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

arXiv: https://arxiv.org/abs/2404.18963 · HTML: https://mlsanity.com/paper/2404.18963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 2 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)

## Abstract (arXiv)

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.