Telecom — Customer Personalization Feedback Loop for Scoring Models

Overview

The primary purpose of this DAG is to create a feedback loop that optimizes scoring models for customer personalization within the telecom industry. It begins by collecting data from various sources, including campaign results and customer interaction logs. The ingestion pipeline processes this data to identify trends and areas for improvement in scoring models. The processing steps include data cleansing, feature extraction, trend analysis, model retraining, and action plan initiation in case o

The primary purpose of this DAG is to create a feedback loop that optimizes scoring models for customer personalization within the telecom industry. It begins by collecting data from various sources, including campaign results and customer interaction logs. The ingestion pipeline processes this data to identify trends and areas for improvement in scoring models. The processing steps include data cleansing, feature extraction, trend analysis, model retraining, and action plan initiation in case of performance failures. Quality controls are integrated throughout the pipeline to ensure data integrity and model accuracy. The outputs of this DAG include updated scoring models, actionable insights for marketing teams, and performance reports. Key performance indicators (KPIs) such as model accuracy, customer engagement rates, and campaign ROI are monitored to assess the effectiveness of the feedback loop. This approach provides significant business value by enhancing customer targeting, increasing campaign effectiveness, and ultimately driving higher customer satisfaction and retention rates.

Part of the Customer Personalization solution for the Telecom industry.

Use cases

• Improved accuracy of customer targeting and segmentation
• Enhanced responsiveness to customer behavior changes
• Increased campaign effectiveness and ROI
• Higher customer satisfaction and loyalty rates
• Streamlined marketing operations through automation