ML Anthology

RE-GrievanceAssist: Enhancing Customer Experience Through ML-Powered Complaint Management

Venkatesh Chandar, Harshit Oberoi, Anurag Kumar Pandey, Anil Goyal, Nikhil Sikka
ECML-PKDD 2024 pp. 394-398
doi:10.1007/978-3-031-70371-3_27 /ecmlpkdd/2024/chandar2024ecmlpkdd-regrievanceassist/

Abstract

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 $\mathtt {RE\text {-}GrievanceAssist}$ 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 by 35% since August 2023. (Demo Video is available at https://www.youtube.com/watch?v=PM4Q3dNTrr4 .)

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Text

Chandar et al. "RE-GrievanceAssist: Enhancing Customer Experience Through ML-Powered Complaint Management." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024. doi:10.1007/978-3-031-70371-3_27

Markdown

[Chandar et al. "RE-GrievanceAssist: Enhancing Customer Experience Through ML-Powered Complaint Management." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024.](https://mlanthology.org/ecmlpkdd/2024/chandar2024ecmlpkdd-regrievanceassist/) doi:10.1007/978-3-031-70371-3_27

BibTeX

@inproceedings{chandar2024ecmlpkdd-regrievanceassist,
  title     = {{RE-GrievanceAssist: Enhancing Customer Experience Through ML-Powered Complaint Management}},
  author    = {Chandar, Venkatesh and Oberoi, Harshit and Pandey, Anurag Kumar and Goyal, Anil and Sikka, Nikhil},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2024},
  pages     = {394-398},
  doi       = {10.1007/978-3-031-70371-3_27},
  url       = {https://mlanthology.org/ecmlpkdd/2024/chandar2024ecmlpkdd-regrievanceassist/}
}