In recent years, digital platform companies have faced increasing challenges in managing customer complaints, driven by widespread consumer adoption. This paper introduces an end-to-end pipeline, named RE-GrievanceAssist, designed specifically for real estate customer complaint management. The pipeline consists of three key components: i) response/no-response ML model using TF-IDF vectorization and XGBoost classifier ; ii) user type classifier using fasttext classifier; iii) issue/sub-issue classifier using TF-IDF vectorization and XGBoost classifier. Finally, it has been deployed as a batch job in Databricks, resulting in a remarkable 40% reduction in overall manual effort with monthly cost reduction of Rs 1,50,000 since August 2023.
Cite
@article{arxiv.2404.18963,
title = {RE-GrievanceAssist: Enhancing Customer Experience through ML-Powered Complaint Management},
author = {Venkatesh C and Harshit Oberoi and Anurag Kumar Pandey and Anil Goyal and Nikhil Sikka},
journal= {arXiv preprint arXiv:2404.18963},
year = {2024}
}