A Cereal Market Share Puzzle

Q: In a certain market there exists two brands of cereal from two competing companies. Brand X and Y. It is known from historical data
• The probability that a customer using brand X would hop to brand Y in a given year is $$12\%$$
• The probability that a customer using brand Y would hop to brand X in a given year is $$10\%$$
After spending some money on advertising brand X is known to have $$30\%$$ market share. What would the market share of X be after 3 years?

Fifty Challenging Problems in Probability with Solutions (Dover Books on Mathematics) A: Situations like the above arise often in the industry and are best modelled using Transition Matrices . To begin with, assume the customer can be modelled as being in a particular "state" of loyalty. From the given data, this can shown in a transition matrix $$T$$ form as below
$$T = \pmatrix{ 0.88 & 0.12 \\ 0.1 & 0.9}$$
The current state of market is the current state of the system which in turn can be represented as a row matrix in the following form.
$$S_{0} = \pmatrix{0.3 & 0.7}$$
The state of the market one year on is simply $$S_{1}=S_{0}T$$. Likewise the state of the market two years on is $$S_{2} = S_{1}T$$. We can extend this and predict the state of the market $$n$$ years on is $$S_{n} = S_{0}T^{n}$$. For the given set of values, $$S_{0}T^{3}$$ works out to be
$$S_{0}T^{3} = \pmatrix{0.381 & 0.618}$$
Notice, that we are now able to predict the future market share distribution!
You can extend this out to the long term by simply increasing the value of $$n=3$$ to some large number and compute $$S_{0}T^{n}$$. Also note that this is sensitive to your initial choice of the state transition matrix $$S_{0}$$ and by continuously monitoring $$S_{i}$$ you can build out a system to constantly predict future market share distributions and/or the effectiveness of advertisement campaigns in moving these metrics. Here is the R code for extending it out to many years.

#!/usr/bin/Rscript

t = matrix(c(0.88,0.12,0.1,0.9),nrow=2,byrow=TRUE)
s = matrix(c(0.3,0.7),nrow=1,byrow=TRUE)

t.iter = matrix(c(1,0,0,1),nrow=2,byrow=TRUE)

for(i in 1:100){
t.iter = t.iter %*% t
}

# Final State
m = s%*%t.iter
m


Some of the best books to learn the art of probability

Fifty Challenging Problems in Probability with Solutions (Dover Books on Mathematics) This book is a great compilation that covers quite a bit of puzzles. What I like about these puzzles are that they are all tractable and don't require too much advanced mathematics to solve.

Introduction to Algorithms This is a book on algorithms, some of them are probabilistic. But the book is a must have for students, job candidates even full time engineers & data scientists

An Introduction to Probability Theory and Its Applications, Vol. 1, 3rd Edition The Probability Tutoring Book: An Intuitive Course for Engineers and Scientists (and Everyone Else!) Introduction to Probability, 2nd Edition The Mathematics of Poker
Good read. Overall Poker/Blackjack type card games are a good way to get introduced to probability theory

Bundle of Algorithms in Java, Third Edition, Parts 1-5: Fundamentals, Data Structures, Sorting, Searching, and Graph Algorithms (3rd Edition) (Pts. 1-5) An excellent resource (students/engineers/entrepreneurs) if you are looking for some code that you can take and implement directly on the job.

Understanding Probability: Chance Rules in Everyday Life A bit pricy when compared to the first one, but I like the look and feel of the text used. It is simple to read and understand which is vital especially if you are trying to get into the subject Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) This one is a must have if you want to learn machine learning. The book is beautifully written and ideal for the engineer/student who doesn't want to get too much into the details of a machine learned approach but wants a working knowledge of it. There are some great examples and test data in the text book too. Discovering Statistics Using R This is a good book if you are new to statistics & probability while simultaneously getting started with a programming language. The book supports R and is written in a casual humorous way making it an easy read. Great for beginners. Some of the data on the companion website could be missing.

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The Best Books to Learn Probability

If you are looking to buy some books in probability here are some of the best books to learn the art of Probability

The Probability Tutoring Book: An Intuitive Course for Engineers and Scientists (and Everyone Else!)
A good book for graduate level classes: has some practice problems in them which is a good thing. But that doesn't make this book any less of buy for the beginner.

An Introduction to Probability Theory and Its Applications, Vol. 1, 3rd Edition
This is a two volume book and the first volume is what will likely interest a beginner because it covers discrete probability. The book tends to treat probability as a theory on its own

Discovering Statistics Using R
This is a good book if you are new to statistics & probability while simultaneously getting started with a programming language. The book supports R and is written in a casual humorous way making it an easy read. Great for beginners. Some of the data on the companion website could be missing.

Fifty Challenging Probl…

The Best Books for Time Series Analysis

If you are looking to learn time series analysis, the following are some of the best books in time series analysis.

Introductory Time Series with R (Use R!)
This is good book to get one started on time series. A nice aspect of this book is that it has examples in R and some of the data is part of standard R packages which makes good introductory material for learning the R language too. That said this is not exactly a graduate level book, and some of the data links in the book may not be valid.

Econometrics
A great book if you are in an economics stream or want to get into it. The nice thing in the book is it tries to bring out a oneness in all the methods used. Econ majors need to be up-to speed on the grounding mathematics for time series analysis to use this book. Outside of those prerequisites, this is one of the best books on econometrics and time series analysis.

Pattern Recognition and Machine Learning (Information Science and Statistics)
This is excelle…

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The following are some good books to own in the area of Linear Algebra.

Linear Algebra (2nd Edition)
This is the gold standard for linear algebra at an undergraduate level. This book has been around for quite sometime a great book to own.

Linear Algebra: A Modern Introduction
Good book if you want to learn more on the subject of linear algebra however typos in the text could be a problem.

Linear Algebra (Dover Books on Mathematics)
An excellent book to own if you are looking to get into, or want to understand linear algebra. Please keep in mind that you need to have some basic mathematical background before you can use this book.

Linear Algebra Done Right (Undergraduate Texts in Mathematics)
A great book that exposes the method of proof as it used in Linear Algebra. This book is not for the beginner though. You do need some prior knowledge of the basics at least. It would be a good add-on to an existing course you are doing in Linear Algebra.

Linear Algebra, 4th Edition
This is good book …