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| data analysis in marketing: Marketing Analytics Mike Grigsby, 2018-04-03 Who is most likely to buy and what is the best way to target them? How can businesses improve strategy without identifying the key influencing factors? The second edition of Marketing Analytics enables marketers and business analysts to leverage predictive techniques to measure and improve marketing performance. By exploring real-world marketing challenges, it provides clear, jargon-free explanations on how to apply different analytical models for each purpose. From targeted list creation and data segmentation, to testing campaign effectiveness, pricing structures and forecasting demand, this book offers a welcome handbook on how statistics, consumer analytics and modelling can be put to optimal use. The fully revised second edition of Marketing Analytics includes three new chapters on big data analytics, insights and panel regression, including how to collect, separate and analyze big data. All of the advanced tools and techniques for predictive analytics have been updated, translating models such as tobit analysis for customer lifetime value into everyday use. Whether an experienced practitioner or having no prior knowledge, methodologies are simplified to ensure the more complex aspects of data and analytics are fully accessible for any level of application. Complete with downloadable data sets and test bank resources, this book supplies a concrete foundation to optimize marketing analytics for day-to-day business advantage. |
| data analysis in marketing: Data Science for Marketing Analytics Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar, 2019-03-30 Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key FeaturesStudy new techniques for marketing analyticsExplore uses of machine learning to power your marketing analysesWork through each stage of data analytics with the help of multiple examples and exercisesBook Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions. What you will learnAnalyze and visualize data in Python using pandas and MatplotlibStudy clustering techniques, such as hierarchical and k-means clusteringCreate customer segments based on manipulated data Predict customer lifetime value using linear regressionUse classification algorithms to understand customer choiceOptimize classification algorithms to extract maximal informationWho this book is for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. |
| data analysis in marketing: Data Science for Marketing Analytics Mirza Rahim Baig, Gururajan Govindan, Vishwesh Ravi Shrimali, 2021-09-07 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. |
| data analysis in marketing: Marketing and Sales Analytics Cesar A. Brea, 2014 Today, an effective marketing analytics executive is even more important than a brilliant data scientist. That's because successful analytics investments now require managerial orchestration of many elements that go far beyond conventional definitions of analytics. Marketing and Sales Analytics examines the experiences of sales and marketing leaders and practitioners who have successfully built high value analytics capabilities in multiple industries. Then, drawing on their experiences, top analytics consultant Cesar Brea introduces overarching frameworks and specific tools that can help you achieve the same levels of success in your own organization. Brea shows how to: Establish the ecosystemic conditions for analytic success Reconcile the diverse perspectives that impact analytics initiatives (Business v. IT, Sales v. Marketing, Analysts v. Creatives v. Managers, and Everyone v. Finance) Decide what success will look like Agree on the questions to ask Organize both internal and external data Establish operational flexibility, and balance flexibility with efficiency Recruit the right people and organize them optimally Intelligently decide what to do yourself, and what to hire vendors for Balance research, analytics, and testing Implement proven research, analytics, and testing strategies Deliver results through storytelling (and recognize its limitations) Control the biases that creep into analytics research Maintain momentum, implement governance, and keep score |
| data analysis in marketing: Marketing Analytics Wayne L. Winston, 2014-01-08 Helping tech-savvy marketers and data analysts solve real-world business problems with Excel Using data-driven business analytics to understand customers and improve results is a great idea in theory, but in today's busy offices, marketers and analysts need simple, low-cost ways to process and make the most of all that data. This expert book offers the perfect solution. Written by data analysis expert Wayne L. Winston, this practical resource shows you how to tap a simple and cost-effective tool, Microsoft Excel, to solve specific business problems using powerful analytic techniques—and achieve optimum results. Practical exercises in each chapter help you apply and reinforce techniques as you learn. Shows you how to perform sophisticated business analyses using the cost-effective and widely available Microsoft Excel instead of expensive, proprietary analytical tools Reveals how to target and retain profitable customers and avoid high-risk customers Helps you forecast sales and improve response rates for marketing campaigns Explores how to optimize price points for products and services, optimize store layouts, and improve online advertising Covers social media, viral marketing, and how to exploit both effectively Improve your marketing results with Microsoft Excel and the invaluable techniques and ideas in Marketing Analytics: Data-Driven Techniques with Microsoft Excel. |
| data analysis in marketing: R for Marketing Research and Analytics Chris Chapman, Elea McDonnell Feit, 2015-03-25 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. |
