As marketers you must also be aware of the sociocultural influences that help to shape a
consumers buying habits. Most consumers buying habits are not a product of any one influence
or behaviour but are made up of thousands of cues that each person receives from, their friends,
families and social groups.
All of these social and cultural influences add to what the consumer believes is the best course of
action when it comes to buying a product or service. Any marketing campaign must target the
cultural and social difference of each consumer and allow then to envision what that product can
do for them when it comes to functioning within a certain culture or social group.
- Personal Influence
A consumer is often influenced into purchasing something by an opinion leader who they put their
trust in or simply because someone else through word of mouth gives positive feedback about a
product or service they have used.
Opinion leaders can be anybody, movie stars, rocks stars, a pastor anybody that the consumer
trusts enough to believe what the other person is telling them. - Reference Groups
In addition to personal influence their particular reference group also influences consumer-buying
habits. A reference group is a group of people to whom an individual will look to for self-appraisal
or as a source to help in the development of their personal standards, such as family, friends and
co-workers
Reference groups affect a consumer’s buying habits because they influence a person’s information
processing, attitudes and aspiration levels of the product or service they are purchasing. For
instance if a reference group has a strong preference of one product of another the consumer
may be influenced into changing their attitude about that product and buy it. - Consumer Socialization
Is the process by which learn to be consumer. As children we learned from our parents how to
how to be consumers and as parents we also teach our children directly and indirectly how to be
consumers. Some companies, such as Sony with their My First Sony line, are quick to target the
children because they know they will be influenced to want to buy the same things their parents
are buying. - Family Life Cycle
We grow and develop as person and also as a consumer. Over the course of a lifetime a family
will go through many stages of development and with each stage will come a whole new set of
consumer development. A marketer must be aware what kind of marketing campaign works best
at not only targeting the family but also the individual needs of the consumer. For instance a baby
swing will work not only in satisfying the needs of the baby but also the family.
Another aspect of the family life cycle is the development of a particular consumer’s buying habits
over the course of a lifetime. A teenager has different buying habits then does their parents or
grandparents so each of their needs must be met in advertising and marketing. - Family Decision Making
Not only do families make decisions as individuals but they also make purchasing decisions based
on the dynamics of the household. Family decision-making happens in the context of joint
decision-making by the husband and wife or as the children age by the whole family. The other
type of family decision-making process is a spouse dominate decision, where either the husband
or wife is responsible for the purchase.
One area that must not be overlooked in marketing is the influence Generation Y has now in the
consumer buying habits of a household. Teenagers have money and are not afraid to spend it
and marketers must be aware of that buying power in order to take full advantage. - Culture and Subculture
An area that is always growing and changing is the culture and subculture. A culture is a set of
values, ideas and attitudes that learned among a group that influence the groups way of thinking
and acting. A subculture is smaller group within the larger group that functions under a different
or unique set of values, ideas and attitudes than the larger group. An example of an culture would
be Hispanic Americans, African Americans and Asian Americans.
Each of these groups has a different affect on marketing and must not be overlooked when
putting together a marketing campaign. An example of a campaign direct at one of these groups
would be Pepsi’s use of the Spanish language to promote its products on TV and in print ads.
Data Types
Data analysis is the process in which raw data is organised so useful information can be extracted
from it. The process of organising and thinking about data is critical to understanding what the
data does and does not contain. There are a variety of ways in which people can approach data
analysis, and it is very easy to manipulate data during the analysis phase to push certain
conclusions or agendas. It is important to pay attention when data analysis is presented, and to
think critically about the data and the conclusions which were drawn. Remember to always be
mindful of:
Data validity
Data reliability
Data relevance
Types of data can include, but is certainly not limited to:
Formal or informal comments, feedback from clients, customers staff etc.
Government statistics
Industry planning information
Qualitative quantitative data
Sales figures
SWOT analysis of competition and your own business in relation to the needs of the
research
Remember from earlier, there are two types of data sources, Primary and Secondary and two
different types of information (data) that can be gathered; Quantitative or qualitative. When
accessing information you will have to make judgements about whether particular information is
relevant to your research task. Does this source tell me more about my topic? Have similar
considerations been made when conducting previous research? There are two broad types of
information, primary and secondary. Both primary and secondary information can be qualitative or
quantitative.
Quantitative or qualitative
Interpreting data requires one of two approaches. Quantitative or qualitative.
The decision which type of research to use is, of course, dependant on what you are aiming to
achieve with your research. If you want to use numerical information to support your theory then
use quantitative, if you need to explain why something is the case then use qualitative. In order to
analyse market trends, you need to ensure the correct research is used.
