When you want to find marketing analytics r, you may need to consider between many choices. Finding the best marketing analytics r is not an easy task. In this post, we create a very short list about top 5 the best marketing analytics r for you. You can check detail product features, product specifications and also our voting for each product. Let’s start with following top 5 marketing analytics r:
Reviews
1. Automated Time Series Forecasting Made Easy with R: An intuitive Step by Step Introduction for Data Science
Description
Finally, A Blueprint for Automated Time Series Forecasting with R!
Automated Time Series Forecasting Made Easy with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creating your own models to forecast time series data. Whether you are new to time series forecasting or a veteran, this book offers a powerful set of tools for quickly and easily gaining insight from your data using R.NO EXPERIENCE REQUIRED: Through a simple to follow step by step process you will learn how to build time series forecasting models using R. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications.
YOUR PERSONAL BLUE PRINT: Through a simple to follow intuitive step by step process, you will learn how to use the most popular time series forecasting models using R. Once you have mastered the process, it will be easy for you to translate your knowledge to assess your own data.
THIS BOOK IS FOR YOU IF YOU WANT:
TAKE THE SHORTCUT: This guide was written for people who want to get up to speed as quickly as possible.
YOU'LL LEARN HOW TO:
For each time series forecasting technique, every step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks. Using plain language, this book offers a simple, intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R.
Everything you need to get started is contained within this book. Automated Time Series Forecasting Made Easy with R is your very own hands on practical, tactical, easy to follow guide to mastery.
Buy this book today and accelerate your progress!
2. Architecting Experience: A Marketing Science and Digital Analytics Handbook (Advances and Opportunities with Big Data and Analytics)
Description
In a world with a seemingly infinite amount of content and scores of methods for consuming that content, marketing communication today is about appealing to individuals, person by person. Effectively appealing to customers requires delivery of brand experiences built on relevance and recognition of context. Just as in any conversation, delivering relevance in context requires understanding the person one is speaking with and shared environment. Wheeler answers the biggest question facing digital marketers today: "with an ever expanding array of digital touch points at one's disposal, how does one deliver content and experiences around one's brand that build relationships and drives results?" The quick answer to this is "through the application of data and analytics to drive highly relevant, contextual targeted content and adaptive experience", but since this answer is not as easy to achieve as it is to say, Architecting Experience has been designed to help readers develop the understanding of marketing data, technology and analytics required to make this happen.
Readership: Suitable for postgraduate students in Digital and Direct Marketing Master's programs and professionals in IT, Research, and Marketing.
3. R for Marketing Research and Analytics (Use R!)
Feature
R for Marketing Research and Analytics Use RDescription
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.
4. Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)
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Pearson FT PressDescription
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.
5. Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
Feature
WileyDescription
Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner, Third Editionpresents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft Office Excel add-in XLMiner to develop predictive models and learn how to obtain business value from Big Data.
Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes:
- Real-world examples to build a theoretical and practical understanding of key data mining methods
- End-of-chapter exercises that help readers better understand the presented material
- Data-rich case studies to illustrate various applications of data mining techniques
- Completely new chapters on social network analysis and text mining
- A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint slides https://www.dataminingbook.com
- Free 140-day license to use XLMinerfor Education software
Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner, Third Editionis an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.
Praise for the Second Edition
"full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing." Research Magazine
"Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." ComputingReviews.com
"Excellent choice for business analysts...The book is a perfect fit for its intended audience." Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization
Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua Universitys Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters.
Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley.
Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years.