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arXiv · 2404.18963

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

Venkatesh C·Harshit Oberoi·Anurag Kumar Pandey·Anil Goyal·Nikhil Sikka
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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 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.

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Venkatesh C, Harshit Oberoi, Anurag Kumar Pandey, Anil Goyal, Nikhil Sikka. 2024-04-29. RE-GrievanceAssist: Enhancing Customer Experience through ML-Powered Complaint Management. https://arxiv.org/abs/2404.18963

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