Following an application-based approach, this book helps readers to understand the usage of analytics in different marketing contexts, such as identifying customer preferences, customer-segmentation, pricing, forecasting, advertising, competitive analysis, perceptual mapping, using SPSS software (Modeler, Statistics and AMOS Graphics).

Marketing has become increasingly data-driven in recent years as a result of new emerging technologies such as AI, granular data availability and ever-growing analytics tools. With this trend only set to continue, it’s vital for marketers today to be comfortable in their use of data and quantitative approaches and have a thorough grounding in understanding and using marketing analytics in order to gain insights, support strategic decision-making, solve marketing problems, maximise value and achieve success. Taking a very hands-on approach with the use of real-world datasets, case studies and R (a free statistical package), this book supports students and practitioners to explore a range of marketing phenomena using various applied analytics tools, with a balanced mix of technical coverage alongside marketing theory and frameworks. Chapters include learning objectives, figures, tables and questions to help facilitate learning. Also included online with the datasets are software codes and solutions (R Markdowns, HTML files) to use with the book, as well as PowerPoint slides, a teaching guide and a testbank for instructors teaching a marketing analytics course. This book is essential reading for advanced level marketing students and marketing practitioners who want to become cutting-edge marketers. Dr. Gokhan Yildirim is an Associate Professor of Marketing at Imperial College Business School, London. Dr. Raoul V. Kübler is an Associate Professor of Marketing at ESSEC Business School, Paris.

Marketing analytics is important to today's business organizations as it lets them measure performance of their marketing resources and channels and in turn plays a vital role in making business strategies and decisions. The present book, following application-based approach, helps readers to understand the usage of analytics in different marketing contexts such as identifying customer preferences, customer-segmentation, pricing, forecasting, advertising, competitive analysis, perceptual mapping, etc. using SPSS software (Modeler, Statistics and AMOS Graphics). Practical applications in each chapter, with supported screenshots, guide readers to apply different analytical techniques in marketing as they learn. This book is an indispensable companion for the postgraduate students of management with specialization in marketing. Also, the book will prove valuable for the Management Development Programs, Data Analysts, and Researchers in the field. It enables them to identify marketing problems, carry out research efficiently, process the data in a simple way using SPSS, and create reports in a systematic manner. TARGET AUDIENCE • MBA (Marketing) • Data Analysts • Management Development Programme

Marketing Analytics provides guidelines in the application of statistics using IBM SPSS Statistics Software (SPSS) for students and professionals using quantitative methods in marketing and consumer behavior. With simple language and a practical, screenshot-led approach, the book presents 11 multivariate techniques and the steps required to perform analysis. Each chapter contains a brief description of the technique, followed by the possible marketing research applications. One of these applications is then used in detail to illustrate its applicability in a research context, including the needed SPSS commands and illustrations. Each chapter also includes practical exercises that require the readers to perform the technique and interpret the results, equipping students with the necessary skills to apply statistics by means of SPSS in marketing and consumer research. Finally, there is a list of articles employing the technique that can be used for further reading. This textbook provides introductory material for advanced undergraduate and postgraduate students studying marketing and consumer analytics, teaching methods along with practical software-applied training using SPSS. Support material includes two real data sets to illustrate the techniques’ applications and PowerPoint slides providing a step-by-step guide to the analysis and commented outcomes. Professionals are invited to use the book to select and use the appropriate analytics for their specific context.

Marketing researchers, companies and business schools need to be able to use statistical procedures correctly and accurately interpret the outputs, yet generally these people are scared off by the statistics behind the different analyses procedures, thus they often rely on external sources to come up with profound answers to the proposed research questions. In an accessible and step by step approach, the authors show readers which procedures to use in which particular situation and how to practically execute them using IBM® SPSS Statistics. IBM® is one of the largest statistical software providers world-wide and their IBM® SPSS Statistics software offers a very user-friendly environment. The program uses a simple drag-and-drop menu interface, which is also suitable for non-experienced programmers. It is widely employed in companies and many business schools also use this software package. This straightforward, pragmatic reference manual will help: professional marketers who use statistical procedures in in IBM® SPSS Statistics; undergraduate and postgraduate students where marketing research and research methodology are taught; all researchers analyzing survey-based data in a wide range of frontier domains like psychology, finance, accountancy, negotiation, communication, sociology, criminology, management, information systems, etc. IBM®'s next-generation business analytic solutions help organizations of all sizes make sense of information in the context of their business. You can uncover insights more quickly and easily from all types of data-even big data-and on multiple platforms and devices. And, with self-service and built-in expertise and intelligence, you have the freedom and confidence to make smarter decisions that better address your business imperatives.

Marketing Analytics provides guidelines in the application of statistics using IBM SPSS Statistics Software (SPSS) for students and professionals using quantitative methods in marketing and consumer behavior. With simple language and a practical, screenshot-led approach, the book presents 11 multivariate techniques and the steps required to perform analysis. Each chapter contains a brief description of the technique, followed by the possible marketing research applications. One of these applications is then used in detail to illustrate its applicability in a research context, including the needed SPSS commands and illustrations. Each chapter also includes practical exercises that require the readers to perform the technique and interpret the results, equipping students with the necessary skills to apply statistics by means of SPSS in marketing and consumer research. Finally, there is a list of articles employing the technique that can be used for further reading. This textbook provides introductory material for advanced undergraduate and postgraduate students studying marketing and consumer analytics, teaching methods along with practical software-applied training using SPSS. Support material includes two real data sets to illustrate the techniques’ applications and PowerPoint slides providing a step-by-step guide to the analysis and commented outcomes. Professionals are invited to use the book to select and use the appropriate analytics for their specific context.

4.2. Non-Hierarchical K-Means Clustering -- Managerial Problem and Dataset Description -- Data Analysis -- Interpretation -- 4.3. Profiling Clusters -- Managerial Recommendations -- Further Reading -- Chapter 5 Hypothesis Testing -- Objectives -- Fundamentals -- 5.1. Parametric Tests -- 5.1.1. One-Sample T Test -- Managerial Problem -- Translation of the Managerial Problem into Statistical Notions -- Hypotheses -- Dataset Description -- Data Analysis -- Interpretation -- Managerial Recommendations -- 5.1.2. Independent-Samples T Test -- Managerial Problem.