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.

最近几年，数字平台公司在应对广泛消费者采用的情况下，在管理客户投诉方面面临着越来越多的挑战。本文介绍了一种名为 RE-GrievanceAssist 的端到端流程，专门用于房地产客户投诉管理。该流程由三个关键组成部分组成：i) 使用 TF-IDF 向量化和 XGBoost 分类器进行回复 / 不回复机器学习模型；ii) 使用 fasttext 分类器的用户类型分类器；iii）使用 TF-IDF 向量化和 XGBoost 分类器的问题 / 子问题分类器。最后，它已经部署为批处理作业在 Databricks 中，从 2023 年 8 月以来，整体手动工作量减少了 40％，每月成本减少了 Rs 1,50,000。