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

Register now

Telecom — Customer Propensity Scoring Model Training Pipeline

New

This DAG trains propensity scoring models using prepared customer data to enhance personalization. It includes model selection and performance evaluation, ensuring high-quality outputs for CRM integration.

Weeki Logo

Overview

The purpose of this DAG is to train propensity scoring models that leverage customer data to improve personalization strategies in the telecom industry. The pipeline begins by ingesting specific data sources such as customer transaction logs, demographic information, and service usage metrics. These inputs are processed through a series of steps that include data cleaning, feature engineering, and model training. During the model training phase, various algorithms are evaluated to select the bes

The purpose of this DAG is to train propensity scoring models that leverage customer data to improve personalization strategies in the telecom industry. The pipeline begins by ingesting specific data sources such as customer transaction logs, demographic information, and service usage metrics. These inputs are processed through a series of steps that include data cleaning, feature engineering, and model training. During the model training phase, various algorithms are evaluated to select the best-performing model based on defined metrics such as accuracy, precision, and recall. Performance metrics are continuously monitored to ensure the quality of the model, and if any drift is detected, an automatic retraining process is triggered to maintain model relevance. The final outputs, including the trained model and performance reports, are exposed via an API, allowing for seamless integration into existing CRM systems. This integration facilitates personalized marketing strategies and enhances customer engagement. The business value of this DAG lies in its ability to provide actionable insights that drive customer retention and increase revenue through targeted offers.

Part of the Customer Personalization solution for the Telecom industry.

Use cases

  • Enhances customer engagement through personalized marketing
  • Increases revenue by targeting high-propensity customers
  • Reduces churn with timely and relevant offers
  • Improves operational efficiency with automated processes
  • Ensures model reliability with continuous monitoring

Technical Specifications

Inputs

  • • Customer transaction logs
  • • Demographic information datasets
  • • Service usage metrics
  • • Customer feedback data
  • • Historical marketing campaign results

Outputs

  • • Trained propensity scoring model
  • • Performance evaluation report
  • • API endpoint for model access
  • • Customer segmentation insights
  • • Drift detection alerts

Processing Steps

  1. 1. Ingest customer transaction logs and demographic data
  2. 2. Clean and preprocess the data for analysis
  3. 3. Conduct feature engineering to enhance model input
  4. 4. Train multiple machine learning models on the dataset
  5. 5. Evaluate model performance and select the best model
  6. 6. Monitor model performance for drift detection
  7. 7. Expose the trained model and reports via API

Additional Information

DAG ID

WK-0443

Last Updated

2025-02-18

Downloads

107

Tags