Market Research II (BIMM)

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Balaji Institute of Modern Management -Marketing Research II

Marketing Research II

Notes: MR. Prantosh Beneerji BIMM

Collected By: CHAITANYA BANSAL

Marketing Research II BIMM

Chaitanya Bansal – Promoters 2007-2009

Index Table

Definition 3

Implementation and Research proposal 4

Data Reduction 9

Research Design 11

Attitude Scale 12

Degree of Freedom and Null Hypothesis 13

Basic Tech.

1. Cross tab and 17

2. Correlation and Regression 24

3. Anova 34

Adv. Tech.

1. Factor Analysis 43

2. Discriminant Analysis 56

3. Cluster Analysis 70

4. Multidimensional scaling 89

5. Attribute based perceptual mapping 102

6. Conjoint Analysis 109



MARKETING RESEARCH

“Marketing research is a science of identifying data

needs and then collecting, organizing and analyzing data

to help management to take marketing decisions.”

Research

Process: @

CHAITANYA BANSAL



Implementation and research proposal:

Create a research proposal/research plan. Page no. 52 Tull & Hawkins

a. Define the problems/opp. and set objectives (Primary objectives/secondary research

objective).

b. Estimate value of information to be obtained by research.

c. Select the data collection method.

d. Select the measurement techniques.

e. Create the sampling plan.

f. Select data analysis technique in line with objective.

g. Evaluate ethics of research.

h. Finalize time and cost requirements.

i. Prepare the research proposal.

Obtain approval for research plan.

Implement research plan.

a. Implement data collection method

b. Using measurement techniques scales on samples (as per plan)

c. Analyze data obtained to fulfill research objective.

Report findings/ interpretations: Recommendations to client through a

presentation/c.d./hard copy in line with research objectives.

Data collection method:

1. Secondary research

a. Internal secondary research

b. External secondary research.

2. Survey research

a. Telephone interviews

b. Mail interviews

c. Personal interviews

d. Computer interviews.

3. Experimental research

a. Laboratory experiments



b. Field experiments.

Measurement Techniques:

1. Questionnaire

2. Attitude scales

a. Rating scales

b. Comparative scales

c. Perceptual maps

d. Conjoint analysis

3. Observation

4. Projective techniques and Depth interviews

a. Projective techniques

b. Depth interview

Sampling:

a. Population

b. Sample frame

c. Sampling unit

d. Sample size

e. Sample plan

f. Execution.

Research Proposal:

1. Executive Summary

2. Background

3. Objective

4. Research Approach

5. Time and cost

6. Technical appendixes.

Primary research objective (PRO):

“Information needed by management”

Secondary research objective (SRO):

“This is an exhaustive breakdown of PRO, such that fulfillment of each SRO would

automatically ensure that PRO is fulfilled.”



Steps in SRO:

1. Classification of PRO from Senior.

2. Situational analysis.

3. Model development

4. Specification for information required.

e.g.

PRO: What is the criterion that the student of XII pass should have.

SRO:

1. Classification of PRO from senior management i.e. school certificate.

2. Situational: speak to teacher what is being taught e.g. language, science, mathematics.

3. Modal development:- we go for modal the subject.

Language (Hindi & English); Science (Physics, Chemistry, Biology); Mathematics

(mathematics).

4. Quantify : give the marks to each subject

Hindi 50, Physics 100 Mathematics 100

English 100 Chemistry 100

Biology 100

------------------------------------------------------



Case: Fertilizer Company ABC has been involved in the manufactures of fertilizer and L.A.B (active ingredient in

detergent) since 1969. The fertilizers or distribution all India through a distribution network of

800 dealers, LAB is sold directly to detergent manufactures HUL, P&G, Nirma. As on 31st

march 2008 the sales turnover for Rs 920 cr. with a net profit of 112 cr. reserve stood act Rs.

290 cr.

Recently the government has announced that foreign direct investment would be

permitted in the field to fertilizers. This is likely to make the fertilizers industry more

competitive.

Mr. Manish Thackker (Son of Founder & Chairman of ABC LTD senior Thackker). So

Mr. Manish Thackker recently passed out from BIMM and join as M.D. (managing director).

Manish dream is of increasing the annual sale turnover of RS 2000cr in three year time. This

analysis indicates that the best route to achieve this would be through forward integration into

the manufacture of detergents.

The only bottleneck (as per his and his team analysis) appears to be the distribution of

detergents. Manish is of view that if his fertilizer dealer were suitable to distribute detergent

effectively then he should fulfill his vision.

To determine this for a solution he has approach you (researcher).

Q.1 State the PRO

Q.2 Crystallize the SRO

For this you can make relevant practical assumptions. State your assumptions if any and also

justify them.

Soln:

Current sales = 920 cr

Growth = 1200 cr

Detergent market:

Population = 110 cr

User = 75 cr

No. of family =15 cr

Detergent market = 15 x 24 x 12 x 50 = ---------



In this process senior management ask Mr. Thackker about which are the point you are looking

in the distributor, these are as follow and we have to give weight age to each point:

1. Willingness 0.15

2. Financial strength 0.15

3. Geographic convenience 0.15

4. Ware housing 0.10

5. Transportation 0.10

6. Market know how 0.05

7. Market reputation 0.10

8. Past experience 0.05

9. Sales staff 0.05

10. Other product 0.10

Situation analysis: Research collect secondary data about the skill and distribution requirement

for detergents. Then he will visit a few detergent dealers of fertilizers to access their skill. He

will also visit zonal managers to ask them if they can add to his list of attribute.

PRO: To determine suitability of distributor to distribute detergent effectively.

SRO: The points selected were:

1. Financial strength

2. Willingness

3. Geographic location.

Title: To determine whether the financial strength of F.D is suitable for distributing detergent

effectively.

Information needed:

a) Upfront investment

b) Working Capital

c) Contingency fund

What From Whom How

S.N. Information need Respondent Method

1 Upfront investment Dealer

Bank

Structure/ direct /

person

Structure/direct/

mail

2 Working capital Dealer

Bank

Structure/ direct /

person

Structure/direct/

mail

3 Contingency Dealer

Bank

Structure/ direct /

person



Structure/direct/

mail

(Preliminary decision What, Whom, How, are the link between questionnaire and SRO.)

Data Reduction : “Process of getting data ready for analysis is called data reduction.”

The steps involved in the reduction of data are: Steps Time Explanation

1. Field controls While data

collection

Field controls are procedures designed to minimize errors

during the actual collection of data.

It contains the observation of field work by

supervisor/directors as it occurs.

Also checking the accuracy of fieldwork after it has been

conducted.

Also recontacting a sample of respondent to verify

respondent answer to several questions taken from

different parts of the questionnaire.

2. Editing

After data

collection

Unless the questionnaire and analysis are very simple, or

the responses are being entered directly the outcome

definitely go wrong.

So in this process editor should examine every completed

questionnaire before transcribe on the computer.

Also after the data are entered into the computer,

computer editing should be conducted.

3. Coding This is the process of establishing categories and

assigning data to them. Normally it will be a number. For

large data research we use it.

e.g (i) code for close ended:

Breakfast Lunch

------- 2401 -------- 2801

------- 2402 -------- 2802

------- 2403 -------- 2803

(ii) Code for open ended question: if asked people said about SX4

about its style, passion, economy, so in coding we need to define

all this „3‟ sections.

4. Transcribing It is the process of physically transferring data from the

measuring instrument directly into the computer. The

result if this in the creation of a data sheet.

5. Generating new

variables

It is often necessary to create new variables as a part of the

analysis procedure.

New variables are often generated from combination of

other variables in the data. e.g. data on a person‟s age,

marital status, and presence and age of children may be

combined to generate a new variable called „stage in the

family life cycle.‟

6. Summarizing There are two major kinds of summarizing statistics. The first



statistics provides measures of the midpoint of the distribution and is

known as measures of central tendency. The second gives an

indication of the amount of variation in the data comprising the

distribution and is known as measures of dispersion.

Central tendency: mean / median / mode.

Dispersion: Range / variance / standard deviation.

Mean Median Mode A measure of central

tendency; obtained by adding

all observation by adding all

observation and dividing the

sum by number of

observations.

A measure of central

tendency; the value below

which 50% of the observation

lie.

A measure of central

tendency; the value that

occurs most frequently.

Interval/Ratio Ordinal Scale Nominal Scale

Marks frequency

22 : 10

60 : 08

70 : 09

90 : 08

95 : 07

50

B:1 5

G:2 45

50

Tabulation:

Q. How many hours do you study?

Ans 20 – 1

25 – 5

05 – 3

00 – 4

00 – 5

Hour/Day Frequency %

1 20 40%

2 25 50%

3 5 10%

4 0 0%

5+ 0 0%

Total 50 100%



Attitude scale

Direct Indirect

(Direct response attitude scale) (Derived attitude scale)

Rating Attitude Conjoint anys Perceptual mappg

Semantic Likert Staple

Attribute Non attribute

Based Based

Non Comparative Comparative Factor Discriminant Culster Attribute based

Analysis analysis analysis Perceptl map

n

Graphical Itemized Graphical Itemized Specific scale

Constant sum Paired comparison Rank order



Degree of freedom & Null hypothesis:

Suppose we are conducting a survey to know that is consumption of beverage is having regional

impact.