Making a business research questionnaire is no big deal, but making one that successfully derives
the information that is actually required by the business to improve its practices is something not
every person can do. One of the things that help make an online survey more effective is the
understanding of the different types of data that is required out of survey respondents and of the
different ways to measure the response. Different situations demand the use of different types of
data. Remember, different scales have to be designed to derive these different types of data14.
The feedback of survey respondents can be categorized into two basic forms of data – nonparametric
and parametric.
Non-Parametric Data
The sort of data that does not have any direction and cannot be divided is called non-parametric
data. Usually histograms are used to analyze non-parametric data. There are two basic types of
non-parametric data.
Nominal Data
Nominal data refers to alphabetical or numeric data that is used to name people or objects for
symbolic purposes and has no mathematical value.
For example, a questionnaire may ask the respondents to name the brand of shampoo they use.
Numeric data too can also be included in the category of nominal data such as numbers written
on the backs of sportsmen.
Ordinal Data
Ordinal data refers to numeric data that indicates only the relative ranking of different items,
without representing the intensity of the mathematical value or the distance between the values.
For example, respondents to an online survey may be asked to rank different brands of
shampoo. Hence, the ranking of different shampoo brands will inform researchers the relative
preference of survey respondents but will not inform them about the intensity of the difference of
preference of one shampoo brand from another.
Parametric Data
Numeric data that has direction is called parametric data. It can be used to analyze the difference
the different responses and can also be at times divided. There are two different types of
parametric data.
Interval Data
14 Source: Question Pro, as at https://www.questionpro.com/blog/a-primer-on-the-4-data-types-youcan-
collect-in-your-market-research/, as on 4th August, 2017.
The collection of internal data is done on a scale on which all points are equidistant from the ones
next to them. Scales measuring interval data do not have zero because of the nature of thing
being measured. For example, respondents can be asked to rate their happiness on a scale of 1
through 10. Interval data cannot be divided because of the non-absolute nature of the data.
Ratio Data
Ratio data is the most absolute form of numeric data collected from respondents. It can be divided
and altered in different ways to derive more meaning. All absolute mathematical values can be
called ratio data such as income, age, sales, market share, etc.
Although ratio data may seem the most usable form of data and researchers may feel tempted to
ask their survey respondents to answer all the questions in ratio form, it is either not practically
possible to do so or isn’t the best form of data because of the objective of the research being
conducted. An effective online survey questionnaire contains questions that derive the sort of data
that will come handy in getting better insight into the respondents’ minds.
Activity 6
Provide examples of the data that would be collected in each of the data types: Nominal,
ordinal, parametric and interval.
Activity 6
Activity 6
All measurements must take one of four forms and these are described in the opening section of
the chapter. After the properties of the four categories of scale have been explained, various forms
of comparative and non-comparative scales are illustrated. Some of these scales are numeric,
others are semantic and yet others take a graphical form. The marketing researcher who is familiar
with the complete tool kit of scaling measurements is better equipped to understand markets15.
Levels of measurement
Most texts on marketing research explain the four levels of measurement: nominal, ordinal, interval
and ratio and so the treatment given to them here will be brief. However, it is an important topic
since the type of scale used in taking measurements directly impinges on the statistical techniques
which can legitimately be used in the analysis.
Nominal scales
This, the crudest of measurement scales, classifies individuals, companies, products, brands or
other entities into categories where no order is implied. Indeed it is often referred to as a
categorical scale. It is a system of classification and does not place the entity along a continuum. It
involves a simply count of the frequency of the cases assigned to the various categories, and if
desired numbers can be nominally assigned to label each category as in the example below:
Figure 3.1 An example of a nominal scale
Which of the following food items do you tend to buy at least once per month? (Please tick)
Okra Palm Oil Milled Rice
Peppers Prawns Pasteurised milk
The numbers have no arithmetic properties and act only as labels. The only measure of average
which can be used is the mode because this is simply a set of frequency counts. Hypothesis tests
can be carried out on data collected in the nominal form. The most likely would be the Chi-square
test. However, it should be noted that the Chi-square is a test to determine whether two or more
variables are associated and the strength of that relationship. It can tell nothing about the form of
that relationship, where it exists, i.e. it is not capable of establishing cause and effect.
Ordinal scales
Ordinal scales involve the ranking of individuals, attitudes or items along the continuum of the
characteristic being scaled. For example, if a researcher asked farmers to rank 5 brands of pesticide
in order of preference he/she might obtain responses like those in table 3.2 below.