| data analysis in marketing: Data Driven Marketing For Dummies David Semmelroth, 2013-10-07 Embrace data and use it to sell and market your products Data is everywhere and it keeps growing and accumulating. Companies need to embrace big data and make it work harder to help them sell and market their products. Successful data analysis can help marketing professionals spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Data Driven Marketing For Dummies helps companies use all the data at their disposal to make current customers more satisfied, reach new customers, and sell to their most important customer segments more efficiently. Identifying the common characteristics of customers who buy the same products from your company (or who might be likely to leave you) Tips on using data to predict customer purchasing behavior based on past performance Using customer data and marketing analytics to predict when customers will purchase certain items Information on how data collected can help with merchandise planning Breaking down customers into segments for easier market targeting Building a 360 degree view of a customer base Data Driven Marketing For Dummies assists marketing professionals at all levels of business in accelerating sales through analytical insights. |
| data analysis in marketing: Marketing Strategy Robert W. Palmatier, Shrihari Sridhar, 2020-12-31 Marketing Strategy offers a unique and dynamic approach based on four underlying principles that underpin marketing today: All customers differ; All customers change; All competitors react; and All resources are limited. The structured framework of this acclaimed textbook allows marketers to develop effective and flexible strategies to deal with diverse marketing problems under varying circumstances. Uniquely integrating marketing analytics and data driven techniques with fundamental strategic pillars the book exemplifies a contemporary, evidence-based approach. This base toolkit will support students' decision-making processes and equip them for a world driven by big data. The second edition builds on the first's successful core foundation, with additional pedagogy and key updates. Research-based, action-oriented, and authored by world-leading experts, Marketing Strategy is the ideal resource for advanced undergraduate, MBA, and EMBA students of marketing, and executives looking to bring a more systematic approach to corporate marketing strategies. New to this Edition: - Revised and updated throughout to reflect new research and industry developments, including expanded coverage of digital marketing, influencer marketing and social media strategies - Enhanced pedagogy including new Worked Examples of Data Analytics Techniques and unsolved Analytics Driven Case Exercises, to offer students hands-on practice of data manipulation as well as classroom activities to stimulate peer-to-peer discussion - Expanded range of examples to cover over 250 diverse companies from 25 countries and most industry segments - Vibrant visual presentation with a new full colour design |
| data analysis in marketing: Creating Value with Data Analytics in Marketing Peter C. Verhoef, Edwin Kooge, Natasha Walk, Jaap E. Wieringa, 2021-11-07 The key competing texts are practitioner-focused ‘how to’ guides, whilst our book combines rigorous theory with practical insight and examples, with authors from both the academic and business world, making it more adoptable as a student text; Unlike other books on the subject, this has a customer focus and an exploration of how big data can add value to customers as well as organisations; Enables readers to move from big data to big solutions by demonstrating how to integrate data analytics into specific goals and processes for implementation; Highly successful and well regarded both for students and practitioners |
| data analysis in marketing: Market Research Erik Mooi, Marko Sarstedt, Irma Mooi-Reci, 2017-11-01 This book is an easily accessible and comprehensive guide which helps make sound statistical decisions, perform analyses, and interpret the results quickly using Stata. It includes advanced coverage of ANOVA, factor, and cluster analyses in Stata, as well as essential regression and descriptive statistics. It is aimed at those wishing to know more about the process, data management, and most commonly used methods in market research using Stata. The book offers readers an overview of the entire market research process from asking market research questions to collecting and analyzing data by means of quantitative methods. It is engaging, hands-on, and includes many practical examples, tips, and suggestions that help readers apply and interpret quantitative methods, such as regression, factor, and cluster analysis. These methods help researchers provide companies with useful insights. |
| data analysis in marketing: Statistical Modeling and Analysis for Database Marketing Bruce Ratner, 2003-05-28 Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses. Statistical Modeling and Analysis fo |
| data analysis in marketing: Marketing Analytics Mike Grigsby, 2015-06-03 Who is most likely to buy and what is the best way to target them? Marketing Analytics enables marketers and business analysts to answer these questions by leveraging proven methodologies to measure and improve upon the effectiveness of marketing programs. Marketing Analytics demonstrates how statistics, analytics and modeling can be put to optimal use to increase the effectiveness of every day marketing activities, from targeted list creation and data segmentation to testing campaign effectiveness and forecasting demand. The author explores many common marketing challenges and demonstrates how to apply different data models to arrive at viable solutions. Business cases and critical analysis are included to illustrate and reinforce key concepts throughout. Beginners will benefit from clear, jargon-free explanations of methodologies relating to statistics, marketing strategy and consumer behaviour. More experienced practitioners will appreciate the more complex aspects of data analytics and data modeling, discovering new applications of various techniques in every day practice. Readers of Marketing Analytics will come away with a firm foundation in markets analytics and the tools they need to gain competitive edge and increase market share. Online supporting resources for this book include a bank of test questions as well as data sets relating to many of the chapters. |
