Summary of Re-grievanceassist: Enhancing Customer Experience Through Ml-powered Complaint Management, by Venkatesh C et al.
RE-GrievanceAssist: Enhancing Customer Experience through ML-Powered Complaint Management
by Venkatesh C, Harshit Oberoi, Anurag Kumar Pandey, Anil Goyal, Nikhil Sikka
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed RE-GrievanceAssist pipeline is an end-to-end solution for real estate customer complaint management. It consists of three components: a response/no-response model using TF-IDF vectorization and XGBoost classifier; a user type classifier using fasttext classifier; and an issue/sub-issue classifier using TF-IDF vectorization and XGBoost classifier. The pipeline has been deployed as a batch job in Databricks, resulting in a 40% reduction in manual effort and a monthly cost savings of Rs 1,50,000 since August 2023. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The RE-GrievanceAssist pipeline helps real estate companies handle customer complaints more efficiently. It’s like a robot that can understand and respond to customer issues. The system uses special techniques to figure out what kind of issue it is and who the customer is. By using this system, companies can save time and money. |
Keywords
» Artificial intelligence » Fasttext » Tf idf » Vectorization » Xgboost
