2. You have to first know what you are looking for -
this is not always so easy.
If your new chocolate bar isn’t selling well, you
don’t automatically do market research on the
“taste” - because maybe the reason has to do
with the packaging.
Problem Definition
3. hypothesis
After the problem has been defined;
(Step 1), and an exploratory
investigation (Step 2), has been
conducted, it is possible to then
formulate a Hypothesis (Step 3)
4. “A tentative explanation about the
relationship between variables as a starting
point for further testing.”
The way of thinking about how something
works - and using your original “guess” as a
starting point for further investigation
HypothesisHypothesis
5. The selection of areas considered reasonably typical of
the total market, and introducing a new product to
these areas with a total marketing campaign to
determine consumer response before marketing the
product nationally.
Test Marketing
6. SampleSample
Sample is a process of selecting a subset of
rationalised number of members of the population
of the study and collecting data about their
attributes
These limited members are called sampling units
Based on the data gathered on the sample the
analyst draws conclusion about the population
7. What is a sample ?
A subset of some of the units in the population
A subgroup of the population
8. Example
population size = 1000 ( blue collar)
Sample size = 200
(chosen for studying performance of blue collar)
9. Why sampling ?
Too expensive to test the entire population
Impossible to test entire population
Testing the entire population often produces
errors
May give accurate results
Enables to researchers to make estimates of
some unknown characteristics of population in
question
High scope of accuracy and reliability
10. Sampling Methods
Probability Non-Probability
Based on probability theory. Focus on volunteers, easily
available units, or those that just
happen to be present when the
research is done.
Every unit of the population of
interest must be identified, and
all units must have a known,
non-zero chance of being
selected into the sample.
Useful for quick and cheap
studies, for case studies, for
qualitative research, for pilot
studies, and for developing
hypotheses for future research.
12. Simple random sampling
Population = n
Sample = n
All possible sample= n
A random number table is a list of numbers, composed
of the digits 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. Numbers in the
list are arranged so that each digit has no predictable
relationship to the digits that preceded it or to the digits
that followed it. In short, the digits are arranged
randomly. The numbers in a random number table are
random numbers
Link - http://stattrek.com/Tables/Random.aspx
13. Simple random sampling
25 Random Numbers
068 057 036 098 014 015 012 022 094 080 094
052 077 076 006 013 018 002 051 080 066 035
000 004 044
* This table of 25 random numbers was produced according to the following
specifications: Numbers were randomly selected from within the range of 0 to
100. Duplicate numbers were allowed.
14. Systematic Random sampling
• most appropriate practical method for
sampling
• for instance - to select every Xth
item from the
list.
15. Systematic Random sampling
There is a population of 2000 and sample size is 100. apply the
systematic random sampling.
Identify Population size = N
Sample size = n= 100
Interval =k=?
k= N/n = 2000/100 = 20
Now Select a random number –x between 1 and k.
Suppose first xth
number is 12
Then next number is = x+k = 12+20 = 32 so on
16. Question
• Population of people going to night-clubs is on
an average about 250-300 people in a city.
Number of night clubs are 30.
• A researcher wants to select people for
interview among this population .Apply
systematic random sampling to identify the
people.
17. Stratified sampling
• Constructed by classifying the population in sub-
populations (or strata), base on some well-
known characteristics of the population, such as
age, gender or socio-economic status.
• The selection of elements is then made
separately from within each strata, usually by
random or systematic sampling methods.
18. Example
Sample size = n = 30
Population size =N= 8000
Population is divided into three strata ;
N1= 4000 , N2= 2400 , N3= 1600
Find the sample sizes for the different strata by using
proportional allocation : For Proportional Allocation –
P1 = 30 (4000/8000) = 15
P2 and P3 = ??
19. Question
In the class 12th
, pupils are offered Maths,
Physics or Chemistry homework. 28 choose
Maths homework 47 choose Physics
homework 25 choose Chemistry homework. If
you wanted to check the homework of any 20
students, how many of each would you
choose? ..
