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
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.
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.
- 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 \(10\%\)
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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