Energy — Customer Engagement Propensity Scoring Pipeline

Overview

The Customer Engagement Propensity Scoring Pipeline is designed to assess the likelihood of customers engaging with marketing campaigns in the energy sector. The primary purpose of this DAG is to leverage scoring models that analyze customer data to predict interaction rates with promotional offers. Data sources include customer profiles, historical interaction logs, and campaign performance metrics. The ingestion pipeline begins with the collection of these data inputs, followed by data cleanin

The Customer Engagement Propensity Scoring Pipeline is designed to assess the likelihood of customers engaging with marketing campaigns in the energy sector. The primary purpose of this DAG is to leverage scoring models that analyze customer data to predict interaction rates with promotional offers. Data sources include customer profiles, historical interaction logs, and campaign performance metrics. The ingestion pipeline begins with the collection of these data inputs, followed by data cleaning and normalization to ensure consistency and accuracy. The processing steps involve segmenting customers based on their historical engagement and defining triggers for automated interactions. Scoring models are then applied to evaluate each segment's propensity to respond to specific offers. Quality controls are implemented throughout the pipeline to monitor data integrity and model performance. The outputs of this DAG include propensity scores for each customer segment, automated interaction triggers, and detailed reports on campaign effectiveness. Monitoring is achieved through key performance indicators (KPIs) such as conversion rates, customer engagement levels, and return on investment (ROI) from marketing campaigns. By utilizing this DAG, energy companies can enhance their marketing strategies, improve customer engagement, and ultimately drive higher conversion rates, leading to increased revenue and customer satisfaction.

Part of the Customer Personalization solution for the Energy industry.

Use cases

• Increased customer engagement through personalized marketing offers
• Higher conversion rates leading to improved sales performance
• Enhanced understanding of customer behavior and preferences
• Efficient resource allocation for marketing campaigns
• Improved ROI through targeted promotional strategies