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M J XAVIER

This data analysis module was developed by Professor M.J. Xavier in conjunction with the textbook authors for the basis of class discussion rather than to illustrate either effective or ineffective marketing practice.

Copyright © 2004 by Houghton Mifflin Company. All rights reserved. Houghton Mifflin Company hereby grants you permission to print the Houghton Mifflin material contained in this work solely for use with the accompanying Houghton Mifflin textbook. All reproductions must include the Houghton Mifflin copyright notice, and no fee may be collected except to cover the cost of duplication. If you wish to make any other use of this material, including reproducing or transmitting the material or portions thereof in any form or by any electronic or mechanical means including any information storage or retrieval system, you must obtain prior written permission from Houghton Mifflin Company, unless such use is expressly permitted by federal copyright law. If you wish to reproduce material acknowledging a rights holder other than Houghton Mifflin Company, you must obtain permission from the rights holder. Address inquiries to College Permissions, Houghton Mifflin Company, 222 Berkeley Street, Boston, MA 02116-3764.

PREFACE

This practical guide on data analysis has been prepared specifically for the business students majoring in marketing who have an aversion for numbers and statistical methods. The simple step-by-step approach used in the guide should enable students to gain insight into statistical tools and help them develop their skills in interpreting and making meaning out of numbers. The entire range of statistical tools has all been explained using a single data set from a questionnaire on tooth-paste market. The tools covered range from simple frequencies, mean, median etc. to multivariate techniques like factor, cluster and discriminant analysis. The questionnaire, the code sheet and the final report are all given in the appendix. The first chapter on simple analytical methods starts with SPSS data preparation and goes on to explain the use of descriptive statistics to prepare summary results for each question in the survey data. It also highlights the use of charts for displaying data. The second chapter goes into the use of brand rating data for making snake charts, and positioning of brand using factor analysis. The third chapter introduces the concept of correlation coefficient and its sue for getting derived importance weights used for construction of Kano diagram. Chapter 4 uses the importance scores for benefits to do benefit segmentation using cluster analysis. Chapter 5 introduces Correspondence analysis and its use for mapping brand-personality association data. Use of regression analysis in marketing research is explained in Chapter 6. The problem of multicollinearity and talking the same using factor analysis is also explained in the same. Chapter 7 describes the use of discriminant analysis to find out brand drivers for different brands. Chapter 9 explains the use of multi-dimensional scaling for brand positioning. The data files referred to in this text are all available on the student web site as part of this module. I am grateful to the graduate and undergraduate students who enrolled for the marketing research course during the Fall 2003 term for their co-operation in developing the questionnaire and collection of data. I am grateful to Dr. R Krishnan, Director Graduate Program and Dr. Norm Borin, Marketing Area Chair for their support and encouragement for this project.

M J Xavier

CONTENTS

Chapter Topic Page No. 1. Introduction and Simple Analytical methods 3 2. Snake Chart, Factor Analysis and Brand Positioning 13 3. Kano Model 26 4. Cluster Analysis and Benefit Segmentation 31 5. Correspondence Analysis 37 6. Regression Analysis 44 7. Discriminant Analysis 51 8. Multidimensional Scaling 59 APPENDIX Toothpaste Questionnaire 71 Code-sheet 77 Power Point Slides 88

3 Copyright © Houghton Mifflin Company. All rights reserved.

Chapter 1

Introduction and Simple Analytical Methods

Objectives:

1. To understand Data view and Variable view in an SPSS data file. 2. To understand the difference between String and Numeric variables. 3. To become familiar with Labels and Value labels. 4. To learn how to get frequencies of variables and get Pie or Bar charts of those

frequencies. 5. To learn how to create a transformed variable and understand the difference

between the raw variable and the transformed variable. 6. To learn how to calculate the mean, standard deviation, and variance of a variable

using SPSS. 7. To understand how to make a cross-tabulation of two nominal variables and use

the chi-square test to see whether the relationship between the two variables is significant or not.

8. To learn to use the “compare means” command and learn about independent t-test. Data View Versus Variable View: Open the file `descriptives.sav’ At the bottom left hand corner you will see TWO BUTTONS: DATA VIEW AND VARIABLE VIEW Click on the variable view. You will see the complete definition of each variable.

Data View- Variable View


String Versus Numeric Variables Study the variable definitions. Note that Nickname is a `STRING VARIABLE’ All others are `NUMERIC VARIABLES’ Go to Data View and see that String variables are made up of letters or letters and numbers (alpha numeric) while the numeric variables are made up of numbers only. Labels and Value Labels Go to variable view again and study the columns LABEL and VALUES. Click at the right hand corner of VALUES corresponding to the VARIABLE `class’ A small window shown below will open.

These are the codes used for the variable class. Now shift to Data view and then click: VIEW VALUE LABEL You will notice that labels corresponding to the numerical codes appear on the data sheet. Frequencies and Charts: Let us first try to understand the profile of respondents.


Let us start with the age profile. Run the following SPSS Commands to get the distribution of respondents by age. ANALYZE DESCRIPTIVE STATISTICS FREQUENCIES Drag variable `Age[q12a]’ on to the VARIABLE(S) Box CHART PIE CHARTS PERCENTAGES CONTINUE OK Check if you get the following table and the Pie chart from in a new window. Age

Frequency Percent Valid Percent Cumulative

Percent Under 18 years 58 82.9 82.9 82.9

18-24 years 12 17.1 17.1 100.0

Valid

Total 70 100.0 100.0

Age

17.1%

82.9%

18-24 years

Under 18 years


Now repeat the analysis with other demographic variables, namely Household Income, Gender, and Race. You can drag all three variables to the variables box and have the charts made simultaneously. Now do the frequencies with other variables, awareness of Brands and also with trial of brands. Now go the variable Current Brand and change the chart from PIE to Bar and see if you get the following chart.

Current Brand

Current Brand

Others

Arm & Hammer

Mentadent

Crest

Colgate

Aqua-Fresh

Per

cent

40

30

20

10

0

Raw Variable Versus Transformed Variables: Suppose we want to know on an average how many brands a person is aware of, we cannot get it directly from the data. We need to create a new variable from the existing ones.


Try the following commands to create a new variable called ‘aware’ which is derived from other variables. TRANSFORM COMPUTE Type àware’ in the TARGET VARIABLE Box Move variable `q01a’ into the NUMERIC EXPRESSION Box Click on + Move variable `q01b’ into the NUMERIC EXPRESSION Box Click on + Move variable `q01c’ into the NUMERIC EXPRESSION Box Click on + Move variable `q01d’ into the NUMERIC EXPRESSION Box Click on + Move variable `q01e’ into the NUMERIC EXPRESSION Box Click on + Move variable `q01f’ into the NUMERIC EXPRESSION Box Click on + Move variable `q01g’ into the NUMERIC EXPRESSION Box Click on + OK Note that we are forming a numeric expression Aware = q01a + q01b + q01c + q01c + q01d + q01e + q01f + q01g Notice that a new column has been created by SPSS called àware’. While the original variables are called raw variables, the new one formed out of raw variables is called a transformed variable. Go to variable view and type `No. Of Brands Aware’ in the LABEL column corresponding to the variable àware’ Now perform a Frequency analysis on the new variable àware’ and get a bar chart as shown below. No. Of Brands Aware

Frequency Percent Valid Percent Cumulative

Percent 2.00 1 1.4 1.4 1.4 3.00 4 5.7 5.7 7.1 4.00 30 42.9 42.9 50.0 5.00 28 40.0 40.0 90.0 6.00 6 8.6 8.6 98.6 7.00 1 1.4 1.4 100.0

Valid

Total 70 100.0 100.0


No. Of Brands Aware

No. Of Brands Aware

7.006.005.004.003.002.00

Per

cent

50

40

30

20

10

0

In the same way create a new variable called trial (no. Of brands tried by each person) using the following expression. trial = q02a + q02b + q02c + q02d + q02e + q02f + q02g And find the frequency distribution of the number of brands tried. Mean, Standard Deviation and Variance: Note that the two new transformed variables, namely aware and trial, are different from the other variables we have seen earlier. These are ratio scaled variables whereas the other variables are only nominally scaled. We shall see how to calculate mean, standard deviation and variance for a ratio scaled data.


Try the following SPSS Commands. ANALYZE DESCRIPTIVE STATISTICS DESCRIPTIVES drag the variable `aware’ to VARIABLES Box OPTIONS VARIANCE CONTINUE OK You will get the following output Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Variance No. Of Brands Aware 70 2.00 7.00 4.5286 .84650 .717 Valid N (listwise) 70

This shows that on an average a respondent is aware of 4.53 brands and the standard deviation of the same variable is 0.85. Crosstabs and Chi-Square Test: Suppose we want to know whether the use of a particular brand depends on whether the person is a male or female, we need to use the type of analysis called cross-tabulation. Crosstabs is used to explore the relationship between two nominal or categorical variables. Try the following SPSS Commands ANALYZE DESCRIPTIVE STATITICS CROSSTABS Drag Gender to ROW(S) Drag Current Brand to COLUMN(S) CELLS ROW CONTINUE STATISTICS CHI-SQUARE OK


Gender * Current Brand Cross-tabulation

Current Brand

Aqua-Fresh Colgate Crest

Mentadent

Arm & Hammer Others Total

Count 7 9 8 3 3 5 35 Male % within Gender 20.0% 25.7% 22.9% 8.6% 8.6% 14.3% 100.0%

Count 3 11 17 3 0 1 35

Gender

Female % within Gender 8.6% 31.4% 48.6% 8.6% .0% 2.9% 100.0%

Count 10 20 25 6 3 6 70 Total % within Gender 14.3% 28.6% 35.7% 8.6% 4.3% 8.6% 100.0%

It is a convention to keep the independent variable in the row, dependent variable in the column and get row percentages in the cells. In this case, we are trying to explore whether gender has an impact on brand choice. To interpret the table, always look column-wise and see if the percentages vary drastically. In the Aquafresh column, there is a larger percentage of males. Colgate has marginally large percentages of females. Crest has a substantially large percentage of females compared to males. Mentadent has an equal following among males and females. Arm & Hammer appears to be an exclusive male brand. There appears to be some relationship between gender and brand used. To check whether the relationship is significant or not, we need to look at the chi-square value. Chi-Square Tests

Value df Asymp. Sig.

(2-sided) Pearson Chi-Square 10.707(a) 5 .058 Likelihood Ratio 12.230 5 .032 Linear-by-Linear Association 1.452 1 .228

N of Valid Cases 70

a 6 cells (50.0%) have expected count less than 5. The minimum expected count is 1.50. A chi-square value of 10.707 at 5 degrees of freedom is significant at 0.058, i.e. at a confidence level of 94.2. Normally we look for a confidence level of 95% or more. As it is close to 95%, and also given the fact that some of the cells have values less than 5, we can take this as a significant relationship. Note that the cell frequencies should be 5 or more for chi-square test. However SPSS applies a correction factor to take care of this deficiency and makes it all the more difficult to attain significance.


The same rule for the significance level of 0.05 or less applies to all the tests that we are going to learn, be it t-test, f-test or any other test. Degrees of freedom = (no. of rows -1) x (no. of columns – 1) The same way construct cross tabs for age, income, and race against Current Brand and check if the relationships are significant using chi-square values. Compare Means Suppose we want to know whether the mean number of brands aware across males and females, we could use the following commands. ANALYZE COMPARE MEANS MEANS Drag variable `aware’ to DEPENDENT LIST gender to INDEPENDENT LIST OK The following output will be obtained. Report No. Of Brands Aware

Gender Mean N Std. Deviation Male 4.6286 35 1.00252 Female 4.4286 35 .65465 Total 4.5286 70 .84650

4.6 and 4.2 are very close values. The difference between Male and Female appears to be very marginal. Now do the analysis with other classification variables, race, income, age and class. Independent t-Test: Suppose we want to know whether the difference of 0.4 in the number of brand aware of between male and female populations is statistically significant, we need to conduct a t-test.


Run the following SPSS Commands ANALYZE COMPARE MEANS INDEPENDENT SAMPLES t-TEST Drag variable aware to the TEST VARIABLE(S) Drag variable q12c to GROUPING VARIABLE DEFINE GROUPS GROUP 1 (Type 1) GROUP 2 (Type 2) CONTINUE OK

Independent Samples Test

Levene's Test for Equality of

Variances t-test for Equality of Means 95%

Confidence Interval of the

Difference

F Sig. t df Sig. (2-tailed)

Mean Differe

nce

Std. Error

Difference Lower Upper

Equal variances assumed

3.608 .062 .988 68 .327 .2000 .20239 -

.20386

.60386

No. Of Brands Aware

Equal variances not assumed

.988 58.535 .327 .2000 .20239

-.2050

4

.60504

Our Null Hypothesis is that the means for males and females are the same. The alternate Hypothesis is that the means are significantly different. As we do not know which one should be greater we use a 2-tailled significance test. Notice that the t-value of 0.988 at 68 degrees of freedom has significance of only 0.327. The corresponding confidence level is 67.3% which is too low. Unless the significance level is less than 0.05 the mean values are not significantly different. Note that the degrees of freedom in this case are number of observations minus two. Conduct the t-test for other variables – age, race and income to see whether the mean brands aware of vary by any of these categories.


Chapter 2

Snake Chart, Factor Analysis and Brand Positioning Objectives: To understand how to compute mean ratings of brands and to construct snake charts. To learn how to run factor analysis and understand the following concepts:

- variance explained - factor loading - eigenvalue - communality - rotation - factor score

Use factor analysis for brand positioning. Data Structure: Open the file factor.sav Study the file structure. This new file has been created out of the master data file by re-arranging the variables q06a01 to q06d11 as indicated below. q06a01 … ... q06a11 q06b01 … ... q06b11 q06c01 … ... q06c11 q06d01 … ... q06d11 1. 2. . . . . . 70

1. 2. . . . . . 70

1. 2. . . . . . 70

1. 2. . . . . . 70

Original Data


Brand Code

q06a01 … ... q06a11

Data

q06b01 … ... q06b11

Data

q06c01 … ... q06c11

Data

q06d01 … ... q06d11 Data

Rearranged Data for Further Analysis

Note that blank rows have been deleted in the new file and a new variable brand code has been created. Snake Chart: We need to calculate the mean ratings of brands, before we can construct the snake chart. Use the following commands to obtain the mean ratings. ANALYZE COMPARE MEANS MEANS Highlight and Drag `q06_01… q06_10’ to DEPENDENT LIST Highlight `brand’ and drag to INDEPENDENT LIST OPTIONS Uncheck NUMBER OF CASES Uncheck STANDARAD DEVIATION CONTINUE OK


Brand Fighting Cavities

Whitening Teeth

Cleaning Stains

Good Taste

Likeable Flavor

Freshening Breath

Brand Image Color

Attractive Packaging

Innovative features

Aquafresh 7.2 6.1 6.7 6.8 6.9 7.4 6.6 7.0 7.1 6.4 Colgate 8.1 6.9 7.5 6.9 7.0 7.7 7.9 7.0 7.0 6.8 Crest 8.4 7.6 7.9 8.2 8.0 8.3 8.7 7.8 7.8 7.4 Mentadent 9.3 8.1 8.9 9.3 9.6 9.6 7.9 8.7 8.3 7.4 Arm & Hammer 9.0 8.3 8.3 6.0 5.0 8.7 7.3 7.7 7.3 8.0 Others 8.6 6.2 6.8 9.0 8.8 9.4 5.8 7.2 6.0 8.4

Mean Ratings for Brands

Right click on the table, copy and paste onto an excel worksheet. Highlight the relevant portions and click Chart Wizard. Choose Line, press Next and Finish to get the following snake chart.

0.0

2.0

4.06.0

8.0

10.0

12.0

Fighti

ng C

avitie

s

Whiten

ing Te

eth

Cleanin

g Stai

ns/Ta

rtar

Good T

aste

Likea

ble Fl

avor

Fresh

ening

Brea

th

Brand

Imag

eColo

r

Attrac

tive Pa

ckag

ing

Innov

ative

featu

res/In

gredie

nts

Aqua-Fresh

Colgate

Crest

Mentadent

Arm & Hammer

Others

This chart can be used to study the relative positioning of brands on different attributes. We can see that Mentadent and Arm&Hammer are rated highly on selected attributes while Crest scores consistently higher rating on all the attributes. Aquafresh has lower rating and Colgate is stuck in the middle. Here the points are very cluttered and it is difficult to see finer distinctions. Factor analysis will help us do sharper positioning.


Factor Analysis Factor analysis is used to understand the underlying dimensions of a set of variables having high correlation among them. Execute the following commands to get the factor analysis output. ANALYZE DATA REDUCTION FACTOR Highlight and Drag `q06_01… q06_10’ to VARIABLES DESCRIPTIVES Check COEFFICIENTS CONTINUE EXTRACTION SCREE PLOT CONTINUE ROTATION VARIMAX CONTINUE SCORES SAVE VARIABLES CONTINUE OPTIONS SORTED BY SIZE CONTINUE OK Take a look at the Correlation Matrix and notice that the variables are correlated among themselves. For example the correlation between good taste and likeable flavor is as high as 0.928. The correlations are sufficient for conducting a factor analysis is confirmed by Bartlett’s Test of Sphericity which is significant.


