http://hdl.handle.net/2451/75927 Fri, 14 Aug 2026 22:18:32 GMT 2026-08-14T22:18:32Z http://hdl.handle.net/2451/75928 Title: APPLIED BUSINESS ANALYTICS FOR MARKETING DECISION-MAKING Authors: Mendoza, Jose Abstract: Applied Business Analytics for Marketing Decision-Making is an applied textbook designed to help students use data, analytical methods, and visualization to support marketing and business decisions. Written for graduate students in marketing, integrated marketing, business analytics, and related professional programs, the book assumes limited prior experience with programming or statistics and emphasizes conceptual understanding, practical application, and managerial interpretation. The textbook guides readers through the complete analytics workflow, including problem framing, measurement, data preparation, exploratory and descriptive analysis, customer segmentation, regression, predictive modeling, classification, forecasting, experimentation, data visualization, and the communication of evidence-based recommendations. Students use Python in Google Colab and Tableau to analyze realistic marketing datasets, evaluate analytical outputs, and translate findings into actionable business insights. A distinctive feature of the book is its treatment of artificial intelligence as part of contemporary analytics practice. Students learn to use AI assistants to generate and troubleshoot code, explore analytical approaches, and improve communication while remaining responsible for verifying results, explaining methods, documenting AI use, and exercising professional judgment. The book’s recurring workflow—specify, predict and verify, explain, and document—reinforces analytical accountability throughout. Each chapter functions as a self-contained learning unit and includes a marketing decision context, clearly explained concepts, worked examples, hands-on applications, verification checks, ethical considerations, exercises, a glossary, and further readings. As an open educational resource, the textbook is freely accessible and supported by companion datasets, notebooks, templates, and instructional materials. Wed, 01 Jul 2026 00:00:00 GMT http://hdl.handle.net/2451/75928 2026-07-01T00:00:00Z