During the summer of 2023, I returned to the Amazon's Alexa HumanFX Team as a UX Designer Intern. I worked with a Senior Technical Product Manager on am upcoming Echo Dot feature meant to drive customer engagement and purchase decision.

During the internship, I had to conduct a quantitative survey (420 respondents) via Qualtrics' Market Research Panel to uncover customer needs. It was a costly endeavor with thousands of dollars spent recruiting the right people to fill out the survey.

Once respondents finish filling out the survey, one major hassle was to conduct a "scrub" of the survey data to identify low-quality responses. For example, in the image below, respondents might have rushed through the question by selecting the same response every time. (Otherwise known as "straight-lining")

Some other participants would provide careless responses by alternating between responses in a predictable way. (I call it a "zigzagging patttern")

My team's existing solution is to export all responses into a csv file and manually tagging each low-quality response. It was time-consuming for large quantitative survey with hundreds of data points.

To expedite the data scrubbing workflow for our UX Researchers, I coded a web-based platform that automates the discovery of straight-lining and zigzagging responses.