| data analysis in marketing: Basic Marketing Research Alvin C. Burns, Ronald F. Bush, 2004-07-01 For undergraduate Marketing Research courses. Best-selling authors Burns and Bush are proud to introduce Basic Marketing Research, the first textbook to utitlize EXCEL as a data analysis tool. Each copy includes XL Data Analyst(R), a user-friendly Excel add-in for data analysis. This book is also a first in that it's a streamlined paperback with an orientation that leans more toward how to use marketing research information to make decisions vs. how to be a provider of marketing research information. |
| data analysis in marketing: Data Analytics in Marketing, Entrepreneurship, and Innovation Mounir Kehal, Shahira El Alfy, 2021-01-12 Innovation based in data analytics is a contemporary approach to developing empirically supported advances that encourage entrepreneurial activity inspired by novel marketing inferences. Data Analytics in Marketing, Entrepreneurship, and Innovation covers techniques, processes, models, tools, and practices for creating business opportunities through data analytics. It features case studies that provide realistic examples of applications. This multifaceted examination of data analytics looks at: Business analytics Applying predictive analytics Using discrete choice analysis for decision-making Marketing and customer analytics Developing new products Technopreneurship Disruptive versus incremental innovation The book gives researchers and practitioners insight into how data analytics is used in the areas of innovation, entrepreneurship, and marketing. Innovation analytics helps identify opportunities to develop new products and services, and improve existing methods of product manufacturing and service delivery. Entrepreneurial analytics facilitates the transformation of innovative ideas into strategy and helps entrepreneurs make critical decisions based on data-driven techniques. Marketing analytics is used in collecting, managing, assessing, and analyzing marketing data to predict trends, investigate customer preferences, and launch campaigns. |
| data analysis in marketing: Big Data Analytics Kiran Chaudhary, Mansaf Alam, 2022-01-19 Big Data Analytics: Applications in Business and Marketing explores the concepts and applications related to marketing and business as well as future research directions. It also examines how this emerging field could be extended to performance management and decision-making. Investment in business and marketing analytics can create value through proper allocation of resources and resource orchestration process. The use of data analytics tools can be used to diagnose and improve performance. The book is divided into five parts. The first part introduces data science, big data, and data analytics. The second part focuses on applications of business analytics including: Big data analytics and algorithm Market basket analysis Anticipating consumer purchase behavior Variation in shopping patterns Big data analytics for market intelligence The third part looks at business intelligence and features an evaluation study of churn prediction models for business Intelligence. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and of consumer behavior. The book concludes with digital marketing, marketing by consumer analytics, web analytics for digital marketing, and smart retailing. This book covers the concepts, applications and research trends of marketing and business analytics with the aim of helping organizations increase profitability by improving decision-making through data analytics. |
| data analysis in marketing: Marketing Analytics José Marcos Carvalho de Mesquita, Erik Kostelijk, 2021-11-02 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, which 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. |
| data analysis in marketing: Python for Marketing Research and Analytics Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit, 2020-11-03 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 analysis in marketing: Cutting-edge Marketing Analytics Rajkumar Venkatesan, Paul Farris, Ronald T. Wilcox, 2015 Master practical strategic marketing analysis through real-life case studies and hands-on examples. In Cutting Edge Marketing Analytics, three pioneering experts integrate all three core areas of marketing analytics: statistical analysis, experiments, and managerial intuition. They fully detail a best-practice marketing analytics methodology, augmenting it with case studies that illustrate the quantitative and data analysis tools you'll need to allocate resources, define optimal marketing mixes; perform effective analysis of customers and digital marketing campaigns, and create high-value dashboards and metrics. For each marketing problem, the authors help you: Identify the right data and analytics techniques Conduct the analysis and obtain insights from it Outline what-if scenarios and define optimal solutions Connect your insights to strategic decision-making Each chapter contains technical notes, statistical knowledge, case studies, and real data you can use to perform the analysis yourself. As you proceed, you'll gain an in-depth understanding of: The real value of marketing analytics How to integrate quantitative analysis with managerial sensibility How to apply linear regression, logistic regression, cluster analysis, and Anova models The crucial role of careful experimental design For all marketing professionals specializing in marketing analytics and/or business intelligence; and for students and faculty in all graduate-level business courses covering Marketing Analytics, Marketing Effectiveness, or Marketing Metrics |
