RE-GrievanceAssist: Enhancing Customer Experience through ML-Powered Complaint Management
<|reference_start|>RE-GrievanceAssist: Enhancing Customer Experience through ML-Powered Complaint Management: 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.<|reference_end|>
@article{c2024re-grievanceassist:,
    title={RE-GrievanceAssist: Enhancing Customer Experience through ML-Powered
  Complaint Management},
    author={Venkatesh C, Harshit Oberoi, Anurag Kumar Pandey, Anil Goyal, and
  Nikhil Sikka},
    journal={arXiv preprint arXiv:2404.18963},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18963},
    primaryClass={cs.LG cs.CL}
}