We asked two questions to respondent

1. Where do you come from?

a) North

b) South

2. What is your preferred choice of beverage early in the morning?

a) Tea

b) Coffee

Region Frequency

North 100

South 100

200

Now respondent give the answer in such a way:

Region Beverage

….. choice

Tea Coffee Total

North 62(60) 38(40) 100.

South 58(60) 42(40) 100

120 80 200

The terms written in bracket are expected values.

Deviation we measure in terms of ᵡ2

Its deviation = 2/10 & 2/10

Suppose the data is like this then

Region Beverage

….. choice

Tea Coffee Total

North 610 (600) 390 (400) 1000

South 590 (600) 410 (400) 1000

1200 800 2000

Deviation = 10/600 & 10/600

Deviation of I case > Deviation of II case

Beverage choice Frequency

Tea 120

Coffee 80

200



Hypothesis – Understanding

Null hypothesis – Ground floor to understanding

Null hypothesis put always a negative sentence.

Null hypothesis either be rejected or not rejected, here there is no place for accepted, if the

hypothesis becomes true then it does mean we accept the hypothesis.

e.g. ground floor says there is building but it doesn‟t say how many floors are there.

H0= Region has no significant impact on choice of beverage at 70% confidence level. no: shows the negativity of the statement.

Confidence level: you should have to mention in each and every null hypothesis.

More the Degree of freedom more will be the variation.

Fig. 1 Fig. 2

Fig.1 – Degree of freedom is less

Fig.2 – Degree of freedom is more

Degree of freedom = ᵡ2

value = Probability of occurrence

and vice versa.

R\B Tea Coffee Total

North X 100

South X X 100

120 80 200

Now see we can put any value in „north- tea‟ box because we put 99 in it we could adjust it by

„north- coffee‟ box and in column wise it can be adjusted by „south- tea‟ .



But once this value we fixed then we can‟t put value in any box so

Degree of freedom = 1

R\B Tea Coffee Other Total

North X 100

South X 100

East X

West X X X

120 80 200

Degree of Freedom = 6

Degree of freedom = (R-1)x(C-1)

R- no. of rows

C- no. of columns

If ᵡ2observed > ᵡ2 Benchmark

Then reject H0

If ᵡ2observed < ᵡ2 Benchmark

Then not reject H0

Now suppose these data we have collected from respondent

Region Beverage

….. choice

Tea Coffee Total

North 72(60) 28(40) 100.

South 48(60) 52(40) 100

120 80 200

The terms written in bracket are expected values.

Ho = Region has no significant impact on the choice of beverage significantly at 80%

confidence level

= 122/60 + 12

2/20 + 12

2/60 + 12

2/40

= 144/60 + 144/60+ 144/60 + 144/40

= 2.4 + 3.6 + 3.6 +2.4

ᵡ2observed = 12

ᵡ2B.M.= 1.64



ᵡ2observed > ᵡ

2B.M

Reject Ho

In SPSS the values given in the form of „P‟ value

For the value „1.64‟ Confidence level = 80% so „P‟ value = (1-0.8) = 0.2

For the value „12‟ Confidence level = 99.99% so „P‟ value = (1-0.99) = 0.01

So if then reject Ho.

Hypothesis: An “educated guess” about the outcome of an empirical test designed to answer a

research question.



Chi-Square Test: A statistical test for analyzing significance in analysis of frequencies.

Case: Educational Background A business school desires to determine whether educational background of a student has any

significant impact on academic performance of the student during MBA at 90% confidence

level.

For this purpose the business school has conducted a research in which students were asked to

indicate their educational background and their academic performance at MBA. An extract of

the data of the research is provided below for your analysis.

Data:Variable view:



Data view:

this is label view option The table 1 we have coded for SPSS work but table 2 is the actual data that we collected, we have to compile it by giving

them code as shown in variable view.

SPSS Procedure: @CHAITANYA BANSAL



Q.1 Frame the null hypothesis.

Soln: Ho = Educational background has no significant impact on the academic performance at

90% confidence level.

Output: Q.2 Test out the null hypothesis using suitable statistical measure.

Soln

P Observed = 0.089

P Benchmark = 0.10

so reject Ho



Case: Soft Drink A soft drink manufacturer desires to understand whether consumer‟s age has any significant

impact of choice of soft drink at 80% confidence level. For this purpose they have conducted a

research in which respondent who have asked to indicate column(a) – age ; column(b) – (prefer

choice of soft drink). A data extract of the research is provided below for your analysis.

Data: Variable view:

AGE:

SOFT DRINK:



Data view:

this is label view option The table 1 we have coded for SPSS work but table 2 is the actual data that we collected, we have to compile it by giving

them code as shown in variable view.




Q.1 Frame the null hypothesis.

Soln: Ho = Age has no significant impact on the choice of beverages of soft drink at 80%

confidence level.

Output: Q.2 Test out the null hypothesis using suitable statistical measure.

Soln

P Observed = 0.833

P Benchmark = 0.20

so not reject Ho.



# In marketing we studied about different buying behavior. Different products

command different behavior of the consumers e.g. for the bike, age can be the

important factor so we have to find which behavior is suitable for our product

through analysis.

# Sales fore casting

Sales = fn( x1, x2,x3,…………………………………xn)

x1= Product quality

x2 = Price

x3 = Advertisement expenditure

x4 = Distribution

Sales (Y) = k + a1x1+ a2x2+ a3x3……………+ anxn

a1- Magnitude of impact of x1 over sales (Y).

x1- Independent variable e.g. product quality

k – constant

We calculate a(1,2,3….n) and k by past data and calculate the value of Sales.

For this purpose we need to do Correlation and Regression analysis.



# Correlation and Regression Analysis:

Correlation: „A number between -1 and +1 that reflects the nature (direct or inverse) and the

degree of the association between two or more variables.‟

Correlation analysis used to measure degree of association between two sets of quantitative

data. e.g. Advertising, expenses and sales

Researcher‟s discretion/judgment required judicious use/interpretation of correlation analysis.

Normally correlation analysis is followed by regression analysis.

Regression analysis is used to explain variation in dependent variable based on variation of

independent variables.

e.g. Sales = fn(advertising, staff, outlets)

Typically correlation and regression analysis used for sales/demand forecasting.

Case: Consumer durable manufacturer: A consumer durable manufacturer desire

to create a sales forecasting model that would be practically implementable by the firm. For this

purpose we have conducted preliminary research and identify the variables that had a significant

impact on sales at confidence level of 90%. The short list variable were;

a) Market potential

b) No. of dealers

c) No. of sales staff

d) Competition activity index on 5 point scale „1‟ stood for very low and „5‟ for very high.

e) Existing customer base

Historical data was collected territory wise on sales and the above variables. A data abstract

of research is provided below for analysis.




Data view:

Q.1 Determine the correlation between the variable.

Q.2 Develop alternative regression model indicating relationship between sales and the other variables.

Q.3 Determine goodness of fit of each model recommended a suitable model for practical use by the firm.




Select the method that you want (enter, backward, forward) then click ok

Forward Method:

See this two table:

This table gives us the value of R square which shows the correlation between the variables.

This table shows us the regression model.

Back ward method:



We take the „4‟th

value because it doesn‟t carry competitor activity which is quite difficult to calculate.

This is the regression model for backward method.

Forward Method:



Method R square Model

1. Enter 97.7% Sales = .227(market potential) + 0.819 (dealer) + 1.091(sales staff) –

1.893(competitor)- .549(service)+ 0.066(customer) -3.173

2. Backwar

d

97.1% Sales = 0.2(market potential) + 1.101 (dealer) + 1.120(sales staff) –10.288

3. Forward 96.0% Sales = 0.243(market potential) + 1.424(sales staff) –10.616

Enter solution we can‟t take all the variable including competitor activity as it is difficult to

calculate and it is not a good solution.

Backward method is better because it doesn‟t take „competitor activity‟ and also the value of

R square is not much less than enter solution.

The Forward solution also doesn‟t take the „competitor activity‟ factor and also does not take

the „dealer‟ but the value of R square is quite less.

In this for 1.1% (97.1- 96.0) we can easily calculate the dealer factor in Backward method. So

Backward method is most suitable solution in this situation



Case: Pizza hut

Pizza hut desires to create a practice sales forecasting model to forecast sales territory vise for

this purpose they have conducted a suitable research. The shortlisted variables having impact on

sales at 90% confidence level were:

a) No. of delivery boys

b) Advertising spend

c) No. of outlets

d) Varieties of pizza sold

e) Competitor activity measured on 5 point scale „1‟ stood for very low and „5‟ for very

high.

f) Existing customer base.

A data extract of research on sales and the above variables is provided below. For your analysis

Q.1 Determine the correlation between the variable.

Q.2 Develop alternative regression model indicating relationship between sales and the other

variables.

Q.3 Determine goodness of fit of each model recommended a suitable model for practical use

by the firm.

Q.4 Recommend a practical model for Pizza hut. Justify your answer.




Data view:


Select the method that you want (enter, backward, forward) then click ok



Forward Method:

See this two table:

This table gives us the value of R square which shows the correlation between the variables.

This table shows us the regression model.

Back ward method:

We take the „2‟nd

value because it doesn‟t carry competitor activity which is quite difficult to calculate with the

highest R square value.



This is the regression model for backward method.

