O-Lab
For nearly 2 years I was at Opinionlab where we helped companies gather feedback from their customers. In addition to collecting their NPS we could aggregate data to help brands make more timely and impactful decisions based on real customer pain points. We offered different forms of analysis of all this data through the use of reports, consulting services, and a self service dashboard application that I was in charge of.
Comment Card
Our company all started with a feedback tab and a comment card. Living on our clients websites we would be present for customers to leave their sentiment, give feedback and submit an NPS.
Data Viz
The new generation application was still a brand new offering, slowing manifesting itself as we included more features.
From a competitive landscape we aimed to include the complexity of tools like Qualtrics and Looker, yet give the ease of use like Survey Monkey and Usabilla.
O-Trend Report
A 20 page report that was utilized by 75% of our clients to digest their data. A lot of the features we prioritized derived from this one report. The report focused on specified sections or custom reports that would track month over month and year over year trends to easily identify user behavior and engagement. Our first goal was to improve on our filtering allowing our users functionality to replicate reports from the original report.
Advanced Filtering
Filtering was going to be a long ongoing improvement. We planned to include all of our our taxonomies, variables and custom IDs that clients could have control of. However we knew the upgrade would have to be released gradually, slowly increasing the complexity.
With a lot of the projects I worked on, I started my journey learning from subject matter experts, and looking at the data of current usage . Through surveys and internal critiques I would gather feedback on my decisions by showing my work in progress.
Mapping Complexity
The hardest part of managing all the changing components was having multiple versions of the product. There was the working web portal, a newer version we planned to release, then a future version for user testing. It was simpler to map out all the components from their most complex state and work backwards to make sure the patterns we were using would hold up.
Part of the challenge was the use of our own likert scale. There was a marketing need to prove its worth as a valuable and informative measure of our clients users engagement with their brand.