Correlation Matrix

Fighting Cavities

Whitening Teeth

Cleaning Stains

Good Taste

Likeable

Flavor Freshening Breath

Brand Image Color

Attractive

Packaging

Innovative features

Fighting Cavities 1.000 .385 .610 .221 .203 .450 .367 .142 .210 .228 Whitening Teeth .385 1.000 .613 .278 .223 .403 .354 .198 .249 .501 Cleaning Stains/Tartar .610 .613 1.000 .218 .161 .470 .461 .188 .314 .488 Good Taste .221 .278 .218 1.000 .928 .475 .313 .436 .303 .366 Likeable Flavor .203 .223 .161 .928 1.000 .481 .276 .436 .300 .318 Freshening Breath .450 .403 .470 .475 .481 1.000 .384 .349 .358 .390 Brand Image .367 .354 .461 .313 .276 .384 1.000 .576 .624 .373 Color .142 .198 .188 .436 .436 .349 .576 1.000 .650 .419 Attractive Packaging .210 .249 .314 .303 .300 .358 .624 .650 1.000 .413 Innovative features .228 .501 .488 .366 .318 .390 .373 .419 .413 1.000

Now take a look at the variance explained matrix.

Total Variance Explained Component Initial Eigenvalues Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % 1 4.438 44.378 44.378 2.651 26.512 26.512 2 1.577 15.773 60.152 2.392 23.915 50.428 3 1.239 12.391 72.543 2.211 22.115 72.543 4 .804 8.043 80.585 5 .500 5.004 85.590 6 .440 4.399 89.989 7 .343 3.432 93.421 8 .329 3.291 96.711 9 .261 2.613 99.324 10 .068 .676 100.000

Read ‘component’ as ‘factors’ in the table. Technically the 10 original variables can be converted into 10 new factors which are orthogonal to each other (i.e., will have zero correlation among them). The first such factor will account for 44.378 percent variance in the original data, second one will account for 15.773 percent and so on. In statistics, variance is information. As 72.543 percent of information (variance) is summarized by three variables, it is enough to work with three factors. We shall see what the eigenvalue and rotation mean later.


Now go to the data view in the SPSS data file. You will notice that three new variables, namely, fact1_1, fact2_1 and fact3_1 have been added by the system. The values that these variables take are called factor scores. Basically the original 10 inter-correlated variables have been converted to 3 new factors which are orthogonal to each other. To check the orthogonality, do the following analysis. ANALYSE CORRELATE BIVARIATE Highlight and drag `fact1_1, fact2_1 and fact3_1’ to VARIABLES OK You will get the following output which shows that the factors have zero correlation between them. Correlations

REGR factor score 1 for analysis 1



Pearson Correlation 1 .000 .000 Sig. (2-tailed) . 1.000 1.000


N 199 199 199 Pearson Correlation .000 1 .000 Sig. (2-tailed) 1.000 . 1.000


N 199 199 199 Pearson Correlation .000 .000 1 Sig. (2-tailed) 1.000 1.000 .


N 199 199 199

Now the problem is to find out what these factors mean. Obviously the three new factors summarize the information present in the original ten variables. We need to establish which variables go into which factor. Look at the rotated component matrix


Rotated Component Matrix (a)

Component

1 2 3 Cleaning Stains/Tartar .878 .203 .014 Fighting Cavities .763 .051 .103 Whitening Teeth .758 .150 .126 Freshening Breath .550 .220 .484 Innovative features/Ingredients .482 .437 .234

Attractive Packaging .154 .867 .115 Color .013 .830 .323 Brand Image .364 .757 .077 Likeable Flavor .098 .183 .950 Good Taste .152 .195 .933

The cells contain factor loadings, i.e., correlation coefficients of original variables with the new factors. Conduct the following analysis to confirm the above statement. Conduct a correlational analysis of the first variable Cleaning of Stains/Tartar with the three new factors (fact1_1, fact2_1 and fact3_1) to get the first row in the rotated component matrix. The variable `cleaning stains/tartar’ has a correlation coefficient of 0.878 with the first factor, 0.203 with the second factor, and 0.014 with factor 3. What it means is that the variable `cleaning stains/tartar’ belongs to first factor. The same way the variables highlighted in the column corresponding to factor 1 belong to the same factor. For the moment ignore the variable `innovative features/ingredients’ as it is highlighted in two columns. Looking at the variables that go into each factor we can name them as Dental Hygiene, Visibility and Sensory Benefits. Factor -1 Factor – 2 Factor – 3 Cleaning Stains/Tartar Fighting Cavities Whitening Teeth Freshening breath

Attractive Packaging Color Brand Image

Likeable Flavor Good Taste

Dental Hygiene Visibility Sensory Benefits The variable `innovative features/ingredients’ has a high correlation with Dental Hygiene as well as Visibility. Suppose that a brand claims in its advertisements that it has a new ingredient that whitens the teeth, it contributes to Dental Hygiene as well the Visibility of the brand. That is how it features in two factors.


Now take a look at the communalities matrix.

Communalities

Initial Extraction Fighting Cavities 1.000 .596 Whitening Teeth 1.000 .613 Cleaning Stains/Tartar 1.000 .812 Good Taste 1.000 .931 Likeable Flavor 1.000 .945 Freshening Breath 1.000 .585 Brand Image 1.000 .712 Color 1.000 .794 Attractive Packaging 1.000 .789 Innovative features/Ingredients 1.000 .478

Communalities refer to the amount of information that has been extracted from each variable. Notice that more than 90 percent of information (variance) has been extracted from variables ‘good taste’ and ‘likeable flavor’ whereas less than 50% is extracted from ‘innovative features/ingredients.’ If we work with large number of variables, say more than 20, it may be a good idea to leave out variables with low communality while naming factors. In the same way, the eigenvalues are directly proportional to the amount of variance explained by each factor. The sum of all eigenvalues always equals the total number of variables. Hence the proportion of variance explained by each factor can be calculated by dviding the corresponding eigenvalue by the total number of variables. Now take a look at the variance explained table to verify the same. As the first eigenvalue is 4.438, the variance explained by the first factor can be calculated by diving 4.438 by 10 (total number of variables) and multiplying bv 100. Now take a look at the Scree Plot


Scree Plot

Component Number

10987654321

Eig

enva

lue

5

4

3

2

1

0

Scree plot gives an idea as to how many factors to extract. The rule normally applied is to stop at where the arm bends. In this case it is three factors. After three factors the curve gets flat indicating that the gain will be marginal if we go beyond three factors. The default in SPSS is that it stops when the eigenvalue gets to less than one. To understand the concept of rotation, take a look at the unrotated component matrix. If we plot factor2 and factor3 we get the graph shown below.


It is difficult to interpret this type of data as we find that the cluster of variables likeable flavor and good taste are mid-way between factor 2 and factor 3. If we rotate the y-axis so as to pass through the cluster we can interpret the y-axis as Sensory Benefits. In the same way the x-axis can be rotated to pass through the cluster that corresponds to Dental Hygiene. Rotation is done to make it easy to interpret the output. Note that the angle between X and Y axis in our rotation is more than 90 degrees. If the angle is maintained at 90o it is called an orthogonal rotation otherwise it is known as oblique rotation. Note that we used Varimax rotation which is an orthogonal rotation method. What we have achieved by conducting a factor analysis is that we have converted the original ten variables into 3 new factors. Now we can use these three new variables to do brand positioning. We can bring both the variables and the brands on to the same map.

FACTOR2 .8.6.4.

2 -.0-.2-.4-.6

FAC

TOR

3

.6

.4

.2

-.0

-.2

-.4

-.6

Fighting Cavities Whitening Teeth

Likeable Flavor

Color


Cleaning Stains/Tart

Innovative features/

Good Taste

Brand Image

Freshening Breath


Brand Positioning Using Factor Scores: We shall now find out the mean ratings of brands for the three new factors. First of all go to the variables view and label those new factors as:

1. Dental Hygiene 2. Visibility 3. Sensory Benefits

Then compute mean ratings by executing the following commands. ANALYZE COMPARE MEANS MEANS Highlight and Drag `Dental Hygiene’, `Visibility’, and `Sensory Benefits’ to DEPENDENT LIST Drag `brand’ to INDEPENDENT LIST OPTIONS Highlight `number of case’ and `standard deviation’ and send back to STATISTICS CONTINUE OK You will get the following output.

Brand Dental

Hygiene Visibility Sensory Benefits

Aqua-Fresh -.5622039 -.1069474 -.1044542 Colgate .0723164 -.0603488 -.2591244 Crest .3170127 .2179352 .2061569 Mentadent .7780827 .0560046 .9165143 Arm & Hammer .9514446 .0165516 -.9228734 Others .0827401 -.8240165 1.1187287 Total .0000000 .0000000 .0000000

We already have the coordinates of the variables in the rotated components matrix. Create combined table, which has the coordinates of both the brands and attributes as below.


Brand/Attribute Dental Hygiene Visibility

Sensory Benefits

Aqua-Fresh -0.56 -0.11 -0.1 Colgate 0.07 -0.06 -0.26 Crest 0.32 0.22 0.21 Mentadent 0.78 0.06 0.92 Arm & Hammer 0.95 0.02 -0.92 Others 0.08 -0.82 1.12 Cleaning Stains/Tartar 0.88 0.2 0.01 Fighting Cavities 0.76 0.05 0.1 Whitening Teeth 0.76 0.15 0.13 Freshening Breath 0.55 0.22 0.48 Innovative features/Ingredient 0.48 0.44 0.23 Attractive Packaging 0.15 0.87 0.12 Color 0.01 0.83 0.32 Brand Image 0.36 0.76 0.08 Likeable Flavor 0.1 0.18 0.95 Good Taste 0.15 0.2 0.93

Using this data create a new SPSS file – factor1.sav To get the positioning map of the first two factors use the following commands. GRAPHS SCATTER SIMPLE DEFINE Drag `Dental Hygiene’ to X-AXIS Drag `Visibility’ to Y-AXIS Drag `Brand/Attribute’ to LABEL CASES BY OPTIONS Check DISPLAY CHART WITH CASE LABELS OK The resulting plot can be taken to Power point to have it annotated as shown below. Note that the attributes are represented as vectors and brands as points.


While Arm & Hammer and Mentadent are seen as better in Dental Hygiene, Crest scores better on Visibility. In the same way get the other two plots, namely, Dental Hygiene Versus Sensory Benefits and Visibility Versus Sensory Benefits.

Dental Hygiene 1.0.8.6.4.2-.0 -.2-.4 -.6

Vis

ibili

ty

1.0

.5

0.0

-.5

-1.0

Good Taste

Likeable

Brand Image Color


Innovative features

Freshening Breath

Fighting Cavities

Cleaning Stains

Others

Arm & Hammer

Mentadent Crest

Colgate Aqua-Fresh

Whitening Teeth


Chapter 3 KANO Model

Objectives: To understand the basics of Kano Model To learn how to calculate derived Importance weights for attributes To learn how to plot the Kano Model and interpret the same Data Files: We will be using two different data files for this analysis.

1. cluster.sav 2. factor.sav

Stated Importance: This will be the mean importance rating given to attributes by the respondents. To calculate the means, open the file cluster.sav and run the following commands. ANALYZE DESCRIPTIVE STATISTICS DESCRIPTIVES Highlight and drag variables `q05a… .q05j’ OPTIONS Check DESCENDING MEANS CONTINUE OK


Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Freshening Breadth 70 4.00 7.00 6.3000 .76802 Fighting Cavities 70 1.00 7.00 6.1143 1.44004 Cleaning Stains/Tartar 70 1.00 7.00 5.6714 1.34834 Whitening Teeth 70 1.00 7.00 5.6429 1.41458 Good Taste 70 1.00 7.00 5.3429 1.50252 Likeable Flavor 70 1.00 7.00 5.1714 1.52250 American Dental Association Recommendation

70 1.00 7.00 4.4857 1.88620

Innovative Feature/new ingredient 70 1.00 7.00 4.0714 1.36543

High Prestige Brand 70 1.00 7.00 3.4714 1.50093 Attractive Packaging 70 1.00 7.00 3.4286 1.68171 Valid N (listwise) 70

You find that Freshening Breath is the most important attribute with a mean rating of 6.3 on a 1-7 scale. Attractive Packaging is the least important attribute. To convert the means into importance weights, we need to normalize the means. Take the above table to Excel and find the sum of means. Then calculate: Importance Weight = (Mean/Sum of Means)*100 Develop the following Stated Importance weights table.

Attribute Mean Importance Weight

Freshening Breadth 6.30 12.68 Fighting Cavities 6.11 12.30 Cleaning Stains/Tartar 5.67 11.41 Whitening Teeth 5.64 11.35 Good Taste 5.34 10.75 Likeable Flavor 5.17 10.41 American Dental Association Recommendation 4.49 9.03 Innovative Feature/new ingredient 4.07 8.19 High Prestige Brand 3.47 6.98 Attractive Packaging 3.43 6.90 Sum 49.70 100.00

Stated Importance


Derived Importance: In order to obtain the derived importance we are going to use the file factor.sav. By correlating rating of attributes with the overall rating we get the derived importance of attributes. Use the following commands. ANALYZE CORRELATE BIVARIATE Highlight variables q06_01 to q06_11 and drag to VARIABLES OK You will get an 11 by 11 matrix of correlations. We are interested in the last column only which has the correlation of individual attributes with the overall rating. As correlations can range from -1 to +1, take the r2 value for derived importance. Once again these values can be normalized by taking the sum of all the r2 values. The resultant table is given below.

Attribute r r2 Importance Weights

Freshening Breath 0.65 0.43 13.26 Good Taste 0.63 0.39 12.22 Likeable Flavor 0.59 0.35 10.84 Brand Image 0.59 0.35 10.81 Innovative features/Ingredients 0.57 0.33 10.22 Color 0.57 0.32 9.96 Cleaning Stains/Tartar 0.54 0.29 9.04 Whitening Teeth 0.53 0.28 8.77 Fighting Cavities 0.52 0.27 8.46 Attractive Packaging 0.45 0.21 6.41 Sum 3.21 100.00

Derived Importance Weights

Bring stated and derived importance to a common table as shown below. Now plot derived Stated Importance against derived Importance to develop the Kano Model.


Attribute Stated Importance

Derived Importance

Attractive Packaging 6.90 6.41 Cleaning Stains/Tartar 11.41 9.04 Fighting Cavities 12.30 8.46 Freshening Breadth 12.68 13.26 Good Taste 10.75 12.22 High Prestige Brand 6.98 10.81 Innovative Feature/new ingredient 8.19 10.22 Likeable Flavor 10.41 10.84 Whitening Teeth 11.35 8.77

Stated Versus Derived Importance

Use the commands: GRAPH SCATTER Drag stated importance to X-AXIS Drag derived importance to Y-AXIS Drag attribute to LABEL CASES BY OPTIONS Check DISPLAY CHART WITH CASE LABELS CONTINUE OK On the graph use 10 as a cut off for High and Low values of importance and illustrate by taking it to PowerPoint.


KANO’s Model According to Kano’s model attributes that have a high stated and low derived importance are Minimum expected attributes. Attributes like whitening teeth, fighting cavities, cleaning stains are the minimum expected in a toothpaste. Attributes with low stated and high derived importance are called Delight attributes. The marketers should concentrate on these attributes. In this study the innovative ingredients and brand image emerge as the delight attributes. Others are linear attributes. If they are important then pay attention. The most important attribute is freshening breath, as the stated and derived importance is high. If they have low importance one should not do over engineering of those attributes. In this case spending too much on packaging may not produce commensurate returns.

Stated Importance

13

12

11

10

9876

Der

ived

Impo

rtanc

e

14

13

12 11

10

9

8

7

6

Whitening teeth teethTeeth

Likeable flavor Flavor Innovative Ingredients

High Prestige brand Brand

Good Taste Taste

Freshening breath Breadth

Fighting cavities Cavities

Cleaning Stains/Tartar

Attractive packaging Packaging

Minimum Expected Attributes

Delight Attributes

High

High

Low

Low


Chapter 4 Cluster Analysis and Benefit Segmentation

Objectives:

• To learn how cluster analysis can be used for grouping of subjects.

• To understand the difference between hierarchical clustering and k-means clustering.

• To use SPSS to perform cluster analysis and interpret the results.

• To learn how to use cluster analysis for benefit segmentation.

Cluster Analysis: We shall use the cluster.sav file for this session. Let us first calculate the descriptives. Execute the following commands. ANALYZE DESCRIPTIVE STATISTICS DESCRIPTIVES Drag variables q05a to q05j to VARIABLES OK If you sort the output according to descending values of standard deviation you will get this output.