| data analysis in marketing: Essentials of Marketing Research Kenneth E. Clow, Karen E. James, 2013-01-09 Essentials of Marketing Research takes an applied approach to the fundamentals of marketing research by providing examples from the business world of marketing research and showing students how to apply marketing research results. This text focuses on understanding and interpreting marketing research studies. Focusing on the 'how-to' and 'so what' of marketing research helps students understand the value of marketing research and how they can put marketing research into practice. There is a strong emphasis on how to use marketing research to make better management decisions. The unique feature set integrates data analysis, interpretation, application, and decision-making throughout the entire text. The text opens with a discussion of the role of marketing research, along with a breakdown of the marketing research process. The text then moves into a section discussing types of marketing research, including secondary resources, qualitative research, observation research, and survey research. Newer methods (e.g. using blogs or Twitter feeds as secondary resources and using online focus groups) are discussed as extensions of traditional methods such. The third section discusses sampling procedures, measurement methods, marketing scales, and questionnaires. Finally, a section on analyzing and reporting marketing research focuses on the fundamental data analysis skills that students will use in their marketing careers. Features of this text include: - Chapter Openers describe the results of a research study that apply to the topics being presented in that chapter. These are taken from a variety of industries, with a greater emphasis on social media and the Internet. - A Global Concerns section appears in each chapter, helping prepare students to conduct market research on an international scale.This text emphasizes the presentation of research results and uses graphs, tables, and figures extensively. - A Statistics Review section emphasizes the practical interpretation and application of statistical principles being reviewed in each chapter. - Dealing with Data sections in each chapter provide students with opportunities to practice interpreting data and applying results to marketing decisions. Multiple SPSS data sets and step-by-step instructions are available on the companion site to use with this feature. - Each Chapter Summary is tied to the chapter-opening Learning Objectives. - A Continuing Case Study follows a group of students through the research process. It shows potential trade-offs, difficulties and flaws that often occur during the implementation of research project. Accompanying case questions can be used for class discussion, in-class group work, or individual assignments. - End-of-Chapter Critical Thinking Exercises are applied in nature and emphasize key chapter concepts. These can be used as assignments to test students' understanding of marketing research results and how results can be applied to decision-making. - End-of-chapter Your Research Project provides more challenging opportunities for students to apply chapter knowledge on an in-depth basis, and thus olearn by doing. |
| data analysis in marketing: Data-Driven Marketing Mark Jeffery, 2010-02-08 NAMED BEST MARKETING BOOK OF 2011 BY THE AMERICAN MARKETING ASSOCIATION How organizations can deliver significant performance gains through strategic investment in marketing In the new era of tight marketing budgets, no organization can continue to spend on marketing without knowing what's working and what's wasted. Data-driven marketing improves efficiency and effectiveness of marketing expenditures across the spectrum of marketing activities from branding and awareness, trail and loyalty, to new product launch and Internet marketing. Based on new research from the Kellogg School of Management, this book is a clear and convincing guide to using a more rigorous, data-driven strategic approach to deliver significant performance gains from your marketing. Explains how to use data-driven marketing to deliver return on marketing investment (ROMI) in any organization In-depth discussion of the fifteen key metrics every marketer should know Based on original research from America's leading marketing business school, complemented by experience teaching ROMI to executives at Microsoft, DuPont, Nisan, Philips, Sony and many other firms Uses data from a rigorous survey on strategic marketing performance management of 252 Fortune 1000 firms, capturing $53 billion of annual marketing spending In-depth examples of how to apply the principles in small and large organizations Free downloadable ROMI templates for all examples given in the book With every department under the microscope looking for results, those who properly use data to optimize their marketing are going to come out on top every time. |
| data analysis in marketing: Digital Analytics for Marketing A. Karim Feroz, Gohar F. Khan, Marshall Sponder, 2024-01-25 This second edition of Digital Analytics for Marketing provides students with a comprehensive overview of the tools needed to measure digital activity and implement best practices when using data to inform marketing strategy. It is the first text of its kind to introduce students to analytics platforms from a practical marketing perspective. Demonstrating how to integrate large amounts of data from web, digital, social, and search platforms, this helpful guide offers actionable insights into data analysis, explaining how to connect the dots and humanize information to make effective marketing decisions. The authors cover timely topics, such as social media, web analytics, marketing analytics challenges, and dashboards, helping students to make sense of business measurement challenges, extract insights, and take effective actions. The book’s experiential approach, combined with chapter objectives, summaries, and review questions, will engage readers, deepening their learning by helping them to think outside the box. Filled with engaging, interactive exercises and interesting insights from industry experts, this book will appeal to undergraduate and postgraduate students of digital marketing, online marketing, and analytics. Online support materials for this book include an instructor’s manual, test bank, and PowerPoint slides. |
