APPLICATION OF MARKOV CHAIN ANALYSIS IN AN ORGANISATION

1. MARKOV ANALYSIS
AND ITS
APPLICATIONSSUBMITTED BY:
SUBMITTED TO SUBMITTED BY
MS SHALINI KAPOOR VIVEK TYAGI BM 016282
SUHMA KUMARI BM016257
VEDANGI BHATNAGAR BM016269
SUCHITA SINGHAL BM016245
SHREY KUMAR BM016231

2. MARKOV PROCESS
• A Markov analysis looks at a sequence of events, and analyzes the tendency
of one event to be followed by another. Using this analysis, you can generate
a new sequence of random but related events, which will look similar to the
original.

3. MARKOV CHAIN
• A Markov process is useful for analyzing dependent random events - that is,
events whose likelihood depends on what happened last. It would NOT be a
good way to model a coin flip, for example, since every time you toss the
coin, it has no memory of what happened before. The sequence of heads
and tails are not inter- related. They are independent events.
• But many random events are affected by what happened before. For
example, yesterday's weather does have an influence on what today's weather
is. They are not independent events.

4. MARKOV CHAIN
• Markov Processes • A Markov system (or Markov process or Markov chain)
is a system that can be in one of several (numbered) states, and can pass
from one state to another each time step according to fixed probabilities. • If
a Markov system is in state i, there is a fixed probability, pij, of it going into
state j the next time step, and pij is called a transition probability

5. EXAMPLE
• Markov Analysis In an industry with 3 firms we could look at the market
share of each firm at any time and the shares have to add up to 100%. If we
had information about how customers might change from one firm to the
next then we could predict future market shares. This is just one example of
Markov Analysis. In general we use current probabilities and transitional
information to figure future probabilities.

6. EXAMPLE
• Say in the accounts receivable department, accounts are in one of 4 states,
or categories: state 1 - s1, paid, state 2 – s2, bad debt, here defined as
overdue more than three months and company writes off the debt, state 3 –
s3, overdue less than one month, state 4 – s4, overdue between one and three
months. Note the states are mutually exclusive and collectively exhaustive. At
any given time there will be a certain fraction of accounts in each state. Say
in the current period we have the % of accounts receivable in each state. In
general we have a row vector of probabilities (s1, s2, s3, s4).

7. APPLICATION OF MARKOV CHAIN
• Frequently used to describe consumer behavior
• Used for forecasting long term market share in an oligopolistic market •
• Brand loyalty and consumer behavior in the same can be analyzed
• Useful in prediction of brand switching and their effect on individual’s
market share
• Sales forecasting

8. Advantages
• Markov models are relatively easy to derive (or infer) from successional data.
• Does not require deep insight into the mechanisms of dynamic change
• Can help to indicate areas where deep study would be valuable and hence act as
both a guide and stimulator to further research.
• Transition matrix summarizes all the essential parameters of dynamic change.
• The results of the analysis are readily adaptable to graphical presentation and hence
easily understood by resource managers and decision- makers.
• The computational requirements are modest and can easily be met by small
computers or for small numbers of states by simple calculators.

9. LIMITATIONS
• Customers do not always buy products in certain intervals and they do not always buy the same amount of a
certain product.
• Two or more brands may be bought at the same time
• Customers always enter and leave markets, and therefore markets are never stable •
• The transition probabilities of a customer switching from an i brand to an j brand are not constant for all
customers
• These transitional probabilities may change according to the average time between buying situations
• The time between different buying situations may be a function of the last brand bought.
• The other areas of the marketing environment such as sales promotions, advertising, competition etc. were
not included in these models

10. Markov Chain Analysis Applied To FMCG
Product
• Q) Suppose that there are three brands of Biscuits namely Good Day, Monaco,
Marie selling in a market. The market has been observed continuously month after
month for changes in the brand loyalty – that is to say, whether and how customers
change their brand of biscuits over time. Let us say that rate of brand switching has
settled over time as follows: Every month customers are being drawn from brand
Good Day to Monaco to the extent of 30%, 10% drawn to Marie and the remaining
60% staying with Good Day itself. However, 20% of those eating Monaco in a
given month are drawn to Good Day; one-half of them do not change the brand
and uses this same brand in the next month as well, while the remaining 30% shift
to Marie. Similarly the behaviour with regard to Marie is found to be like this- 80%
of the customers stick to Marie, 15% shift to Good Day, while remaining 5% shift
to Monaco, each month

11. QUESTION
• Given these conditions about brand switching, assuming no further entry or
exit and given further that the market share for these three brands for the
Month March is 30%,45%,25% for Good Day, Monaco, Marie respectively.
Determine : 1) What would be the market share of these three brands after 2
months (Short Run)?

12. MARKOV ANALYSIS

17. •THANK YOU