Consumer Products — Customer Personalization Based on Behavioral Data

Overview

The purpose of this DAG is to create personalized recommendations for customers in the consumer products industry by analyzing their behavioral data. The workflow begins with data ingestion from various sources, including CRM systems and interaction platforms, which provide a comprehensive view of customer interactions and preferences. Once ingested, the data undergoes a series of processing steps where it is cleaned, transformed, and segmented based on behavioral patterns. This segmentation all

The purpose of this DAG is to create personalized recommendations for customers in the consumer products industry by analyzing their behavioral data. The workflow begins with data ingestion from various sources, including CRM systems and interaction platforms, which provide a comprehensive view of customer interactions and preferences. Once ingested, the data undergoes a series of processing steps where it is cleaned, transformed, and segmented based on behavioral patterns. This segmentation allows for the identification of distinct customer groups, which is crucial for generating tailored recommendations. Quality control measures are implemented throughout the process to ensure the relevance and accuracy of the recommendations produced. The final outputs of this DAG are personalized recommendation reports that are published on customer portals, directly enhancing customer engagement and satisfaction levels. Monitoring key performance indicators (KPIs) such as recommendation click-through rates and customer feedback scores is essential for assessing the effectiveness of the personalization efforts. The business value of this DAG lies in its ability to improve customer loyalty and drive sales through targeted marketing strategies.

Part of the Data & Model Catalog solution for the Consumer Products industry.

Use cases

• Enhances customer engagement through personalized experiences
• Increases customer satisfaction and loyalty
• Drives sales through targeted recommendations
• Improves marketing efficiency with data-driven insights
• Facilitates better understanding of customer preferences