Title: Developing a Model for AI-Based Predictive Analytics in Marketing: Strategies for Customer Acquisition and Retention
Authors: Seun Akinfolarin, Ekene Cynthia Onukwulu Mercy Odochi Agho Naomi Chukwurah
Volume: 9
Issue: 3
Pages: 309-324
Publication Date: 2025/03/28
Abstract:
In the rapidly evolving landscape of digital marketing, leveraging artificial intelligence (AI) for predictive analytics has emerged as a transformative strategy for customer acquisition and retention. This review presents a model designed to harness AI-based predictive analytics to optimize marketing strategies, focusing on enhancing customer engagement and driving business growth. The model integrates advanced machine learning algorithms and data analytics to forecast customer behavior, preferences, and trends, enabling marketers to tailor their strategies more effectively. The core of the model revolves around data collection and analysis, emphasizing the importance of gathering comprehensive customer data from various touchpoints, including social media, online interactions, and transactional records. By applying AI-driven analytics to this data, the model identifies patterns and predicts future customer actions, allowing for the creation of personalized marketing campaigns that resonate with individual preferences and needs. For customer acquisition, the model proposes using predictive analytics to identify high-potential leads and optimize targeting strategies. It involves segmenting potential customers based on predictive scoring, which assesses the likelihood of conversion. This enables marketers to allocate resources more efficiently and develop targeted outreach programs that improve conversion rates. In terms of customer retention, the model advocates for predictive insights to enhance customer relationship management. By analyzing customer behavior and feedback, AI can predict churn and suggest proactive measures to retain customers. This includes personalized retention offers, tailored communication strategies, and improved customer service initiatives. The model also highlights the role of continuous learning and adaptation. AI systems can evolve with changing customer behaviors and market conditions, providing ongoing refinement of predictive models to maintain their accuracy and relevance. This study develops a model for leveraging AI-based predictive analytics to optimize marketing strategies for customer acquisition and retention. The model focuses on the application of machine learning techniques to analyze vast datasets, identify customer behavior patterns, and predict future purchasing trends. By utilizing AI-driven insights, marketers can create targeted campaigns that effectively attract and retain customers. The paper discusses the technical and operational aspects of implementing predictive analytics and the implications for marketing strategy, offering a roadmap for businesses seeking to improve their customer management practices. In conclusion, the proposed model for AI-based predictive analytics in marketing offers a robust framework for optimizing customer acquisition and retention strategies. By leveraging AI to anticipate customer needs and behaviors, businesses can enhance their marketing effectiveness, drive higher engagement, and achieve sustained growth.