Attribute N Mean Std. Deviation

American Dental Association Recommendation 70 4.49 1.89 Attractive Packaging 70 3.43 1.68 Likeable Flavor 70 5.17 1.52 Good Taste 70 5.34 1.50 High Prestige Brand 70 3.47 1.50 Fighting Cavities 70 6.11 1.44 Whitening Teeth 70 5.64 1.41 Innovative Feature/new ingredient 70 4.07 1.37 Cleaning Stains/Tartar 70 5.67 1.35 Freshening Breadth 70 6.30 0.77

Mean Rating of Benefits Sought


From the output, it is clear that the five variables with high standard deviation are: • American Dental Association Recommendation • Attractive Packaging • Likeable Flavor • Good Taste • High Prestige Brand • Fighting Cavities • Whitening Teeth

These are the attributes where the opinion of the respondents varies much. Hence for clustering and segmentation we shall use only these seven variables. Hierarchical Clustering: Let us start with hierarchical clustering. Execute the following commands. ANALYZE CLASSIFY HIERARCHICAL CLUSTER Highlight and drag the seven variables to VARIABLE(S) Drag nickname to LABEL CASES BY PLOTS Check DENDROGRAM Check NONE CONTINUE If you look at the output you will find a tree structure. If you leave out three cases 38 bearing the nickname `Warden’, 2 (Bud) and 9 (Hank) there are three major branches. That gives us some idea about how many clusters to ask for when we go to K-means clustering.


K-Means Clustering: In this method the respondents will get allocated to different clusters based on the number of clusters the researcher asks for. Based on the results of the hierarchical cluster we have decided to ask for three clusters. ANALAYZE CLASSIFY K-MEANS CLUSTER Highlight and drag the five variables to VARIABLES box Drag `nickname’ to LABEL CASES BY NUMBER OF CLUSTERS change from 2 to 3 SAVE CLUSTER MEMEBERSHIP CONTINUE OPTION CLUSTER INFORMATION FOR EACH CASE Look at FINAL CLUSTER CENTERS. Attribute Cluster 1 Cluster 2 Cluster 3 Attractive Packaging 2.63 3.46 3.82 American Dental Association Recommendation 4.63 2.08 5.24 Likeable Flavor 3.32 5.77 5.89 Good Taste 3.53 6.00 6.03 High Prestige Brand 2.63 3.08 4.03 Whitening Teeth 5.58 5.85 5.61 Fighting Cavities 6.68 3.77 6.63

Yellow filling indicates rank one across the row and green indicates rank 2. Benefit Segmentation: Cluster 1 members are primarily concerned with fighting cavities and are also interested in American Dental Association recommendation. So the benefit sought is `medically proven cavity fighter.’ Cluster 2 is primarily interested in whitening teeth. They have also given relatively high rating for likeable flavor and good taste. Though they get the second rank on attractive packaging and high prestige brand, the ratings themselves are low in absolute terms. the benefit sought by this group is white teeth plus good taste and flavor. Cluster 3 wants everything; they look for a balanced paste that provides dental care and sensory benefits (taste & flavor).


So the benefit segments that we have devised are as follows: 1. Proven cavity fighter 2. Tasty flavorful paste for white teeth 3. Balanced paste which provides dental care as well as good taste and flavor

From the table on number of cases in each cluster we find that 19 are in cluster 1, 13 are in cluster 2 and 38 are in cluster 3. The majority (54%) of the people want a balanced paste, 27% want a cavity fighter, and 19% are for white teeth.

Number of Cases in Each Cluster

Cluster 1 19.000 2 13.000 3 38.000 Valid 70.000 Missing .000

Now take a look at the cluster membership table. This gives the information about the cluster membership of each individual. The same information is also stored in the SPSS data file as a new variable crated qcl_1. Insert label values for the new variable as given below:

1. Cavity Fighter 2. White Teeth 3. Balanced Paste

Cross Classification with Demographic Variables: Cross tabulation of the new variable qcl_1 Vs race will produce the following table. Crosstab

Race/Ethnicity White Others Total

Count 16 3 19 Cavity fighter % within Cluster Number of Case 84.2% 15.8% 100.0%

Count 9 4 13 White teeth % within Cluster Number of Case 69.2% 30.8% 100.0%

Count 30 8 38

Cluster Number of Case

Balanced Paste % within Cluster Number of Case 78.9% 21.1% 100.0%

Count 55 15 70 Total % within Cluster Number of Case 78.6% 21.4% 100.0%


While cavity fighting is important for more proportion of whites, white teeth seems to be of more importance to non-whites. However the chi-square does not show a significant relationship between benefits segments and race. Chi-Square Tests


(2-sided) Pearson Chi-Square 1.036(a) 2 .596 Likelihood Ratio 1.005 2 .605 Linear-by-Linear Association .097 1 .755

N of Valid Cases 70

a 2 cells (33.3%) have expected count less than 5. The minimum expected count is 2.79. The same kind of analysis can be done with age, income, and gender. The benefit segments versus current brand produced the following table. Crosstab

Current Brand

Aqua-fresh Colgate Crest Others Total

Count 4 5 6 4 19 Cavity fighter % within Cluster Number of Case

21.1% 26.3% 31.6% 21.1% 100.0%

Count 1 7 2 3 13 White teeth % within Cluster Number of Case

7.7% 53.8% 15.4% 23.1% 100.0%

Count 5 8 17 8 38


Balanced Paste % within


13.2% 21.1% 44.7% 21.1% 100.0%

Count 10 20 25 15 70 Total % within Cluster Number of Case

14.3% 28.6% 35.7% 21.4% 100.0%

Aquafresh has a large proportion of cavity fighters; Colgate has a large proportion of white-teeth seekers; and Crest has a large proportion of the balanced paste segment. Once again the chi-square is not significant. We need to take these results with a pinch of salt.


Chi-Square Tests


(2-sided) Pearson Chi-Square 7.213(a) 6 .302 Likelihood Ratio 7.038 6 .317 Linear-by-Linear Association .674 1 .412

N of Valid Cases 70

a 6 cells (50.0%) have expected count less than 5. The minimum expected count is 1.86.


Chapter 5 Correspondence Analysis

Objectives:

• To understand the basic nature of correspondence analysis.

• To conduct correspondence analysis using SPSS and interpret the results. What is Correspondence Analysis? Correspondence analysis is typically used to get a graphical representation of contingency tables. Suppose we did a sample study in which we obtained the income level and the brand used by 36 respondents. The following table summarizes the responses.

Brand Brand A Brand B Brand C Brand D Total

Less than $1000 5 2 1 1 9

$1000 to $3000 2 4 1 2 9

$3001 to $5000 2 2 4 1 9

Income

Above $5000 2 1 1 5 9 Total 11 9 7 9 36

Data Preparation: To get a better insight on the relationship between income and brand used, we could use correspondence analysis. To run correspondence analysis using SPSS we need to have the data organized in the following format.


Income Income

Code Brand Brand

Code Frequency

Less Than $1000 1 Brand A 1 5 Less Than $1000 1 Brand B 2 2 Less Than $1000 1 Brand C 3 1 Less Than $1000 1 Brand D 4 1 $1000 to $ 3000 2 Brand A 1 2 $1000 to $ 3000 2 Brand B 2 4 $1000 to $ 3000 2 Brand C 3 1 $1000 to $ 3000 2 Brand D 4 2 $3001 to $5000 3 Brand A 1 2 $3001 to $5000 3 Brand B 2 2 $3001 to $5000 3 Brand C 3 4 $3001 to $5000 3 Brand D 4 1 Above $5000 4 Brand A 1 2 Above $5000 4 Brand B 2 1 Above $5000 4 Brand C 3 1 Above $5000 4 Brand D 4 5

SPSS Commands: The same data has been used to create a data file corres1.sav. Use the following SPSS commands to run correspondence analysis. DATA WEIGHT CASES WEIGHT CASES BY Drag `freq’ to FREQUENCY VARIABLE OK ANALYZE DATA REDUCTION CORRESPONDENCE ANALYSIS Drag `income’ to ROW VARIABLE DEFINE RANGE MINIMUM VALUE `1’ MAXIMUM VALUE `4’ UPDATE CONTINUE Drag `brand’ to COLUMN VARIABLE DEFINE RANGE MINIMUM VALUE `1’ MAXIMUM VALUE `4’ UPDATE CONTINUE OK


Interpretation of Results: The data we used as input for the analysis is printed in the correspondence table. Correspondence Table

BRAND INCOME Brand A Brand B Brand C Brand D Active Margin Less Than $1000 5 2 1 1 9 $1000 to $3000 2 4 1 2 9 $3001 to $ 5000 2 2 4 1 9 Above $5000 2 1 1 5 9 Active Margin 11 9 7 9 36

From the summary table we can infer that the first two dimensions account for 81.4% of inertia. This is pretty similar to the eigenvalues in factor analysis. It is enough to work with two dimensions.

Summary

Dimension

Singular Value Inertia

Chi Square Sig. Proportion of Inertia

Accounted

for Cumulati

ve

1 .427 .182 .497 .497 2 .341 .116 .317 .814 3 .261 .068 .186 1.000 Total .367 13.201 .154(a) 1.000 1.000

a 9 degrees of freedom Correspondence analysis decomposes the original matrix into row and column points.

Overview Row Points(a)

Score in Dimension Contribution

Of Point to Inertia of Dimension

Of Dimension to Inertia of Point

INCOME Mass 1 2 Inertia 1 2 1 2 Total Less Than $1000 .250 .310 .801 .080 .056 .470 .128 .682 .810

$1000 to $3000 .250 .020 .245 .053 .000 .044 .001 .096 .097

$3001 to $ 5000 .250 .717 -.764 .106 .301 .428 .518 .469 .987

Above $5000 .250 -1.047 -.282 .127 .642 .058 .920 .053 .974 Active Total 1.000 .367 1.000 1.000


The score in Dimension gives the co-ordinates for the row variables in the joint plot. In the same way you will find the co-ordinates for the column variables in the net matrix.

Overview Column Points(a)

Score in Dimension Contribution Of Point to Inertia of

Dimension Of Dimension to Inertia of Point

BRAND Mass 1 2 Inertia 1 2 1 2 Total Brand A .306 .198 .640 .068 .028 .367 .075 .627 .702 Brand B .250 .283 .251 .059 .047 .046 .146 .092 .238 Brand C .194 .720 -.960 .107 .236 .526 .402 .571 .972 Brand D .250 -1.085 -.287 .133 .689 .060 .947 .053 1.000 Active Total 1.000 .367 1.000 1.000

Using the coordinates for the row and column co-ordinates the program produces a joint map which is given below.

Dimension 1

1.0 .5 0.0 -.5 -1.0 -1.5

Dim

ensi

on 2

1.0

.5

0.0

-.5

-1.0

BRAND INCOME

Brand D

Brand C

Brand B

Brand A

Above $5000

$3001 to $ 5000

$1000 to $3000

Less Than $1000


From the chart it is very clear that there is a one-to-one relationship between brand used and the income category. It also shows that Brand A and B are closer to each other than the other brands. Toothpaste Data: Now let us turn our attention to the brand-personality association data collected in the toothpaste study. The data has been arranged in the file correspond.sav Open file corresponds, study the way the data is arranged and run the following SPSS Commands. DATA WEIGHT CASES WEIGHT CASES BY Drag `freq’ to FREQUENCY VARIABLE OK ANALYZE DATA REDUCTION CORRESPONDENCE ANALYSIS Drag `attri’ to ROW VARIABLE DEFINE RANGE MINIMUM VALUE `1’ MAXIMUM VALUE `11’ UPDATE CONTINUE Drag `brand’ to COLUMN VARIABLE DEFINE RANGE MINIMUM VALUE `1’ MAXIMUM VALUE `3’ UPDATE CONTINUE OK


Notice that 100 percent inertia has been accounted for the first two dimensions. It is enough to work with two dimensions. Summary

Proportion of Inertia Confidence Singular

Value Correlati

on Dimension

Singular Value Inertia

Chi Square Sig.

Accounted for

Cumulative

Standard

Deviation 2

1 .286 .082 .793 .793 .035 .043 2 .146 .021 .207 1.000 .036 Total .103 72.795 .000(a) 1.000 1.000

a 20 degrees of freedom The resulting correspondence map can be taken to PowerPoint and annotated as given below.

q Crest is seen to be used by Ambitious Achievers. q Colgate is seen to be used by the Traditional, Overcautious, Masculine person. q Aquafresh is seen as Fun-loving, Feminine and Outgoing. q There is no brand available for Romantic, sensuous types… . Here is an

opportunity for a new product.

Dimension 1 1.0 .5 0.0 -.5 -1.0

Dim

ensi

on 2

.8

.6

.4

.2

0.0

-.2

-.4

-.6

-.8

Brand

Personality Trait

Crest

Colgate Aquafresh

Masculine

Feminine

Overcautious

Ambitious

Traditional

Romantic

Achiever

Fun Loving

Hedonist

Sensuous Outgoing


Correspondence analysis is a powerful tool for visualization of data from contingency tables. It has no restrictions on the sample size or on the scale used. Association between any two categorical variables can be easily analyzed using this technique.


Chapter 6 Regression Analysis

Objectives:

• To understand the meaning of regression.

• To conduct simple regression and interpret the results.

• To conduct multiple regression and interpret the results.

• To understand the problem of multi-collinearity and a method to overcome the same.

Simple Regression: Open the file regression.sav Study the file structure to understand that it is the same field which was used for factor analysis along with three new variables corresponding to the factor scores that we created using factor analysis. Variables q06_01 to q06_10 refer to rating scores for different brands on different attributes. Variable q06_11 correspond to overall rating given to different brands. We are going to fit regression equations with overall rating as dependent variable and attribute ratings as independent variables. Linear regression refers to fitting of a linear mathematical model between one dependent variable and one or more independent variables. We shall first conduct a regression analysis using just two variables. Use the following SPSS commands to fit a regression model with Fighting Cavities (q06_01) as independent variable and Overall Rating (q06_11) as the dependent variable. ANALYZE REGRESSION LINEAR Drag Fighting Cavities (q06_01) to INDEPENDENT Drag Overall Rating(q06_11) to DEPENDENT OK Take a look at the Coefficients table.


Coefficients(a)

Unstandardized Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig. (Constant) 3.771 .477 7.905 .000 1 Fighting Cavities .505 .059 .521 8.576 .000

a Dependent Variable: Overall rating The unstandardized coefficients give the linear mathematical model: Y = 3.771 + 0.505 X Y - Overall rating X – Fighting Cavities The strength of relationship between the independent and dependent variables is given by the correlation coefficient R given in the Model Summary table. Model Summary

Model R R Square Adjusted R

Square Std. Error of the Estimate

1 .521(a) .272 .268 1.21679 a Predictors: (Constant), Fighting Cavities The value of R2 0.272 is somewhat low. Normally an R2 value of 0.7 and above is supposed to signify a strong relationship. In the present case we cannot rule out that there is no relationship between the two variables as the F value is significant in the ANOVA Table and the t-value corresponding to the variable Fighting Cavities is significant in the coefficients Table. ANOVA(b)

Model Sum of

Squares df Mean Square F Sig. Regression 108.880 1 108.880 73.540 .000(a)

Residual 291.672 197 1.481

1

Total 400.553 198 a Predictors: (Constant), Fighting Cavities b Dependent Variable: Overall rating


Coefficients(a)



Model B Std. Error Beta t Sig. (Constant) 3.771 .477 7.905 .000 1 Fighting Cavities .505 .059 .521 8.576 .000

a Dependent Variable: Overall rating In the same way, conduct a simple regression of each rating variable with the dependent variable and interpret the results. Multiple Regression: Now we shall conduct regression analysis with all the 10 attribute ratings as independent variables. ANALYZE REGRESSION LINEAR Drag variables q06_01 to q06_10 to INDEPENDENT Drag Overall Rating(q06_11) to DEPENDENT OK Note that the R2 value dramatically improves to 0.743 Model Summary



1 .862(a) .743 .730 .74117 a Predictors: (Constant), Innovative features/Ingredients, Fighting Cavities, Likeable Flavor, Attractive Packaging, Whitening Teeth, Brand Image, Freshening Breath, Color, Cleaning Stains/Tartar, Good Taste From the coefficients table, we can construct the following mathematical model to depict the relationship between the 10 independent variables and the overall rating. Y = 0.598 + 0.208X1 + 0.097X2 + 0.016X3 + 0.109X4 + 0.060X5 + 0.150X6 + 0.135X7 + 0.154X8 – 0.096X9 + 0.120X10 The negative sign for variable nine (Attractive Packaging) connotes that the same has a negative relationship with overall rating. That is, the paste receiving higher rating on attractive packaging has a diminishing effect on the overall rating.