15 Source: FAO Corporate Document Repository, as at
http://www.fao.org/docrep/w3241e/w3241e04.htm, as on 4th September, 2017.
Figure 3.2 An example of an ordinal scale used to determine farmers’ preferences among 5 brands
of pesticide.
Order of preference Brand
1 Rambo
2 R.I.P.
3 Killalot
4 D.O.A.
5 Bugdeath
From such a table the researcher knows the order of preference but nothing about how much
more one brand is preferred to another, that is there is no information about the interval between
any two brands. All of the information a nominal scale would have given is available from an
ordinal scale. In addition, positional statistics such as the median, quartile and percentile can be
determined.
It is possible to test for order correlation with ranked data. The two main methods are Spearman’s
Ranked Correlation Coefficient and Kendall’s Coefficient of Concordance. Using either procedure
one can, for example, ascertain the degree to which two or more survey respondents agree in
their ranking of a set of items. Consider again the ranking of pesticides example in figure 3.2. The
researcher might wish to measure similarities and differences in the rankings of pesticide brands
according to whether the respondents’ farm enterprises were classified as “arable” or “mixed” (a
combination of crops and livestock). The resultant coefficient takes a value in the range 0 to 1. A
zero would mean that there was no agreement between the two groups, and 1 would indicate
total agreement. It is more likely that an answer somewhere between these two extremes would be
found.
The only other permissible hypothesis testing procedures are the runs test and sign test. The runs
test (also known as the Wald-Wolfowitz). Test is used to determine whether a sequence of
binomial data – meaning it can take only one of two possible values e.g. African/non-African,
yes/no, male/female – is random or contains systematic ‘runs’ of one or other value. Sign tests are
employed when the objective is to determine whether there is a significant difference between
matched pairs of data. The sign test tells the analyst if the number of positive differences in ranking
is approximately equal to the number of negative rankings, in which case the distribution of
rankings is random, i.e. apparent differences are not significant. The test takes into account only
the direction of differences and ignores their magnitude and hence it is compatible with ordinal
data.
Interval scales
It is only with an interval scaled data that researchers can justify the use of the arithmetic mean as
the measure of average. The interval or cardinal scale has equal units of measurement, thus
making it possible to interpret not only the order of scale scores but also the distance between
them. However, it must be recognised that the zero point on an interval scale is arbitrary and is
not a true zero. This of course has implications for the type of data manipulation and analysis we
can carry out on data collected in this form. It is possible to add or subtract a constant to all of the
scale values without affecting the form of the scale but one cannot multiply or divide the values. It
can be said that two respondents with scale positions 1 and 2 are as far apart as two respondents
with scale positions 4 and 5, but not that a person with score 10 feels twice as strongly as one with
score 5. Temperature is interval scaled, being measured either in Centigrade or Fahrenheit. We
cannot speak of 50°F being twice as hot as 25°F since the corresponding temperatures on the
centigrade scale, 10°C and -3.9°C, are not in the ratio 2:1.
Interval scales may be either numeric or semantic. Study the examples below in figure 3.3.
Figure 3.3 Examples of interval scales in numeric and semantic formats
Please indicate your views on Balkan Olives by scoring them on a scale of 5 down to 1 (i.e. 5 =
Excellent; = Poor) on each of the criteria listed
Balkan Olives are: Circle the appropriate score on each line
Succulence 5 4 3 2 1
Fresh tasting 5 4 3 2 1
Free of skin blemish 5 4 3 2 1
Good value 5 4 3 2 1
Attractively packaged 5 4 3 2 1
(a)
Please indicate your views on Balkan Olives by ticking the appropriate responses below:
Excellent Very Good Good Fair Poor
Succulent
Freshness
Freedom from skin blemish
Value for money
Attractiveness of packaging
(b)
Most of the common statistical methods of analysis require only interval scales in order that they
might be used. These are not recounted here because they are so common and can be found in
virtually all basic texts on statistics.
Ratio scales
The highest level of measurement is a ratio scale. This has the properties of an interval scale
together with a fixed origin or zero point. Examples of variables which are ratio scaled include
weights, lengths and times. Ratio scales permit the researcher to compare both differences in
scores and the relative magnitude of scores. For instance the difference between 5 and 10 minutes
is the same as that between 10 and 15 minutes, and 10 minutes is twice as long as 5 minutes.
Given that sociological and management research seldom aspires beyond the interval level of
measurement, it is not proposed that particular attention be given to this level of analysis. Suffice it
to say that virtually all statistical operations can be performed on ratio scales.
