Maths and STATS for Web Analytics and Conversion Optimization

Author: Himanshu Sharma
Publisher: Blurb
ISBN: 9781364849184
Category : Computers
Languages : en
Pages : 430

Book Description
The role of maths and statistics in the world of web analytics is not clear to many marketers. Not many talk or write about the usage of statistics and data science in conversion optimization. This book has been written to fill this knowledge gap.This expert guide will teach you exactly what you need to know. It will teach you how to leverage the knowledge of maths and statistics in order to accurately interpret data and take actions which can quickly improve the bottom-line of your online business. Every topic covered in this book has something to do directly with your day to day job. This book covers vital topics on maths and statistics which every internet marketer/web analyst should get familiar with in order to achieve optimum results from their analysis, marketing campaigns and conversion optimization efforts. It explains some of the most useful statistics terms/concepts one by one and will also show you their practical use in web analytics and conversion optimization, so that you can take advantage of them straightaway. "

Attribution Modelling in Google Analytics and Beyond

Author: Himanshu Sharma
Publisher:
ISBN: 9781366694577
Category :
Languages : en
Pages :

Book Description
Attribution modelling is the process of understanding and assigning credit to the marketing channels which eventually lead to conversions. The objective of attribution modelling is to understand the buying behaviour of your website visitors and to determine the most effective marketing channels for investment.What is this book about?This book has been written to help you implement attribution modelling in your organization. It will teach you to leverage the knowledge of attribution modelling while allocating your marketing budget and help you to understand your user's buying behaviour. In this book there is a strong focus on using Google Analytics and other Google tools and technologies, such as Google AdWords. I have explained various attribution models mainly in the context of Google Analytics. What is not included in this book?This book is not about traditional marketing mix modelling (MMM) or understanding and finding the optimal mix of the 4Ps of marketing (product, price, place and promotion). There are no chapters on regression analysis and predictive modelling. Consequently, there is little to no use of statistics in this book. This book is not about getting started with Google Analytics or how to master Google Analytics.What do you need to know in advance in order to benefit from this book?In order to get the maximum benefit from this book, you will need a working knowledge of Google Analytics and e-commerce. No knowledge of statistics or traditional marketing mix modelling is required.Who should read this book?Any person who wants to improve the online performance of their business and marketing campaigns should read this book. Online marketers, web analysts and data scientists will benefit the most from this book. If you have a strong background in traditional marketing mix modelling (MMM) you will benefit tremendously from this book because it will enable you to integrate attribution modelling data with your MMM data.

Attribution Modelling in Google Ads and Facebook

Author: Himanshu Sharma
Publisher: Blurb
ISBN: 9780368079610
Category :
Languages : en
Pages : 478

Book Description
Attribution modelling is the process of understanding the customer purchasing journey and determining the most effective marketing channels / campaigns for investment at a particular point in time. A lot has been said about attribution modelling over the years. However, talking about attribution is the easy bit, implementing it is the real challenge. This book has been written to help you implement attribution modelling in Google Ads (formerly Google AdWords) and Facebook. This expert guide will teach you, how to leverage the knowledge of attribution modelling in order to understand the customer purchasing journey and to determine the most effective marketing channels for investment.

Digital marketing like a PRO

Author: Clo Willaerts
Publisher: Lannoo Meulenhoff - Belgium
ISBN: 9401490767
Category : Business & Economics
Languages : en
Pages : 200

Book Description
How do you translate your business objectives into a digital marketing strategy? What are today's best practices for content marketing, email marketing, social media and online advertising? What is the practical use for new technologies like Augmented and Virtual Reality, the Internet of Things, Artificial Intelligence and chatbots? Digital Marketing like a PRO is an easy to follow, step-by-step marketing guide that helps you maximize all digital channels. Packed with models, information and examples to help you succeed, it is free of fluff and noise so you can get to work quickly. Whether you're new to marketing or a long-time professional seeking to boost your skills, this digital marketing handbook will help you save time, energy and resources as you learn from tested industry models and best practices.

Digital Marketing Strategy

Author: Simon Kingsnorth
Publisher: Kogan Page Publishers
ISBN: 1398605980
Category : Business & Economics
Languages : en
Pages : 417

Book Description
Build an effective and practical digital marketing strategy with this bestselling guide, covering everything from automation and analytics to integrating AI. Digital Marketing Strategy is a global bestseller, and a one-stop guide to structuring and building a more strategic approach to digital marketing. Now fully updated, this third edition covers the integration of AI in marketing, e-commerce, marketing automation, affiliate marketing and how to use digital analytical tools, plus new strategies for the latest cookie changes and privacy protection. Digital Marketing Strategy will show you how to effectively select, align and manage digital channels and operations, to streamline a successful digital marketing strategy for measurable, optimized results. Recommended by the Chartered Institute of Marketing (CIM), it is supported by real-world case studies from the likes of Coca-Cola, Spotify, Airbnb, Adidas and Hostelworld as well as checklists, key terms and insights from leading industry practitioners to help you develop your own digital marketing strategy. This book is an invaluable guide for both digital marketing students and entry-level to mid-management marketing professionals. Accompanying online resources consist of practical implementation guides spanning SEO, paid-search, email, lead-generation, as well as presentation slides and activity sheets.

Mathematics for Machine Learning

Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392

Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

The Book of R

Author: Tilman M. Davies
Publisher: No Starch Press
ISBN: 1593276516
Category : Computers
Languages : en
Pages : 833

Book Description
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.

Understanding Machine Learning

Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415

Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Practical Statistics for Data Scientists

Author: Peter Bruce
Publisher: "O'Reilly Media, Inc."
ISBN: 1491952911
Category : Computers
Languages : en
Pages : 322

Book Description
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Linear Models in Statistics

Author: Alvin C. Rencher
Publisher: John Wiley & Sons
ISBN: 0470192607
Category : Mathematics
Languages : en
Pages : 690

Book Description
The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.