ERP-Driven Data Analytics for Personalizing Advertising and Strengthening CRM Strategies
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.
Publication Details
- Journal
- Pacific Journal of Business Innovation and Strategy
- Published
- 2025-01-01
- DOI
- https://doi.org/10.70818/pjbis.2025.v02i01.019
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00