Tracking Outcomes: Using Importance-Performance Analysis to Analyze Feat in the Workplace

What makes an initiative successful? That’s exactly what Feat wants to find out. Now that Feat is moving into the corporate work environment, we are looking for ways to track the success of our operational programs. There are many systems available to assess a program, but we found that Importance-performance analysis (IPA) is particularly helpful and relevant when assessing the outcome of Feat initiatives.

What is IPA?

Importance-performance analysis, or IPA, is used to measure how people feel about the quality of service they have received and the effectiveness of certain characteristics of a program. IPA can be used to make decisions and prioritize resources by identifying the level of importance and satisfaction clients associate with specific attributes of a program or service.

Such assessment is done through a survey of participants in which they are given importance and satisfaction questions.

The purpose of asking importance and satisfaction questions is to evaluate the relevancy between responses and assess participant priorities and satisfaction level. This analysis is useful to improve the Feat service as a whole and to repeat it in a similar environment, all while providing valuable insight for the company regarding employee reactions to specific scenarios.

Understanding IPA

From survey data, IPA uses a pair of coordinate axis to evaluate importance (y-axis) and performance (x-axis) and make deductions based on the data. The graph is thus divided into four quadrants.

Each quadrant represents a characterization of the program.

Quadrant A: Concentrate here // high importance, low performance

• Attributes in this quadrant demand immediate attention and highlight where the main focal points should be in creating a next possible iteration of a similar program.

Quadrant B: Keep up with the good work // high importance, high performance

• Attributes in this quadrant indicate high satisfaction and value. They should be repeated in next possible iterations.

Quadrant C: Low priority // low importance, low performance

• Attributes in this quadrant do not require as much emphasis or attention, as participants find them to be not important nor satisfactory.

Quadrant D: Possible overkill // low importance, high performance

• Attributes in this quadrant imply that resources have been wasted on strengthening potentially unnecessary attributes and in next possible iterations these attributes would be better implemented elsewhere

Using IPA with Feat.Corp for Barilla

Using data taken from a survey given to 90 program participants with nine importance questions and ten satisfaction questions, Feat was able to gain the following insight into this particular program iteration.

Both derived importance and stated importance charts show participants are happy with a balanced diet inspired by the Mediterranean model and using technology to improve lifestyle. These two elements are rated high importance and high satisfaction which means Feat did a good job promoting such elements.

A wellness program for employees, environmental impact creation through healthy practices, and work environment improvement are three elements that participants received with high satisfaction even if they weren’t initially valued as highly important. Feat will focus on improving the promotion of these elements in a next possible iteration. Barilla can understand from this data that these values might be promoted more internally.

Entertainment and social interaction were not properly delivered. Feat is now focusing on stronger and more effective engagement and interaction design patterns. This would be an important aspect to test again in a further possible iteration.

Finally, rewards resulted as the weakest element. Although participants didn’t consider rewards as a fundamental aspect, they were still unsatisfied with their quality and appeal. A next possible iteration should definitely have more attractive and effective rewards.

IPA for Future Iterations

Feat.Corp for Barilla was the first Feat program in a workspace. By using IPA analytics, Feat was able to understand where to concentrate efforts for similar programs. Now that Feat has launched its Airbnb program, feedback from Feat for Barilla played a crucial role in helping plan the new iteration.

Knowing where to focus energy and attention in a program is an important aspect when developing a program as versatile as Feat. It makes sense as a tool to gauge participant interest and set goals for future iterations.