Academy Gain new skills, enhance your expertise and take high-impact courses.

Register now

Telecom — Customer Personalization Model Deployment Pipeline

Popular

This DAG deploys propensity scoring models into a production environment, ensuring reliable customer interactions. It integrates results into the CRM while monitoring model performance in real-time.

Weeki Logo

Overview

The purpose of this DAG is to deploy propensity scoring models within a production environment, specifically tailored for the telecom industry. The workflow begins with the ingestion of data from various sources, including customer transaction histories, call detail records, and CRM data. These inputs are processed through a series of validation and testing steps to ensure the models' reliability and accuracy. Quality controls are implemented to verify the integrity of the scoring models, which

The purpose of this DAG is to deploy propensity scoring models within a production environment, specifically tailored for the telecom industry. The workflow begins with the ingestion of data from various sources, including customer transaction histories, call detail records, and CRM data. These inputs are processed through a series of validation and testing steps to ensure the models' reliability and accuracy. Quality controls are implemented to verify the integrity of the scoring models, which include end-to-end testing to validate performance under real-world conditions. Once validated, the scoring results are integrated into the Customer Relationship Management (CRM) system, enabling personalized customer interactions based on the propensity scores. The DAG also includes monitoring features that track key performance indicators (KPIs) such as model accuracy and customer engagement metrics in real-time. In the event of a model failure, an automated rollback mechanism is triggered to revert to the last stable version, ensuring uninterrupted service. This deployment pipeline not only enhances customer experience through tailored interactions but also drives business value by improving customer retention and engagement in the competitive telecom market.

Part of the Customer Personalization solution for the Telecom industry.

Use cases

  • Enhanced customer experience through personalized interactions
  • Increased customer retention rates via targeted engagement
  • Improved decision-making based on accurate propensity scores
  • Reduction in operational risks with rollback capabilities
  • Real-time insights into model performance for quick adjustments

Technical Specifications

Inputs

  • • Customer transaction histories
  • • Call detail records
  • • CRM customer profiles
  • • Marketing campaign responses
  • • Network usage statistics

Outputs

  • • Propensity scores for customer segments
  • • Integrated scoring results in CRM
  • • Performance reports on model accuracy
  • • Alerts for model performance issues
  • • Rollback logs for model failures

Processing Steps

  1. 1. Ingest data from multiple sources
  2. 2. Validate input data for accuracy
  3. 3. Run propensity scoring models
  4. 4. Test models through end-to-end validation
  5. 5. Integrate results into the CRM system
  6. 6. Monitor model performance in real-time
  7. 7. Trigger rollback if model fails

Additional Information

DAG ID

WK-0444

Last Updated

2025-04-04

Downloads

119

Tags