How do companies know how to grow? How can they create products that they are sure customers want to buy? Can innovation be more than a game of hit and miss? Harvard Business School professor Clayton Christensen has the answer. A generation ago, Christensen revolutionized business with his groundbreaking theory of disruptive innovation. Now, he goes further, offering powerful new insights.
After years of research, Christensen has come to one critical conclusion: our long held maxim—that understanding the customer is the crux of innovation—is wrong. Customers don’t buy products or services; they "hire" them to do a job. Understanding customers does not drive innovation success, he argues. Understanding customer jobs does. The "Jobs to Be Done" approach can be seen in some of the world’s most respected companies and fast-growing startups, including Amazon, Intuit, Uber, Airbnb, and Chobani yogurt, to name just a few. But this book is not about celebrating these successes—it’s about predicting new ones.
Christensen contends that by understanding what causes customers to "hire" a product or service, any business can improve its innovation track record, creating products that customers not only want to hire, but that they’ll pay premium prices to bring into their lives. Jobs theory offers new hope for growth to companies frustrated by their hit and miss efforts.
This book carefully lays down Christensen’s provocative framework, providing a comprehensive explanation of the theory and why it is predictive, how to use it in the real world—and, most importantly, how not to squander the insights it provides.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates