Digital Analytics: Data Driven Decision Making in Digital World

352 Pages·2017·16.16 MB·English

by  Jumin Kamki

Notion Press
Old No. 38, New No. 6
McNichols Road, Chetpet
Chennai - 600 031
First Published by Notion Press 2016
Copyright © Jumin Kamki 2016
All Rights Reserved.
ISBN 978-1-946556-20-2
This book has been published with all efforts taken to make the material error-
free after the consent of the author. However, the author and the publisher do
not assume and hereby disclaim any liability to any party for any loss, damage,
or disruption caused by errors or omissions, whether such errors or omissions
result from negligence, accident, or any other cause.
No part of this book may be used, reproduced in any manner whatsoever
without written permission from the author, except in the case of brief
quotations embodied in critical articles and reviews.
Dedicated
To
My Wife
Kasturika Saikia
&
My Son
Eevan aka Minyansh Kamki
ABOUT THE AUTHOR
JUMIN KAMKI
PGDM from IIM Ahmedabad with more than 10 years of experience in Retail
and Ecommerce industries across different verticals. Keen interest in Analytics
and Data Driven decision making, he has been spearheading Analytics
initiatives in multiple organizations focused on building digital capabilities to
transform the organization and bringing strategic shift towards data driven
decision making. His area of interest includes Game Theory, Machine Learning,
Digital Marketing and Economics. He has worked with multiple organization –
Reliance Industries Limited, Aditya Birla Retail Ltd, Utsavfashion.com, Askme
Group and Tata Insights and Quants in his professional career. More about him
on
Blog: www.juminkamki.com
LinkedIn: https://in.linkedin.com/in/juminkamki
CONTENTS
Preface
Acknowledgement
Chapter - I : DATA ANALYTICS FOUNDATION
Section - I : MEASURING CENTRAL TENDENCY AND DISPERSION
Section - II : PROBABILITY THEORY
Section - III : SAMPLING AND HYPOTHESIS TESTING
Section - IV : LINEAR PROGRAMMING
Chapter - II : ANALYTICS SYSTEM
Section - I : BUSINESS INTELLIGENCE SYSTEM
Section - II : R BASICS
Chapter - III : WEB ANALYTICS
Section - I : GOOGLE ANALYTICS
Chapter - IV : CUSTOMER ANALYTICS
Section - I : CUSTOMER ANALYTICS
Chapter - V : DIGITAL MARKETING
Section - I : DIGITAL MARKETING BASIC
Section- II : DIGITAL CHANNEL OPTIMIZATION
Chapter - VI : FORECASTING AND PREDICTION
Section - I : REGRESSION
Section - II : TIME SERIES FORECASTING
Chapter - VII : INVENTORY MANAGEMENT
Section - I : INVENTORY MODEL
Section -II : TRANSPORTATION PROBLEM
Chapter - VIII : ADVANCED TOPICS
PREFACE
The genesis of this book is an idea that comes to me while interacting with
many young aspirants who want to build career in analytics. Most of the
candidates and colleagues I interacted have good knowledge on the certain
areas of the analytics but there is gap in understanding the basic knowledge of
the analytics from overall analytics domain perspective. This book is an
attempt to provide a comprehensive guide to the readers who want to build a
career in analytics. As such analytics is a vast domain with each industry
having different practices due to demand of the system and processes of that
industry but there is underlying common thread in analytics that cut across all
industries that is the digital side of the analytics. The orientation of the book is
more towards ecommerce industry and retail industry but it is equally useful
for those in insurance and finance domain doing digital analytics.
The book is comprehensive in sense that all areas of analytics being covered
to some extent. Intent is to provide as many aspect of digital analytics as
possible in a single book; such that a reader should not need to consult any
other book while going through this book. Respecting the freedom and style of
each analytics person, I have not prescribe a single model to be followed; It has
been left for reader to explore each topic and figure out their own model and
style from knowledge gained from the book.
My intent was to create something that a person without prior knowledge of
analytics can enter the book and come out as an expert in the digital analytics
at the end of the book. This book is intended for people with no knowledge of
digital analytics and people with some level of digital analytics and want to
enhance it. Some people can use this book as a reference in their work as well.
This book is not intended for someone looking for advance topic in analytics
such as big data, machine learning, and internet of thing and so on.
Flow of the Book
The beauty of this book is that one can read a chapter as an independent unit
because each chapter is self-contain and start with very basic understanding of
the concept and then it has been taken to a higher level of understanding.
However there are many interconnection at the overall scheme of the book. For
example you would need analytics foundation chapter to understand concept of
probability distribution and central limit theorem which has been used in say
Inventory Management chapter. Similarly you would need knowledge of R in
Analytics System Chapter to use R codes in Customer Analytics chapter.
Therefore depending on the level of knowledge one has, she can pick any
chapter and gain something out of the chapter.
My intent of covering vast topic is to make reader without knowledge of
analytics or with little analytics knowledge, a rock star in Digital Analytics.
CHAPTER I: First chapter of the book provides one with basic foundation
of the Analytics worlds that is the statistical knowledge. Section I of the
chapter deals with the concept of central tendencies- mean, mode & median,
variance & standard deviation, correlation and graphs. Section II of the chapter
talks about probability concepts, probability distribution and central limit
theorem. Section III deals with the hypothesis testing, chi-square and ANOVA.
Section IV provides concept of optimization techniques called Linear
Programming.
CHAPTER II: Second chapter of book is about how to build and use
analytics system. As an example of the analytics system in section I, I have
used Pentaho stack to provide overall understanding of concept of Extraction,
Transformation & Loading (ETL), Online Analytics Processing (OLAP) cube
and Dashboard. Section II provides understanding of Analytics System called
R which is open source tool for statistical analysis.
CHAPTER III: Third chapter is extension of second chapter as it deals with
another analytics system known as Google Analytics. Google Analytics is
different from previous system because it deals with collection and reporting of
website data.
CHAPTER IV: Fourth chapter deals talks about ways and means to analyze
online and offline customer data. It not only provide theoretical concept but R
codes with examples for self-practice.
CHAPTER V: Fifth chapter provides rigorous understanding of the digital
marketing concepts and tools in the section I. Using concept from section I, the
optimization of the digital channel and attribution modeling is discussed in
section II.
CHAPTER VI: Sixth chapter is all about prediction and forecasting. Section
I deals with predictive models such as regression and section II talks about
time series forecasting including smoothening and ARIMA model.
CHAPTER VII: Seventh chapter deals with the operation part of the retail
and ecommerce companies. Section I is all about optimizing inventory and
section II is about optimizing the transportation. Both section combine provides
ammunition to optimize overall supply chain optimization.
CHAPTER VIII: Eight chapter is all about more advanced topic which are
not covered in detail in this book but give a glimpse of the subject matter so
that interested readers can pick up from here to specific book on the subject.