His reasons for buying the paper are much more specific. He has a lot of characteristics, but none of them has caused him to go out and buy the New York Times. He and his wife have sent all their children off to college. Why is this misguided? Consider the case of one of this article’s coauthors, Clayton Christensen. And though it’s no surprise that correlation isn’t causality, we suspect that most managers have grown comfortable basing decisions on correlations. While it’s exciting to find patterns in the numbers, they don’t mean that one thing actually caused another. The fundamental problem is, most of the masses of customer data companies create is structured to show correlations: This customer looks like that one, or 68% of customers say they prefer version A to version B. But for most of them, innovation is still painfully hit-or-miss.
From the outside, it looks as if companies have mastered a precise, scientific process. Most firms carefully calculate and mitigate innovations’ risks. Many firms have established structured, disciplined innovation processes and brought in highly skilled talent to run them. Thanks to the big data revolution, companies now can collect an enormous variety and volume of customer information, at unprecedented speed, and perform sophisticated analyses of it. Never have businesses known more about their customers. Most people would agree that the vast majority of innovations fall far short of ambitions. In a recent McKinsey poll, 84% of global executives reported that innovation was extremely important to their growth strategies, but a staggering 94% were dissatisfied with their organizations’ innovation performance. Successful innovators identify poorly performed “jobs” in customers’ lives-and then design products, experiences, and processes around those jobs.įor as long as we can remember, innovation has been a top priority-and a top frustration-for leaders. Marketers and product developers focus too much on customer profiles and on correlations unearthed in data, and not enough on what customers are trying to achieve in a particular circumstance. Innovation success rates are shockingly low worldwide, and have been for decades. The key to successful innovation is identifying jobs that are poorly performed in customers’ lives and then designing products, experiences, and processes around those jobs. Instead of adding more features to the condos, they created services assisting buyers with the move and with their decisions about what to keep and to discard. Sales were weak until the developers realized their business was not construction but transitioning lives. Consider the experiences of condo developers targeting retirees who wanted to downsize their homes. And the circumstances in which customers try to do them are more critical than any buyer characteristics. They’re never simply about function they have powerful social and emotional dimensions.
If it does a crummy job, we “fire” it and look for something else to solve the problem. If it does the job well, we hire it again. When we buy a product, we essentially “hire” it to help us do a job. Some jobs are little (pass the time) some are big (find a more fulfilling career). To create offerings that people truly want to buy, firms instead need to home in on the job the customer is trying to get done.
Why? According to Christensen and his coauthors, product developers focus too much on building customer profiles and looking for correlations in data. Firms have never known more about their customers, but their innovation processes remain hit-or-miss.
0 kommentar(er)