Forward Method:



Method R square Model

1. Enter 95.3% Sales = +6.372+ 0.919(Delivery boy) + 0.699 (Advertisement) +

1.620(Outlet) – 1.978(Variety)+ .66(competitor)+ 0.242(customer base)

2. Backward 95.3% Sales = +7.061+ 0.920(Delivery boy) + 0.678 (Advertisement) +

1.629(Outlet) – 2.014(Variety)+ 0.242(customer base)

3. Forward 94.0% Sales = -11.817 + 1.753(outlet) + 1.64(Delivery boy)

Enter solution we can‟t take the entire variable including competitor activity so it is difficult to

calculate and is not a good solution.

Backward method is better because it doesn‟t take „competitor activity‟ and also the value of

R square is same as Enter solution.

The Forward solution also don‟t take the „competitor activity‟ factor and also not take the

„Advertisement & customer base‟ factor but the value of R square is quite less.

In this for 1.3% (95.3- 94.0) we can calculate the „Advertisement & customer base‟ factor

which is not difficult to calculate in Backward method.

So, backward method is most suitable solution.



Anova:

We conducted a research that, Do engineers have better performance than non engineers.

We have to calculate the mean to the data.

If the data is as follows:

Non

Engi.

7.4 7.5 7.45 7.85 7.55 7.3 7.35 7.7 7.5

Engi. 6.4 6.75 6.45 6.25 6.9 6.15 6.1 6.4 6.5

We can directly conclude that non engineers are better than engineers. But if the data is like

this then:

Non

Engi.

0.5 3.5 2.5 5.5 8.5 9.0 6.5 5.5 7.5

Engi. 3.5 7.5 4.5 6.5 8.9 9.5 3.5 1.5 6.5

We can‟t take the decision that who is better.

The difference between the two data of a research is called as Dispersion.

Dispersion is more in table – (II).

Dispersion is found out by variance

If the dispersion is high as in table II we calculate it by Anova.

In table I data is like this. They are distinctly distinguished.



In table II data is overlapped so we can‟t conclude that who is better.

S.O.S (with in):

e.g. If Dual D studies „3‟ hours a day & in other class (let it be Dual „C‟) all students study for

more or less than „3‟ hours, then they are not distinctly distinguished.

But if Dual D studies „3‟ hours a day and Dual „C‟ studies for „0.5‟ hours then they are

distinctly distinguished.

We take difference among the member and square it down. It is called „sum of square

distances within‟ ( S.O.S. within).

S.O.S (among/between):

Group 1 and Group 2 both are distinctly different from each other, but Group 2 may not be

distinctly different than Group 1 because the data value is quite conserved in group 1 and in

group 2 quite spread .So the distance ,even though is same but if we see group wise then it is

different.

So we have to take second parameter also i.e. „sum of square distances among/between‟



Anything more than 1 states that the groups are distinctly distinguished from each other.

e.g. Big Bazaar:

Shelf height: {eye level, waist level, knee level} – Among group

Degree of freedom of all among group = 3-1 =2

Sales are different at different level. Now don‟t you think sales also vary with in weekend days,

also in different time in a day?

Day/time Parameter: {week day, weekend, day, evening} – Within group

4 parameters of Day time so (4-1) = 3

But for all „3‟ we have „3‟ parameter (eye, waist, knee level) so it become 3x3 = 9

Degree of freedom of all with in group: 9

3 ≠ 9

Degree of freedom (df ) among ≠ Degree of freedom(df ) within

f ratio = Eigen value (only when df =1) {means only when there are two group and two variable}

If f observed > f table then reject Ho (page no. 252 „Tull and Hawkin‟).

This whole process is called „ANOVA‟.



Case: Ad agency (Manually) An ad agency desires to determine which of three ad campaigns (theme A, B & C) should be

launch in the market for this purpose it has conducted a research in which respondent were

asked to evaluate the three ad team on a scale of 1-10, where 1- not liked at all. 10- Liked very

much.

A data extract of the research is provided below for your analysis.

Q.1 State the null hypothesis at 90% confidence level.

data provided.

Respondent no. Theme „A‟ Theme „B‟ Theme „C‟

1 3 7 0

2 3 9 1

3 4 6 2

4 6 6 1

Total 16 28 4

Soln: Ho = Theme has no significant impact on market.

Group mean:

1) 16/4 = 4

2) 28/4 = 7

3) 4/4 = 1

Grand mean:

(4+7+1)/3 = 4

SOS among = {(Group mean – Grand mean)2 x (no. of respondent)}

1) (4-4)2 x 4= 0

2) (7-4)2 x 4 =36

3) (1-4)2 x 4 = 36

SOS within = (individual respondent 1 – group mean)2+ (individual respondent 2 – group

mean)2 + (individual respondent 3 – group mean)2

… (individual respondent n – group

mean)2

(were n = total no. of respondent)

1) (3-4) 2 + (3-4)

2 + (4-4)

2 + (6-4)

2 = 6

2) (7-7) 2 + (9-7)

2 + (6-7)

2 + (6-7)

2 = 6

3) (0-1) 2 + (1-1)

2 + (2-1)

2 + (1-1)

2 = 2

Theme „A‟ Theme „B‟ Theme „C‟ Total

SOS within 6 6 2 14

SOS among 0 36 36 72



SOS total 6 42 38 88

# One Way Anova Case: Food processing company

A food processing company desires to determine whether the self height at which their products

are placed has any impact on sales of that product. For this purpose they have conducted a

research in which the product was placed at three different height.

a) Eye level

b) Waist level

c) Knee level

Sales were found to vary based on col.(a) – day of the week (week/ week end), col.(b) time of

the day (morning/evening).

A data abstract of research tracking sales is provided below for your analysis.

Q.1 Determine whether the group mean for each shelf height are significantly different or nor at

99% confidence level.

Frame a suitable hypothesis for this and test it out with a suitable statistical test justify your

answer.




Data view:

Q.1 Determine whether the group mean for each shelf height are significantly different or not at 99%

confidence level.

SolN :




Output:

Our condition is satisfied here see in above table ‘sig.’= 0.001>>0.01 (i.e 99% c.l.) so reject Ho.

Ans: Shelf height has significant impact on the sales of goods.



Case: Advertising agency (by SPSS)

An ad agency has created three ad theme A,B and C for an advertising campaign. The agency

desires to determine which ad is the best at a confidence level at 75% so that the best ad can be

launch in the market. For this purpose an agency has conducted a research in which respondent

were asked to evaluate the three ads on scales of 1-10 where 1 – not liked at all, 10 – like very

much. A data extract of the research is provide below for your analyze.

Q.1 Develop the null hypothesis Ho at confidence level of 75%.

Q.2 Test out the null hypothesis using statistical test.

Q.3 What conclusion can you draw? Justify your answer.


Data view:




Output:

Ho – theme has no significant impact on rating at 75% confidence level.

P Ho = 0.25 , P Observed = 0.203

P Observed <PHo

Reject the null hypothesis.



# Perceptual Mapping

Perceptual map: A graphic representation of the perceived relationships among elements in a

set. (Where the elements could be brands, service, or product categories.)

Perceptual mapping involves mathematical approaches design to describe consumer perception

of an object or objects on one or a series of spatial maps so that the relationship between them

can be seen easily.

Perceptual map are used for:

a) Market gap analysis

b) To understand brand positioning ( own brand as well as competitor‟s brand)

c) To reconcile between brand identity and brand image.

# Factor Analysis

“It is a type of analysis used to determine the underlying dimension of set of data, to determine

relationships among variables, and to condense and simplify a data set.”

The objective of factor analysis is to summarize a large number of original variables into a

small number of synthetic variables, called „factor‟. Determining the factor which is present in

the data has number of application in marketing. e .g.

1. Developing perceptual maps.

2. Determining the underlying dimensions of the data.

3. Identifying market segments and positioning products.

4. Condensing and simplifying data.

5. Testing hypotheses about the structure of a data set.

Brands/ Objects have a large no. of attributes which defines them. Each attribute may/may not

have some correlation. Impact of attribute on consumer is often in the form of combination or

underlying dimensions. Combinations may contain some common and some unique attributes.

Underlying dimension/combination are called factors.

Factor analysis deals with the identification of factors most important to the consumers.



Perceptual maps are drawn with factors as the axes. The original attributes are incorporated into

the map as vectors such that direction of line indicates nature of association with the factors and

length of line indicates strength of association.

A1, A2, A3, A4, A5, A6 these are the attributes and we take two factors F1 and F2. Now we

calculate the relation of this attributes with F1 and F2. Now we calculate the relation of this

attribute with F1 and F2. We find the correlation with factor. Correlation could be (-1 to +1), so

suppose

F1 = (A1(+) , A2(-) , A4 (+), A5(+))

F2 = (A3 (-), A4 (-), A6(+))



Basic approach to generate Perceptual Map

Case: Life Insurance Company

A life insurance company has been losing market share in recent month. The reason for this is

that they are not able to attract first time insurers in adequate numbers. The problem appears to

be due to company image. Before embarking on rectification program the firm desires to

understand that influences consumer buying behavior.

To do this they had initially conducted an exploratory research and identified exhaustive list of

variables that could possibly influenced buying behavior.

These variables were then evaluated for the significance of their impact at 90% confidence level

through a suitable statistical test. The short listed variables were then converted into

questionnaire to which respondent were asked to provide these responses on five point Likert

scale where 1: strongly agree; 5: strongly disagree.



A sample of questionnaire is provided below for your analysis.

