Retail — Multi-Source Customer Service Data Ingestion Pipeline

Overview

The primary purpose of this DAG is to facilitate the ingestion of multi-source data to support customer service functions in the retail industry. It collects data from various input sources, including Customer Relationship Management (CRM) systems, IT Service Management (ITSM) platforms, and interaction logs. The ingestion pipeline begins with the extraction of raw data, which is then subjected to normalization processes to ensure consistency across different formats. Following normalization, th

The primary purpose of this DAG is to facilitate the ingestion of multi-source data to support customer service functions in the retail industry. It collects data from various input sources, including Customer Relationship Management (CRM) systems, IT Service Management (ITSM) platforms, and interaction logs. The ingestion pipeline begins with the extraction of raw data, which is then subjected to normalization processes to ensure consistency across different formats. Following normalization, the data undergoes validation checks to guarantee its quality and reliability. Once validated, the cleansed data is stored in a centralized data warehouse, where it can be accessed for further analysis. To monitor the performance of this DAG, key performance indicators (KPIs) such as data ingestion latency and volume metrics are tracked. Alerts are configured to notify stakeholders in the event of any ingestion failures, ensuring prompt resolution. The outputs of this DAG include enriched datasets that can be utilized for advanced analytics and reporting, ultimately driving better customer engagement strategies. By leveraging this pipeline, retail businesses can enhance their operational efficiency, improve customer satisfaction, and make data-driven decisions that align with market demands.

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

Use cases

• Enhances customer service responsiveness and effectiveness.
• Improves data-driven decision-making capabilities.
• Increases operational efficiency through streamlined processes.
• Reduces data quality issues and inconsistencies.
• Facilitates better customer engagement strategies.