The Other Side of Innovation: Solving the Execution Challenge by Govindarajan Vijay; Trimble Chris

Author:Govindarajan, Vijay; Trimble, Chris
Language: eng
Format: epub

A Process for Learning from Experiments

The basic steps for learning from experiments are straightforward:

Before launching an experiment, write down what you plan to do, what you expect to

happen, and why. Draw lessons learned by analyzing differences between what you

thought would happen and what actual y happened. Then, based on lessons learned,

revise the plan. (See figure 4-2.)

That is the whole bal game! If you can execute this process rigorously and

dispassionately, your chances of drawing the right lessons learned from a business

experiment go up dramatical y. The crucial learning step is the analysis of the disparities

between predictions and outcomes. Through this analysis, assumptions are either

validated or invalidated. Lessons are learned. Predictions improve.

This process has a formal name—the scientific method. For many, the phrase

scientific method surfaces bad memories from youth. Perhaps the last time you thought

about the scientific method you were studying high school chemistry, and maybe you

didn’t even like chemistry that much. Nonetheless, the scientific method is the

innovator’s indispensable friend. The process of learning through experimentation is the

scientific method.

When running innovation initiatives, businesspeople need to behave more like

scientists. We believe that disciplined experimentation should take its place in the

business school curriculum right alongside marketing and finance.

FIGURE 4-2

Formalizing an experiment

Each Project Needs Its Own Plan

With a basic process that is so easy to describe, what could possibly go wrong? Oh, if

only the list were short. The learning process can break down in any number of ways.

Most of these breakdowns originate in an uncomfortable juxtaposition. For a

scientist, formal experimentation is routine. For a businessperson, however, ongoing

operations are routine and experiments are the exception. Many common disruptions in

the learning process arise from awkward col isions between what’s good for ongoing

operations and what’s good for experiments.

We’ve just given a basic description of a formal learning process. But business

organizations already have such a process. It is, of course, the planning process. The

basics are the same. As a part of routine planning, managers make predictions and

later analyze the differences between predictions and outcomes.

Unfortunately, these high-level similarities mask fundamental differences. For

example, plans for innovation initiatives have a much different purpose than plans for

ongoing operations. A Performance Engine leader’s central focus is to execute the plan.

In an innovation initiative, however, the plan is a hypothesis. Your central focus should be

testing and improving the hypothesis as quickly and efficiently as possible. Doing so

leads to the best possible results. If your initiative is an entirely new business, your

hypothesis is your strategy. Your job is not to execute the strategy, as it is in the

Performance Engine, but to test and improve it.

This distinct purpose requires distinct thinking when analyzing results. The most

fundamental premise underlying analysis in the Performance Engine is that a business is

an ongoing concern; in other words, that past is precedent. This premise—that past is

precedent—is the underpinning for accounting rules. Given that innovation is inherently

new and uncertain—that there is no precedent — this recognition should make you

squirm.

As a result, each innovation initiative must have its own, dedicated, separate, stand-

alone plan. The stand-alone plan creates much-needed space for distinct thinking. It also

prevents lessons learned from being obscured.

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