Applied Marketing Analytics Using R


Book Description

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.




Applied Marketing Analytics Using R


Book Description

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.




R for Marketing Research and Analytics


Book Description

This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.




APPLIED MARKETING ANALYTICS USING SPSS


Book Description

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




Customer and Business Analytics


Book Description

Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the tex




Python for Marketing Research and Analytics


Book Description

This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.




Data Science for Marketing Analytics


Book Description

Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language Key FeaturesUse data analytics and machine learning in a sales and marketing contextGain insights from data to make better business decisionsBuild your experience and confidence with realistic hands-on practiceBook Description Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making. What you will learnLoad, clean, and explore sales and marketing data using pandasForm and test hypotheses using real data sets and analytics toolsVisualize patterns in customer behavior using MatplotlibUse advanced machine learning models like random forest and SVMUse various unsupervised learning algorithms for customer segmentationUse supervised learning techniques for sales predictionEvaluate and compare different models to get the best outcomesOptimize models with hyperparameter tuning and SMOTEWho this book is for This marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path. Basic prior knowledge of Python and experience working with data will help you access this book more easily.




Applied Marketing


Book Description

Tomorrow's professionals need a practical, customer-centric understanding of marketing's role in business and critical thinking skills to help their organizations succeed. Applied Marketing, 1st Canadian Edition helps students learn practical, modern marketing concepts appropriate for the principles of marketing course by applying them to the latest business scenarios of relatable brands like This Bar Saves Lives and GoPro. This comprehensive yet concise text is co-authored by Professors Rochelle Grayson and Daniel Padgett and practitioner Andrew Loos, and blends current academic theory with an agency-owner perspective to help students get an insider's look at how top businesses operate. With many Canadian specific examples created specifically for this course, students can relate concepts learned in the classroom to marketing topics and events taking place in their backyard.




Marketing Calculator


Book Description

This book uncovers the components of driving increased marketing effectiveness and can be applied to just about every industry and marketing challenge. It demystifies how marketers can significantly improve their measurement and management infrastructure in order to improve their return on marketing effectiveness and ROI. They will be able to significantly improve their tactical and strategic decision-making and finally be able to respond to John Wannamachers' "half of my advertising is wasted; I just don't know which half." With this in hand, they will be able to avoid the budget cutting ax, become a critical component of corporate success and enhance their careers. Even in a crowded theoretical marketing environment there are three new concepts being introduced: 1. The Marketing Effectiveness Framework to help marketers talk the talk of marketing effectiveness within marketing and with the C-Suite. 2. The Marketing Effectiveness Continuum to help marketers understand the organizational issues and change management associated with delivering long lasting enhanced marketing effectiveness. 3. The Marketing Accountability Framework to help marketers begin to collect data that is meaningful to improving their marketing effectiveness and to become accountable for their results. It is one of the only marketing books covering the topic at a global level. It includes a great number of specific case studies from North America, Asia, Europe and Africa. The cases cover the following industries: Telecommunications, consumer packaged goods, home repair services, travel, utilities, software, restaurants, alcoholic and non-alcoholic beverages and others. It can also be used to support marketing education at the university level. Whether the reader is a marketer, business analyst, C-level executive, this book will help them to understand the key issues surrounding the measurement of marketing effectiveness. More than that however, is how each of the concepts can be directly applied to their marketing environment. Each of the concepts are applied to the different types of businesses (business-to-business, OEM, consumer, NGO and others) so they can quickly make them actionable.




Applied Predictive Modeling


Book Description

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.