Automating Classification of Customer Queries
Improving Efficiency and Quality at Alpha Bank's Machine Learning Lab
Introduction
- Presenting the problem of categorizing customer queries at Alpha Bank
- Discussing the challenges and complexities involved in the process
- Highlighting the importance of automating the classification task
- Exploring the benefits of automation for both employees and customers
Classification Process
- Describing the communication process between customers and bank employees
- Explaining the need for a unified classifier and its hierarchical structure
- Highlighting the importance of accurate classification for better decision-making
- Discussing the various thematic categories and their relevance to different banking products
Data Analysis and Reporting
- Analyzing the collected data to generate insights and reports
- Examining the distribution of thematic categories over time
- Identifying trends and anomalies in customer queries
- Exploring the use of data analysis for making informed management decisions
Modeling and Improvement
- Introducing the task of text classification using machine learning
- Exploring different modeling approaches and techniques
- Discussing the challenges faced and possible solutions
- Presenting the results of initial modeling experiments and their implications
Data Annotation and Quality Control
- Describing the process of data annotation by human experts
- Explaining the need for quality control measures
- Discussing the benefits of cross-verification and feedback
- Presenting the results of data annotation and the achieved quality
Incorporating User Feedback
- Addressing the challenge of incorporating user feedback into the classification process
- Exploring the use of customer's existing banking products as features
- Highlighting the impact of feature inclusion on model performance
- Presenting the results and demonstrating the improved accuracy
Pilot Implementation
- Describing the pilot implementation of the automated classification system
- Discussing the results and benefits achieved
- Highlighting the reduction in manual classification workload
- Presenting the improved accuracy in comparison to human operators
Conclusion
- Summarizing the key points and achievements
- Highlighting the importance of automation in improving efficiency and quality
- Addressing the future scope for further improvements
- Encouraging the adoption of automated classification systems in similar contexts