20. Cluster
• Suitable for conducting research studies that
cover large geographic area.
• Once the cluster is formed the researcher can
either go for one stage, two stages, or multi
stage cluster sampling.
21. Example
Suppose that the Department of Agriculture wishes
to investigate the use of pesticides by farmers in
England.
A cluster sample could be taken by identifying the
different counties in England as clusters.
A sample of these counties (clusters) would then be
chosen at random, so all farmers in those counties
selected would be included in the sample. It is
easier to visit several farmers in the same county
than it is to travel to each farm in a random sample
to observe the use of pesticides.
23. Convenience
An exploratory research where the researcher
is interested in getting an inexpensive
approximation of the truth. As the name
implies, the sample is selected because they
are convenient.
24. Purposive
The researcher selects the units with some
purpose in mind, for example, students who
live in dorms on campus, or experts on urban
development.
25. Quota
widely used in opinion polling and market
research.
Interviewers are each given a quota of subjects
of specified type to attempt to recruit for
example, an interviewer might be told to go out
and select 20 adult men and 20 adult women,
10 teenage girls and 10 teenage boys so that
they could interview them about their television
viewing.
26. Differences between Probability
Sampling and Non-Probability
Probability (Random) Sampling Non-Probability (Non-Random)
Sampling
Allows use of statistics, tests
hypotheses
Exploratory research, generates
hypotheses
Can estimate population
parameters
Population parameters are not of
interest
Eliminates bias Adequacy of the sample can't be
known
Must have random selection of
units
Cheaper, easier, quicker to carry
out
27. 27
What is a Good Sample?
Issue Criterion
Population Definition Consistency of target population and
study population
Truly representative sample
Sampling Method To select any member of study
population equally likely
Precision of Estimate Estimate precise enough to inform
decision making
May results in a small sampling error.
28. Steps in Sampling Design
Type of universe: The universe can be finite or
infinite. In finite universe the number of items is
certain, but in case of an infinite universe (city
population, factory workers etc,) the number of
items is infinite, i.e., we cannot have any idea
about the total number of items ( number of
stars).
Sampling Unit: Sampling Unit may be geographical
one such as state, district, village, etc., or a
construction unit such as house flat., it may be
individual.
28
29. Steps in Sampling Design
(contd..)
Source list: It is also known as ‘sampling
frame’ from which sample is to be drawn. It
contains the names of all items of a universe
(incase of finite universe only). If the source
list is not available, the researcher has to
prepare it. Such list should be comprehensive,
correct, reliable and appropriate. It is
extremely important for the source list to be
as representative of the population as
possible.
29
30. Steps in Sampling Design (contd..)
Size of sample: The sample size should neither be
excessively large, nor too small. It should be optimum.
An optimum sample is one which fulfills the
requirements of efficiency, appropriate representation,
reliability and flexibility. While deciding the sample size,
researcher must determine the desired precision as also
an acceptable confidence level for the estimate.
31. Steps in Sampling Design (contd..)
Parameters of interest: In determining the sample
design, one must consider the question of the
specific population parameters which are of
interest. For instance, we may be interested in
estimating the proportion of persons with some
characteristic in the population, or we may be
interested in knowing some average or the other
measure concerning the population.
31
32. Steps in Sampling Design (contd..)
Sampling procedure: Finally, the researcher
must decide the type of sample he will use
i.e., he must decide about the technique to be
used in selecting the items for the sample. In
fact, this technique or procedure stands for
the sample design itself. There are several
sample design out of which the researcher
must choose one for his study.
32
33. Definition
A set of routine procedures to continuously collect,
monitor, and present internal and external
information on company performance and
opportunities in the marketplace.
Marketing Information Systems
34. For some companies, market knowledge comes in on a regular
basis.
Some stuff is “Data”, and some is “Information”
Data = statistics, opinions in surveys, facts, predictions etc.
Information = data RELEVANT to the Marketing Manager in
making decisions
Marketing Information Systems
35. All of this depends on the ability of the
company to use technology to help it be
better than the competition
Marketing Information Systems
