Data-Driven Marketing: Predictive Models and ROI Personalization

Goals

• Explain the core principles and applications of predictive analytics in modern marketing contexts.
• Identify and integrate relevant data sources to support predictive modeling and customer insights.
• Apply machine learning techniques to predict customer behaviors such as churn, purchase intent, and lifetime value.
• Design and interpret customer journey maps that align with business goals and customer expectations.
• Combine predictive analytics with customer journey mapping to optimize marketing strategies across multiple touchpoints.
• Utilize segmentation and personalization techniques to enhance customer engagement and conversion rates.
• Evaluate the performance of predictive models using key metrics such as accuracy, precision, and ROI.
• Implement ethical and transparent data practices in predictive marketing processes.
• Leverage AI-driven tools to automate and refine customer journey analytics in real time.
• Formulate data-informed marketing strategies that improve customer experience and long-term brand loyalty.

Prerequisites

• Learners are expected to have a foundational understanding of marketing principles, digital marketing strategies, and customer relationship management concepts. 
• Basic knowledge of statistics, data analysis, and familiarity with spreadsheet or analytical tools (such as Excel, Google Analytics, or introductory Python/R) is recommended.
•  Prior experience or coursework in marketing analytics, consumer behavior, or data-driven decision-making will be beneficial but not mandatory. 
• A willingness to engage with data interpretation and analytical problem-solving is essential for successful course completion.