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| Home - Industry Article - Sep 03 Issue |
The Innovation Equation |
By Anthony W. Ulwick, Chief Executive Officer, Strategyn
When companies were experiencing manufacturing yields as low as 10 percent just 35 years ago, they applied statistical process control (SPC) to help reduce process variability and over time achieved six-sigma, virtually eliminating process variability – and failures. The same thinking can be applied to the process of innovation. By identifying the steps that comprise the process and eliminating the causal factors that introduce process variability, it too can be refined to yield high success rates – and to dramatically reduce wasted development expense.
To begin this journey, innovation leaders in marketing, business development, R&D and other functions must first understand just what the innovation process is and know what steps are required to devise ideas and solutions that deliver customers new and significant value. Conceptually, the robust execution of the innovation process can be broken down into 3 steps in which companies should be trying to:
Define the criteria customers use to measure the value of a solution.
Define ideas and solutions that will potentially meet those criteria.
Determine which solution best satisfies the criteria and delivers the most value.
To follow these steps, for example, a cell phone manufacturer should first determine how customers measure value. In doing so they may find that users, for example, want to minimize the number of calls that are dialed incorrectly; minimize the time it takes to locate a needed phone number or minimize the likelihood of a call being placed inadvertently. We call these criteria or measures of value the customers desired outcomes, as they describe what customers are trying to achieve while using a cell phone. [The concept of desired outcomes was first introduced in the January 2002 issue of the Harvard Business Review, Turn Customer Input Into Innovation.] Once it is known what 50 to 100 outcomes customers want to achieve, new ideas and technologies should be devised specifically to address those outcomes, e.g., new software or hardware solutions that make locating and dialing a number effortless. With dozens of ideas to consider and dozens of outcomes to satisfy, all possible combinations should then be evaluated for their ability to satisfy the underserved outcomes.
When framed in this manner, executing the innovation process is analogous to solving a complex simultaneous equation. In effect, companies must determine which combination of ideas and solutions will best satisfy the greatest number of important and currently unsatisfied outcomes. Solving this "innovation equation" requires structure and discipline – and this is where the traditional approach to innovation falls short, as it gets the process backwards. Rather than first defining the customers desired outcomes – and using them to guide the creation of valued solutions, most companies come up with ideas and solutions and then test them with customers to see if they will buy – without ever knowing how customers measure value. This trial-and-error approach is like guessing at the answer to a complex simultaneous equation – and the chances of getting it right are slim.
The innovation process, as defined by most companies, consists of six discreet activities: (1) obtaining customer inputs, (2) segmenting the market, (3) prioritizing opportunities, (4) defining the competitive position, (5) formulating concepts and (6) evaluating concepts. Over the past 15 years, I have studied how most companies perform these activities and identified the factors that introduce process variability – and cause the process to fail. Let’s look at these activities in turn and explain what traditional practices have hindered management’s ability to solve the innovation equation.
Step 1: Obtaining Customer Inputs
When solving the innovation equation, companies must capture two distinct types of information – (1) the customer’s desired outcomes and (2) the ideas or solutions that will satisfy them. They should capture desired outcomes from customers and rely on trained engineers, technologists, marketers and other experts to devise valued solutions. In practice, it rarely works this way.
When companies talk with customers, they invariably try to gather ideas for new products and services or for new product features. Consequently, they are not only likely to get weak ideas but more importantly they fail to capture their customer’s desired outcomes. A cell phone manufacturer for example, may hear from customers that they want voice activated dialing or raised buttons on the dial pad but fail to uncover the outcomes they desire, e.g., minimize the number of calls that are dialed incorrectly.
Innovation leaders know the output of the innovation process should be a big idea – so this is what they look for – and customers, who want to be helpful, are willing to offer their ideas. These leaders rarely attempt to capture the customer’s desired outcomes because they do not know how critical they are to the successful execution of the innovation process and even when they do, they may not know how to capture them.
The lead-user process and most other Voice-of-the-Customer and requirements gathering methods are not focused on capturing desired outcomes. The information that is collected – solutions, vague statements and incomplete outcomes – introduces variability into the innovation process. Not surprisingly, if companies do not know how customers measure value, then creating it is a risky proposition.
Step 2: Segmenting the Market
Much of the innovation process is dependent on segmentation: identifying groups of customers in the market that are so similar that the same product or service will appeal to all members of the group. In an attempt to more effectively use resources, many companies segment their markets by product type, price point, business size, geography, age, or by other demographic or psychographic classifications. These segment classifications are convenient to use because data exists to size these segments, making it easy to collect, track and report customer data.
Unfortunately, these classifications rarely contain a homogeneous population and invalidate the basic tenets of solid segmentation theory, undermining the reasons for segmentation. In reality, these segments are simply classifications that companies impose on customers. They are phantom targets that misguide the application of company resources and introduce variability into the innovation process.
Through 15 years of study, I have found that customers can be more precisely segmented by what outcomes they are trying to achieve – forming what I like to call the market’s "natural order" or segmentation. By using the customer’s desired outcomes as the basis for segmentation a cell phone manufacturer, for example, may find one segment of users values speed of communication, while another segment values safety of operating the phone under different circumstances. Delivering products that are optimized around unique customer outcomes is far more efficient than designing products around different demographic groupings – an age-old practice that targets resources and products at customer targets that do not exist.
Step 3: Prioritizing Opportunities
Through dozens of studies, I have found that customers often consider over 50 desired outcomes when measuring the value of a solution. Innovation leaders must know which of those outcomes is most important and least satisfied in order to determine the best opportunities for improvement – but they rarely do.
A cell phone manufacturer, for example, may not know that the customer’s desire to "minimize the number of call dialed incorrectly" is already well satisfied – and that additional improvements in this area will not be recognized as valuable. It may also be unaware that the desired outcome that is most important and least satisfied is to "minimize the number of seconds my eyes are diverted from the road when trying to call someone while driving". Without this knowledge, product development teams typically consider the solutions customers have requested and give them those they know how to create or those that are easy or inexpensive to create. As a result, they often miss the greatest opportunities for improvement.
Using what I call the "opportunity algorithm", [first introduced in the January 2002 issue of HBR, Turn Customer Input Into Innovation], companies are able to determine which desired outcomes are most important and least satisfied and to effectively prioritize which represent the greatest opportunity for improvement. With this knowledge, companies can focus their resources on the best opportunities for growth.
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