Leveraging non-respondent data in customer satisfaction modeling

Morteza Zihayat, Anteneh Ayanso, Heidar Davoudi, Mehdi Kargar, Nigussie Mengesha
January 2021

Abstract

This paper presents methods for leveraging non-respondent data in customer satisfaction modeling, improving prediction accuracy and business insights.

Type
Journal article
Publication
Journal of Business Research

This work explores innovative approaches to incorporate non-respondent data in customer satisfaction analysis.

Customer Satisfaction Non-Respondent Data Business Research Data Modeling Prediction
Morteza Zihayat
Morteza Zihayat
Principal Investigator

Dr. Morteza Zihayat is a Canada Research Chair (CRC) in Human-Centered AI and Associate Professor at Toronto Metropolitan University, Faculty of Engineering and Architectural Science. He also holds appointments as Adjunct Associate Professor at the University of Waterloo (Management Sciences) and IBM Faculty Fellow at IBM Centre for Advanced Studies. He is the Director of the Human-Centered Machine Intelligence Lab.