Coefficients(a)



Model B Std. Error Beta t Sig. (Constant) .598 .349 1.716 .088 Fighting Cavities .208 .048 .215 4.368 .000

Whitening Teeth .097 .037 .130 2.606 .010

Cleaning Stains/Tartar .016 .052 .018 .307 .759

Good Taste .109 .072 .154 1.513 .132 Likeable Flavor .060 .071 .086 .844 .400 Freshening Breath .150 .045 .173 3.311 .001

Brand Image .135 .038 .195 3.582 .000 Color .154 .037 .238 4.187 .000 Attractive Packaging -.096 .041 -.132 -2.361 .019

1

Innovative features/Ingredients

.120 .033 .181 3.660 .000

a Dependent Variable: Overall rating Now the question arises as to which variable is contributing more to the dependent variable. This can be inferred by looking at the standardized coefficients. From the β values we can see that the most important variable is Color (0.238) and the second important variable is fighting cavities (0.215) and so on From a statistical standpoint also see which are the variables that are significant. The significance of β is given by the t-values. Note that only the following variables are significant at 95 percent confidence level.

• Fighting cavities (0.000) • Freshening breath (0.001) • Brand Image (0.000) • Color (0.000) and • Innovative features (0.000)

Other variables have significance values above 0.05. Can we conclude that other variables are not important? We cannot say that unless we are sure that the independent variables are not correlated among them.


Multicollinearity: When the independent variables are correlated among themselves, we call it a problem of multicollinearity. Consider the variables Good Taste and Likeable Flavor. They are highly correlated among themselves. ANALYZE CORRELATE BIVARIATE Drag Good Taste and Likeable Flavor to VARIABLES OK You will get the following output. Correlations

Good Taste Likeable Flavor

Pearson Correlation 1 .932(**)

Sig. (2-tailed) . .000

Good Taste

N 202 201 Pearson Correlation .932(**) 1

Sig. (2-tailed) .000 .

Likeable Flavor

N 201 201 ** Correlation is significant at the 0.01 level (2-tailed). The correlation coefficient between the two variables is as high as 0.932. Now we shall use only these two variables as independent variable and conduct multiple regressions. ANALYZE REGRESSION LINEAR Drag variables Good Taste and Likeable Flavor to INDEPENDENT Drag Overall Rating(q06_11) to DEPENDENT OK The output indicates that only the variable good taste is significant (t = 3.762 and sig. = 0.000) and Likeable flavor is not significant (t = 0.431 and sig. = 0.667). Can we conclude here that likeable flavor is not an important attribute? No. Since the two variables are highly correlated, the β values get distorted. As these two are highly correlated, only one of them attains significance.


Coefficients(a)



Model B Std. Error Beta t Sig. (Constant) 4.547 .301 15.086 .000 Good Taste .391 .104 .566 3.762 .000

1

Likeable Flavor .044 .101 .065 .431 .667

a Dependent Variable: Overall rating To get over the problem of multi-collinearity, we could factor analyze the independent variables and work with the resulting new factors. Using Factor Scores: Refer to the module on factor analysis where we reduced the original ten variables to three new factors, which were named as Dental Hygiene, Visibility and Sensory benefits. The resulting factor scores for these new variables have been saved in the regression.sav file. We shall now conduct a multiple regression with these three new variables as independent variables and overall rating as the dependent variable. ANALYZE REGRESSION LINEAR Drag variables Dental Hygiene, Visibility and Sensory Benefits to INDEPENDENT Drag Overall Rating(q06_11) to DEPENDENT OK We get an R2 value of 0.710 which shows that the relationship is quite strong between the three factors and the overall rating. Model Summary



1 .842(a) .710 .705 .77397 a Predictors: (Constant), sensory benefits, visibility, dental hygiene


From the coefficients table we see that all three variables are significant. From the β values we can conclude that dental hygiene is the most important attribute, followed by sensory benefits and then visibility. Coefficients(a)



Model B Std. Error Beta t Sig. (Constant) 7.803 .055 141.025 .000 dental hygiene .774 .056 .538 13.824 .000

Visibility .612 .055 .429 11.028 .000

1

sensory benefits .684 .057 .465 11.937 .000

a Dependent Variable: Overall rating The resulting regression equation is: Y = 7.803+ 0.774 (Dental Hygiene) + 0.612 (Visibility) + 0.684 (Sensory Benefits) In this case we used ‘overall rating’ as the dependent variable. If we get respondents ‘intention to buy’ score on a rating scale, the same could also be used as a dependent variable. We could then build a regression equation model to predict the intention to buy rating from the attribute rating scores.


Chapter 7

Discriminant Analysis

Objectives: • To understand what discriminant analysis is. • To run discriminant analysis and interpret the results:

- discriminant function - discriminant score - Wilke’s lambda - canonical correlation - structure matrix - standardized and unstandardized discriminant coefficients - cut-off point - confusion matrix

• To understand the use of discriminant analysis to identify Brand Drivers. Basic Ideas: We shall use the disc_aquafresh.sav file to understand how to use dicriminant analysis. Discriminant analysis is a dependent technique where the dependent variable is categorical in nature. The variables 1 – 10 in the file refer to attribute ratings for the brand Aquafresh. 11-13 are the factor scores obtained from earlier analysis (refer factor analysis). We need this information as we may face the same problem of multi-collinearity as we experienced in multiple regression (refer to the section on multiple regression). We are going to use the first 10 variables as independent variables and later use 11-13, the factor scores for the three new factors. The dependent variable is whether the respondent is a user of Aquafresh or not. This is a transformed variable developed from the current and previous brand variables in the master data file. Note that the dependent variable is dichotomous and we are going to fit a model that will help us predict whether someone is a user or non-user of Aquafresh. Use the following SPSS commands.


SPSS Commands: ANALYZE CLASSIFY DISCRIMINANT Drag usage to GROUPING VARIABLE DEFINE RANGE Type 1 MINIMUM VALUE Type 2 MAXIMUM VALUE CONTINUE Drag Variables 1-10 to INDEPENDENTS STATISTICS MEANS Check UNSTANDARDIZED COEFFICINETS CONTINUE CLASSIFY Check COMPUTE FROM GROUP SIZES Check SUMMARY TABLE CONTINUE SAVE Check PREDICTED GROUP MEMBERSHIP Check DISCRIMINANT SCORE CONTINUE OK

Groups Fighting Cavities

White-ning Teeth

Cleaning Stains/ Tartar

Good Taste

Likeable Flavor

Fresh-ening Breath

Brand Image Color

Attra-ctive Pack-aging

Inno-vative feat-ures

Users 7.95 6.65 7.20 7.10 7.30 7.30 6.25 6.85 6.95 6.20 Non Users 6.74 5.76 6.39 6.82 6.87 7.45 6.95 7.16 7.29 6.53 Overall 7.16 6.07 6.67 6.91 7.02 7.40 6.71 7.05 7.17 6.41

Notice the difference in the ratings given by users and non-users. In the first five attributes, the users have given a higher rating for Aquafresh and in the last five attributes non-users have given a marginally higher rating for Aquafresh. Canonical Correlation and Wilke’s Lambda: In the next Table, find something called canonical correlation. The interpretation of this is similar to the R value we had in regression analysis.


Eigenvalues

Function Eigenvalue % of Variance Cumulative % Canonical Correlation

1 .662(a) 100.0 100.0 .631 a First 1 canonical discriminant functions were used in the analysis. The next Table shows Wilke’s λ, which can be interpreted the same way as the F-test in the multiple regression output. The discriminant equation that has been constructed is significant. Wilks' Lambda

Test of Function(s) Wilks'

Lambda Chi-square df Sig. 1 .602 25.919 10 .004

Discriminant Function and Classification: Now take a look at the canonical discriminant function coefficients (unstandardized coefficients). Using that we can form the discriminant function shown below. D = - 3.041 + 0.702X1 + 0.324X2 + 0.081X3 + 0.056X4 + 0.167X5 - 0.427X6 -0.491X7 + 0.002X8 + 0.201X9 - 0.165X10 Canonical Discriminant Function Coefficients

Function 1 Fighting Cavities .702 Whitening Teeth .324 Cleaning Stains/Tartar .081 Good Taste .056 Likeable Flavor .167 Freshening Breath -.427 Brand Image -.491 Color .002 Attractive Packaging .201 Innovative features/Ingredients -.165

(Constant) -3.041 Unstandardized coefficients


Using this kind of an equation, a discriminant score is calculated for each respondent and the program uses a cut-off point to classify respondents as users or non-users. Now go to the data file and find two new variables added there – predicted group (dis_1) and discriminant scores (dis1_1). Change to data view and find the discriminant scores. To compute the cut-off point, calculate the mean discriminant score for each group and use the following formula: Cut-off point = (N2D1 + N1D2)/( N1 + N2) where: N1 = No. of Users N2 = No. of Non Users D1 = Average Discriminant Score for Users D2 = Average Discriminant Score for Non Users These values are available in the output in the table: Functions at Group Centroids. Functions at Group Centroids

Function User of Aquafresh 1 user 1.102 Non-user -.580

Unstandardized canonical discriminant functions evaluated at group means We know from the next table that we have 20 users and 38 non-users.

Cases Used in Analysis

User of Aqua- fresh Prior Unweighted Weighted user 0.345 20 20 Non-user 0.655 38 38 Total 1 58 58

We can calculate the cut-off point as 0.522. Respondents with discriminant scores above 0.522 are classified as users and the others are classified as non-users.


Confusion Matrix: If we cross-tabulate the actual versus predicted, we can get the extent of misclassification. Classification Results(a)

Predicted Group Membership

User of Aquafresh user Non-user Total User 14 6 20 Count Non-user 5 33 38 User 70.0 30.0 100.0

Original

% Non-user 13.2 86.8 100.0

a 81.0% of original grouped cases correctly classified. From the output we can see that the classification accuracy is 81%. The above table is also known as a confusion matrix. Structure Matrix: Now to determine which variables were effective in discriminating the users from non-users, we could look at the Table of Standardized Canonical Discriminant Coefficients. These coefficients can be interpreted the same way as the β values were interpreted in multiple regression. Fighting cavities appears to be the most important in predicting whether a respondent is an Aquafresh user or not. The least important one seems to be color. This has to be taken with a pinch of salt as the coefficients can get distorted if the independent variables are correlated. Standardized Canonical Discriminant Function Coefficients

Function 1 Fighting Cavities .986 Whitening Teeth .589 Cleaning Stains/Tartar .132 Good Taste .129 Likeable Flavor .402 Freshening Breath -.831 Brand Image -.903 Color .006 Attractive Packaging .405 Innovative features/Ingredients -.359


When the independent variables are correlated among themselves, it is safer to look at the Structure Matrix. Structure Matrix

Function 1 Fighting Cavities .514 Cleaning Stains/Tartar .296 Whitening Teeth .290 Brand Image -.225 Likeable Flavor .107 Attractive Packaging -.100 Innovative features/Ingredients -.089

Good Taste .073 Color -.068 Freshening Breath -.045

Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. These values in the Structure Matrix can be interpreted much the way as factor loadings in factor analysis. For example, if you correlate `fighting cavities’ with discriminant score you will get 0.514. The attributes are arranged in their order of importance. The attributes that differentiate Aquafresh users from others are ‘fighting cavities’ and ‘cleaning stains/tartar.’ These are strong dental hygiene factors.


Using Factors Score in Discriminant Analysis: However, it is better to run the analysis with factor scores, as we already have done factor analysis. Use the following commands. ANALYZE CLASSIFY DISCRIMINANT Drag usage to GROUPING VARIABLE DEFINE RANGE Type 1 MINIMUM VALUE Type 2 MAXIMUM VALUE CONTINUE Drag Variables 11-13 to INDEPENDENTS STATISTICS MEANS Check UNSTANDARDIZED COEFFICINETS CONTINUE CLASSIFY Check COMPUTE FROM GROUP SIZES Check SUMMARY TABLE CONTINUE SAVE Check PREDICTED GROUP MEMBERSHIP Check DISCRIMINANT SCORE CONTINUE OK The prediction accuracy is only 65.5. Classification Results(a)

Predicted Group Membership

User of Aquafresh user Non-user Total user 11 9 20 Count Non-user 11 27 38 user 55.0 45.0 100.0

Original

% Non-user 28.9 71.1 100.0

a 65.5% of original grouped cases correctly classified.


Structure Matrix

Function 1 dental Hygiene .864 visibility -.620 sensory benefits .148

Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. Standardized Canonical Discriminant Function Coefficients

Function 1 dental Hygiene .773 visibility -.483 sensory benefits .223

In terms of importance of attributes, we find that `dental hygiene’ is the most discriminating attribute for Aquafresh. Also find that there is no contradictory interpretations arising from standardized discriminant coefficients and the structure matrix. Brand Drivers: The implication for the brand is that dental hygiene is the driver attribute for Aquafresh. They should ensure that the rating on the same attribute does not go down. Conduct discriminant analysis for Colgate and Crest using files disc_colgate.sav and disc_crest.sav and identify the brand drivers.


Chapter 8 Multidimensional Scaling

Objectives: • To understand the basic concepts behind multidimensional scaling. • To learn how to conduct multidimensional scaling analysis using SPSS and interpret

the results: - meaning of stress - stress decomposition - interpreting the dimensions

• To understand how to use multiple regression to fit vectors to the multidimensional

space. Basic Concepts: This is a techniques used to identify the underlying dimensions on which a set of stimuli are differentiated based on the similarity-dissimilarity rating of the stimuli. The stimuli could be brands or some other objects or people or even countries. Suppose we get people to rate a set of six countries on a 0-10 scale of similarity-dissimilarity with 0 when the countries are identical and 10 when they are diametrically opposite to each other. The following is the matrix generated from a single respondent. England USA France Germany India China England 0 2 9 8 8 9 USA 2 0 8 9 9 8 France 9 8 0 1 8 8 Germany 8 9 1 0 8 9 India 8 9 8 8 0 1 China 9 8 8 1 9 0 The above data has been arranged in the form required for analysis in the mds_sample.sav file. Open the same file and study the file structure. The rows and columns are defined by row_id and col_id. The corresponding ratings are given under the variable rating. Run the following SPSS commands and study the output.


ANALYZE SCALE MULTIDIMENSIONAL SCALING (PROXCAL) Check THE PROXIMITIES ARE IN A SINGLE COLUMN DEFINE Drag variable rating to PROXIMITIES Drag row_id to ROWS Drag col_id to COLUMNS MODEL Check INTERVAL CONTINUE OUTPUT Check INPUT DATA Check ITERATION HISTORY CONTINUE OK You will notice that the input data used by us has been reproduced in the Input data matrix. Proximities

England USA France Germany India China England . USA 2.000 . France 9.000 8.000 . Germany 8.000 9.000 1.000 . India 8.000 9.000 8.000 8.000 . China 9.000 8.000 8.000 9.000 1.000 .

The next table shows the improvement in stress value. Stress in MDS refers to the extent of model misfit. It is through an iterative process that the program arrives at the configuration that best fits the dissimilarity rating that we have input into the program. Iteration History

Iteration Normalized Raw Stress Improvement

0 .09053(a) 1 .00423 .08630 2 .00397 .00025 3 .00388 .00009(b)

a Stress of initial configuration: simplex start. b The iteration process has stopped because Improvement has become less than the convergence criterion.


Stress value of less than 0.01 is considered to be very good. Stress values up to 0.03 are acceptable. The stress value of 0.00388 for the current data shows that the model has given a good fit. The program has found a solution in two dimensions and the co-ordinates of the stimuli (countries) are given in the final coordinates matrix. The co-ordinates are plotted on a graph too. Final Coordinates

Dimension 1 2 England .656 -.057 USA .656 .057 France -.337 -.518 Germany -.319 -.572 India -.337 .518 China -.319 .572

A cursory look at the chart shows that the countries that are similar to each other are placed together and the ones that are different from each other are placed apart from each other. Notice that there are three clusters – USA & England, France & Germany and India & China.

Object Points

Common Space

Dimension 1

.8.6.4.20.0-.2-.4

Dim

ensi

on 2

.6

.4

.2

-.0

-.2

-.4

-.6

ChinaIndia

GermanyFrance

USA

England


In order to interpret the chart we shall take this to PowerPoint. Draw lines passing through the origin. Now the next task is to uncover the meaning of the underlying two dimensions.

In order to get better sense out of this graph, rotate the x-axis and y-axis as shown in the next chart.

Dimension 1

.8 .6 .4 .2 0.0 -.2 -.4

Dim

ensi

on 2

.6

.4

.2

-.0

-.2

-.4

-.6

China

India

Germany France

USA

England


Now you can clearly see that the x-axis refers to support for Iraq war and the y-axis refers to economic development. Here we used a judgmental approach to interpreting the dimensions. It is possible to use multiple regression to fix attributes to the above chart, which we shall see later. It raises another question as to how many dimensions will be needed. In general MDS is used mostly for a two dimensional solution. If the researcher has a priori knowledge that more than two dimensions may be required then it has to be planned at the data collection stage itself. If you need more dimensions you will need to get rating on more number of stimuli. The rule is that for every dimension you need a minimum of three stimuli. If you need a three dimensional solution you need to have at least 9 stimuli (in this case countries) rated on a dissimilarity scale. However, if we have data collected from multiple respondents we use the criteria of Stress. If the stress (measure of mis-fit) value is more than 0.03 then go for more dimensions. Here we had only one respondent rating the countries. In a survey typically we will have several respondents rating the stimuli. In the case of tooth-paste survey, we had a total of 43 valid responses to the MDS question (i.e. question 8).

