Machine Learning Improves Customer Satisfaction

Authors

Jessie Guo, University of Nebraska - LincolnFollow
Caitlin McCarthy, University of Nebraska - LincolnFollow
Alexis Delos Reyes, University of Nebraska - LincolnFollow
Taylor Bernt, University of Nebraska - LincolnFollow
Megan Chaffey, University of Nebraska - LincolnFollow

Date of this Version

5-2022

Document Type

Thesis

Citation

McCarthy, C., J. Guo, A. Delos Reyes, M. Chaffey & T. Bernt. 2022. Machine Learning Improves Customer Satisfaction. Undergraduate
Honors Thesis. University of Nebraska-Lincoln.

Comments

Copyright Caitlin McCarthy, Jessie Guo, Alexis Delos Reyes, Megan Chaffey, & Taylor Bernt 2022.

Abstract

Within DMSi’s customer service platform, The Wedge, customers are able to submit new ideas for features using the Idea Portal. Within the Idea Portal, there are too many ideas with limited filtering capabilities. The goal of this project was to improve customer satisfaction and DMSi’s internal teams’ pain points through data available on The Wedge and the Design Studio team’s knowledge in machine learning. The final solution resulted in a recommendation engine geared toward customer satisfaction, and two reports: the Strategic Classification Report, which identifies ideas in line with DMSi’s company-wide strategic initiatives, and the Duplicate Detection Report, which identifies similar or identical ideas that can easily be merged.