This thesis is an analysis looking into consumer experience and the predictability of consumerfeedback based on Net Promoter Score (NPS). The industry focused on in this document is the DTC clear aligner industry. Ideally, this research is meant to be applied in ”real world settings” with the intention that companies can leverage this concept to help improve their product’s own experience. In an effort to achieve that goal, we hope to answer two aspects 1) whether certain events that occur during a customer’s product experience impact score and to what degree and 2) whether machine learning methods can accurately predict scores based on the certain events mentioned. The research begins by performing exploratory data analysis to understand event correlation to score, running a few models while comparing the accuracy and discussing the modelled results while highlighting the importance of the research for companies in the industry.