Dimension 1

.8 .6 .4 .2 0.0 -.2 -.4

Dim

ensi

on 2

.6

.4

.2

-.0

-.2

-.4

-.6

China

India

Germany France

USA

England


I have arranged those 43 responses in 43 columns in the data file md2.sav. Now open this file and study the file structure. ANALYZE SCALE MULTIDIMENSIONAL SCALING (PROXCAL) Check MULTIPLE MATRIX SOURCES Check THE PROXIMITIES ARE IN A SINGLE COLUMN DEFINE Drag variables resp01 to resp69 to PROXIMITIES Drag row_id to ROWS Drag col_id to COLUMNS MODEL Check INTERVAL CONTINUE OUTPUT Check INPUT DATA Check ITERATION HISTORY Check STRESS DECOMPOSITION CONTINUE OK This time we try to fit a model by taking into account the 43 responses. Note that a stress value of 0.05555 has been achieved. The program has gone through 21 iterations to arrive at the final configuration. Iteration History

Iteration Normalized Raw Stress Improvement

0 .12660(a) 1 .08510 .04150 2 .07399 .01111 3 .06833 .00566 4 .06493 .00340 5 .06273 .00220 6 .06126 .00147 7 .06024 .00102 8 .05949 .00075 9 .05891 .00059 10 .05841 .00050 11 .05797 .00044 12 .05756 .00041 13 .05719 .00037 14 .05686 .00034


15 .05656 .00030 16 .05630 .00026 17 .05608 .00022 18 .05591 .00018 19 .05576 .00014 20 .05565 .00012 21 .05555 .00009(b)

a Stress of initial configuration: simplex start. b The iteration process has stopped because Improvement has become less than the convergence criterion. Stress value of 0.05555 is a little too high to make any meaningful interpretation. You will notice another large table that gives the stress decomposed for every individual. Notice that respondents 6,20,23,38,39,41,47,58 and 6y9nhave stress values above 0.07. If we leave out these respondents and run MDS analysis, we can possibly get a solution with lowered stress values. Alternately, we may try for a solution with more than two dimensions. Let us get a solution in three dimensions. Repeat the above analysis using the following commands. ANALYZE SCALE MULTIDIMENSIONAL SCALING (PROXCAL) Check MULTIPLE MATRIX SOURCES Check THE PROXIMITIES ARE IN A SINGLE COLUMN DEFINE Drag variables resp01 to resp69 to PROXIMITIES Drag row_id to ROWS Drag col_id to COLUMNS MODEL Check INTERVAL Check WEIGHTED EUCLIDIAN Change 2 to 3 MINIMUM DIMENSIONS Change 2 to 3 MAXIMUM DIMENSIONS CONTINUE OUTPUT Check INPUT DATA Check ITERATION HISTORY Check STRESS DECOMPOSITION CONTINUE OK Note that we are now asking for 3 dimensions. We get a much lower stress value of 0.02319 which is an acceptable level of error in this type of analysis.


Stress and Fit Measures Normalized Raw Stress .02319 Stress-I .15229(a) Stress-II .98328(a) S-Stress .09732(b) Dispersion Accounted For (D.A.F.) .97681

Tucker's Coefficient of Congruence .98834

PROXSCAL minimizes Normalized Raw Stress. a Optimal scaling factor = 1.024. b Optimal scaling factor = .961. We also get three sets of co-ordinates for the brands. Final Coordinates

Dimension 1 2 3 Aquafresh -.399 .164 -.483 Crest .486 -.189 .360 Colgate .422 -.018 -.471 Mentadent -.482 .063 .408 Arm & Hammer .147 .595 .187 Pepsodent -.175 -.614 -.002

We could plot two at a time to study the relative positioning of brands. However we‘ll need to interpret the dimensions. We used a subjective method of interpretation in the case of six countries. It will be difficult in the case of toothpaste brands as we have a three dimensional solution. We shall use regression method to fit attributes on to the same map where the brands are positioned. Use file mds4.sav to get the co-ordinates for the attributes. Note that the mean ratings of brands on different attributes and three factors are going to be used for this analysis. Perform a multiple regression analysis with the mean rating of attributes as dependent variable and the co-ordinates for the brands as independent variables. The resulting β values give the three co-ordinates for the attributes concerned. ANALYZE REGRESSION LINEAR


Drag Fighting Cavities to DEPENDENT Highlight and drag Dim1, dim2 and dim3 to INDEPENDENT OK The β values given in the coefficients table give the three co-ordinates for the variable `fighting cavities’. Coefficients(a)



Model B Std. Error Beta t Sig. (Constant) 8.297 .421 19.718 .032 Dimension 1 .144 .953 .078 .151 .905

Dimension 2 .701 1.470 .247 .477 .717

1

Dimension 3 1.562 .945 .831 1.652 .347

a Dependent Variable: Fighting Cavities In the same way the coordinates for all the attributes are arrived at and a new data file mds5.sav has been created. This new file contains the coordinates for all the brands and attributes. Now we can plot and interpret the dimensions. GRAPH SCATTER SIMPLE DEFINE Drag dim1 to X-AXIS Drag dim2 to Y-AXIS Drag attri to LABEL CASES BY OPTIONS Check DISPLAY CHART WITH CASE LABELS CONTINUE OK You will get the scatter plot which will have the brands and attributes on the same space. The same can be taken to a PowerPoint and draw vectors for the attributes as shown in the chart below.


Only the long vectors belong to this space. Attributes A3, A1, A6 etc. are too short and need not be considered while interpreting the meaning of Dimensions 1 and 2. Also we can rotate the axis as shown in the picture to get better meaning of the space. You can see that Dimension 1 is basically attribute A7, namely Band Image and Dimension 2 is comprised of attributes A4 and A5 (Good Taste and Likeable Flavor). Crest and Colgate are seen to have a good brand Image. Pepsodent is high on Dimension 2, namely flavor and taste. This result is consistent with the earlier findings. To name dimension 3, plot dim1 Vs dim3. From the plot notice that Dimension 3 comprises of A8 + A9 and A2 + A1 (Color + Attractive Packaging & Fighting cavities + white teeth). Dimension 1 is clearly made up of A7 (brand image) only. In the same way plot dim2 Vs dim3. Dimension 2 comes out as A4 and A5 (Taste and flavor) and dim 3 remains as a combination of four attributes A1, A2, A8 and A9.

Dimension 1

.6 .4 .2 0.0 -.2 -.4 -.6 -.8

Dim

ensi

on 2

.8

.6

.4

.2

.0

-.2

-.4

-.6

-.8 -1.0

A10

A9

A8

A7

A6

A5

A4

A3 A A1

Sensory Benefits

Visibility

Dental Hygiene

Pepsodent

Arm & Hammer

Mentadent Colgate

Crest

Aquafresh

A1. Fighting cavities A2. Whitening Teeth A3. Cleaning Stains A4. Good Taste A5. Likeable Flavor A6. Freshening Breath A7. Brand Image A8. Color A9. Attractive Pack A10. Innovations


Dimension 1

.6 .4 .2 0.0 -.2 -.4 -.6 -.8

Dim

ensi

on 3

1.0

.8

.6

.4

.2

-.0 -.2

-.4

-.6

A1 A9

A8

A7

A6

A5

A4

A3 A2 A1

Sensory Benefits

Visibility Dental Hygiene

Pepsodent Arm & Hammer

Mentadent

Colgate

Crest

Aquafresh


Dimension 2

.8 .6 .4 .2 .0 -.2 -.4 -.6 -.8 -1.0

Dim

ensi

on 3

1.0 .8

.6

.4

.2

-.0

-.2

-.4

-.6

A10

A9 A8

A7

A6

A5

A4

A3 A2 A1

Sensory Benefits

Visibility Dental Hygiene

Pepsodent

Arm & Hammer

Mentadent

Colgate

Crest

Aquafresh



QUESTIONNAIRE ON TOOTH-PASTE BRANDS

This research study is conducted purely for academic purposes only. As the interest is on the collective opinion of the group as a whole, individual identity will not be revealed. Please have the questionnaire filled-in and returned to the instructor. 1. Which of the tooth paste brands listed in the table below question 4 are you aware of? (Please put ?

marks in the appropriate boxes in the Table) 2. Which of these have you tried ever? 3. What brand of tooth paste are you currently using? (If you use more than one brand, please mark `M’

by the side of the most used brand) 4. What was the brand that you used immediately prior to this brand?

Brand

Question 1 Question 2 Question 3 Question 4

Aqua-fresh ? ? ? ? Colgate ? ? ? ? Crest ? ? ? ? Mentadent ? ? ? ? Arm & Hammer ? ? ? ? Pepsodent ? ? ? ? Others1 ________________ ? ? ? ? Others2 ________________ ? ? ? ?

5. Please indicate the relative importance of the following factors in terms of choosing a brand using a seven-point rating scale. (Please circle the appropriate number to indicate the importance to you)

Factor

Not Important Very Important

a. Fighting cavities 1 2 3 4 5 6 7 b. Whitening teeth 1 2 3 4 5 6 7 c. Cleaning Stains/Tartar 1 2 3 4 5 6 7 d. Good taste 1 2 3 4 5 6 7 e. Likeable Flavor 1 2 3 4 5 6 7 f. Freshening Breadth 1 2 3 4 5 6 7 g. High Prestige Brand 1 2 3 4 5 6 7 h. American Dental Association Recommendation

1 2 3 4 5 6 7

i. Attractive Packaging 1 2 3 4 5 6 7 j. Innovative feature/new ingredient 1 2 3 4 5 6 7


6. Please rate Aqua-Fresh, Colgate and Crest on the following factors. If your `current brand’ is other than these three, please rate your current brand too. Use a 0-10 scale with 0 being poor and 10 being good on that attribute. Your response will be a number between 0 and 10 in the boxes below - indicating the extent to which that attribute is present in the brand being rated. (If you have never used any of these brands you may leave the corresponding column(s) blank.)

Attribute

a. Aqua-fresh b. Colgate c. Crest d. Current Brand

1.Fighting Cavities

2. Whitening teeth

3. Cleaning stains/Tartar

4. Good Taste

5. Likeable Flavor

6. Freshening Breath

7. Brand Image

8. Color

9. Attractive Packaging

10. Innovative features/Ingredients

11. Overall rating for brands(out of 10)

7. Kindly put check-marks in the boxes to indicate the personality traits of users that match with the

three brands of tooth pastes mentioned in the following table. You may also check more than one brand for a personality trait.

Personality Trait

a. Aqua-fresh b. Colgate c. Crest

1.Outgoing 2. Sensuous 3. Hedonist 4. Fun loving 5. Achiever 6. Romantic 7. Traditional 8. Ambitious 9. Overcautious 10. Feminine 11. Masculine


8. Please rate the following pairs of toothpaste brands from most similar pair (1) to most dissimilar pair (10). Circle the appropriate number. Please leave out the pairs containing brands that you are not aware of.

Brand Pair

Most Similar Most Dissimilar

a. Aquafresh - Crest 1 2 3 4 5 6 7 8 9 10 b. Aquafresh – Colgate 1 2 3 4 5 6 7 8 9 10 c. Aquafresh – Mentadent 1 2 3 4 5 6 7 8 9 10 d. Aquafresh – Arm & Hammer 1 2 3 4 5 6 7 8 9 10 e. Aquafresh – Pepsodent 1 2 3 4 5 6 7 8 9 10 f. Crest – Colgate 1 2 3 4 5 6 7 8 9 10 g. Crest – Mentadent 1 2 3 4 5 6 7 8 9 10 h. Crest – Arm & Hammer 1 2 3 4 5 6 7 8 9 10 i. Crest – Pepsodent 1 2 3 4 5 6 7 8 9 10 j. Colgate – Mentadent 1 2 3 4 5 6 7 8 9 10 k. Colgate – Arm & Hammer 1 2 3 4 5 6 7 8 9 10 l. Colgate – Pepsodent 1 2 3 4 5 6 7 8 9 10 m. Mentadent – Arm & Hammer 1 2 3 4 5 6 7 8 9 10 n. Mentadent – Pepsodent 1 2 3 4 5 6 7 8 9 10 o. Arm & Hammer – Pepsodent 1 2 3 4 5 6 7 8 9 10

9. Please indicate how likely are you to buy the following new toothpaste concepts by using a seven point

rating scale. Please circle the appropriate number that indicates your preference.

New Product Concept

Definitely Not Buy Will definitely Buy

a. Tooth paste in a big jar (like styling gel) 1 2 3 4 5 6 7 b. Make your own tooth-paste kit (with whitener, mouth wash, foaming agent, baking soda etc. in different tubes)

1 2 3 4 5 6 7

c. Caffeinated Tooth paste for a refreshing feeling 1 2 3 4 5 6 7 d. Toothpaste with weight-control formulae (make you feel full after brushing)

1 2 3 4 5 6 7

e. Toothpaste containing multi-vitamin 1 2 3 4 5 6 7 f. Spicy toothpaste (clove and/or cinnamon) 1 2 3 4 5 6 7 g. Transparent tube to see how much is left inside 1 2 3 4 5 6 7 h. Toothpaste containing colored beads and flavor crystals

1 2 3 4 5 6 7

i. Use and throw away paste-pre-applied tooth brushes (Better Hygiene)

1 2 3 4 5 6 7

j. Tube fitted with dispenser for right amount 1 2 3 4 5 6 7 k. Male /Female toothpaste 1 2 3 4 5 6 7 l. Toothpaste that doubles as shaving cream 1 2 3 4 5 6 7 m. Night paste with sleep inducers 1 2 3 4 5 6 7


10. Please rank the following 15 toothpaste concepts from most preferred (1) to least preferred (15). (Enter your preference ranks in the little eggs in every box) One easy strategy to make the ranking task easier is to do it in two stages. First mark concepts as H (High preference), L (Low Preference) and M (Medium preference) and then rank the cards within each category. It is also suggested that you assign equal number of cards to each category.

Concept 1 Tube Transparent Taste Mint Color White Brand Colgate Price $2

Concept 2 Tube Transparent Taste Spicy* Color Red Brand Aquafresh Price $2

Concept 3 Tube Transparent Taste Caffeinated Color Green Brand Colgate Price $4

Concept 4 Tube Transparent Taste Mint Color White Brand Crest Price $6

Concept 5 Tube Semi-transparent Taste Mint Color Red Brand Crest Price $4

Concept 6 Tube Semi-transparent Taste Spicy* Color White Brand Colgate Price $6

Concept 7 Tube Semi-transparent Taste Caffeinated Color White Brand Aquafresh Price $2

Concept 8 Tube Semi-transparent Taste Mint Color Green Brand Colgate Price $2

Concept 9 Tube Non-transparent Taste Mint Color Green Brand Aquafresh Price $6

Concept 10 Tube Non-transparent Taste Spicy* Color White Brand Colgate Price $4

Concept 11 Tube Non-transparent Taste Caffeinated Color White Brand Crest Price $2

Concept 12 Tube Non-transparent Taste Mint Color Red Brand Colgate Price $2

Concept 13 Tube Transparent Taste Spicy* Color Green Brand Crest Price $2

Concept 14 Tube Transparent Taste Caffeinated Color Red Brand Colgate Price $6

Concept 15 Tube Transparent Taste Mint Color White Brand Aquafresh Price $4

* `spicy taste’ could be due to the addition of clove or cinnamon. They also have medicinal properties apart from being natural ingredients (as opposed to chemicals).


11. Please indicate the extent of your agreement with the following statements using the Agree-Disagree scale given below. Strongly Disagree- 1, Disagree- 2, Neither Agree Nor Disagree - 3, Agree - 4, Strongly Agree-5.

Statement Strongly Strongly Disagree Agree

a. I am very concerned about my looks 1 2 3 4 5 b. I am very inquisitive like a child. 1 2 3 4 5 c. I like to be different in whatever I do. 1 2 3 4 5 d. I love whatever work I do. 1 2 3 4 5 e. I am always playful 1 2 3 4 5 f. I keep thinking about my future. 1 2 3 4 5 g. I take a lot of care about the dress I wear 1 2 3 4 5 h. I like to try new and different things 1 2 3 4 5 i. I am sensitive to others feelings 1 2 3 4 5 j. I believe in keeping myself physically fit. 1 2 3 4 5 k. I dislike being left alone. 1 2 3 4 5 l. I'd say I'm rebelling against the way I was brought up 1 2 3 4 5 m. I try to understand deeply about anything that I study 1 2 3 4 5 n. I always worry about my past failures. 1 2 3 4 5 o. I like to follow what others do. 1 2 3 4 5 p. I am very sensitive to what others think about me. 1 2 3 4 5 q. My objective in life is to acquire wealth to the maximum extent possible.

