TECHNIQUE 2 Outcome Expectations

For the job of cleaning clothes at home, Exhibit 3.2 lists a sampling of expected outcomes, desired and undesired, from both the provider s and customers perspective. See Outcome Expectations (Technique 2) for more guidance on how to identify and document desired and undesired outcomes, as well as how to determine their relative importance. 

To ensure that your innovation will provide real value to customers, it s important to understand the major outcomes that are not currently satisfied by existing solutions. Use Outcome Expectations (Technique 2) to generate a list of expectations associated with the JTBD. Then, on the Project Charter, list the key unmet expectations. Use the format direction (minimize), measurement (time needed to acquire), object of action (breakfast), and context (in the busy morning). 

You don t have to be an expert on the items you select. However, it is important to understand the customer expectations associated with each item. For more information, see Outcome Expectations (Technique 2). 

Prioritizing JTBDs is a function of how important they are, how satisfied customers are with existing solutions, the general potential for developing new (or more ideal) solutions, and the specific potential of the provider for creating new solutions that better meet outcome expectations (see Technique 2). The importance-satisfaction dimensions establish priority from the customers perspective, while new-solution potential forms the basis for prioritization from the provider s perspective. 

For a particular job, identify its associated desired and undesired outcome expectations. These become the drivers of further ideation activities in the innovation process. Outcome expectations are explained in Technique 2. 

It s important to define any outcome expectations associated with a JTBD when pursuing an innovation based on that JTBD. Understanding these expectations, and knowing how satisfied (or unsatisfied) customers are with current solutions, helps you identify unidentified market space and possibly fill that space with better solutions than what exists today. You may need light survey design and sampling help from a statistician to apply this technique, but for the most part it requires no expert assistance. 

In Jobs To Be Done (Technique 1), we provide instructions for how to develop job statements and how to determine which JTBDs are priorities for innovation. Follow these steps to select the JTBD for which you ll develop related outcome expectations. 

Do not confuse this exercise with cataloguing performance and perception expectations (see Technique 30), which are solution-specific performance characteristics, such as candle bum time (target = 32 hours), or PC battery life (target = 6 hours). Outcome expectations are solution-neutral and reside at a higher level they are JTBD-specific desires, such as maximize duration of illumination (using any solution), or maximize operating time (in whatever way possible). 

Consider using focus groups or even Ethnography (Technique 4) to identify unarticulated outcome expectations. 

What job, problem, or task creates the focus for your innovation effort What are its associated customer and provider outcome expectations Have you already created a project statement or job statement See Jobs To Be Done, Outcome Expectations, and Project Charter (Techniques 1, 2, and 10 respectively). 

You can use the Random Stimulus technique to generate solution ideas for a JTBD, or even for an outcome expectation (see Exhibit 21.3 for an example of the latter). 

Using other techniques in this book, you have already scoped the JTBD (see Jobs To Be Done, Technique 1) and identified associated outcome expectations (see Technique 2). Now, ask, What would nature do to solve this problem In doing so, you realize that nature isn t the environment in which your design will live instead it s a model on which to base your design. This subtle shift is important if you want to draw inspiration from nature and follow its guiding principles ... 

Customers have three types of expectations—outcome, performance, and perception. Outcome expectations (see Technique 2), are specific to the/oZ the customer wants to get done. Performance and perception expectations,... 

Each subfunction should fulfill a customer need—either an outcome expectation (see Technique 2) or a performance or perception expectation (see Technique 30).). 

Use Paired Comparison Analysis when you need to compare either more upstream innovation ideas or more downstream design concepts. This technique is especially helpful when you don t have objective data regarding how different ideas could meet your customers outcome expectations (see Technique 2), or when you re uncertain about how different design concepts could meet customer performance and perception expectations (see Technique 30). 

The estimates of an NCE s expected sales trajectory can be analyzed using a Monte Carlo technique, which produces a range of possible outcomes, given uncertainty about the actual magnitudes of various input assumptions. The results of a Monte Carlo analysis... 

There are actually very few. Modern optimization techniques practically guarantee location of a minimum energy structure, and only where the initial geometry provided is too symmetric will this not be the outcome. With a few notable exceptions (Hartree-Fock models applied to molecules with transition metals), Hartree-Fock, density functional and MP2 models provide a remarkably good account of equilibrium structure. Semi-empirical quantum chemical models and molecular mechanics models, generally fare well where they have been explicitly parameterized. Only outside the bounds of their parameterization is extra caution warranted. Be on the alert for surprises. While the majority of molecules assume the structures expected of them, some will not. Treat "unexpected" results with skepticism, but be willing to alter preconceived beliefs. 

As an example, a recent intercomparison (37) included three N02 measurement techniques aTDLAS-based system and two chemical-based systems— the photolysis-ozone chemiluminescence system diagramed in Figure 7 and an instrument based on N02 plus luminol chemiluminescence. Above 2 ppbv the three instruments gave similar results, but at sub-ppbv the results from the three techniques became dissimilar. Tests on the prepared mixtures showed that the luminol results were affected by expected interferences from 03 and PAN. No interferences were found in the TDLAS system, but near the detection limit the data analysis procedures calculated levels of N02 that were too high. The outcome of this intercomparison was close to the ideal the sensitivity, specificity, accuracy, and precision of each instrument were objectively analyzed previous data sets taken by different systems can now be reliably evaluated and each investigator was able to perceive areas in which the technique could be improved. 

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