Data-Driven Optimization of Customer Acquisition Cost: A Business Model Based on Predictive and Prescriptive Analytics

Kabir Chibber, Anand Zutshi

Abstract: This study presents a data-driven framework for optimizing Customer Acquisition Cost (CAC), a key metric for evaluating marketing and operational efficiency. Drawing on a comprehensive dataset from North American supermarkets, the research applies machine learning and segmentation analysis to identify the primary factors influencing CAC. The findings reveal that promotional design and media strategy significantly affect acquisition costs, while store format and amenities offer secondary leverage. A predictive business model is proposed, integrating store characteristics, geographic context, and promotional variables to guide marketing allocation. The results offer actionable insights for businesses aiming to reduce CAC, enhance scalability, and support financial planning through advanced analytics.

Keywords: customer acquisition cost, predictive analytics, business strategy, machine learning, marketing optimization

How to Cite?: Kabir Chibber, Anand Zutshi, "Data-Driven Optimization of Customer Acquisition Cost: A Business Model Based on Predictive and Prescriptive Analytics", Volume 14 Issue 9, September 2025, International Journal of Science and Research (IJSR), Pages: 1018-1027, https://www.ijsr.net/getabstract.php?paperid=SR25920111103, DOI: https://dx.doi.org/10.21275/SR25920111103