| data analysis in marketing: Predictive Analytics for Marketers Barry Leventhal, 2018-02-03 Predictive analytics has revolutionized marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current data and make predictions about unknown future events. In business terms, this enables companies to forecast consumer behaviour and much more. Predictive Analytics for Marketers will guide marketing professionals on how to apply predictive analytical tools to streamline business practices. Including comprehensive coverage of an array of predictive analytic tools and techniques, this book enables readers to harness patterns from past data, to make accurate and useful predictions that can be converted to business success. Truly global in its approach, the insights these techniques offer can be used to manage resources more effectively across all industries and sectors. Written in clear, non-technical language, Predictive Analytics for Marketers contains case studies from the author's more than 25 years of experience and articles from guest contributors, demonstrating how predictive analytics can be used to successfully achieve a range of business purposes. |
| data analysis in marketing: Predictive Marketing Omer Artun, Dominique Levin, 2015-08-06 Make personalized marketing a reality with this practical guide to predictive analytics Predictive Marketing is a predictive analytics primer for organizations large and small, offering practical tips and actionable strategies for implementing more personalized marketing immediately. The marketing paradigm is changing, and this book provides a blueprint for navigating the transition from creative- to data-driven marketing, from one-size-fits-all to one-on-one, and from marketing campaigns to real-time customer experiences. You'll learn how to use machine-learning technologies to improve customer acquisition and customer growth, and how to identify and re-engage at-risk or lapsed customers by implementing an easy, automated approach to predictive analytics. Much more than just theory and testament to the power of personalized marketing, this book focuses on action, helping you understand and actually begin using this revolutionary approach to the customer experience. Predictive analytics can finally make personalized marketing a reality. For the first time, predictive marketing is accessible to all marketers, not just those at large corporations — in fact, many smaller organizations are leapfrogging their larger counterparts with innovative programs. This book shows you how to bring predictive analytics to your organization, with actionable guidance that get you started today. Implement predictive marketing at any size organization Deliver a more personalized marketing experience Automate predictive analytics with machine learning technology Base marketing decisions on concrete data rather than unproven ideas Marketers have long been talking about delivering personalized experiences across channels. All marketers want to deliver happiness, but most still employ a one-size-fits-all approach. Predictive Marketing provides the information and insight you need to lift your organization out of the campaign rut and into the rarefied atmosphere of a truly personalized customer experience. |
| data analysis in marketing: Principles of Marketing Engineering, 2nd Edition Gary L. Lilien, Arvind Rangaswamy, Arnaud De Bruyn, 2013 The 21st century business environment demands more analysis and rigor in marketing decision making. Increasingly, marketing decision making resembles design engineering-putting together concepts, data, analyses, and simulations to learn about the marketplace and to design effective marketing plans. While many view traditional marketing as art and some view it as science, the new marketing increasingly looks like engineering (that is, combining art and science to solve specific problems). Marketing Engineering is the systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technology-enabled and model-supported decision process. (For more information on Excel-based models that support these concepts, visit DecisionPro.biz.) We have designed this book primarily for the business school student or marketing manager, who, with minimal background and technical training, must understand and employ the basic tools and models associated with Marketing Engineering. We offer an accessible overview of the most widely used marketing engineering concepts and tools and show how they drive the collection of the right data and information to perform the right analyses to make better marketing plans, better product designs, and better marketing decisions. What's New In the 2nd Edition While much has changed in the nearly five years since the first edition of Principles of Marketing Engineering was published, much has remained the same. Hence, we have not changed the basic structure or contents of the book. We have, however Updated the examples and references. Added new content on customer lifetime value and customer valuation methods. Added several new pricing models. Added new material on reverse perceptual mapping to describe some exciting enhancements to our Marketing Engineering for Excel software. Provided some new perspectives on the future of Marketing Engineering. Provided better alignment between the content of the text and both the software and cases available with Marketing Engineering for Excel 2.0. |
| data analysis in marketing: Marketing Analytics Robert W. Palmatier, J. Andrew Petersen, Frank Germann, 2022-03-24 Using data analytics and big data in marketing and strategic decision-making is a key priority at many organisations and subsequently a vital part of the skills set for a successful marketing professional operating today. Authored by world-leading authorities in the field, Marketing Analytics provides a thoroughly contemporary overview of marketing analytics and coverage of a wide range of cutting edge data analytics techniques. It offers a powerful framework, organising data analysis techniques around solving four underlying marketing problems: the 'First Principles of Marketing'. In this way, it offers an action-oriented, applied approach to managing marketing complexities and issues, and a sound grounding in making effective decisions based on strong evidence. It is supported by vivid international cases and examples, and applied pedagogical features. The companion website offers comprehensive classroom instruction slides, videos including walk throughs on all the examples and methods in the book, data sets, a test bank and a solution guide for instructors. |
