ERP-Driven Data Analytics for Personalizing Advertising and Strengthening CRM Strategies

Abstract

The rapid growth of digital technologies has made data analytics a cornerstone of modern business strategies, particularly in the realms of advertising and customer relationship management (CRM). ERP-driven data analytics has emerged as a significant contributor to personalizing advertising efforts and enhancing CRM strategies. This study aims to explore how ERP-driven data analytics can personalize advertising, improve customer targeting, and optimize CRM strategies through the integration of large-scale data sets and advanced analytical techniques. A mixed-methods approach was employed, including quantitative analysis through the use of statistical models applied to ERP-generated data from 52 companies across various industries. In-depth case studies of organizations utilizing ERP-driven data analytics for personalized advertising were analyzed, focusing on performance metrics such as customer engagement, conversion rates, and retention. The data was collected from ERP systems over a one-year period. Advanced regression models, predictive analytics, and clustering techniques were applied to examine the effects on CRM strategies. The implementation of ERP-driven data analytics resulted in a 25% increase in the efficiency of advertising campaigns, a 40% improvement in customer segmentation accuracy, and a 15% reduction in customer churn rates. Additionally, businesses reported a 20% growth in customer lifetime value (CLV) and a 30% improvement in customer engagement rates. Predictive analytics models demonstrated a 12% increase in ad conversion rates based on targeted campaigns. ERP-driven data analytics significantly enhances personalized advertising and strengthens CRM strategies, offering businesses substantial improvements in customer targeting, retention, and overall marketing effectiveness.