Consumer Products — Customer Interaction Feature Engineering Pipeline

Overview

The Customer Interaction Feature Engineering Pipeline is designed to optimize machine learning processes by extracting relevant features from customer interactions. This pipeline sources data from two primary inputs: the Customer Relationship Management (CRM) system and agent interaction logs. The ingestion process begins with data normalization, ensuring consistency across different data formats and sources. Following normalization, feature selection is performed to identify the most impactful

The Customer Interaction Feature Engineering Pipeline is designed to optimize machine learning processes by extracting relevant features from customer interactions. This pipeline sources data from two primary inputs: the Customer Relationship Management (CRM) system and agent interaction logs. The ingestion process begins with data normalization, ensuring consistency across different data formats and sources. Following normalization, feature selection is performed to identify the most impactful variables that influence customer behavior. This step is crucial as it reduces dimensionality and enhances model performance. Quality validation checks are then implemented to assess the integrity and reliability of the selected features, ensuring that only high-quality data is used for model training. The outputs of this pipeline include a refined dataset ready for machine learning, which can be utilized for training predictive models that forecast customer behavior. Monitoring Key Performance Indicators (KPIs) such as feature significance and model accuracy are essential for assessing the effectiveness of the feature engineering process. The business value of this DAG lies in its ability to improve customer engagement strategies through data-driven insights, ultimately leading to increased customer satisfaction and retention in the consumer products industry.

Part of the AI Assistants & Contact Center solution for the Consumer Products industry.

Use cases

• Improves customer engagement strategies with data insights.
• Enhances predictive accuracy of customer behavior models.
• Reduces data processing time through efficient feature selection.
• Increases customer satisfaction with tailored interaction strategies.
• Facilitates data-driven decision-making in marketing initiatives.