| data analysis in marketing: Marketing Data Science Thomas W. Miller, 2015-05-02 Now, a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance. |
| data analysis in marketing: Market Data Explained Marc Alvarez, 2011-04-01 Market Data Explained is intended to provide a guide to the universe of data content produced by the global capital markets on a daily basis. Commonly referred to as market data, the universe of content is very wide and the type of information correspondingly diverse. Jargon and acronyms are very common. As a result, users of marker data typically face difficulty in applying the content in analysis and business applications. This guide provides an independent framework for understanding this diversity and streamlining the process of referring to content and how it relates to today's business environment. The book achieves this goal by providing a consistent frame of reference for users of market data. As such, it is built around the concept of a data model – a single, coherent view of the capital markets independent of any one source, such as an exchange. In particular it delineates clearly between the actual data content and how it is delivered (i.e., realtime data streams versus reference data). It shows how the data relates across the universe of securities (i.e., stocks, bonds, derivatives etc.). In this way it provides a logical framework for understanding how new content can be added over time as the business develops. Special features: 1. Uniqueness – this is the first comprehensive catalog and taxonomy to be made available for a business audience 2. Industry Acceptance – the framework described in this book is implemented as a relational data model in the industry today and used by blue chip multinational firms 3. Comprehensiveness – there are no arbitrary distinctions made based on asset class or data type (the legacy approach). The model presented in this book is fully cross asset and makes no distinction between data types (i.e., realtime versus historical/reference data) or sources 4. Independence – the framework is an independent, objective overview of how the data content integrates to provide a coherent view of the data produced by the global capital markets on a daily and intra-day basis. It provides a logical framework for referring to the content and entities that are so intrinsic to this industry - First and only single, comprehensive desk reference to market data produced by the global capital markets on a daily basis - Provides a comprehensive catalog of the market data and a common structure for navigating the complex content and interrelationships - Provides a common taxonomy and naming conventions that handles the highly varied, geographically and language dependent nature of the content |
| data analysis in marketing: Creating Value with Big Data Analytics Peter C. Verhoef, Edwin Kooge, Natasha Walk, 2016-01-08 Our newly digital world is generating an almost unimaginable amount of data about all of us. Such a vast amount of data is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organisations to leverage the information to create value. This book is a refreshingly practical, yet theoretically sound roadmap to leveraging big data and analytics. Creating Value with Big Data Analytics provides a nuanced view of big data development, arguing that big data in itself is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. By tying data and analytics to specific goals and processes for implementation, this is a much-needed book that will be essential reading for students and specialists of data analytics, marketing research, and customer relationship management. |
| data analysis in marketing: Market Segmentation Analysis Sara Dolnicar, Bettina Grün, Friedrich Leisch, 2018-07-20 This book is published open access under a CC BY 4.0 license. This open access book offers something for everyone working with market segmentation: practical guidance for users of market segmentation solutions; organisational guidance on implementation issues; guidance for market researchers in charge of collecting suitable data; and guidance for data analysts with respect to the technical and statistical aspects of market segmentation analysis. Even market segmentation experts will find something new, including an approach to exploring data structure and choosing a suitable number of market segments, and a vast array of useful visualisation techniques that make interpretation of market segments and selection of target segments easier. The book talks the reader through every single step, every single potential pitfall, and every single decision that needs to be made to ensure market segmentation analysis is conducted as well as possible. All calculations are accompanied not only with a detailed explanation, but also with R code that allows readers to replicate any aspect of what is being covered in the book using R, the open-source environment for statistical computing and graphics. |
