Introduction
The changes created by Covid-19 affected how we interact at every level. The sudden onset of the pandemic required a quick transition to virtual program increment planning sessions.
To ensure the transition was as seamless possible I was tasked with developing a plan to collect and track sentiment and help develop recommendations on an ongoing basis.
Timeline
My approach began with helping stakeholders identify goals and objectives of the research. Once a clear set of goals and objectives had been identified I drafted up a research plan outlining my recommended methodology for solution with the team. The recommend solution consisted of a mix method approach to sentiment gathering beginning with a survey and following up with interviews to fill in gaps in data.
Once the research plan was approved I began drafting the survey questions using the business goals and objectives as a guide. After this surveys were deployed to users after survey collection is complete an interview guide is developed using feedback from the initial surveys.
Once interviews are complete I summarize feedback into a presentation that is used to meet with stakeholders and develop recommendations. Recommendations that are adopted by stakeholders are then previewed with a subset of users to gather any additional feedback and identify any unknown risk prior to the next PI planning session. This process is then repeated during the next PI planning event 10 weeks later
Impact
Recommended changes based off of feedback provided from user research have proved to be powerful in lifting overall satisfaction rates. The research has also provided the need push for adaption of new tools and policies that management has sometimes been hesitant to agree to such as transitioning from WebEx Classrooms to Zoom as a VTC tool. Resources have also been more accurately targeted as a result of research.
The research from this imitative has also helped me establish metrics to benchmark success and failure of the PI planning sessions moving forward. Changes in user sentiment are closely monitored so that adjustments can be made to ensure maximum user satisfaction.