Q. Identify the factor from questionnaire intuitively.

1. Will not cancel policy because of age and minor health problems.

2. Tries to handle claim equitably.

3. Difficult to do business with.

4. Provide excellent recommendations about coverage for individual needs.

5. Explain policies fully and clearly.

6. Tends to raise premiums without justification.

7. Policies better than other for older people.

8. Coverage is renewable for life.

9. Take long time to saddle claims.

10. Quick reliable service easily accessible.

Answer:

Factor Contained attribute Name of factor

F1 1,7,8 Handing of age issue

F2 2,3,4,5,6 Service features

F3 9,10 Speed



Theory of FACTOR ANALYSIS:

In marketing we need to know the exact reason why a consumer buys a product.

Possible purchasing criteria could vary in no. from 2-3 to 15-20. We need to

understand the underlying significant drivers of buying behavior for a part product.

Factor analysis helps to reduce the complexity of attributes/features into relevant

factors. Base technique analysis establishes correlations between

attributes/variables to exact factors. Factors thus created provide insight into

relevant psychographics of target customers.

Subjective element in factor analysis will increase or decrease and is controlled by

splitting respondents into two groups & extracting factors separately.

If the factors are similar, the analysis is reliable. In practice, group created could

either be

I. User

II. Non user

This helps in finding out differences in buying criteria for same product category

among two groups.



Case: Two Wheeler

Two wheeler desires to understand consumer preferences. For this purpose they have initially

conducted an exploratory research and identify an exhaustive list of variable that could possibly

influence buyer behavior.

These variables were then evaluated for the significance of impact on consumer preferences at

90% confidence level. The shortlisted variables were then converted into a questionnaire in the

form of statements. Respondents were asked to provide their responses on 7- point Likert scale

where 1: strongly agree, 7: strongly disagree. An extract of questionnaire as well as the data

extract are provided below for your analysis.

The shortlisted variables are:

1. Use to 2 wheeler(2w) because it is affordable

2. It gives me a sense of freedom to own a two wheeler.

3. Low maintenance customer makes it economic in long run.

4. 2w is man vehicle essentially.

5. I feel powerful on my 2 w.

6. Some of my friends who don‟t have a 2 w are jealous of me.

7. I feel good when I see ads for my 2 wheeler.

8. My 2w gives me comfortable ride.

9. I think 2w are a safe way to travel.

10. „3‟ people should be legally allowed to travel on a 2w.

Q.1 Identify the major factors influencing consumer preferences.

Q.2 Determine the % total variance in the data explained by the abstracted factors

cumulatively.

Q.3 Determine the major constituents attributes of each factor.

Q.4 Label each factor based on their dominant characteristics.




Data view:




Output




Ans1: These three factors are selected because only values which are greater than 1.

Q.2 Determine the % total variance in the data explained by the abstracted factors cumulatively.

Ans2: Percentage(%) variance

1- 38.828

2- 27.770

3- 13.747Ans3: For an acceptable factor analysis:

a) The Eigen value of rotated factor should be greater than „1‟.

b) Percentage(%) of total variance explained cumulatively by the factor extracted with this

table which are greater than “0.7” also which is less than „-0.7‟ and name them according

to the variables characteristics ,meaning the name which combine all the variables.





Ans 3&4:

F1- Man vehicle /Powerful / Friend jealous / Ads makes feel good --------- MACHO IMAGE

F2- Comfort / Safety ---------------------------------------------------------PRODUCT FEATURE

F3- Affordable /Legal----------------------------------------------------------------------ECONOMY

Case: airline

The domestic airline industry has been witness intense competitor during last few years. Fare

prices have dropped to a large extent. In the last 6 month the price of aviation fuel has gone up

considerable this has compelled all carrier to increase the price of their tickets. Indian airline

didn‟t increase their prices. Given that average Indian flyer is price sensitive, this move by

Indian airlines was expected to increase the market share considerably. However this didn‟t

happen.

Jet airway continue been the market leader. Hence Indian airline desires to understand the factor

influencing customer preferences.

To determine this they have conducted a suitable research. An extract of questionnaire used

along with a data extract is provided below for your analysis.


Q.2 Determine the % total variance in the data explained by the abstracted factors

cumulatively.



Services Jet airways v/s Indian airline

1: strongly agree



5: strongly disagree

Variable are:

1) They (jet airways) are always on time.

2) The seats are very comfortable.

3) I love their food.

4) The air hostesses are beautiful.

5) My boss/ friends fly on Jet airways.

6) Jet airways have younger air craft.

7) They have a frequent flyer program.

8) The flight timing suit my schedule.

9) My mom/family feel safe when I fly Jet airways.

10) Flying Jet complements my life style and social standing in society.

Variable view:



Data view:



SPSS Procedure:

Output

Ans3: For an acceptable factor analysis:

c) The Eigen value of rotated factor should be greater than „1‟.

d) Percentage(%) of total variance explained cumulatively by the factor extracted with this

table which are greater than “0.7” also which is less than „-0.7‟ and name them according

to the variables characteristics means the name which combine all the variables. e) Rotated Component Matrix(a) f)

Component

1 2 3

Ontime 0.954 -4.19E-03 .153

ccomfort 3.74E-02 9.013E-02 .962

Lvfood .912 3.695E-02 -5.18E-02

airhostess -6.2E-02 .965 9.631E-02

bossfrd .578 .149 .325

yngrarct .959 -3.98E-02 2.061E-02

Freqflyr -2.82E-02 .985 -5.29E-03

suittime -7.65 .175 0.958

feelsafe -1.84 -0.389 -8.61E-02

Lfstyss -1.59E-02 .956 9.722E-02

g) Extraction Method: Principal Component Analysis. h) Rotation Method: Varimax with Kaiser Normalization.



i) a Rotation converged in 5 iterations. j)



Ans 3&4:

F1- On time/love food/ younger plane --------- SERVICE

F2- Airhostess / frequency of flying/ life style------------------------------- INCENTIVE

F3- Comfort / Suit time --------------------------------------------------------------CONVENIENCE

# Discriminant Analysis: “It is a statistical technique for classifying persons or objects into two or more

categories, using a set of intervally scaled predictor variables.”

The objective of discriminant analysis is to classify persons or objects into two or more

categories, using a set of variables which are scaled intervallic. e.g.

Classification of buyer v/s non buyer, good v/s bad risks, early v/s late timing of the market.

When independent variable (prediction variable) is interval or ratio scale and the dependent

variable (criterion variable) is nominal scale (categorical).

e.g. 1) assessment of application for loans i.e. to determine credit worthiness

2) How do loyal consumers vary from non loyal consumer in terms of demographics

psychographic?

3) How do doctors, lawyers, banker differ in terms of their preference forecast.

Discriminant Z1 = k + a1x1 + a2x2 + a3x3

Regression Y = k + a1x1 + a2x2 + a3x3

In discriminant analysis the dependent variable is nominal/ categorical. „Z‟ can take only

limited range and in regression analysis the dependent variable is Ratio/ Interval.



„Y‟ can take any value.

Find linear composite of independent variables in order to separate groups based on their

attribute score for this consider variation among relative to variation within group.

Create discriminant function and discriminant criteria, create perceptual map using above

(discriminant function) classify discriminant function and discriminant criteria create perceptual

map using above (discriminant function) classify new function into category in the map created.

Conceptual case: Ready to eat cereal for children

Research is to determine consumer attitude towards nutritional addition.

1) Proteins

2) Vitamin.

X1- The amount of protein in group in each serving.

X2- Percentage (%) of vitamin required daily in each serving.

10 type of cereal are cereal are rated by 10 person for like/dislike.

Protein Vitamin

Person Evaluation X1 X2

1 D 2 4

2 D 3 2

3 D 4 5

4 D 5 4

5 D 6 7

Total 20 22

Average 20/5 = 4 22/5 = 4.4

Protein Vitamin

Person Evaluation X1 X2

6 L 7 4

7 L 8 6

8 L 9 7

9 L 10 6

10 L 11 9

Total 45 32

Average 45/5 = 9 32/5 = 6.4

Grand mean (9+4)/2 =6.5 (4.4+6.4)/2 = 5.4



S.D. 3.028 2.011

The locus of the point is called discriminant function.

If the points are distinguish like this then it is very easy to locate discriminant function. But

don‟t you think even a single point coordinate will not allow us to such discriminant function.

e.g. a person who is credit worthy but not paid loan on time then „ ‟ will jump to that side.

Case: Public Sector Bank

A public sector bank has been in the credit card business for last 15 year during last „3‟ year the

bank has witnessed a sharp increase in payment defaults. Even though the bank charges a

penalty interest on late payment this high default has began to impact the bank profitability

adversely.

The problem appears to be the screening criteria used by the bank at the time of credit card

allotment. Hence the bank desires to revamp screening mechanism for credit card applicants in

future. To do this the bank has carried out a suitable research. Initially an exploratory research

was conducted and a detailed set of variable that could impact credit worthiness were identified.

These variables were evaluated for the significance of their impact on credit worthiness at 90%

confidence level.

The shortlisted variables were:



1) Consumer age

2) Monthly income

3) No. of years married.

Based on past experience, customers were categories as follows:

a) High risk (code =1)

b) Low risk (code = 2)

Historical data for the last three years was collected on three variables an extract of this data is

provided below for your analysis.

Q.1 Build a discriminant function that would distinguish between high risk and low risk

customer.

Q.2 State the classification accuracy of the discriminant function.

Q.3 Determine the statistical significance of discriminant function.

Q.4 Which variable is the Best discriminator?