| data analysis in marketing: Advanced Digital Marketing Strategies in a Data-Driven Era Saura, Jose Ramon, 2021-06-25 In the last decade, the use of data sciences in the digital marketing environment has increased. Digital marketing has transformed how companies communicate with their customers around the world. The increase in the use of social networks and how users communicate with companies on the internet has given rise to new business models based on the bidirectionality of communication between companies and internet users. Digital marketing, new business models, data-driven approaches, online advertising campaigns, and other digital strategies have gathered user opinions and comments through this new online channel. In this way, companies are beginning to see the digital ecosystem as not only the present but also the future. However, despite these advances, relevant evidence on the measures to improve the management of data sciences in digital marketing remains scarce. Advanced Digital Marketing Strategies in a Data-Driven Era contains high-quality research that presents a holistic overview of the main applications of data sciences to digital marketing and generates insights related to the creation of innovative data mining and knowledge discovery techniques applied to traditional and digital marketing strategies. The book analyzes how companies are adopting these new data-driven methods and how these strategies influence digital marketing. Discussing topics such as digital strategies, social media marketing, big data, marketing analytics, and data sciences, this book is essential for marketers, digital marketers, advertisers, brand managers, managers, executives, social media analysts, IT specialists, data scientists, students, researchers, and academicians in the field. |
| data analysis in marketing: Hands-On Data Science for Marketing Yoon Hyup Hwang, 2019-03-29 Optimize your marketing strategies through analytics and machine learning Key FeaturesUnderstand how data science drives successful marketing campaignsUse machine learning for better customer engagement, retention, and product recommendationsExtract insights from your data to optimize marketing strategies and increase profitabilityBook Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learnLearn how to compute and visualize marketing KPIs in Python and RMaster what drives successful marketing campaigns with data scienceUse machine learning to predict customer engagement and lifetime valueMake product recommendations that customers are most likely to buyLearn how to use A/B testing for better marketing decision makingImplement machine learning to understand different customer segmentsWho this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples. |
| data analysis in marketing: Essentials of Marketing Analytics Joseph F. Hair (Jr.), Dana E. Harrison, Haya Ajjan, 2024 Preface We developed this new book with enthusiasm and great optimism. Marketing analytics is an exciting field to study, and there are numerous emerging opportunities for students at the undergraduate level, and particularly at the masterís level. We live in a global, highly competitive, rapidly changing world that is increasingly influenced by digital data, expanded analytical capabilities, information technology, social media, artificial intelligence, and many other recent developments. We believe this book will become the premier source for new and essential knowledge in data analytics, particularly for situations related to decision making that can benefit from marketing analytics, which is likely 80 percent of all challenges faced by organizations. Many of you have been asking us to write this book, and we are confident you will be pleased it is now available. This second edition of Essentials of Marketing Analytics was written to meet the needs of you, our customers. The text is concise, highly readable, and value-priced, yet it delivers the basic knowledge needed for an introductory text on marketing analytics. We provide you and your students with an exciting, up-to-date text and an extensive sup-plement package. In the following sections, we summarize what you will find when you examineóand we hope, adoptóthe second edition of Essentials of Marketing Analytics-- |
| data analysis in marketing: Excel 2019 for Marketing Statistics Thomas J. Quirk, Eric Rhiney, 2021-02-23 This book shows the capabilities of Microsoft Excel in teaching marketing statistics effectively. It is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical marketing problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in marketing courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. Excel 2019 for Marketing Statistics: A Guide to Solving Practical Problems capitalizes on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. In this new edition, each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand marketing problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full practice test (with answers in an appendix) that allows readers to test what they have learned. |
| data analysis in marketing: Research Methods and Data Analysis for Business Decisions James E. Sallis, Geir Gripsrud, Ulf Henning Olsson, Ragnhild Silkoset, 2021-10-30 This introductory textbook presents research methods and data analysis tools in non-technical language. It explains the research process and the basics of qualitative and quantitative data analysis, including procedures and methods, analysis, interpretation, and applications using hands-on data examples in QDA Miner Lite and IBM SPSS Statistics software. The book is divided into four parts that address study and research design; data collection, qualitative methods and surveys; statistical methods, including hypothesis testing, regression, cluster and factor analysis; and reporting. The intended audience is business and social science students learning scientific research methods, however, given its business context, the book will be equally useful for decision-makers in businesses and organizations. |
| data analysis in marketing: Data Analytics in Marketing, Entrepreneurship, and Innovation Mounir Kehal, Shahira El Alfy, 2021-01-12 Innovation based in data analytics is a contemporary approach to developing empirically supported advances that encourage entrepreneurial activity inspired by novel marketing inferences. Data Analytics in Marketing, Entrepreneurship, and Innovation covers techniques, processes, models, tools, and practices for creating business opportunities through data analytics. It features case studies that provide realistic examples of applications. This multifaceted examination of data analytics looks at: Business analytics Applying predictive analytics Using discrete choice analysis for decision-making Marketing and customer analytics Developing new products Technopreneurship Disruptive versus incremental innovation The book gives researchers and practitioners insight into how data analytics is used in the areas of innovation, entrepreneurship, and marketing. Innovation analytics helps identify opportunities to develop new products and services, and improve existing methods of product manufacturing and service delivery. Entrepreneurial analytics facilitates the transformation of innovative ideas into strategy and helps entrepreneurs make critical decisions based on data-driven techniques. Marketing analytics is used in collecting, managing, assessing, and analyzing marketing data to predict trends, investigate customer preferences, and launch campaigns. |
