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Retail — Customer Purchase Propensity Model Training Pipeline

Free

This DAG trains machine learning models to predict customer purchase propensity using interaction and segmentation data. It enhances customer personalization strategies and optimizes marketing efforts.

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Overview

The purpose of this DAG is to train machine learning models that predict the likelihood of customer purchases, thereby facilitating enhanced customer personalization in the retail sector. The primary data sources include customer segmentation data and interaction logs, which are ingested into the pipeline for processing. The data ingestion step ensures that the models are trained on the most relevant and up-to-date information available. The processing steps involve data cleaning, feature engine

The purpose of this DAG is to train machine learning models that predict the likelihood of customer purchases, thereby facilitating enhanced customer personalization in the retail sector. The primary data sources include customer segmentation data and interaction logs, which are ingested into the pipeline for processing. The data ingestion step ensures that the models are trained on the most relevant and up-to-date information available. The processing steps involve data cleaning, feature engineering, model training, and evaluation. During model training, various algorithms are applied to identify the best-performing models based on their predictive accuracy and recall metrics. Quality control measures are implemented to ensure that the models meet the required performance standards. The outputs of this DAG include the selected models ready for deployment, along with performance metrics that indicate their effectiveness. Monitoring KPIs such as precision and recall are tracked to assess model performance continually. Additionally, a recovery mechanism is in place to retry training in case of failures, ensuring robustness in the workflow. The business value of this DAG lies in its ability to provide actionable insights into customer behavior, enabling retailers to tailor their marketing strategies and improve customer engagement significantly.

Part of the Customer Personalization solution for the Retail industry.

Use cases

  • Improves marketing effectiveness through targeted campaigns
  • Enhances customer experience with personalized recommendations
  • Increases sales by predicting customer purchase behaviors
  • Reduces churn by identifying at-risk customers
  • Optimizes resource allocation based on predictive insights

Technical Specifications

Inputs

  • • Customer segmentation data
  • • Customer interaction logs
  • • Historical purchase data

Outputs

  • • Trained machine learning models
  • • Model performance metrics
  • • Deployment-ready model artifacts

Processing Steps

  1. 1. Ingest customer segmentation and interaction data
  2. 2. Clean and preprocess the data for analysis
  3. 3. Perform feature engineering to enhance model inputs
  4. 4. Train multiple machine learning models
  5. 5. Evaluate models based on precision and recall
  6. 6. Select the best-performing model for deployment
  7. 7. Output model artifacts and performance metrics

Additional Information

DAG ID

WK-0303

Last Updated

2026-01-27

Downloads

97

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