1 2 3 4 5

r. I am very organized 1 2 3 4 5 s. I'm a "spender" rather than a "saver." 1 2 3 4 5 t. Love and sex are great distractions to achieving ones objectives.

1 2 3 4 5

u. I act on my hunches 1 2 3 4 5 v. I am more conventional than experimental 1 2 3 4 5 w. I am a little fickle minded 1 2 3 4 5 x. I love to have good food every day. 1 2 3 4 5


12. Please provide the following information about yourself.

a. Which of these age groups do you fall into? ? Below 25 Years ? 25 or More ? refused

b. What is your household yearly income?

(Remember, the survey is anonymous) ? $30,000 or Less ? $30,001 to $80,000 ? $80,001 to $125,000 ? Over $125,000

c. Gender: ? Male

? Female

d. Race/Ethnicity: (adopted from the census) ? White ? Others ? Refused

e. Your nickname: _________________________ (This name will appear in the data base and also you can see where you stand vis-à-vis others in your class during analysis – Please do not give your real name.)

Thank You for Your Time


CODE SHEET FOR TOOTH PASTE SURVEY

Column No.

Variable Name

Variable Label Label Values

1 no Respondent No. 2 class Close-up 1. GSB-573-02

2. MKT-347-01 3. MKT-347-02

3 q01a Aquafresh 0. Unaware 1. Aware

4 q01b Colgate 0. Unaware 1. Aware

5 q01c Crest 0. Unaware 1. Aware

6 q01d Mentadent 0. Unaware 1. Aware

7 q01e Arm & Hammer 0. Unaware 1. Aware

8 q01f Pepsodent 0. Unaware 1. Aware

9 q01g Others 0. Unaware 1. Aware

10 q02a Aquafresh 0. Never Used 1. Used

11 q02b Colgate 0. Never Used 1. Used

12 q0ac Crest 0. Never Used 1. Used

13 q02d Mentadent 0. Never Used 1. Used

14 q02e Arm & Hammer 0. Never Used 1. Used

15 q02f Pepsodent 0. Never Used 1. Used

16 q02g Others 0. Never Used 1. Used

17 q03a Current Brand 1 1. Aquafresh 2. Colgate 3. Crest 4. Mentadent 5. Arm & Hammer 6. Pepsodent 7. Others


18 q03b Current Brand 2 1. Aquafresh 2. Colgate 3. Crest 4. Mentadent 5. Arm & Hammer 6. Pepsodent 7. Others 8. No Second Brand

19 q04 Previous Brand 1. Aquafresh 2. Colgate 3. Crest 4. Mentadent 5. Arm & Hammer 6. Pepsodent 7. Others

20 q05a Fighting Cavities 1. Not Important To 7. Very Important

21 q05b Whitening Teeth 1. Not Important To 7. Very Important

22 q05c Cleaning Stains/Tartar 1. Not Important To 7. Very Important

23 q05d Good Taste 1. Not Important To 7. Very Important

24 q05e Likeable Flavor 1. Not Important To 7. Very Important

25 q05f Freshening Breath 1. Not Important To 7. Very Important

26 q05g High Prestige Brand 1. Not Important To 7. Very Important

27 q05h American Dental Association Recommendation

1. Not Important To 7. Very Important

28 q05i Attractive Packaging 1. Not Important To 7. Very Important

29 q05j Innovative Features/new Ingredients

1. Not Important To 7. Very Important

30 q06a01 Aquafresh - Fighting Cavities 0 – Poor


To 10 – Good

31 q06a02 Aquafresh - Whitening Teeth 0 – Poor To 10 – Good

32 q06a03 Aquafresh - Cleaning Stains/Tartar

0 – Poor To 10 – Good

33 q06a04 Aquafresh - Good Taste 0 – Poor To 10 – Good

34 q06a05 Aquafresh - Likeable Flavor 0 – Poor To 10 – Good

35 q06a06 Aquafresh - Freshening Breath 0 – Poor To 10 – Good

36 q06a07 Aquafresh - High Prestige Brand 0 – Poor To 10 – Good

37 q06a08 Aquafresh - Color 0 – Poor To 10 – Good

38 q06a09 Aquafresh - Attractive Packaging


39 q06a10 Aquafresh - Innovative Features/new Ingredients


40 q06a11 Aquafresh - Overall Rating 0 – Poor To 10 – Good

41 q06b01 Colgate - Fighting Cavities 0 – Poor To 10 – Good

42 q06b02 Colgate - Whitening Teeth 0 – Poor To 10 – Good

43 q06b03 Colgate - Cleaning Stains/Tartar 0 – Poor To 10 – Good

44 q06b04 Colgate - Good Taste 0 – Poor To 10 – Good

45 q06b05 Colgate - Likeable Flavor 0 – Poor To


10 – Good 46 q06b06 Colgate - Freshening Breath 0 – Poor

To 10 – Good

47 q06b07 Colgate - High Prestige Brand 0 – Poor To 10 – Good

48 q06b08 Colgate - Color 0 – Poor To 10 – Good

49 q06b09 Colgate - Attractive Packaging 0 – Poor To 10 – Good

50 q06b10 Colgate - Innovative Features/new Ingredients


51 q06b11 Colgate - Overall Rating 0 – Poor To 10 – Good

52 q06c01 Crest - Fighting Cavities 0 – Poor To 10 – Good

53 q06c02 Crest - Whitening Teeth 0 – Poor To 10 – Good

54 q06c03 Crest - Cleaning Stains/Tartar 0 – Poor To 10 – Good

55 q06c04 Crest - Good Taste 0 – Poor To 10 – Good

56 q06c05 Crest - Likeable Flavor 0 – Poor To 10 – Good

57 q06c06 Crest - Freshening Breath 0 – Poor To 10 – Good

58 q06c07 Crest - High Prestige Brand 0 – Poor To 10 – Good

59 q06c08 Crest - Color 0 – Poor To 10 – Good

60 q06c09 Crest - Attractive Packaging 0 – Poor To 10 – Good


61 q06c10 Crest - Innovative Features/new Ingredients


62 q06c11 Crest - Overall Rating 0 – Poor To 10 – Good

63 q06d01 Current Brand - Fighting Cavities


64 q06d02 Current Brand - Whitening Teeth 0 – Poor To 10 – Good

65 q06d03 Current Brand - Cleaning Stains/Tartar


66 q06d04 Current Brand - Good Taste 0 – Poor To 10 – Good

67 q06d05 Current Brand - Likeable Flavor 0 – Poor To 10 – Good

68 q06d06 Current Brand - Freshening Breath


69 q06d07 Current Brand - High Prestige Brand


70 q06d08 Current Brand - Color 0 – Poor To 10 – Good

71 q06d09 Current Brand - Attractive Packaging


72 q06d10 Current Brand - Innovative Features/new Ingredients


73 q06d11 Current Brand - Overall Rating 0 – Poor To 10 – Good

74 q07a01 Aquafresh - Outgoing `1’ If Marked `0’ Otherwise

75 q07a02 Aquafresh - Sensuous `1’ If Marked ‘0’ Otherwise

76 q07a03 Aquafresh - Hedonist `1’ If Marked ‘0’ Otherwise

77 q07a04 Aquafresh - Fun Loving `1’ If Marked


‘0’ Otherwise 78 q07a05 Aquafresh - Achiever `1’ If Marked

‘0’ Otherwise 79 q07a06 Aquafresh - Romantic `1’ If Marked

‘0’ Otherwise 80 q07a07 Aquafresh - Traditional `1’ If Marked

‘0’ Otherwise 81 q07a08 Aquafresh - Ambitious `1’ If Marked

‘0’ Otherwise 82 q07a09 Aquafresh - Overcautious `1’ If Marked

‘0’ Otherwise 83 q07a10 Aquafresh - Feminine `1’ If Marked

‘0’ Otherwise 84 q07a11 Aquafresh - Masculine `1’ If Marked

‘0’ Otherwise 85 q07b01 Colgate - Outgoing `1’ If Marked

‘0’ Otherwise 86 q07b02 Colgate - Sensuous `1’ If Marked

‘0’ Otherwise 87 q07b03 Colgate - Hedonist `1’ If Marked

‘0’ Otherwise 88 q07b04 Colgate - Fun Loving `1’ If Marked

‘0’ Otherwise 89 q07b05 Colgate - Achiever `1’ If Marked

‘0’ Otherwise 90 q07b06 Colgate - Romantic `1’ If Marked

‘0’ Otherwise 91 q07b07 Colgate - Traditional `1’ If Marked

‘0’ Otherwise 92 q07b08 Colgate - Ambitious `1’ If Marked

‘0’ Otherwise 93 q07b09 Colgate - Overcautious `1’ If Marked

‘0’ Otherwise 94 q07b10 Colgate - Feminine `1’ If Marked

‘0’ Otherwise 95 q07b11 Colgate - Masculine `1’ If Marked

‘0’ Otherwise 96 q07c01 Crest - Outgoing `1’ If Marked

‘0’ Otherwise 97 q07c02 Crest - Sensuous `1’ If Marked

‘0’ Otherwise 98 q07c03 Crest - Hedonist `1’ If Marked

‘0’ Otherwise 99 q07c04 Crest - Fun Loving `1’ If Marked

‘0’ Otherwise 100 q07c05 Crest - Achiever `1’ If Marked


‘0’ Otherwise 101 q07c06 Crest - Romantic `1’ If Marked

‘0’ Otherwise 102 q07c07 Crest - Traditional `1’ If Marked

‘0’ Otherwise 103 q07c08 Crest - Ambitious `1’ If Marked

‘0’ Otherwise 104 q07c09 Crest - Overcautious `1’ If Marked

‘0’ Otherwise 105 q07c10 Crest - Feminine `1’ If Marked

‘0’ Otherwise 106 q07c11 Crest - Masculine `1’ If Marked

‘0’ Otherwise 107 q08a Aquafresh - Crest 1 – Most Similar

To 10 – Most Dissimilar

108 q08b Aquafresh – Colgate 1 – Most Similar To 10 – Most Dissimilar

109 q08c Aquafresh – Mentadent 1 – Most Similar To 10 – Most Dissimilar

110 q08d Aquafresh – Arm & Hammer 1 – Most Similar To 10 – Most Dissimilar

111 q08e Aquafresh – Pepsodent 1 – Most Similar To 10 – Most Dissimilar

112 q08f Crest – Colgate 1 – Most Similar To 10 – Most Dissimilar

113 q08g Crest – Mentadent 1 – Most Similar To 10 – Most Dissimilar

114 q08h Crest – Arm & Hammer 1 – Most Similar To 10 – Most Dissimilar

115 q08i Crest – Pepsodent 1 – Most Similar To 10 – Most Dissimilar

116 q08j Colgate – Mentadent 1 – Most Similar To 10 – Most Dissimilar

117 q08k Colgate – Arm & Hammer 1 – Most Similar To 10 – Most Dissimilar


118 q08l Colgate – Pepsodent 1 – Most Similar To 10 – Most Dissimilar

119 q08m Mentadent – Arm & Hammer 1 – Most Similar To 10 – Most Dissimilar

120 q08n Mentadent – Pepsodent 1 – Most Similar To 10 – Most Dissimilar

121 q08o Arm & Hammer – Pepsodent 1 – Most Similar To 10 – Most Dissimilar

122 q09a Tooth paste in a big jar (like styling gel)

1 – Definitely Not Buy To 2 – Will Definitely Buy

123 q09b Make your own tooth-paste kit (with whitener, mouth wash, foaming agent, baking soda etc. in different tubes)


124 q09c Caffeinated Tooth paste for a refreshing feeling


125 q09d Toothpaste with weight-control formulae (make you feel full after brushing)


126 q09e Toothpaste containing multi-vitamin


127 q09f Spicy toothpaste (clove and/or cinnamon)


128 q09g Transparent tube to see how much is left inside


129 q09h Toothpaste containing colored beads and flavor crystals


130 q09i Use and throw away paste-pre-applied tooth brushes (Better Hygiene)


131 q09j Tube fitted with dispenser for right amount


132 q09k Male /Female toothpaste 1 – Definitely Not Buy To 2 – Will Definitely Buy


134 q09l Toothpaste that doubles as shaving cream


135 q09m Night paste with sleep inducers 1 – Definitely Not Buy To 2 – Will Definitely Buy

136 q10a Concept 1 Rank 1 To 15 137 q10b Concept 2 Rank 1 To 15 138 q10c Concept 3 Rank 1 To 15 139 q10d Concept 4 Rank 1 To 15 140 q10e Concept 5 Rank 1 To 15 141 q10f Concept 6 Rank 1 To 15 142 q10g Concept 7 Rank 1 To 15 143 q10h Concept 8 Rank 1 To 15 144 q10i Concept 9 Rank 1 To 15 145 q10j Concept 10 Rank 1 To 15 146 q10k Concept 11 Rank 1 To 15 147 q10l Concept 12 Rank 1 To 15 148 q10m Concept 13 Rank 1 To 15 149 q10n Concept 14 Rank 1 To 15 150 q10o Concept 15 Rank 1 To 15 151 q11a I am very concerned about my

looks 1 – Strongly Disagree To 5 – Strongly Agree

152 q11b I am very inquisitive like a child.

1 – Strongly Disagree To 5 – Strongly Agree

153 q11c I like to be different in whatever I do.


154 q11d I love whatever work I do. 1 – Strongly Disagree To 5 – Strongly Agree

155 q11e I am always playful 1 – Strongly Disagree To 5 – Strongly Agree

156 q11f I keep thinking about my future.


157 q11g I take a lot of care about the dress I wear


158 q11h I like to try new and different things



159 q11i I am sensitive to others feelings


160 q11j I believe in keeping myself physically fit.


161 q11k I dislike being left alone. 1 – Strongly Disagree To 5 – Strongly Agree

162 q11l I'd say I'm rebelling against the way I was brought up


163 q11m I try to understand deeply about anything that I study


164 q11n I always worry about my past failures.


165 q11o I like to follow what others do. 1 – Strongly Disagree To 5 – Strongly Agree

166 q11p I am very sensitive to what others think about me.


167 q11q My objective in life is to acquire wealth to the maximum extent possible.


168 q11r I am very organized 1 – Strongly Disagree To 5 – Strongly Agree

169 q11s I'm a "spender" rather than a "saver."


170 q11t Love and sex are great distractions to achieving ones objectives.


171 q11u I act on my hunches 1 – Strongly Disagree To 5 – Strongly Agree

172 q11v I am more conventional than experimental


173 q11w I am a little fickle minded 1 – Strongly Disagree To 5 – Strongly Agree

173 q11x I love to have good food every 1 – Strongly Disagree


day. To 5 – Strongly Agree

174 q12a Age 1. Less Than 25 2. 25 and Above

175 q12b Household Income 1. $30,000 or less 2. $30,001 to $80,000 3. $80,001 to $125,000 4. Above $125,000

176 q12c Gender 1. Male 2. Female

177 q12d Race/Ethnicity 1. White 2. Others

178 q12e Nickname Enter as given 179 aware Number of Brands Aware 180 trial Number of Brands Tried 181 qcl_1 Benefit Clusters 1. Dental Care

2. Sensory benefits 3. a la carte

182 fact1_1 External Locus of Control 183 fact2_1 Internal Locus of Control 184 fact3_1 Creative 185 fact4_1 Unique 186 fact5_1 Goal Oriented 187 fact6_1 Fun Loving 188 fact7_1 Rule Bound 189 fact8_1 Rebel 190 qcl_2 Psychographic Clusters 1. Middle of the Road

2. Creative/unique 3. Goal directed rebels 4. Systematic Achiever 5. Inner Directed

Individual Variables 2 & 178 are string variables Variables 179 and 180 are computed by the researcher from variables defined already. These are called transformed variables. Variables 181-190 are SPSS System created variables and labels given by the researcher.


Slide 1

1

A Market Survey of Toothpaste Brands Among University Students

BYProf. M J Xavier

Slide 2

2

Contents

• Executive Summary - 3• Profile of Respondents - 8• Awareness, Trial and Usage - 15• Benefits Sought and Benefit Segmentation - 28• Competitive Positioning of Brands - 37• Evaluation of New Product Ideas - 48• Psycho-graphic Segmentation - 65


Slide 3

3

Executive Summary• A study on toothpaste market was carried out among the university

students during September-November, 2003 to asses the preferences and to evaluate new product ideas.

• The study was done in two phases. The qualitative phase used focus groups to understand the nature of the market and to come up with a number of new product ideas. In the quantitative phase, aquestionnaire was developed and administered to 70 students.

