Want To Launch a Steady Stream of Successful New Products?

Then Address Important And Underserved Outcomes – Here’s How

The real power of Jobs-To-Be-Done (J2BD) innovation approach comes from identifying and prioritizing a set of desired outcomes a customer wants to achieve in executing his job. Customers may not know “how” to get their jobs done better, but they will know quite accurately their level of satisfaction in getting their current jobs done. Not just the ultimate outcome, but all the steps along the way from beginning to end.

Now of course, don’t expect a customer to be able to precisely articulate all of his desired outcomes to you. It’s not quite that easy because most customers aren’t thinking about their jobs in a structured innovation framework. It is up to us as developers and innovators to listen to and observe the jobs customers are trying to get done, and then translating the raw inputs into actionable information.

We need to probe deeply into the what, why, when and circumstances a customer faces when executing his job using qualitative research techniques. Qualitative research can include site visits,  in-depth interviews, ethnographic research and a combination thereof. Refer to an early article Putting Theory Into Practice Part 10: Testing the Key Assumptions with VoC  for more information on conducting voice-of-customer (VoC) research.

Depending on the complexity of a “job,” we can expect to uncover 50 to more than 150 desired outcomes form our qualitative research. The initial qualitative research will uncover a rich set of desired outcomes for us, but the information will come in the form of “unstructured data.”  We will need to translate the data into a common structure that we can further test and prioritize in a second round of quantitative research.

Structuring an Outcome Statement

A desired outcome typically states a direction of improvement (minimize or increase); a unit of measurement (number, time, frequency, likelihood); and what outcome is desired. Here is an illustration of the basic outcome statement structure:

Here are examples of structured outcome statements from last week’s article Understanding Desired Outcomes Customers Want In Doing A Job

1. Increase the likelihood of finding the right tool

2. Decrease the time it takes to find the right tool

3. Decrease the time it takes to store a tool

4. Increase the likelihood the tool will not be misplaced

5. Increase the likelihood of knowing if a tool is missing

6. Increase the security of storing tools

With this simple and unambiguous structure, we can determine the importance and satisfaction level of each outcome with a set of targeted job executers by doing follow-up quantitative research. For each desired outcome  we extracted from the qualitative research, ask our follow up survey participants to rate:

1. The importance of all the jobs, outcomes and constraints using a scale of 1 to 5 where 5 means critically important and 1 means not important at all.

2. And the degree to which they are satisfied with how the solution they are using today is addresses those jobs, outcomes, and constraints using a scale of 1 to 5, where 5 is totally satisfied and 1 means not satisfied at all.

Ranking The Outcomes Using the “Opportunity Algorithm”

The priority of outcome statements from the perspective of the customer is determined using the following formula:

Opportunity = Importance  +  Max(Importance – Satisfaction, 0)

To use the formula, we need determine the percentage of participants giving a rating of 4 or 5 for each “importance” and corresponding “satisfaction.”

So for example in the illustration below, outcome one has a  score 9.6 for importance which means that 96% of the participants ranked the importance of “outcome one” a 4 or a 5. And 2.8 satisfaction means 28% of the participants rank their level of satisfaction a 4 or 5. Thus for “outcome one,” 9.6 is inserted in the importance variable, and 2.8 is inserted into the satisfaction variable. Plugging it into the formula we get an opportunity index of 16.4. See figure 2.

In figure 2 we can immediately see the greater the importance of an outcome and the lower the satisfaction level for the outcome, the higher the opportunity rating is. Note also for “outcome N,” where the satisfaction level is greater than the importance, the formula tells us to insert a 0 for satisfaction (max(importance – satisfaction,0)) resulting in an opportunity rating of 8.5.

A thorough discussion of using the Opportunity Algorithm is provided in Tony Ulwick’s book “What Customers Want”  chapter 3.

Next week’s article will talk a bit more how to use the opportunity ranking to create a differentiated competitive solution.

Here’s to discovering real opportunities,

Kevin