MARKETING DATA SCIENCE

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Marketing Data Science

Author : Thomas W. Miller
ISBN : 9780133887341
Genre : Business & Economics
File Size : 38.50 MB
Format : PDF, Kindle
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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.
Category: Business & Economics

Data Science For Marketing Analytics

Author : Tommy Blanchard
ISBN : 9781789952100
Genre : Computers
File Size : 34.77 MB
Format : PDF, Docs
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Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features Study new techniques for marketing analytics Explore uses of machine learning to power your marketing analyses Work through each stage of data analytics with the help of multiple examples and exercises Book 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 learn Analyze and visualize data in Python using pandas and Matplotlib Study clustering techniques, such as hierarchical and k-means clustering Create customer segments based on manipulated data Predict customer lifetime value using linear regression Use classification algorithms to understand customer choice Optimize classification algorithms to extract maximal information Who 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.
Category: Computers

Hands On Data Science For Marketing

Author : Yoon Hyup Hwang
ISBN : 9781789348828
Genre : Computers
File Size : 45.59 MB
Format : PDF, ePub
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Optimize your marketing strategies through analytics and machine learning Key Features Understand how data science drives successful marketing campaigns Use machine learning for better customer engagement, retention, and product recommendations Extract insights from your data to optimize marketing strategies and increase profitability Book 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 learn Learn how to compute and visualize marketing KPIs in Python and R Master what drives successful marketing campaigns with data science Use machine learning to predict customer engagement and lifetime value Make product recommendations that customers are most likely to buy Learn how to use A/B testing for better marketing decision making Implement machine learning to understand different customer segments Who 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.
Category: Computers

A Hands On Introduction To Data Science

Author : Chirag Shah
ISBN : 9781108472449
Genre : Business & Economics
File Size : 44.45 MB
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An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.
Category: Business & Economics

The Data Science Of Marketing

Author :
ISBN : OCLC:1116963656
Genre :
File Size : 58.12 MB
Format : PDF, Kindle
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Discover how to leverage R, Python, and Tableau to gain robust insights from large data sets.
Category:

Innovations In Classification Data Science And Information Systems

Author : Daniel Baier
ISBN : 9783540269816
Genre : Language Arts & Disciplines
File Size : 66.80 MB
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The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques.
Category: Language Arts & Disciplines

Marketing Analytics

Author : Mike Grigsby
ISBN : 9780749482176
Genre : Business & Economics
File Size : 79.40 MB
Format : PDF, Kindle
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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.
Category: Business & Economics

