Energy — User Intent Classification for Enhanced Search Relevance

Overview

The purpose of this DAG is to analyze user queries in the energy sector to classify their intents, ultimately enhancing the relevance of search results. The architecture consists of a data ingestion pipeline that collects user query logs from various sources such as web interfaces, mobile applications, and customer feedback systems. These inputs are processed using advanced natural language processing techniques to identify and categorize user intents, such as information seeking, transaction re

The purpose of this DAG is to analyze user queries in the energy sector to classify their intents, ultimately enhancing the relevance of search results. The architecture consists of a data ingestion pipeline that collects user query logs from various sources such as web interfaces, mobile applications, and customer feedback systems. These inputs are processed using advanced natural language processing techniques to identify and categorize user intents, such as information seeking, transaction requests, or service inquiries. The processing steps include data cleansing, intent recognition, classification, and feedback loop integration, where results are used to adjust search algorithms dynamically. Quality controls are implemented to ensure the accuracy of intent classification, with performance metrics tracked to evaluate the effectiveness of the model. Key performance indicators include classification accuracy, user engagement rates, and search result satisfaction scores. The outputs of this DAG consist of classified user intents, updated search algorithm parameters, and performance reports. Monitoring is crucial, with alerts set up to detect anomalies in user behavior or classification performance, enabling quick responses to any issues. The business value lies in improved user experience, higher search result relevance, and increased customer satisfaction, ultimately leading to better engagement and retention in the energy sector.

Part of the Literature Review solution for the Energy industry.

Use cases

• Increases user satisfaction through relevant search results
• Reduces search time for users, enhancing efficiency
• Improves engagement rates with tailored responses
• Facilitates data-driven decision-making for search algorithms
• Boosts overall customer retention in the energy sector