The Lean Startup - Eric Ries [104]
This formula is effective in for-profit companies as well. At Toyota, the manager in charge of developing a new vehicle from start to finish is called the shusa, or chief engineer:
Shusa are often called heavy-weight project managers in the U.S. literature, but this name understates their real roles as design leaders. Toyota employees translate the term as chief engineer, and they refer to the vehicle under development as the shusa’s car. They assured us that the shusa has final, absolute authority over every aspect of vehicle development.1
On the flip side, I know an extremely high-profile technology company that has a reputation for having an innovative culture, yet its track record of producing new products is disappointing. The company boasts an internal reward system that is based on large financial and status awards to teams that do something extraordinary, but those awards are handed out by senior management on the basis of—no one knows what. There are no objective criteria by which a team can gauge whether it will win this coveted lottery. Teams have little confidence that they will receive any long-term ownership of their innovations. Thus, teams rarely are motivated to take real risks, instead focusing their energies on projects that are expected to win the approval of senior management.
CREATING A PLATFORM FOR EXPERIMENTATION
Next, it is important to focus on establishing the ground rules under which autonomous startup teams operate: how to protect the parent organization, how to hold entrepreneurial managers accountable, and how to reintegrate an innovation back into the parent organization if it is successful. Recall the “island of freedom” that enabled the SnapTax team—in Chapter 2—to successfully create a startup within Intuit. That’s what a platform for experimentation can do.
Protecting the Parent Organization
Conventionally, advice about internal innovators focuses on protecting the startup from the parent organization. I believe it is necessary to turn this model on its head.
Let me begin by describing a fairly typical meeting from one of my consulting clients, a large company. Senior management had gathered to make decisions about what to include in the next version of its product. As part of the company’s commitment to being data-driven, it had tried to conduct an experiment on pricing. The first part of the meeting was taken up with interpreting the data from the experiment.
One problem was that nobody could agree on what the data meant. Many custom reports had been created for the meeting; the data warehouse team was at the meeting too. The more they were asked to explain the details of each row on the spreadsheet, the more evident it became that nobody understood how those numbers had been derived. What we were left looking at was the number of gross sales of the product at a variety of different price points, broken down by quarter and by customer segment. It was a lot of data to try to comprehend.
Worse, nobody was sure which customers had been exposed to the experiment. Different teams had been responsible for implementing it, and so different parts of the product had been updated at different times. The whole process had taken many months, and by this point, the people who had conceived the experiment had been moved to a division separate from that of the people who had executed it.