| Author | : Clayton M. Christensen |
| File Size | : 44,9 Mb |
| Publisher | : HarperCollins |
| Language | : English |
| Release Date | : 04 October 2016 |
| ISBN | : 9780062435637 |
| Pages | : 259 pages |
Competing Against Luck by Clayton M. Christensen Book PDF Summary
The foremost authority on innovation and growth presents a path-breaking book every company needs to transform innovation from a game of chance to one in which they develop products and services customers not only want to buy, but are willing to pay premium prices for. 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.