In our last article, Outcome Statements Define the Customer’s Success Metrics …,” we explored how to take raw qualitative data from our exploratory research effort, and structure it into unambiguous outcome statements that describe the desired outcomes job executors wish to achieve every step of the way of getting important jobs done.
A typical job chain will have anywhere between 50 to more than 200 outcome statements depending on the complexity of the job-to-be-done. Outcome statements will fall into two basic categories:
- Desired Outcomes job executors want more of. For example: Decrease amount of inventory in manufacturing warehouse.
- Undesired Outcomes job executors want less of or preferably eliminate. For example: Decrease inventory outages on production line.
Designing around every raw outcome statement will prove to be impracticable and not provide us with any real insight to innovate around.
Instead we need to do more analysis to discover which of the outcomes are important, and of the important outcomes which ones are underserved (i.e. customers are not satisfied with the current outcome), and which outcomes are overserved (customers are very satisfied with the outcome – improving overserved outcomes results in diminishing returns – a form of waste).
Analyzing Outcome Statements Using Quantitative Research
Using the simple and unambiguous structure of outcome statements, we can determine the importance and satisfaction level of each outcome with a set of targeted job executors by doing follow-up quantitative research. For each desired outcome we ask our follow up survey participants to rate:
- 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.
- 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 1.
Figure 1: Ranking Outcomes Using “Opportunity Algorithm”
In figure 1 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 we will look at using opportunity rankings to create a differentiated competitive solution – also known as a Blue Ocean Strategy.
Here’s to discovering real opportunities and innovating around them.