system get sick—it’s effectively “sick care” rather than “health care.” When the members are healthy, the providers make little. In other words, the Jobs to Be Done of members and providers are not aligned in the US health care system.

Peter Drucker famously cautioned us: “The customer rarely buys what the company thinks it is selling him.”