Q.5 Create a discriminant criteria or cutoff value that would enable the bank to classify future

applicant into high risk and low risk category. Justify your answer.

Data:

Variable view:



Data view:

SPSS Procedure @CHAITANYA BANSAL



when the grouping variable “risk” is

sifted then we need to define the range. Click on „Define range button‟ then this table comes

Output:




customer.

Ans1:

Z= 0.194(age) + 0.00009573(income) + 0.160 (years marriage) – 9.076 (constant)


Ans2: Classification Accuracy = 88.9%


Ans3: Statistical significance of the Discriminant Function we found by

a. Eigen value ( >1 )

b. Wilks Lambda ( <0.5 )



Both are ok.


Ans4: Best Discriminator

is Income because its value is greatest among all three.

Q.5 Create an discriminant criteria or cutoff value that would enable the bank to classify

future applicant into high risk and low risk category. Justify your answer.

Ans5 : Discriminant criteria



We have to take Grand mean = = 0

Now for new data(new respondent) we take the value of their age, income and yrs of marriage

in discriminant function we get the value of Z

If Z< 0 then it HIGH RISK

& if Z > 0 then LOW RISK

Tables to remember Things to be Find out SPSS Output Table

Discriminant Function Canonical discriminant Function

Classification Accuracy Classification Results

Statistical Significance Eigen values and Wilks Lambda

Best Discriminator Standard Canonical discriminant Function

Discriminant criteria Functions at Group Centroids @CHAITANYA BANSAL

Case: Retail chain



A national Retail chain desires to categories its customers into loyal customers & normal

customers. To do this the firm has conducted a suitable research. The variable that have a

significant impact on customer loyalty was found to be:

a) Frequency of purchase.

b) Amount of purchase.

c) No. of years purchasing.

Based on past experience customers were categories into two groups:

a) Normal customers (code =1)

b) Loyal customers (code = 2)

Historical data for the last 10years was collected from companies own records. An extract of

this data is providing below for your analysis.


customer.




Q.5 Create a discriminant criteria or cutoff value that would enable the bank to classify future

applicant into high risk and low risk category. Justify your answer.




Data view:




when the grouping variable “risk” is sifted then we need to define the range. Click on „Define

range button‟ then this table comes



Output:


customer.

Ans1:

Z= 0.092(frequency of purchase) + 0.0000622(amount purchase) + 0.140 (years purchase) –

4.958 (constant)


Ans2: Classification Accuracy = 94.4%




Ans3: Statistical significance of the Discriminant Function we found by

a. Eigen value ( >1 )

b. Wilks Lambda ( <0.5 )

Both are ok.


Ans4: Best Discriminator

is „Frequency of purchase‟ because its value is greatest (i.e. = .777) among all three.

Q.5 Create an discriminant criteria or cutoff value that would enable the bank to classify

future applicant into high risk and low risk category. Justify your answer.

Ans5: Discriminant criteria



We have to take Grand mean = = 0

Now for new data(new respondent) we take the value of their age, income and yrs of marriage

in discriminant function we get the value of Z

If Z< 0 then it is Normal Customer.

& if Z > 0 then it is Loyal customer.



# Cluster Analysis “It is a set of techniques for separating objects into mutually exclusive groups such that the

groups are relatively homogeneous.”

Cluster is group of target customer who are similar in

1) Buying behavior

2) Demographic

3) Psychographic.

Cluster analysis is done for:

Consumer characteristics

Consumer response

Cluster analysis used for market segmentation. The methods for cluster analysis are:

1) Hierarchical clustering / Linkage method.

2) Non hierarchical clustering /Nodal method.

1. Hierarchical clustering: (HC): In this method no. of clusters to be extracted are not pre-

specified. Solution would provide range of cluster which the researcher may decide from.

In HC linkage could be:

Single

Complete

Average.

2. Nodal method (K- means approach): This method require No. of clusters to be pre-

specified.

Typically both method are Euclidean distance (measure of proximity) to create clusters.

A research is conducted in which we asked respondent about

What they do in weekend on seven point Likert scale.

Q.1 Normally read book during weekends

Q.2 Normally go out for a movie during weekend

This is seven point Likert scale.



Suppose respondent given answer of Q.1 is mildly disagree and Q.2 strongly agree then write it

in R n (5,1).

R1 (4,3); {means on answer of Q.1 Neither Agree Nor Disagree,& Q.2 mildly agree}

R2 (1,2); R3(7,7); R4(7,6); R5(4,4); R6(3,6); R7(2,1); R8(1,1); R9(3,3), R10(6,7)

Now these points are located on the graph.

We have to make cluster of these point. For jointing of these point there are rule:

Rule1: those points will joint which are most near to each other.

Rule2: If the points are at the same distance then we take that point which is having highest „y‟

value means highest coordinate value.

e.g. (3,4) and (3,10) among these we choose (3,10) because “10>4”.

Rule3: Even if the points are at same distance and also their „y‟ coordinate is same then we take

the point in which the value of „X‟ is minimum.

e.g. (1, 9) & (6,9) we choose (1,9) because “1<6”



Rule4: The points will join at the midpoint of those points and the name is given of the point

having least value.

e.g. if (3,10) will join then the name of new point will be „3‟ only but the value of „3‟ will

different but name will be „3‟ only

now this points R1 (4,3); R2 (1,2); R3(7,7); R4(7,6); R5(4,4); R6(3,6); R7(2,1); R8(1,1); R9(3,3),

R10(6,7).

We choose those point which are closer to each other (rule 1). i.e. (R2&R8); (R1&R9);

(R5&R6); (R3&R10) so the points are (2,8); (1,9), (5,6) ,(3,10)

Take the point with highest „Y‟ value i.e. (3, 10) next (1, 9), next (2, 8) next (5, 6)

Agglomeration Schedule:

Stage Cluster Combined Fusion coeff. Difference

between coeff. C1 C2

1 3 10 1

2 1 9 1 0

3 2 8 1 0

4 5 6 1 0

5 1 5 1 0

6 2 7 1.12 1.12

7 3 4 1.12 0

8 1 2 3.22 2.1

9 1 3 4.62 1.4

There are „9‟ stage because the no. of respondent = 10

Stage = (No. of respondent -1)

The process of combining all the data is called agglomeration.

e.g. Mumbai suburban agglomeration means Mumbai and the area which included to Mumbai.

The solution provides the range of cluster. The table of fusion coeff is showing range of cluster.

Now see the differences in each stage.

Till stage „4‟ the differences are „0‟ means the combined groups are equidistance from each

other.

In „5‟ & „6‟th

stage it is slightly greater but it is small. In „7‟ stage the difference is 0 means

there are also the point are equidistance from each other.

In „8‟th stage (while combine 7, 8) the difference is quite large so we don‟t combine (7,8) & we

stop at stage „7‟.

Now to combine

a) (1,7) {3,10,4) b) (2,4,5) {1,5,6,9) c) (3,6) {2,7,8} The above are „2‟ variables so we combine them just difference.



To combine „3‟ variables there is simple distance formula i.e.

Similarly for „4‟ variables

Dendrogram:

The word Dendrogram came from „Dendron‟. In human body the cells are connected with each

other by Dendron. Like segmentation these cell are joint with one by one but connect with

different cell by Dendron. Like this graphical representation cell „1‟ ,„2‟ „3‟ ,„4‟ are join with

each other. But as you see in figure cell „1‟ connected with cell „4‟ but cell „2‟ not so. In

segmentation also points are serially given but they are connected with different point

irrespective of their number. This is represented by Dendrogram.

K means: See this Stage „1‟

Points 1,2 & 9,10 are very close to each other, they should have to join; but

see due to change of sequence how they look like.



So to take care of such mistake „K‟ means is there. It takes place in iteration.

Now see suppose there are 40 respondents. Spread like this: Stage 1

we divided them randomly & then cluster. Now suppose

more or less each cluster contain 10 respondents. They had given them1 to 5 scale ranking. We

find the mean of that ranking to find the Centroids of those „4‟ clusters differently.

Now we find out the distance of each respondent from centroid and sum it up.

d1 – for I cluster

d2 – for II cluster and so on



D = ∑ii=4 di

Here i=4

Stage 2

Distance is the shortest path between two points. So we cannot reduce „D‟. But we can change

the cluster of some point so that the total distance „D‟ could reduce. This is done through

iteration method and it is done till we reach the smallest value of „D‟.

The whole process is called „K‟ means.

Stage 3



Case: FMCG Company

A FMCG co. desires to understand the segments in its market based on customer lifestyle

attitude and perceptions. To determine this they have conducted a initial research and identity

the variable that have a significant impact on customer preference. The shortlisted were then

converted into a questionnaire a sample of which is provided below for your ready reference.

Respondent were ask to provide the responses on „5‟ point Likert scale were „1‟ stood for

strongly agree and „5‟ stood for strongly disagree.

A data extract of the research is provided below for your analysis.

Life style/attitude/ perception segmentation statements (questionnaire)

1. I prefer email to post

2. I feel that quality comes at a price.

3. I think twice before I buy anything.

4. T.V. is a major source of entertainment.

5. A car is necessarily not a luxury.

6. I prefer fast food and ready to eat food.

7. People are more health conscious today.

8. Entry of foreign companies has increase the efficiency of Indian companies.

9. Women are the active participants in purchase decisions.

10. I believe politicians can play a positive role.

11. If I get a chance I would like to settle abroad.

12. I always buy branded products.

13. I frequently go out of weekends.

14. I prefer to buy in credit rather than buy on cash.

Q.1 Identify the no. segment from data provided.

Q.2 Determine which respondent belongs to which Cluster (segment)

Q.3 Determine the variable that distinguish between clusters at 90% C.L.

Q.4 Profile each cluster based on their dominant distinguish characteristics.

Q.5 Label each cluster suitably after profiling. Justify your answer.



Data View:




Then @CHAITANYA BANSAL



Output:

Q.1 Identify the no. segment from data provided.

Ans1: No. of segment find out by manual method from this table



See column 2&3

Now see group (4,5). (19,20).( 2.6).(3,4). (13,16)

Till now no numeric repeated but in 6 member (2,18) but (2,6) are already there so

we group them (2,6,18)

Likewise we find cluster (4,5,3,12,2,6,18,13,16,9). (19,20,11,10). (1,14,15). (7,17).

So there are “4” cluster.

See Dendrogram we find the same cluster



Q.2 Determine which respondent belongs to which Cluster (segment)

Ans:

Cluster

no.

No. of

member

Respondent no.

C1 5 1,2,9,14,15

C2 6 6,8,13,16,17,18

C3 5 7,10,11,19,20

C4 4 3,4,5,12



Q.3 Determine the variable that distinguish between cluster at 90% C.L.

Ans: In this table the member whose value less than „0.1‟ are the distinguishing

variable at 90% C.L. @CHAITANYA BANSAL

Q.4 Profile each cluster based on their dominant distinguish characteristics.

Ans: In Final cluster center table we see only those variables which are

distinguishing variables.



Now take first variable

Email over letter : here we have to spread this 4 points into „5‟ Point „Likert Scale‟

Least value = 1.6 Highest Value = 3.5

Difference = (3.5 – 1.6) = 1.9

Divided it by „4‟ i.e. 1.9/4= 0.475

We divide it by „4‟ because „5‟ point „Likert Scale‟ contains „4‟ interval in between.

Now add „0.475‟ to 1.6 till „3.5‟ we got 5 value like this.

1.6 + 0.475 = 2.075

2.075 + 0.475 = 2.5

2.5 + 0.475 = 3.025

3.5 + 0.475 = 3.5



email now we are locating our points on this scale

Health Conscious

Frgn co.

Women active part shopping

Settle abroad

C3 C1 C4 C2

C1

C1

C1

C2

C2

C2

C3

C3

C3

C4

C4

C4



Branded product

Go out

@CHAITANYA BANSAL

Credit cards

C1

C1

C1

C2

C2

C2

C3

C3

C3

C4

C4

C4



From this scale we conclude following @CHAITANYA BANSAL

C1: 1. Highly Prefer Email over post

2. People are health conscious

3. Agree that Foreign co. increases

efficiency of Indian company

4. Women may or may not take

active part in purchase

5. Highly prefer to settle abroad

6. Wearing branded Product always

7. Always go out in Weekends

8. Strongly avoid to purchase on

credit

C2: 1. Highly prefer post over email

2. People aren‟t health conscious

3. Foreign co. highly influence the

efficiency of Indian co.

4. Agree that women take part in

active Shopping

5. Prefer to stay in home country

6. Doesn‟t prefer branded pdt.

7. Not go out in weekends

8. Buy on credit

C3: 1. Moderately prefer email over post

2. People are strongly health

conscious

3. Foreign co. doesn‟t increase the

efficiency of Indian company

4. Strongly agree that women take

active part in shopping

5. Prefer to live in home country

over abroad

6. Prefer branded product over

unbranded

7. Go out in weekend

8. Strongly prefer to buy on credit

C4: 1. Mdrtly prefer post over email.

2. People are not health conscious

3. Foreign co. increase efficiency of

Indian co.

4. Believe that woman does not take

active part in shopping

5. Want to live in home country

always.

6. Never wear branded product.

7. Stay at home in weekend

8. Prefer to buy on Cash

@CHAITANYA BANSAL

Q.5 Label each cluster suitably after profiling justify your answer.

Ans: From the ans4 table we give then suitable name according to their characteristics @CHAITANYA

BANSAL

C1 Modern

C2 Believers

C3 Nationalistic

C4 Strivers



Tables to remember Things to be Find out SPSS Output Table

No. of segment Agglomeration Schedule

Respondent Belongs Cluster membership

Variable that distinguish Anova

Dominant distinguish characteristics Final Cluster Center @CHAITANYA BANSAL



Multi Dimensional Scaling: Understanding the raw perception of consumer without any aid or stating any attribute to

respondent is called multi dimensional scaling.

Suppose you are asking to a consumer why you are drinking cold drink such as „coke‟ we have

these attributes:

Shape of bottle, colour, sweetness, advertisement, Amir Khan……

Don‟t you think respondent say “apne ko nahi pata baba yeh sab” (I don‟t know these things).

These attribute create biasing with in respondent.

MDS primarily used to create perceptual map of product/Brand positioning in the minds of the

consumer for a particular group of products/brands.

Positions of competing brands in a product category are found out through MDS.

Raw perception: It knows consumer preference without influencing your view or attribute.

Respondent may be thinking about the attribute, that might be don‟t know, but the necessary

thing is that we don‟t have to tell these attribute.

Now suppose research is conducted for the distance between the cities.

Pune Raipur Delhi Nagpur Indore Kanpur

Pune - 1100 1600 750 800 1600

Raipur 1100 - 1200 350 850 1350

Delhi 1600 1200 - 750 650 450

Nagpur 750 350 750 - 400 900

Indore 800 850 650 400 - 600

Kanpur 1600 1350 450 900 600 -

Now if the question asked:



Q. How many directions do you want to draw on the map such that Nagpur and other cities are

located on where they are on map assuming that above distances are equal?

Ans: None; we don‟t require any direction.

Suppose we take any point on this sheet, the Pune- Nagpur distance is 600Km so we draw circle

of radius r = 600 because the Pune can‟t go beyond this radius. This is the locus of Pune.

Now distance between Delhi and Nagpur is 1000 but the distance between Delhi and Pune is

1500 so by keeping this we draw the circle above the point Nagpur.

We get two point D1 and D2. By this process we can locate all the point is map. The maximum

can happen that the map of India came „laterally inverted‟. Like this

Don‟t you think the brand has also are at some distance in mind of consumer. e.g. difference

between Pepsi and Coke = 15 ; Pepsi and Sprite = 80.

Consumer are thinking on their own , don‟t influence the respondent by our attribute.

This method is called Multidimensional Scaling (MDS).

WHY MDS: In this we get perceptual map of each brand.



Normally it may be possible to identify for a particular product category the important attribute

form a customer point of view.

These attribute may have an impact on the positioning of each brand in the customer mind and

on the customer buying behavior. Further these attribute may be taken „2‟/ „3‟ at a time the plot

created to understand specific position of each brand.

Such plot or map may be relatively straight forward but it may not capture the consumer mind

completely. This is because customer thinks simultaneously on multiple product dimensions/

attributes while evaluating the products.

Hence such a procedure is an approximation. A better technique is MDS. So MDS would

capture complex interaction between attributes, brands and would derive dimensions which

explain the position given by customer to various brands.

Method: There are two methods of MDS:

1. Attribute based.

2. Non attribute based.

The non attribute based procedure is based on similarity or preference. It is also called the

similarity - dissimilarity approach.

In similarity and dissimilarity approach:

1. Conceptual distance measure is used between brands being rated.

2. Distance measure could also be a ranking of distances between specific brand and other

brand.

S1 S2

MDS DRAS (attribute based)

1. No. of dimension / parameters used

by respondent to evaluate brands.

Score of each brand on the each attribute.

2. Score of each brand on the each

dimension.

-

Map data of DRAS /respondent into dimensions obtained from MDS responses to interpret

meaning of dimensions.



Attribute – We find out from ᵡ2(chi –square test).

Dimension – Derived from attribute using DRAS and MDS responses.

We are conducting a research on soap. Customer/ respondent are shown cards with name of

two brands on each card. The name on each card is varying.

All pairs of brands studied are shown. Respondent have to decide brand proximity or

differences. Respondent are asked to convey their preferences or difference between brands in

terms of their perceptual distances numerically. Scales may vary from 0-10, 0-20, 0-25,0-100

etc.

These distances are average out across respondents and then converted into a matrix. The MDS

procedure is run to determine:

a. No. of dimensions used by respondent to differentiate the brands.

b. Scores are stimulus on each dimension/stimulus coordinates. The derived are interpreted

by mapping the output of DRAS research on to the dimensions of MDS research.

Kruskal‟s Stress:

Perceptual maps are drawn with the dimensions as the axis, brands are plotted based on

stimulus coordinate score. The location of brands on map indicates brand positioning.

The customer has given two dimension for „4‟ brands (A, B, C, D) dimension are D1 & D2.



When two dimension scale are converted into one dimension we need to take the distance from

origin. The original distance between AB, BC, CD, AC & BD has changed. It shrunk.

Suppose D (1) – Distance when dimension is one.

D (or) –This is the original distance when there are two dimensions.

D (1) surely be less than D (or).

So to find the misfit between the converting of two dimension into one dimension is called

Kruskal‟s stress.

Kruskal‟s stress is a measure of the extent of misfit of a specific MDS solution. Values of

Kruskal‟s stress range from 0 to 1. Value close to „1‟ indicate high level of misfit & values

close to „0‟ indicate low level of misfit.