• Respondent Profile– The sample comprised of 15 Graduate Students and 55 undergraduate

students from Orfalea College of Business.– 35 were males and 35 were female students.– 58 of them were below the age of 25– The sample had a wide income distribution– In terms of ethnicity 80% were whites

Slide 4

4

Executive Summary - Continued• Awareness Trial and Usage:

– Major Brands – Colgate, Aqua-fresh and Crest have close to 100 percent awareness while others have low levels of awareness

– On an average each respondent is aware of 4 to 5 brands.– The three major brands – Aqua-fresh, Colgate and Crest have close to

95% trial– Every person has tried 4 brands on an average– Crest has a larger share (35.7%) compared to the other two major

brands Colgate (28.6%)and Aqua-fresh (14.3%) though they match Crest in terms of awareness and trial. The niche brands Mentadent and Arm&Hammer have reasonable shares.

– 5 out of 70 respondents have two current brands– Current Brand Vs previous brand analysis shows that there is very low

level of Brand Loyalty is seen. Crest has the highest retention figure of 37.5%. People seek variety. This offers great opportunity for manufacturers to offer variety through innovative ingredients.


Slide 5

5

Executive Summary - Continued• Benefits Sought and Benefit Segmentation

– The most Important benefit is `Freshening of Breath’ with an average score of 6.3 on a 1 to 7 Importance scale and the least Important attribute is `Attractive Packaging’ with a score of 3.4. The high Standard Deviation (Above 1.3) for all the attributes except `Freshening Breath’ indicates that there are segments that seek differing benefits.

– The derived importance of benefits obtained by correlating rating of brands on attributes with overall rating showed a different pattern.

– The stated importance was plotted against derived importance andinterpreted using Kano model. The delight attributes are brand image and innovative features which have low stated and high derived importance. Cleaning stains/tartar is a minimum expected attribute.

– Cluster analysis of the Importance ratings yielded the following three benefit segments.

• Cluster 1 has 12 respondents and the attributes that they look for are – fighting cavities, whitening teeth and American Dental Association Recommendation –This can be named as the `dental care’ segment.

• Cluster 2 with 16 respondents is interested in `sensory benefits’ – good taste, likeable flavor and white teeth.

• Cluster 3 with 42 respondents want a bit of everything – we shall call this a la carte cluster.

– 50% of cluster 2 members use Colgate which clearly indicates that Colgate is perceived to be high on Oral sensory benefits. 42.9% of a la carte cluster uses Crest which indicates that Crest is seen as a balanced tooth paste offering dental care as well as sensory benefits.

Slide 6

6

Executive Summary - Continued• Competitive Positioning of Brands

– Crest scores over the other two competitors on all attributes. Colgate scores over Aqua Fresh on all attributes except Attractive Packaging.

– Correspondence Map of Brand-Personality Profile Indicates the following• Crest is seen to be used by Ambitious Achiever• Colgate is seen to be used by Traditional, overcautious, masculine person• Aqua Fresh is seen as Fun-loving, Feminine and Outgoing.• There is no brand available for Romantic, sensuous types… Opportunity for new product

– MDS based positioning indicates the following:• Crest and Colgate are seen as very similar.• Mentadent, Arm&Hammer and Pepsodent are seen to be of a different category.• Aqua Fresh stands out as a unique category.

– Factor analysis of brand ratings resulted in the three factors listed below and positioning was done on those three factors.

• The first factor can be named as `Dental Hygiene’ as it comprises of Cleaning Stains/Tartar, Fighting Cavities, Whitening Teeth and Freshening Breath. The variable Innovative features/Ingredients are seen as offering dental hygiene as well as contributing to the second factor on Visibility/Brand Image.

• The Second Factor is `visibility’ in the store and in the media comprising of Attractive Packaging, Color and Brand Image

• The third variable refers to `sensory benefits’ – Flavor and Taste


Slide 7

7

Executive Summary - Continued• Evaluation of New Product Ideas

– 13 new product ideas were rated for intention to buy and the following four got short-listed as best ideas.

o Transparent tube to see how much is left insideo Toothpaste containing multi-vitamino Tube fitted with dispenser for right amounto Toothpaste containing colored beads and flavor crystals

• More males prefer Night-paste with sleep inducers more females prefer Toothpaste with weight control formulae

• Members of the oral sensation cluster have a higher preference for a paste with weight-control formulae

• Conjoint analysis indicates that taste and price are the two most important attributes• Transparent tube appears to be the preferred choice of majority of respondents

• Psycho-graphic Segmentation– Factor analysis of the ratings of 24 statements yielded eight factors.– These eight factor scores were clustered to arrive at the following five segments

• Cluster 1 - Middle of the Road• Cluster 2 - Creative /Unique • Cluster 3 - Goal Directed Rebels• Cluster 4 - Systematic Achiever• Cluster 5 - Inner Directed individual

– The relationship between brand used and the psycho graphic segments is not statistically significant. Large sample sizes will be needed to establish firm relationships.

Slide 8

8

Profile of Respondents


Slide 9

9

Class

15 21.4 21.423 32.9 54.332 45.7 100.070 100.0

GBS-573-02MKT-347-01MKT-347-02

Total

FrequencyValid Percent

CumulativePercent 45.7%

32.9%

21.4%

MKT-347-02

MKT-347-01

GBS-573-02

The sample comprised of 15 Graduate Students and 55 undergraduate students from Orfalea College of Business, Calpoly.

Slide 10

10

Age Profile

10070Total

1001130-34 years

99161125-29 years

83835818-24 years

Cumulative Percentage

Valid Percent

Frequ-encyAge 1.4%

15.7%

82.9%

30-34 years

25-29 years

18-24 years

More than 80% of respondents are in the less than 25 age group


Slide 11

11

Gender

50.0%

50.0%Female

Male

35 50.0 50.0 50.035 50.0 50.0 100.070 100.0 100.0

MaleFemaleTotal

ValidFrequency Percent Valid Percent

CumulativePercent

Gender has a perfect 50:50 split

Slide 12

12

Income Distribution

20.0%

15.7%

14.3%

50.0%

Above $125,000

$80,001 to $125,000

$30,000 to $80,000

Less than $30,000Income distribution is fairly representative of the population

35 50.0 50.0 50.010 14.3 14.3 64.311 15.7 15.7 80.014 20.0 20.0 100.070 100.0 100.0

Less than $30,000$30,000 to $80,000$80,001 to $125,000Above $125,000Total

ValidFrequency Percent Valid Percent

CumulativePercent


Slide 13

13

Race/Ethnicity

6 8.6 8.63 4.3 12.9

55 78.6 91.43 4.3 95.73 4.3 100.0

70 100.0

Asian/Pacific IslanderHispanicWhiteMulti-racialRefusedTotal

Frequency PercentCumulativePercent

4.3%

4.3%

78.6%

4.3%

8.6%

Refused

Multi-racial

White

Hispanic

Asian/Pacific Islander

Close to 80% respondents are whites

Slide 14

14

Respondents Profile - Summary

• The sample comprised of 15 Graduate Students and 55 undergraduate students from Oracle College of Business.

• 35 were males and 35 were female students.• 58 of them were below the age of 25• The sample had a wide income distribution• In terms of ethnicity 80% were whites


Slide 15

15

Awareness, Trial and Usage

Slide 16

16

Brand AwarenessAqua-fresh

100.0%Aware

Pepsodent7.1%

92.9%

Aware

Unaware

Colgate

Aware

Unaware1.4%

98.6%

Crest

95.7%

4.3%

Aware

Unaware

Mentadent

88.6%

11.4%

Aware

Unaware

Arm & Hammer

25.7%

74.3%

Aware

Unaware

Major Brands – Colgate, Aqua-fresh and Crest have close to 100 percent awareness while others have low levels of awareness


Slide 17

17

Number of Brands Aware

AWARE

6.005.004.003.002.00

Per

cent

50

40

30

20

10

0

1.4%

5.7%

47.1%

37.1%

8.6%

On an average each respondent is aware of 4 to 5 brands.

Slide 18

18

Number of Brands Aware ByDemographic Characteristics

AWARE * INCOME

Mean

4.48574.40004.45454.42864.4571

INCOMELess than $30,000$30,000 to $80,000$80,001 to $125,000Above $125,000Total

AWAREAWARE * RACE

Mean

4.47274.40004.4571

RACEWhiteOthersTotal

AWARE

AWARE * Gender

Mean

4.51434.40004.4571

GenderMaleFemaleTotal

AWARE

AWARE * Age

Mean

4.48284.33334.4571

Age18-24 years25-29 yearsTotal

AWARE

There is no variation in awareness across different demographic groups


Slide 19

19

Usage/TrialAqua-fresh

97.1%

2.9%

Used

Never Used

Colgate

94.3%

5.7%

Used

Never Used

Crest

94.3%

5.7%

Used

Never Used

Mentadent

55.7%

44.3%Used

Never Used

Arm & Hammer

21.4%

78.6%

Used

Never Used

Pepsodent

2.9%

97.1%

Used

Never Used

The three major brands – Aqua-fresh, Colgate and Crest have close to 95% trial

Slide 20

20

Number of Brands Tried

TRIAL

7.006.005.004.003.002.001.00

Per

cent

60

50

40

30

20

10

0

Every person has tried 4 brands on an average


Slide 21

21

Number of Brands Tried byDemographic Groups

TRIAL * INCOME

Mean

3.91433.80004.09093.85713.9143

INCOMELess than $30,000$30,000 to $80,000$80,001 to $125,000Above $125,000Total

TRIAL

TRIAL * RACE

Mean

3.92733.86673.9143

RACEWhiteOthersTotal

TRIAL

TRIAL * Gender

Mean

3.85713.97143.9143

GenderMaleFemaleTotal

TRIAL

TRIAL * Age

Mean

3.96553.66673.9143

Age18-24 years25-29 yearsTotal

TRIAL

There is no variation in the number of brands tried across different demographic categories

Slide 22

22

Current Brand

Current Brand

Others

Arm & Hammer

Mentadent

Crest

Colgate

Aqua-Fresh

Per

cent

40

30

20

10

0

Crest has a larger share compared to the other two major brands Colgate and Aqua-fresh though they match Crest in terms of awareness and trial. The niche

brands Mentadent and Arm&Hammer have reasonable shares.

14.3%

28.6%

35.7%

8.6%

4.3%

8.6%

Base - 70


Slide 23

23

Current Brand Vs Gender

Chi-square value of 8.3 at 3 degrees of freedom is significant at 96% confidence level.While Crest is popular among Females, Aqua-fresh and other brands are favored more by males than femalesOther demographic variables ñIncome, Race and Age ñhave no significant affect on Current Brand

7 9 8 11 3520.0% 25.7% 22.9% 31.4% 100.0%

3 11 17 4 358.6% 31.4% 48.6% 11.4% 100.0%

10 20 25 15 7014.3% 28.6% 35.7% 21.4% 100.0%

Count% within GenderCount% within GenderCount% within Gender

Male

Female

Gender

Total

Aqua-Fresh Colgate Crest OthersCURRENT

Total

Slide 24

24

Use of Two Brands

7.1%

92.9%

Two Brand Usage

No Second Brand

5 out of 70 respondents have two current brands


Slide 25

25

Previous BrandPrevious Brand

Previous Brand

Others

Arm & Hammer

Mentadent

Crest

Colgate

Aqua-Fresh

Per

cent

50

40

30

20

10

0

21.4%22.9%

45.7%

7.1%

1.4% 1.4%Base - 70

The pattern is more or less the same as the current Brand shares

Slide 26

26

Current Brand Vs Previous Brand (Brand Loyalty)

3 4 6 1 0 1 1520.0% 26.7% 40.0% 6.7% .0% 6.7% 100.0%

2 3 6 1 1 3 1612.5% 18.8% 37.5% 6.3% 6.3% 18.8% 100.0%

4 11 12 3 1 1 3212.5% 34.4% 37.5% 9.4% 3.1% 3.1% 100.0%

1 2 0 1 1 0 520.0% 40.0% .0% 20.0% 20.0% .0% 100.0%

0 0 1 0 0 0 1.0% .0% 100.0% .0% .0% .0% 100.0%

0 0 0 0 0 1 1.0% .0% .0% .0% .0% 100.0% 100.0%10 20 25 6 3 6 70

14.3% 28.6% 35.7% 8.6% 4.3% 8.6% 100.0%

Count% within Previous BrandCount% within Previous BrandCount% within Previous BrandCount% within Previous BrandCount% within Previous BrandCount% within Previous BrandCount% within Previous Brand

Aqua-Fresh

Colgate

Crest

Mentadent

Arm & Hammer

Others

PreviousBrand

Total

Aqua-Fresh Colgate Crest MentadentArm &

Hammer Others

Current Brand

Total

Very low level of Brand Loyalty is seen. Crest has the highest retention figure of 37.5%. People seek variety. This offers great opportunity for

manufacturers to offer variety through innovative ingredients.


Slide 27

27

Awareness, Trial and Usage -Summary

• Major Brands – Colgate, Aqua-fresh and Crest have close to 100 percent awareness while others have low levels of awareness

• On an average each respondent is aware of 4 to 5 brands.• The three major brands – Aqua-fresh, Colgate and Crest have close

to 95% trial• Every person has tried 4 brands on an average• Crest has a larger share (35.7%) compared to the other two major

brands Colgate (28.6%)and Aqua-fresh (14.3%) though they match Crest in terms of awareness and trial. The niche brands Mentadent and Arm&Hammer have reasonable shares.

• 5 out of 70 respondents have two current brands• Current Brand Vs previous brand analysis shows that there is very

low level of Brand Loyalty is seen. Crest has the highest retention figure of 37.5%. People seek variety. This offers great opportunity for manufacturers to offer variety through innovative ingredients.

Slide 28

28

Benefits Sought& Benefit Segmentation


Slide 29

29

Relative Importance of Benefits Sought (Direct Method)

70 4.00 7.00 6.3000 .7680270 1.00 7.00 6.1143 1.4400470 1.00 7.00 5.6714 1.3483470 1.00 7.00 5.6429 1.4145870 1.00 7.00 5.3429 1.5025270 1.00 7.00 5.1714 1.52250

70 1.00 7.00 4.4857 1.88620

70 1.00 7.00 4.0714 1.36543

70 1.00 7.00 3.4714 1.5009370 1.00 7.00 3.4286 1.6817170

Freshening BreadthFighting CavitiesCleaning Stains/TartarWhitening TeethGood TasteLikeable FlavorAmerican DentalAssociationRecommendationInnovative Feature/newingredientHigh Prestige BrandAttractive PackagingValid N (Base)

N Minimum Maximum Mean Std. Deviation

The most Important benefit is `Freshening of Breath’ with an average score of 6.3 on a 1 to 7 Importance scale and the least Important attribute is `Attractive Packaging’ with a score of 3.4. The high Standard Deviation (Above 1.3) for all the attributes except `Freshening Breath’ indicates that there are segments that seek differing benefits.

Slide 30

30

Benefits Sought By Different Demographic Segments

No. Mean St. Deviation t-value d.f. sig.Cleaning Stains/Tartar Male 35 5.17 1.56 -3.32 68 0.001 Female 35 6.17 0.86Whitening Teeth 18-24 years 58 5.79 1.18 1.995 68 0.05 25-29 years 12 4.92 2.15Cleaning Stains/Tartar 18-24 years 58 5.84 1.20 2.45 68 0.017 25-29 years 12 4.83 1.75High Prestige Brand Less than $30,000 35 3.77 1.42 2.091 47 0.042 Above $125,000 14 2.86 1.29

The differences on other benefits were not statistically significant


Slide 31

31

Indirect Method of deriving Importance Scores of Benefits

• Conduct a multiple regression of ratings of brands on different attributes with the overall rating for corresponding brands.

• The standardized ß coefficients give the relative importance of attributes.

• The data for this analysis is available in the data file paste_final_data2.sav

Slide 32

32

Relative Importance of Benefits Sought (Indirect Method)

Coefficientsa

.598 .349 1.716 .088

.208 .048 .215 4.368 .000

.097 .037 .130 2.606 .010

.016 .052 .018 .307 .759

.109 .072 .154 1.513 .132

.060 .071 .086 .844 .400

.150 .045 .173 3.311 .001

.135 .038 .195 3.582 .000

.154 .037 .238 4.187 .000-.096 .041 -.132 -2.361 .019

.120 .033 .181 3.660 .000

(Constant)Fighting CavitiesWhitening TeethCleaning Stains/TartarGood TasteLikeable FlavorFreshening BreathBrand ImageColorAttractive PackagingInnovativefeatures/Ingredients

Model1

B Std. Error

Un-standardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: Overall ratinga.

Though the most important and the least important attributes remain the same, there are several changes that can be noticed in relation to the Importance of attributes obtained using direct method.


