Energy — Customer Interaction Feature Engineering Pipeline

Overview

The primary purpose of this DAG is to create robust feature pipelines that facilitate the analysis of customer interactions, thereby enhancing the performance of AI assistants within the energy sector. The data sources include CRM systems, ITSM platforms, and customer feedback logs, which are essential for understanding customer behavior and optimizing interactions. The ingestion pipeline begins with extracting raw data from these systems, followed by a series of processing steps including data

The primary purpose of this DAG is to create robust feature pipelines that facilitate the analysis of customer interactions, thereby enhancing the performance of AI assistants within the energy sector. The data sources include CRM systems, ITSM platforms, and customer feedback logs, which are essential for understanding customer behavior and optimizing interactions. The ingestion pipeline begins with extracting raw data from these systems, followed by a series of processing steps including data transformation, feature engineering, and quality validation. During data transformation, raw inputs are cleansed and formatted to ensure consistency, while feature engineering involves identifying and creating relevant features that can significantly impact model accuracy. Quality controls are implemented to validate the integrity and reliability of the data, ensuring that only high-quality features are utilized. The outputs of this DAG include structured feature sets ready for machine learning model training, performance metrics for agent interactions, and insights into customer behavior patterns. Monitoring KPIs such as feature impact on agent efficiency and customer satisfaction scores are tracked to evaluate the effectiveness of the features developed. This rigorous approach not only enhances the predictive power of AI models but also drives significant business value by improving customer service quality and operational efficiency in the energy industry.

Part of the AI Assistants & Contact Center solution for the Energy industry.

Use cases

• Improved customer satisfaction through enhanced interaction quality
• Increased agent efficiency by leveraging data-driven insights
• Faster response times to customer inquiries and issues
• Higher accuracy in predictive modeling for customer behavior
• Streamlined operations leading to cost savings and better resource allocation