For an acceptable MDS solution Kruskal‟s stress should be less than „0.15‟.

Another measure to evaluate a specific MDS solution is R2.

R2 is the % of original variance explained by a specific MDS solution.

For an acceptable MDS solution R2 should be greater than 70%.

For an acceptable solution

Kruskal‟s stress < 0.15

R2 > 0.70



Case: T.V.

A T.V. producer desires to understand the brand positioning of „8‟ T.V. brands as per customer

perception. For this purpose, they have identified the following brands:

(1) Aiwa, (2)Videocon, (3)L.G, (4) Samsung, (5) Sony, (6) Onida, (7) Thomson, (8) BPL

They have conducted a research in two parts in part „A‟ MDS Questionnaire were shown to

respondent. Respondent were ask to indicate on scale of 0-10, the conceptual distances between

each brand pair as per their perception.

The data obtained was then average out across respondent & is converted into misfit. The data

is given below for your analysis.

In part „B‟ a match sample was shown as „DRAS‟ based questionnaire on a set of attribute.

Respondent were asked to evaluate the same „8‟ brands on the „7‟ point „Semantic differential

scale‟. A summary of this data is provided below for your analysis.

DRAS Table

Attribute Low High

Picture

quality

- Samsung BPL Thomson

and LG

Videocon Sony and

Aiwa

Onida

Sound

quality

- Samsung

and

Thomson

L.G Videocon Sony and

Onida

Aiwa

Price

(switched)

Thomson

and BPL

- L.G and

Samsung

Videocon Sony - Aiwa and

Onida

After sale

service

L.G Sony Samsung BPL Onida and

Aiwa

- Videocon

& Thomson

Brand

image

Samsung Onida and

Thomson

Sony Aiwa LG Videocon BPL



Data: Variable View:

Data View:



SPSS Procedure:

@CHAITANYA BANSAL

@CHAITANYA BANSAL



Output:

Q.1 Determine the no. of dimensions used by respondent to evaluate the brands.

Ans: For an acceptable solution we see two things

a) Kruskal Stress < 0.15

b) R2 > 0.70

till the dimensions not able to got this value SPSS increase the no. of the dimension

and check until these value comes. In our case „3‟ dimensions are used by

respondents.

Q.2 Interpret the dimensions by mapping the data summery of the DRAS Search on

to the dimension obtained from MDS research.



Ans:

See this table to evaluate each dimension take dimension one by one

e.g. we take dimension „3‟ we compare it with DRAS table, follow the steps:

1. See which is the highest value in dimension „3‟ i.e. „1.3724‟- BPL now see in

DRAS(direct response attribute scale) table that for which attribute BPL in having

High value.

Here it is Brand image in which it highest

2. Now see the lowest value in dimension „3‟ i.e. „-1.6871‟- Samsung

now if Brand image contains Samsung as lowest value then there is a possibility

that Dimension „3‟ = Brand image

3. Now to check this we do check the second highest value in this table and if this

equals to DRAS then it sure that

Dimension „3‟ = Brand image

It may be noted that a dimension contains more than „1‟ attribute and it may not

match with DRAS table as it exactly matching here. We need to take some

Flexibility here.



Q.3 Label each dimension based on their dominant characteristics.

Ans: By this Procedure we get

Dimension

No.

Attribute contains Name

1 Sound quality + Price + Picture

quality

Value for money

2 After sale service After sale service

3 Brand image Brand image @CHAITANYA BANSAL If one dimension contains more than one attribute then we have to Label the dimension by

keeping all attribute in mind.

Q.4 Create perceptual map with the dimension as the axis. Position the brand on

this map based on their stimulus coordinate scope on each dimension. Justify your

answer.

Ans: Here we have to prepare three graphs betn

a) Value for money and After sale service

Brand

name

Value

for

money

After

sale

service

AIWA 1.9545 0.2962

VIDEOCON 0.0613 1.137

LG -0.6209 -1.2429

SS -0.9221 -0.4411

SONY 0.9783 -1.0898

ONIDA 0.892 0.4307

THOMSON -1.0686 1.6324

BPL -1.2746 -0.7225



b) After sale service and Brand image

Brand

name

After sale

service

Brand

image

AIWA 0.2962 0.3459

VIDEOCON 1.137 0.9848

LG -1.2429 0.3122

SS -0.4411 -1.6871

SONY -1.0898 -0.2014

ONIDA 0.4307 -0.8364

THOMSON 1.6324 -0.2905

BPL -0.7225 1.3724



c) Brand image and Value for money

Brand name Brand image Value for money

AIWA 0.3459 1.9545

VIDEOCON 0.9848 0.0613

LG 0.3122 -0.6209

SS -1.6871 -0.9221

SONY -0.2014 0.9783

ONIDA -0.8364 0.892

THOMSON -0.2905 -1.0686

BPL 1.3724 -1.2746

and prepare graphs for this table by Chats- Scatter plot option name the dimension

in x and y axis and the points located and the position of that brand.



Attribute Based Perceptual Mapping Using Discriminant Analysis:

MDS used to create perceptual maps without using attribute i.e. non attribute based perceptual

mapping.

In practical situations may not be easy for a respondent to paired comparisons between a large

numbers of brands all the time. Further the respondent may be to compare our own brand with a

few other leading brands with clarity on differences.

In such cases techniques to create perceptual maps could be attribute based using discriminant

analysis..

Procedure: Brands to be evaluated are short listed. The attribute to evaluate them on are

finalized based on prior research/exploratory research. These attribute evaluated by chi-square

test.

Case: Chocolate

A chocolate company desires to understand of comparison of three leading brands as per

customer perception. The brands are:

1. Nestle

2. Cadbury

3. Amul.

To determine this they have initially conduct a preliminary research and identify the variable

that have a significant impact on consumer brand preferences

The attribute short listed were

A. Price

B. Quality apart from taste.

C. Availability

D. Packaging

E. Taste.

A questionnaire was created based on above „5‟ variables



Respondent were asked to provide responses on numeric staple scale. The data extract of

research is provide below for your analysis.


Data view

SPSS Procedure:



Output:

Q.1 Identify the functions that discriminate consumer brand preferences based on the

independent variables.

Ans:



Z1 = 0.143 (price) + 0.189(quality) + 0.011(availability) - 0.385(packaging) - 0.60(taste) –

5.948

Z2 = 0.483(price) - 0.087(quality) - 0.001(availability) - 0.284(packaging) +

0.435(taste) – 7.486

Q.2 Determine the classification accuracy and the statistical significance of the discriminant

functions.

Ans:

The classification accuracy = 86.7%

And for statistical significance we need to see two things a) Eigen value > 1, b) Wilks lambda <

0.5 for both in F1 and F2 see these tables

By this table we can conclude that for both Z1 and Z2 this is an acceptable solution.



Q.3 Determine the major constituent variable of discriminant function label each function based

on dominant characteristics.

Ans: By structure matrix we can conclude that which variable constitutes which function.

Z1 = fn ( Availability, Quality, Packaging) = Convenience

Z2 = fn

(Taste, Price) = V.F.M

Q.4 a) Create perceptual maps with the discriminate function as the axis.

b) Depict the brand on this map based on their Centroids score on each discriminant functions.

c) Show the Attributes on the same map as factors based on their correlations to each function.

Ans: a) For discriminant we have to take coordinate of structure matrix i.e.



b) To depict the brands on the map we have to take coordinate of „Functions at group

Centroids‟ table i.e.

but to plot the value of structure matrix are very small we need to multiply this value by the ceiling

value of the term which is highest among the values in „functions at group Centroids‟ table

here the highest value is „2.745‟ we need to take its ceiling value i.e. = 3

if that value was suppose 4.243 then its ceiling value = 5

now multiply this „3‟ to all value of „structure matrix‟ table values

this now be as:

Availability 1.992303 0.883406 Quality 1.550649 -1.00939 Packaging 0.8035 -0.6097 Taste 1.293022 2.004916 Price -0.13216 0.703714



@CHAITANYA BANSAL

Q.5 Interpret the map and develop the positioning statement for each brand as per customer

perceptions. Justify your answer.

Ans.

A) Nestle: Good availability comparison to Cadbury.

Nestle is perceived as moderately good in packaging compare to Cadbury.

Perceived as moderately superior in quality compare to Cadbury

Price perceived to low

In the matter to taste it perceived to be inferior than Cadbury.

B) Cadbury:1. Comparatively less availability than Nestle

2. Cadbury perceived almost similar somewhat inferior to Nestle

3. Perceived as lower in quality

4. Price is high compare to Nestle.

C) Amul: Candidate for repositioning.



Conjoint Analysis:

“It is a set of technique used to derive the relative importance respondents assign to each

attribute when selecting from among several brands. It also allows an estimate of the best

combination of attributes.”

While creating a new product, if respondent were asked questions on product feature, they are

likely to indicate that they want max. benefit at lowest price.

e.g. In motorcycles consumer may indicate a requirement for the fastest, most powerful, most

fuel efficient more stylish motor cycle at lowest cost such combinations may be impractical or

unrealistic.

Hence consumers need to make tradeoffs, e.g. the tradeoff between power and mileage/ tradeoff

between style and price.

To determine this trade off we use conjoint analysis.



For „2‟ wheeler research is conducted. These are the parameters on which the responses we

have get.

Please remember that the conjoint analysis done after segmentation.

C.D.V is used to determine:

1) The relative importance of each attribute to a consumer.

