Retail — Customer Intent Classification for Enhanced Service Delivery

Overview

The primary purpose of this DAG is to classify customer intents to enhance service delivery and satisfaction in the retail sector. It ingests data from multiple sources, including CRM systems, support tickets, and customer feedback forms. The data pipeline begins with the normalization of these inputs to ensure consistency across different formats. Following normalization, the data undergoes an analysis phase where machine learning algorithms are applied to identify and classify customer intents

The primary purpose of this DAG is to classify customer intents to enhance service delivery and satisfaction in the retail sector. It ingests data from multiple sources, including CRM systems, support tickets, and customer feedback forms. The data pipeline begins with the normalization of these inputs to ensure consistency across different formats. Following normalization, the data undergoes an analysis phase where machine learning algorithms are applied to identify and classify customer intents accurately. Quality control measures are implemented throughout the process to validate the accuracy of classifications, ensuring that the insights generated are reliable. The classified intents are then integrated into a customer interaction management system, allowing for improved tracking of performance metrics. Key performance indicators (KPIs) such as customer satisfaction rates, response times, and intent classification accuracy are monitored to assess the effectiveness of the system. The business value of this DAG lies in its ability to provide actionable insights that drive better customer engagement strategies, ultimately leading to increased customer loyalty and retention in the competitive retail landscape.

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

Use cases

• Improves customer satisfaction through accurate intent recognition
• Increases operational efficiency in handling customer inquiries
• Facilitates data-driven decision-making in customer service
• Enhances customer loyalty with personalized interactions
• Provides insights for continuous service improvement