Analytics is taking up an increasingly larger role in a marketer’s everyday decision-making. With a change in the customers’ nature of buying behaviour, there is a need for marketers to understand and use technology-enhanced data collection and analysis methods. Marketing analytics: for strategic decision-making has been written from a marketing perspective to provide a comprehensive overview of the analytical methods that are pertinent and important for a marketer through illustrations that make use of appropriate data sets and apply software such as R, SPSS and excel. Designed primarily for the students of MBA specializing in marketing and written in a lucid manner, The book will also be useful for marketing professionals trying to improve their understanding of marketing analytics. Key features provides comprehensive coverage of marketing analytics and its applications in the real world includes a dedicated br>Chapter on understanding machine learning for marketing analytics presents all exercises demonstrating analytical concepts in at least two of the following analytics software: r-language programming, SPSS or Excel includes ample number of examples for students to practise and learn the basics of marketing analytics online resources instructor resources (a) PowerPoint slides (B) instructor manual table of Contents section I: The need for marketing analytics marketing analytics and marketing Research marketing analytics: data including web analytics descriptive analysis a Primer on machine learning for marketing analytics section II: understanding the consumer and customer: using structured data correlation and Regression experimental design advertising analytics consumer perception, consumer preference and customer portfolio management customer acquisition customer retention section III: understanding the consumer and customer: using unstructured data collecting and understanding social media data br>Chapter 12: visualizing consumer engagement br>Chapter 13: simulating social media data generating mechanisms br>Chapter 14: analyzing social network data br>Chapter 15: mining meaning from text br>Chapter 16: collecting unstructured data in offline marketing Research section IV: putting it all together br>Chapter 17: coda.