Competing Against Luck
《Competing Against Luck》(Clayton M. Christensen)内容概览、经典语录与核心观点。
本页介绍《Competing Against Luck》(Clayton M. Christensen)——一本热门的商业类图书。下方有简介、金句与相关推荐。搜索「Competing Against Luck 摘要」「Competing Against Luck 语录」时可参考本页。
In Competing Against Luck, Clayton M. Christensen and coauthors Taddy Hall, Karen Dillon, and David S. Duncan argue that the theory of disruptive innovation needed a companion: the jobs-to-be-done theory of innovation. Their central claim is that customers do not really buy products or services; they hire them to make progress in a particular circumstance. Understanding that job, not the customer's demographic profile or product category, is the key to predictable growth. The book's signature framework is the "job to be done," illustrated by the famous milkshake study. A fast-food chain found that roughly 40 percent of milkshakes sold were bought in the early morning by commuters who hired the shake to occupy a long, boring drive and to stave off hunger until lunch. Seen through product categories, the shake competed with all other breakfast items; seen through the job, it competed against bananas, bagels, and Snickers bars. The authors also introduce related ideas such as "consumption," "big hire versus little hire," and the "forces of progress" that push customers toward and pull them away from a new solution. Subsequent chapters explore why companies miss the job, how to see the job through customers' eyes, how to build purpose brands and customer experiences around it, and how to measure success by progress rather than attributes. Throughout, the authors cite examples from Intuit, IKEA, American Girl, and other firms to show how focusing on circumstances, rather than correlation or luck, allows organizations to innovate with far greater predictive confidence.
正在翻译简介…
关于本书
《Competing Against Luck》讲什么?
《Competing Against Luck》(Clayton M. Christensen)在 Book Drop 书库提供核心内容概览,请阅读上方简介。
哪里可以看《Competing Against Luck》的语录?
本页列出《Competing Against Luck》的经典语录,请下滑到语录部分。
如何获取《Competing Against Luck》的摘要?
可先阅读本页概览,或打开 Book Drop Telegram 机器人获取更完整的 AI 摘要。