The Routledge Companion To The Future Of Marketing

Author : Luiz Moutinho
ISBN : 9781136242854
Genre : Business & Economics
File Size : 52.31 MB
Format : PDF, ePub
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Marketing has changed substantially in the last few years. With more and more research conducted in marketing and consumer behaviour fields, and technological advances and applications occurring on a regular basis, the future of marketing opens up a world of exciting opportunities. Going beyond a state-of-the-art view of the discipline, this innovative volume focuses on the advances being made in many different areas such as; critical thinking, new paradigms, novel conceptualisations, as well as key technological innovations with a direct impact on the theory and practice of marketing. Each chapter presents an expert overview, and an analytical and engaging discussion of the topic, as well as introducing a specific research agenda paving the way for the future. The Routledge Companion to the Future of Marketing provides the reader with a comprehensive set of visionary insights into the future of marketing. This prestigious collection aims to challenge the mindset of marketing scholars, transforming current thinking into new perspectives and advances in marketing knowledge. Foreword Wayne S. DeSarbo, Smeal College of Business, Pennsylvania State Univerity, USA "The Future of Marketing" presents 22 different chapters written by some of the top scholars in the field of Marketing. These 22 chapters are organized into four topical areas: (1) New paradigms and philosophical insights (Chapters 1-5), (2) Contributions from other scientific fields (Chapters 6-9), (3) Reconnecting with consumers and markets (Chapters 10-17), and (4) New methodological insights in scholarly research in the field (Chapters 18-22). Thus, there are a number of diverse areas treated here ranging from futuristic managerial philosophies to state of the art qualitative and quantitative methodologies applicable to the various types of Marketing problems to be faced in the future. There are a number of implicit guidelines (and future research areas and needs) that can be gleaned for (quantitative) modelers in terms of the issues and considerations that their constructed models should explicitly accommodate in future empirical endeavors: Heterogeneity When modeling consumer perceptions, preferences, utility structures, choices, etc., it is important to avoid potential masking issues that aggregate models are subject to in many cases. In the simple case, consider a regression scenario where there are two equal sized segments whose utility functions (as a function of price) are opposite reflections of each other. Aggregating the sample in one large analysis yields a non-significant price elasticity coefficient, whereas estimating separate utility functions by segment displays the true structure in the data. While latent structure and hierarchical Bayesian methods have been developed for disaggregate analyses, a number of methodological issues exist with such existent approaches that provide fertile ground for future research. Competition Many quantitative models are estimated at a brand level and reflect only the efforts of that sole brand. For example, in many customer satisfaction studies, attention is often paid to the consumers of a particular client brand or service in an effort to portray their performance and derive the important drivers of satisfaction. Financial optimization models are then often constructed to examine where a company should invest its resources to best improve sales, retention, word of mouth, loyalty, etc. These studies need to occur in a fully competitive setting where one derives a full picture of the competitive market place. Managers need to know the relative importance of the drivers of satisfaction for their brand/service as well as for their competitors. In addition, knowledge of the relative performance of their brand relative to competitors is necessary information for strategy formation. Ideally, one would hope to see modeling efforts which also examine cross effects in terms of how Brand A’s policy affects other brands. Over time, competitive dynamics are also important as discussed next. Dynamics As seen in the various chapters, this can assume many different manifestations. Related to the previous category above related to competition, it is often necessary to examine competitive dynamics as opposed to comparative statics where the modeler of the future examines simultaneous and/or sequential optimization by each of the competitors in a market place in a game theoretic context. In such a manner, it will not be the case that all competitors end up enacting the same exact identical strategies. Alternatively, the models of the future should be adaptive and have the ability to "learn" from past data, as well as benefit from informed managerial expert input and constraints. Parameter values that change/adapt during the duration of the data are also a desirable feature. Non-Linearity Traditional linear response functions do not typically yield realistic normative managerial guidelines or optimized solutions. End point solutions that suggest "all or none" types of resource allocations are useless in most realistic Marketing applications. A large amount of work is required in this area as Marketing often lacks the strong theory necessary to provide such insight regarding the models that are constructed. In addition, multiple objective functions need to be accommodated with the use of multicriterion optimization methods Endogeneity Often times, there are hidden effects embedded in the various independent variables the Marketer believes are exogenous and truly independent. These may be due to effect of lagged variables, managerial decision making practice, etc. To ignore such effects, threatens the integrity of the models Marketers construct. For example, in traditional regression models, such endogeneity often produces a correlation between the independent variable in question and the error term, often resulting in biased estimates when employing ordinary least-squares estimation. Moderation/Mediation There are times particularly in regression approaches where the relationships between two variables are affected by values of a third variable. In such cases, we need to employ selected interaction effects to measure such moderated effects. Interaction effects are often needed to model the synergistic or catalytic effects of various independent variables. Alternatively, in a mediation regression model, rather than hypothesizing a direct causal relationship between the independent variable and the dependent variable, a mediational model hypothesizes that the independent variable influences the mediator variable, which in turn influences the dependent variable. Thus, such moderator and mediator variables serve to clarify the nature of the relationship between the independent and dependent variables. Marketers need to be aware of such potential inter-relationships. Models Guided by Theory Ideally, the models we construct should be more than just data analytic structures which approximate the relationships found in the data. Where possible, models should be constructed on the basis of available sound Marketing theory describing the process being modeled. One of the advantages of structural equation models is that one can utilize such a methodology to test and implement some a priori theory describing the relationship or causal nature of various inter-related constructs. This feature has been lacking in the general modeling efforts to date. A major reason for this is due to the lack of adequate theory development for most of the processes encountered in Marketing. For example, we have no solid Marketing theory regarding the structure of marketing mix response models. Thus progress must be advanced in such areas so that the models we construct are more robust and explainable. I wish to personally thank the co-editors and various authors of the "Future of Marketing" for opening the door to get a glimpse of the future in the field of Marketing. The hope is that this new book will provide fresh ideas to guide future research to improve the field of Marketing and define the next generation of research efforts as the torch gets passed to future generations.
Category: Business & Economics

Handbook Of Marketing Analytics

Author : Natalie Mizik
ISBN : 9781784716752
Genre :
File Size : 57.25 MB
Format : PDF, Mobi
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Marketing Science contributes significantly to the development and validation of analytical tools with a wide range of applications in business, public policy and litigation support. The Handbook of Marketing Analytics showcases the analytical methods used in marketing and their high-impact real-life applications. Fourteen chapters provide an overview of specific marketing analytic methods in some technical detail and 22 case studies present thorough examples of the use of each method in marketing management, public policy, and litigation support. All contributing authors are recognized authorities in their area of specialty.
Category:

Ai In Marketing Sales And Service

Author : Peter Gentsch
ISBN : 9783319899572
Genre : Business & Economics
File Size : 57.37 MB
Format : PDF, ePub, Docs
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AI and Algorithmics have already optimized and automated production and logistics processes. Now it is time to unleash AI on the administrative, planning and even creative procedures in marketing, sales and management. This book provides an easy-to-understand guide to assessing the value and potential of AI and Algorithmics. It systematically draws together the technologies and methods of AI with clear business scenarios on an entrepreneurial level. With interviews and case studies from those cutting edge businesses and executives who are already leading the way, this book shows you: how customer and market potential can be automatically identified and profiled; how media planning can be intelligently automated and optimized with AI and Big Data; how (chat)bots and digital assistants can make communication between companies and consumers more efficient and smarter; how you can optimize Customer Journeys based on Algorithmics and AI; and how to conduct market research in more efficient and smarter way. A decade from now, all businesses will be AI businesses – Gentsch shows you how to make sure yours makes that transition better than your competitors.
Category: Business & Economics