2) The importance of each level of attribute (part- worth p-w) to a consumer.

Process of data collection:

Out of all possible new product concepts an orthogonal array of simplified option are created.

This is done by:

a) Eliminating impractical/unrealistic option

b) Eliminating very inferior options that customer would just not buy.

c) Respondents are presented with a questionnaire in which the shortlisted new product

concept is listed out serially.

The full new product concept is described on each attribute being considered. Hence the

respondent is not asked to evaluate each attribute individually in isolation. Hence tradeoffs can

be understood.

Respondent are asked to provide rating or ranking to each new product or option. The full

product description is thus evaluated. Analysis is to determine part- worth and utilities.



Questionnaire Table

S.N Power

(HP)

Mileage

(KMPH)

Price

($)

Style

(Yes/No)

Comfort

(Yes/No)

Respondent 1 Respondent 2

Rating Ranking Rating Ranking

1 8 65 60 N Y 50 12

2 8 75 30 Y N 78 7

3 8 85 45 N N 66 9

4 10 65 60 Y N 15 18

5 10 75 30 N Y 72 8

6 10 85 45 N N 32 15

7 12 65 30 N N 90 4

8 12 75 45 Y N 80 6

9 12 85 60 N Y 84 5

10 8 65 45 N N 52 10

11 8 75 60 N N 28 16

12 8 85 30 Y Y 95 1

13 10 65 30 N N 42 13

14 10 75 45 N Y 50 11

15 10 85 60 Y N 20 17

16 12 65 45 Y Y 92 2

17 12 75 60 N N 38 14

18 12 85 30 N N 90 3



How the research is being conducted:

Who can give the best response for „2‟ wheeler?

Ans: The people who have used our bike for at least 1-2 years .

Now to conduct research we call all the people/customer of our bike to dealer centre for free

service. First plan the cities let‟s say we plan „10‟ cities. Let the no. of people who brought our

bike be 1 lakh. Then it is assumed that out of the total at least 1000 people will come for

servicing.

While the servicing is going on,(let‟s say „1‟ bike take „1‟ hr to get full serviced) people don‟t

have any work at that time. We arrange a T.V for them so that they can pass their time. In the

mean time the company representative can go to one of the customers and ask him yo fill the

questionnaire in the following way.

“Hello sir, I am Senior head in-charge of this company, we are developing a new model. Will

you want to be the part- of this project? This will cost you nothing”

Then if the answer is yes then we give him our questionnaire. Don‟t you think this is the best

time to ask questions because the person is free at that time and able to give to answer to the

question very appropriately?

Let‟s say out of 10,000 people, 5000 people say yes. Suppose 2500 give the answer seriously.

Now the sample collected is 2500 from „1‟ city. We have 10 cities.

Total no. of respondents are 2500 x 10 = 25,000 respondent.

Don‟t you think this is the best data to conduct research?

Method analysis: Dummy variable in linear regression using effect coding. In this if we reduce

level per attribute then „13‟ level come down to 8 so this variable is called dummy variable in

linear regression.

For analysis we can take rating as they are. We can invert the ranking to cross check our result.

In inverted ranking ---------------------------------- “Rank 1” = “Rank18”



“Rank 2” = “Rank17” and so on.

Treat one level of each attribute as a dummy variable i.e. omit one level of each attribute from a

regression model, which level to omit may be decided arbitrarily.

In this case the level omitted are:

Power – 10 bhp, Mileage – 75kmph, Price – 65k, Style – ordinary(no), Comfort – ordinary(no).

Create a regression model

Rating = k1 + b1(12bhp + b2(8bhp)+ b3(65kmph)+ b4(85kmph)+ b5(30k)+ b6(s=n) + b7(c=n)

Inverted Rating = k1 + c1 (12bhp + c2 (8bhp) + c3 (65kmph) + c4 (85kmph) + c5 (30k) +

c6(s=y) + c7(c=y).

In the MODEL CODE the independent variable as follows:

(+1) – Implies level of attribute is present in new product option being rated/ranked also in

regression model. (Means present in both).

(0) – Implies that the level of attribute is absent in new product option but present in regression

model. (Means Present in regression model and Absent in new product option).

(-1) – Implies that the level of attribute is present in the new product option being rated/ranked

but absent in regression model. i.e. it is the dummy variable.(Means present in new product

option by absent in regression model).

New Product option Regression Model option

+1

0 X

-1 X

“ ” -Present, X- Absent.

This is called as effect code.

How we make effect code table. Now see our questionnaire table respondent „1‟, new product

option table



S.N Power (HP) Mileage

(KMPH)

Price ($) Style (Yes/No) Comfort

(Yes/No)

Respondent 1 Respondent 2

Rating Ranking Rating Ranking

1 8 65 60 N Y 50 12

Now match it which our regression model

Rating = k1 + b1(12bhp) + b2(8bhp)+ b3(65kmph)+ b4(85kmph)+ b5(30k)+ b6(s=n) + b7(c=n)

See 1. Power (8) present in new product option but not present in regression model

So power score X1 – (0) and for X2 – (1) because present in both.

2. Mileage (65) present in new product option table and also in regression model so mileage

scores X3 – (1), 65 is in regression model but for (65≠85) X4 so X4 – (0)

3. Price (60k) is a dummy variable so for both X5and X6 score is (-1) because it is present in

new product option but for both X5 and X6 regression model it is absent. Same can be apply for

style and comfort.

Create effects coding table based on the above coding system.

12bhp 8bhp 65kmph 85kmph 30k 45k S =Y C =Y

S.N X1 X2 X3 X4 X5 X6 X7 X8

1 0 1 1 0 0 -1 -1 1

2 0 1 -1 -1 1 0 1 -1

3 0 1 0 1 0 1 -1 -1

4 -1 1 1 0 -1 -1 1 -1

5 -1 -1 -1 -1 1 0 -1 1

6 -1 -1 0 1 0 1 -1 -1

7 1 0 1 0 1 0 -1 -1

8 1 0 -1 -1 0 1 1 -1

9 1 0 0 1 -1 -1 -1 1



10 0 1 1 0 0 1 -1 -1

11 0 1 -1 -1 -1 -1 -1 -1

12 0 1 0 1 1 0 1 1

13 -1 -1 1 0 1 0 -1 -1

14 -1 -1 -1 -1 0 1 -1 1

15 -1 -1 0 1 -1 -1 1 -1

16 1 0 1 0 0 1 1 1

17 1 0 -1 -1 -1 -1 -1 -1

18 1 0 0 1 1 0 -1 -1

Procedure:

then

Regression

Input Y range (dependent variable) – Ranking 1

Input X range (independent variable) – total data (12- c =y)

Mark label like this because first row contain label. Then click on “ok”.

Select first two column of solution, by these we know the regression equation using ratings.

Rating = 64.13 + 19.33(12bhp) + 1.833(8bhp) – 2.83(65kmph) + 4.833 (85kmph) + 18.17(30k)

+2.333(45k) + 2.75(s=y) + 10.69(c=y).



Now do the same procedure for inverted ranking.

Inverted rating = 10.5 + 3.833(12bhp) + 0.333(8bhp) – 0.33(65kmph) + 1.167 (85kmph) +

3.5(30k) + 0.667(45k) + 0.75(s=y) + 2.25(c=y).

The coefficients in the regression model indicate the impact that a particular level of attribute

has on rating or inverted ranking. Hence this is the part- worth of that level of attribute.

To determine part- worth of dummy variable (which we leave earlier) we can take either rating

or inverted rating and use other to cross check.

Method: sum of part- worth for any attribute = 0.

Take inverted rating part- worth.

Calculations:

Power: 0.33 + X + 3.83 = 0 X = -4.16

Mileage: -0.33 + 1.167 + X = 0 X = 0.834

Price: 3.5 + 0.667 + X = 0 X = -4.167

Style: 0.75 + X = 0 X = -0.75

Comfort: 2.25 + X = 0 X = -2.25

To determine relative importance of each attribute. Relative importance of an attribute is the

difference between the largest part- worth and the smallest part- worth of a attribute relative to

the same difference for all the attribute put together.



Logic: A large part- worth / large range in part- worth indicate says that the consumer have

higher important to that attribute.

Ri Ai =

Attribute Largest part-

worth

Smallest part-

worth

Range Relative

importance

U j

Power 3.83 -4.16 7.99 0.3377 33.77%

Mileage 1.667 -0.834 2.007 0.08456 8.456%

Price 3.5 -4.167 7.667 0.3240 32.4%

Style 0.75 -0.75 1.5 0.0634 6.3%

Comfort 2.25 -2.25 5.5 0.1902 19.02%

In this case the respondent gives maximum weights to power and price, moderate to comfort

and low weight age to style and mileage.

CDV = ∑ U j x PW ij

By the set of U j & PW ij we have different attribute. By this attribute we create the one which is

best.

We prepare the top 15 option and handle this to R&D department top 5 option R&D cannot

make because they may be unrealistic. But top 5-10 R&D should do within time frame.

This is how conjoint analysis has takes place. Conjoint analysis has two issues. (don‟t you think

this data is filled up by only „one‟ respondent) so if we have to measure two issue as follows)

1. Average of rating: This method is easier, technically not so accurate, used when

respondent are 5000-10000, and means it is used when respondent are large.



2. Average out part- worth‟s: More tedious but technically very good and accurate, used

when respondent are less.

Conjoint analysis must always be done within segments.

Market Research II (BIMM)

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