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

arXiv 2404.18963 ↗First posted 29 Apr 2024cs.LG cs.CLcited by 0

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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.

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.

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References · 2

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2016FastText.zip: Compressing text classification modelscited by 335

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