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 \(\mathtt {RE\text {-}GrievanceAssist}\), 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 by 35% since August 2023. (Demo Video is available at https://www.youtube.com/watch?v=PM4Q3dNTrr4.)
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Notes
- 1.
Demo Video is available at https://www.youtube.com/watch?v=PM4Q3dNTrr4.
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Chandar, V., Oberoi, H., Pandey, A.K., Goyal, A., Sikka, N. (2024). RE-GrievanceAssist: Enhancing Customer Experience Through ML-Powered Complaint Management. In: Bifet, A., et al. Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14948. Springer, Cham. https://doi.org/10.1007/978-3-031-70371-3_27
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DOI: https://doi.org/10.1007/978-3-031-70371-3_27
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