| data analysis in marketing: Cutting Edge Marketing Analytics Rajkumar Venkatesan, Paul Farris, Ronald T. Wilcox, 2014-06-10 Master practical strategic marketing analysis through real-life case studies and hands-on examples. In Cutting Edge Marketing Analytics, three pioneering experts integrate all three core areas of marketing analytics: statistical analysis, experiments, and managerial intuition. They fully detail a best-practice marketing analytics methodology, augmenting it with case studies that illustrate the quantitative and data analysis tools you'll need to allocate resources, define optimal marketing mixes; perform effective analysis of customers and digital marketing campaigns, and create high-value dashboards and metrics. For each marketing problem, the authors help you: Identify the right data and analytics techniques Conduct the analysis and obtain insights from it Outline what-if scenarios and define optimal solutions Connect your insights to strategic decision-making Each chapter contains technical notes, statistical knowledge, case studies, and real data you can use to perform the analysis yourself. As you proceed, you'll gain an in-depth understanding of: The real value of marketing analytics How to integrate quantitative analysis with managerial sensibility How to apply linear regression, logistic regression, cluster analysis, and Anova models The crucial role of careful experimental design For all marketing professionals specializing in marketing analytics and/or business intelligence; and for students and faculty in all graduate-level business courses covering Marketing Analytics, Marketing Effectiveness, or Marketing Metrics |
| data analysis in marketing: Basic Marketing Research Alvin C. Burns, Ronald F. Bush, 2008 This book is targeted for those who need to learn the marketing research process and basic data analysis techniques. This is the first book on the market that offers a software add-in (XL Data Analyst ) designed specifically to use the power of Excel for marketing research applications. XL Data Analyst also features data analysis output that is exceptionally easy to understand and in professional table/report-ready format. All marketing research concepts are presented simply and intuitively. KEY TOPICS: 11 Step marketing research process; the industry; ethical issues, global applications. Basic descriptive statistics, confidence intervals and hypothesis testing, differences tests through one-way ANOVA, cross-tabulation/Chi-square, Pearson Correlation, and simple and multiple regression are all comprehensively covered. This is the ideal book for those who wish to teach basic marketing research to undergraduate students without learning cumbersome statistical analysis software but who have access to Excel. The XL Data Analysis Add-in is easy to master and use and performs analyses such as tests of proportions and hypothesis tests for percentages, not typically available on standard statistical analysis software. |
| data analysis in marketing: A Concise Guide to Market Research Marko Sarstedt, Erik Mooi, 2014-08-07 This accessible, practice-oriented and compact text provides a hands-on introduction to market research. Using the market research process as a framework, it explains how to collect and describe data and presents the most important and frequently used quantitative analysis techniques, such as ANOVA, regression analysis, factor analysis and cluster analysis. The book describes the theoretical choices a market researcher has to make with regard to each technique, discusses how these are converted into actions in IBM SPSS version 22 and how to interpret the output. Each chapter concludes with a case study that illustrates the process using real-world data. A comprehensive Web appendix includes additional analysis techniques, datasets, video files and case studies. Tags in the text allow readers to quickly access Web content with their mobile device. The new edition features: Stronger emphasis on the gathering and analysis of secondary data (e.g., internet and social networking data) New material on data description (e.g., outlier detection and missing value analysis) Improved use of educational elements such as learning objectives, keywords, self-assessment tests, case studies, and much more Streamlined and simplified coverage of the data analysis techniques with more rules-of-thumb Uses IBM SPSS version 22 |
| data analysis in marketing: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data analysis in marketing: Statistics for Marketing and Consumer Research Mario Mazzocchi, 2008-05-22 Balancing simplicity with technical rigour, this practical guide to the statistical techniques essential to research in marketing and related fields, describes each method as well as showing how they are applied. The book is accompanied by two real data sets to replicate examples and with exercises to solve, as well as detailed guidance on the use of appropriate software including: - 750 powerpoint slides with lecture notes and step-by-step guides to run analyses in SPSS (also includes screenshots) - 136 multiple choice questions for tests This is augmented by in-depth discussion of topics including: - Sampling - Data management and statistical packages - Hypothesis testing - Cluster analysis - Structural equation modelling |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
