- Plan outcome-based interviews
- Capture desired outcomes
- Organize the outcomes
- Rate outcomes for importance and satisfaction
- Use the outcomes to jump-start innovation
For the subject of our research, the four of us team members selected Oncourse, an intranet tool used within Indiana University for the purposes of facilitating classroom document sharing, calendaring, and communication, among other uses. We found the ODI methodology to be especially helpful in breaking through the feature requests for and complaints about Oncourse that were flung at us as we interviewed users, allowing us to see the truly greatest areas of opportunity for innovation of the product. Our summary memo is below.
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Executive Summary
After conducting primary research and applying the opportunity algorithm of the Outcome Driven Innovation (ODI) model, we concluded that the outcome with the most opportunity for improvement is one that maximizes student preparation level for class.
Background
Oncourse was selected for several reasons - it is a well-known product among students at Indiana University, playing a central role in the students’ academic experience. However, since each student uses Oncourse in a unique way, we thought there was opportunity for innovation since a commonly agreed-upon usage method does not exist. We explored various outcomes students are trying to accomplish using Oncourse, helping us understand specific stress points with the product and also innovations that would enhance the product in the most meaningful ways.
Application of the Outcome Driven Innovation Methodology
The ODI methodology identifies a qualitative as well as quantitative way of fine-tuning outcomes that customers are trying to accomplish, solving one of the problems with qualitative research: customers can rarely tell you what they want. For instance, when asked about a time of frustration using Oncourse, one student responded “professors upload files in different ways, there is no standard method.” While this comment referred to a feature, specifically file structure, we had to probe him for a deeper understanding behind his response. Therefore, we asked him what feelings he wanted to experience or avoid using Oncourse, to which he responded, “I don’t want to become impatient. I want to feel calm and the process should not require too much of a thought process.” By understanding the emotional context of the high level feature comments and suggestions, we were able to analyze and aggregate the student experience with Oncourse. To replicate a representative sample population, we selected males, females, Kelley students, non-Kelley students, domestic students, and international students.
Upon analysis of all the responses, we discovered six distinct outcomes that students were trying to achieve with the product. These outcomes addressed specific issues with the product that are important to students, including preparation for exams, access to supplementary course material, and making up for a missed class. Next, we followed up with the respondents by having them rate the outcomes on importance and the level to which they were currently satisfied by the product. We then utilized the opportunity algorithm to identify the areas of greatest opportunity, finding that the three greatest opportunity areas were maximization of preparation for class, minimization of stress related to exams and assignments, and minimization of time necessary to see everything related to the school experience. Finally, we brainstormed possible directions for innovation based on the outcomes with the greatest opportunities.
Conclusions
We learned that Oncourse has several benefits but also several areas of improvement. The respondents use Oncourse to satisfy the basic jobs that the software was designed to perform: obtain the material needed for classes, exams, homework, and even updates on social events. However, emotions behind the responses indicated that Oncourse could perform these jobs either in more efficient, innovative, or personable methods.
The opportunity matrix revealed that maximization of students’ preparation level for class is the outcome with the widest gap in the current version of the product. In order to address this, we identified ways to better organize class information to make it easier to consume and more relevant for the student. Sample ideas include: a uniform interface across classes, a list of required vs. recommended actions for class preparation, and outlook calendar sync with class schedules and deliverable dates.
At the same time, we identified challenges with the ODI data gathering process: it is difficult to steer interviewees away from merely suggesting features or logging complaints, and equally difficult to draw out clearly distinct outcomes.
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