Measurement scales
The various types of scales used in marketing research fall into two broad categories: comparative
and non comparative. In comparative scaling, the respondent is asked to compare one brand or
product against another. With noncomparative scaling respondents need only evaluate a single
product or brand. Their evaluation is independent of the other product and/or brands which the
marketing researcher is studying.
Noncomparative scaling is frequently referred to as monadic scaling and this is the more widely
used type of scale in commercial marketing research studies.
Comparative scales
Paired comparison2: It is sometimes the case that marketing researchers wish to find out which are
the most important factors in determining the demand for a product. Conversely they may wish to
know which are the most important factors acting to prevent the widespread adoption of a
product. Take, for example, the very poor farmer response to the first design of an animal-drawn
mould board plough. A combination of exploratory research and shrewd observation suggested
that the following factors played a role in the shaping of the attitudes of those farmers who feel
negatively towards the design:
· Does not ridge
· Does not work for inter-cropping
· Far too expensive
· New technology too risky
· Too difficult to carry.
Suppose the organisation responsible wants to know which factors is foremost in the farmer’s
mind. It may well be the case that if those factors that are most important to the farmer than the
others, being of a relatively minor nature, will cease to prevent widespread adoption. The
alternatives are to abandon the product’s re-development or to completely re-design it which is
not only expensive and time-consuming, but may well be subject to a new set of objections.
The process of rank ordering the objections from most to least important is best approached
through the questioning technique known as ‘paired comparison’. Each of the objections is paired
by the researcher so that with 5 factors, as in this example, there are 10 pairsIn
‘paired comparisons’ every factor has to be paired with every other factor in turn. However, only
one pair is ever put to the farmer at any one time.
The question might be put as follows:
Which of the following was the more important in making you decide not to buy the plough?
· The plough was too expensive
· It proved too difficult to transport
In most cases the question, and the alternatives, would be put to the farmer verbally. He/she then
indicates which of the two was the more important and the researcher ticks the box on his
questionnaire. The question is repeated with a second set of factors and the appropriate box
ticked again. This process continues until all possible combinations are exhausted, in this case 10
pairs. It is good practice to mix the pairs of factors so that there is no systematic bias. The
researcher should try to ensure that any particular factor is sometimes the first of the pair to be
mentioned and sometimes the second. The researcher would never, for example, take the first
factor (on this occasion ‘Does not ridge’) and systematically compare it to each of the others in
succession. That is likely to cause systematic bias.
Below labels have been given to the factors so that the worked example will be easier to
understand. The letters A – E have been allocated as follows:
A = Does not ridge
B = Far too expensive
C = New technology too risky
D = Does not work for inter-cropping
E = Too difficult to carry.
The data is then arranged into a matrix. Assume that 200 farmers have been interviewed and their
responses are arranged in the grid below. Further assume that the matrix is so arranged that we
read from top to side. This means, for example, that 164 out of 200 farmers said the fact that the
plough was too expensive was a greater deterrent than the fact that it was not capable of ridging.
Similarly, 174 farmers said that the plough’s inability to inter-crop was more important than the
inability to ridge when deciding not to buy the plough.
Figure 3.4 A preference matrix
A B C D E
A 100 164 120 174 180
B 36 100 160 176 166
C 80 40 100 168 124
D 26 24 32 100 102
E 20 34 76 98 100
If the grid is carefully read, it can be seen that the rank order of the factors is –
Most important E Too difficult to carry
D Does not inter crop
C New technology/high risk
B Too expensive
Least important A Does not ridge.
It can be seen that it is more important for designers to concentrate on improving transportability
and, if possible, to give it an inter-cropping capability rather than focusing on its ridging
capabilities (remember that the example is entirely hypothetical).
One major advantage to this type of questioning is that whilst it is possible to obtain a measure of
the order of importance of five or more factors from the respondent, he is never asked to think
about more than two factors at any one time. This is especially useful when dealing with illiterate
farmers. Having said that, the researcher has to be careful not to present too many pairs of factors
to the farmer during the interview. If he does, he will find that the farmer will quickly get tired
and/or bored. It is as well to remember the formula of n(n – 1)/2. For ten factors, brands or product
attributes this would give 45 pairs. Clearly the farmer should not be asked to subject himself to
having the same question put to him 45 times. For practical purposes, six factors is possibly the
limit, giving 15 pairs.
It should be clear from the procedures described in these notes that the paired comparison scale
gives ordinal data.
Dollar Metric Comparisons3: This type of scale is an extension of the paired comparison method in
that it requires respondents to indicate both their preference and how much they are willing to pay
for their preference. This scaling technique gives the marketing researcher an interval – scaled
measurement. An example is given in figure 3.5.
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