Slide 33

33

Stated Vs Derived Importance of Benefits (Kano’s Model)

1413121110987

16

14

12

10

8

6

4

2

Innovative features


Brand ImageFreshening Breath

Likeable Flavor

Good Taste

Cleaning Stains/Tarter

Whitening Teeth

Fighting Cavities

DE

RIV

ED

IMP

OR

TAN

CE

STATED IMPORTANCE

Low High

Low

High

Minimum Expected

Delight Attributes

Linea

r Attri

butes

Toothpaste Manufacturers must concentrate on the delight attributes –Brand Image and Innovative Features

Slide 34

34

Benefit Segmentation

Number of Cases in each Cluster

12.00016.00042.00070.000

.000

123

Cluster

ValidMissing

Final Cluster Centers

6.92 4.19 6.626.42 6.06 5.263.17 5.81 5.793.17 5.63 5.57

6.00 2.19 4.93

Fighting CavitiesWhitening TeethGood TasteLikeable FlavorAmerican DentalAssociationRecommendation

1 2 3Cluster

Cluster 1 has 12 respondents and the attributes that they look for are –fighting cavities, whitening teeth and American Dental Association Recommendation – We call this `dental care’ cluster

Cluster 2 with 16 respondents is interested in oral sensation – good taste, likeable flavor and white teeth – We shall name it as `sensory benefits’ cluster

Cluster 3 with 42 respondents want a bit of everything – we shall call this a la carte cluster.


Slide 35

35

Current Brand Vs Benefit Clusters

2 3 4 1 2 0 12

16.7% 25.0% 33.3% 8.3% 16.7% .0% 100.0%

2 8 3 1 0 2 16

12.5% 50.0% 18.8% 6.3% .0% 12.5% 100.0%

6 9 18 4 1 4 42

14.3% 21.4% 42.9% 9.5% 2.4% 9.5% 100.0%

10 20 25 6 3 6 70

14.3% 28.6% 35.7% 8.6% 4.3% 8.6% 100.0%

Dental Care

Sensory Benefits

A la carte

BenefitClusters

Total

Aqua-Fresh Colgate Crest MentadentArm &

Hammer Others

Current Brand

Total

50% of cluster 2 members use Colgate which clearly indicates that Colgate is perceived to be high on Oral sensory benefits

42.9% of a la carte cluster uses Crest which indicates that Crest is seen as a balanced tooth paste offering dental care as well as sensory benefits.

Though Arm Hammer has a large proportion of cluster 1 members (caution - the numbers are too low to give any meaningful interpretation) there is no paste that has a clear positioning for `dental hygiene’. It offers an opportunity for launching a new product with new ingredients like clove to fight cavities and offer total `dental care’.

Aqua-fresh and Mentadent are stuck in the middle with no clear positioning.

Slide 36

36

Benefits Sought & Benefit Segmentation - Summary

• The most Important benefit is `Freshening of Breath’ with an average score of 6.3 on a 1 to 7 Importance scale and the least Important attribute is `Attractive Packaging’ with a score of 3.4. The high Standard Deviation (Above 1.3) for all the attributes except `Freshening Breath’ indicates that there are segments that seek differing benefits.

• The derived importance of benefits obtained by correlating rating of brands on attributes with overall rating showed a different pattern.

• The stated importance was plotted against derived importance and interpreted using Kano model. The delight attributes are brand image and innovative features which have low stated and high derived importance. Cleaning stains/tartar is a minimum expected attribute.

• Cluster analysis of the Importance ratings yielded the following three benefit segments.

– Cluster 1 has 12 respondents and the attributes that they look for are – fighting cavities, whitening teeth and American Dental Association Recommendation – This can be named as the `dental care’ segment.

– Cluster 2 with 16 respondents is interested in `sensory benefits’ – good taste, likeable flavor and white teeth.

– Cluster 3 with 42 respondents want a bit of everything – we shall call this a la cartecluster.

• 50% of cluster 2 members use Colgate which clearly indicates that Colgate is perceived to be high on Oral sensory benefits. 42.9% of a la carte cluster uses Crest which indicates that Crest is seen as a balanced tooth paste offering dental care as well as sensory benefits.


Slide 37

37

Competitive Positioning of Brands

Slide 38

38

Comparative Rating of Brands

7.1639 6.0984 6.6885 6.8361 6.9333 7.4000 6.6393 7.0000 7.1148 6.393461 61 61 61 60 60 61 61 61 61

8.0794 6.9206 7.4839 6.9048 6.9524 7.6508 7.8889 7.0159 7.0317 6.761963 63 62 63 63 63 63 63 63 63

8.4127 7.5714 7.9206 8.1587 8.0476 8.3175 8.6984 7.7619 7.7937 7.412763 63 63 63 63 63 63 63 63 63

9.0000 8.3333 8.3333 6.0000 5.0000 8.6667 7.3333 7.6667 7.3333 8.00003 3 3 3 3 3 3 3 3 3

7.9105 6.8947 7.3862 7.2842 7.2804 7.8095 7.7474 7.2684 7.3158 6.8789190 190 189 190 189 189 190 190 190 190

MeanNMeanNMeanNMeanNMeanN

BrandAqua-Fresh

Colgate

Crest

Arm & Hammer

Total

FightingCavities

WhiteningTeeth

CleaningStains/Tartar Good Taste

LikeableFlavor

FresheningBreath Brand Image Color

AttractivePackaging

Innovativefeatures/Ingredients

Crest has been consistently rated as high on almost all the attributes. The high rating for Arm & Hammer on some of the attributes will have to understood against the fact that only three respondents have rated the same.


Slide 39

39

Competitive Positioning Map

456789

10

Fighti

ng Cav

ities

Whit

ening

Teeth

Cleanin

g Stai

ns/Ta

rtar

Good T

aste

Likea

ble Fl

avor

Fresh

ening

Brea

th

Brand

Imag

eColo

r

Attrac

tive Pa

ckagin

g

Innov

ative

featu

res/In

gredie

nts

Aqua-Fresh

Colgate

Crest

Arm & Hammer

Crest scores over the other two competitors on all attributes. Colgate scores over Aqua Fresh on all attributes except Attractive Packaging.

Slide 40

40

Brand-Personality Association Data

32 18 33 8338.6% 21.7% 39.8% 100.0%

13 15 18 4628.3% 32.6% 39.1% 100.0%

12 14 9 3534.3% 40.0% 25.7% 100.0%

36 14 29 7945.6% 17.7% 36.7% 100.0%

9 24 34 6713.4% 35.8% 50.7% 100.0%

15 18 21 5427.8% 33.3% 38.9% 100.0%

13 37 33 8315.7% 44.6% 39.8% 100.0%

16 17 31 6425.0% 26.6% 48.4% 100.0%

12 29 21 6219.4% 46.8% 33.9% 100.0%

26 11 27 6440.6% 17.2% 42.2% 100.0%

13 39 17 6918.8% 56.5% 24.6% 100.0%

197 236 273 70627.9% 33.4% 38.7% 100.0%

Count% within ATTRICount% within ATTRICount% within ATTRICount% within ATTRICount% within ATTRICount% within ATTRICount% within ATTRICount% within ATTRICount% within ATTRICount% within ATTRICount% within ATTRICount% within ATTRI

Outgoing

Sensuous

Hedonist

Fun Loving

Achiever

Romantic

Traditional

Ambitious

Overcautious

Feminine

Masculine

ATTRI

Total

Aqua Fresh Colgate CrestBrand

Total

The chi-square value of 77.8 at 20 degrees of freedom is significant at 99.999 percent confidence indicating a strong relationship between brand and personality


Slide 41

41

Correspondence Map of Brand-Personality Profile

Dimension 11.0.50.0-.5-1.0

Dim

ensi

on 2

.8

.6

.4

.2

0.0

-.2

-.4

-.6

-.8

Brand

Personality Trait

Crest

Colgate

Aqua Fresh

Masculine

Feminine

Overcautious

Ambitious

Traditional

Romantic

Achiever

Fun Loving

Hedonist

SensuousOutgoing

q Crest is seen to be used by Ambitious Achiever

q Colgate is seen to be used by Traditional, overcautious, masculine person

q Aqua Fresh is seen as Fun-loving, Feminine and Outgoing.

q There is no brand available for Romantic, sensuous types… Opportunity for new product

Slide 42

42

Brand Positioning Using Multi-Dimensional Scaling

Dimension 12.01.51.0.50.0-.5-1.0-1.5-2.0

Dim

ensi

on 2

1.0

.5

0.0

-.5

-1.0

-1.5

Pepsodent

Arm&Hammer

MentadentColgate

Crest

Aqua Fresh

q Crest and Colgate are seen as very similar.

q Mentadent, Arm&Hammer and Pepsodent are seen to be of a different category.

q Aqua Fresh stands out as a unique category.


Slide 43

43

Factor Analysis of Brand Rating Scores

Rotated Component Matrixa

.878 .203 .014

.763 .051 .103

.758 .150 .126

.550 .220 .484

.482 .437 .234

.154 .867 .115

.013 .830 .323

.364 .757 .077

.098 .183 .950

.152 .195 .933

Cleaning Stains/TartarFighting CavitiesWhitening TeethFreshening BreathInnovativefeatures/IngredientsAttractive PackagingColorBrand ImageLikeable FlavorGood Taste

1 2 3Component

q The first factor can be named as `Dental Hygiene’ as it comprises of Cleaning Stains/Tartar, Fighting Cavities, Whitening Teeth and Freshening Breath. The variable Innovative features/Ingredients are seen as offering dental hygiene as well as contributing to the second factor on Visibility/Brand Image.

q The Second Factor is `visibility’ in the store and in the media comprising of Attractive Packaging, Color and Brand Image

q The third variable refers to `sensory benefits’ – Flavor and Taste

Slide 44

44

Factor Analysis Based Positioning-Dental Hygiene Vs Visibility

Dental Hygiene

1.0.8.6.4.2-.0-.2-.4-.6

Vis

ibili

ty

.3

.2

.1

0.0

-.1

-.2

Arm & Hammer

Mentadent

Crest

Colgate

Aqua-Fresh

Mentadent and Arm&Hammer are seen to deliver high level of dental hygiene while Crest is seen to have high visibility


Slide 45

45

Factor Analysis Based Positioning-Dental Hygiene Vs Sensory Benefits

Dental Hygiene

1.0.8.6.4.2-.0-.2-.4-.6

Sen

sory

Ben

efits

1.0

.5

0.0

-.5

-1.0Arm & Hammer

Mentadent

Crest

ColgateAqua-Fresh

Mentadent scores high on sensory benefits. Arm&Hammer scores low on sensory benefits.

Slide 46

46

Factor Analysis Based Positioning-Visibility Vs Sensory Benefits

Visibility

.3.2.10.0-.1-.2

Sen

sory

Ben

efits

1.0

.5

0.0

-.5

-1.0

Arm & Hammer

Mentadent

Crest

Colgate

Aqua-Fresh

Colgate and Aqua Fresh get lower rating on both sensory and visibility factors


Slide 47

47

Competitive Positioning of Brands -Summary

• Crest scores over the other two competitors on all attributes. Colgate scores over Aqua Fresh on all attributes except Attractive Packaging.

• Correspondence Map of Brand-Personality Profile Indicates the following– Crest is seen to be used by Ambitious Achiever– Colgate is seen to be used by Traditional, overcautious, masculine person– Aqua Fresh is seen as Fun-loving, Feminine and Outgoing.– There is no brand available for Romantic, sensuous types… Opportunity for new

product• MDS based positioning indicates the following:

– Crest and Colgate are seen as very similar.– Mentadent, Arm&Hammer and Pepsodent are seen to be of a different category.– Aqua Fresh stands out as a unique category.

• Factor analysis of brand ratings resulted in the three factors listed below and positioning was done on those three factors.

– The first factor can be named as `Dental Hygiene’ as it comprises of Cleaning Stains/Tartar, Fighting Cavities, Whitening Teeth and Freshening Breath. The variable Innovative features/Ingredients are seen as offering dental hygiene as well as contributing to the second factor on Visibility/Brand Image.

– The Second Factor is `visibility’ in the store and in the media comprising of Attractive Packaging, Color and Brand Image

– The third variable refers to `sensory benefits’ – Flavor and Taste

Slide 48

48

Evaluation of New Product Ideas


Slide 49

49

Ratings for New Product Ideas

1.301.5470Toothpaste that doubles as shaving cream

1.361.7970Tooth paste in a big jar (like styling gel)

1.882.7170Cafinated Tooth paste for a refreshing feeling

1.532.8070Male /Female toothpaste

1.762.8170Spicy toothpaste (clove and/or cinnamon)

1.892.8370Use and throw away paste-pre-applied tooth brushes (Better

Hygiene)

2.243.2370Toothpaste with weight-control formulae (make you feel full after

brushing)

2.063.2770Make your own tooth-paste kit

2.293.4070Night paste with sleep inducers

1.773.5970Toothpaste containing colored beads and flavor crystals

1.764.3070Tube fitted with dispenser for right amount

1.744.7568Toothpaste containing multi-vitamin

1.495.0170Transparent tube to see how much is left inside

Std. DeviationMeanNNew product Ideas

Slide 50

50

The best and Unacceptable Ideasq Best Ideas

o Transparent tube to see how much is left insideo Toothpaste containing multi-vitamino Tube fitted with dispenser for right amounto Toothpaste containing colored beads and flavor crystals

q Unacceptable Ideaso Toothpaste that doubles as shaving creamo Tooth paste in a big jar (like styling gel)

q Ideas where the opinion varies (Ones with High Standard Deviation)o Night paste with sleep inducerso Toothpaste with weight-control formulae (make you feel full after

brushing)o Make your own tooth-paste kit (with whitener, mouth wash, foaming

agent, baking soda etc. in different tubes)


Slide 51

51

Preference for New Ideas-Male Vs Female

3.6857 2.6571 3.3143

35 35 35

2.29797 1.98439 2.15258

3.1143 3.8000 3.2286

35 35 35

2.28514 2.36146 1.98651

3.4000 3.2286 3.2714

70 70 70

2.29303 2.24042 2.05660

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Gender

Male

Female

Total

Night pastewith sleepinducers

Toothpastewith

weight-controlformulae(make you

feel full afterbrushing)

Make yourown

tooth-paste kit(with whitener,mouth wash,

foamingagent, bakingsoda etc. in

differenttubes)

While more males prefer Night-paste with sleep inducers more females prefer Toothpaste with weight control formulae

Slide 52

52

Age-wise Preference for New Ideas

3.6034 3.4655 3.379358 58 58

2.34663 2.26503 2.059022.4167 2.0833 2.7500

12 12 121.78164 1.78164 2.050503.4000 3.2286 3.2714

70 70 702.29303 2.24042 2.05660

MeanNStd. DeviationMeanNStd. DeviationMeanNStd. Deviation

Age18-24 years

25 and above

Total


Toothpastewith



Make yourown



differenttubes)

Younger people are more receptive to new ideas than mature students.


Slide 53

53

Benefit Clusters and New product Ideas

3.5833 2.9167 3.666712 12 12

2.60971 2.57464 2.674233.7500 4.0625 3.3125

16 16 162.35230 2.11246 1.887463.2143 3.0000 3.1429

42 42 422.21454 2.16401 1.957743.4000 3.2286 3.2714

70 70 702.29303 2.24042 2.05660

MeanNStd. DeviationMeanNStd. DeviationMeanNStd. DeviationMeanNStd. Deviation

Benefit ClustersFight Cavities

Oral Sensation

A la carte

Total


Toothpastewith



Make yourown



differenttubes)

Members of the oral sensation cluster have a higher preference for a paste with weight-control formulae

Slide 54

54

Utilities – Derived through Conjoint Analysis

0.3167

-0.1583 -0.1583

-0.2-0.1

00.10.20.30.4

Transparent Semi-transparentNon-Transparent

TUBE4.15

-3.45 -0.7

-4-3-2-1012345

Mint Spicy Cafinated

TASTE

1.4833

-1.3667 -0.1167

-1.5-1

-0.50

0.51

1.5

White Red Green

COLOR

0.3167 -1.6583

1.3417

-2-1.5

-1-0.5

00.5

11.5

Colgate Aqua Fresh Crest

BRAND

3.4833

-0.6167 -2.8667

-3-2-101234

2 Dollars 4 Dollars 6 Dollars

PRICE

2.3% 37.5%

31.3%

14.1% 14.8%

Taste and price are the two most important attributes

Preferred Levels

q Transparent Tube

q Mint Taste

q White Color

q Crest Brand

q Price - $2


Slide 55

55

Benefit Clusters - Tube

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

Transparent

Semi-Transparent

Non-Transparent

Fight Cavities

Oral sensationA la carte

While the tube is immaterial for `a la carte’ cluster, transparent tube is most important for the Oral Sensation Cluster

Transparent Semi-Transparent Non-Transparent ImportanceFight Cavities 0.95 -0.475 -0.475 7.37Oral sensation 1.1333 0.8083 -1.9417 14.14A la carte 0.2667 0.1167 -0.3833 3.17

Slide 56

56

Benefit Clusters - Taste

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

MintSpicy

Cafinated

Fight Cavities

Oral sensationA la carte

Oral sensation cluster has a positive preference for cafinated toothpaste

Mint Spicy Cafinated ImportanceFight Cavities 4.45 -1.85 -2.6 36.48Oral sensation 2.6333 -4.567 1.9333 33.10A la carte 4.2667 -3.383 -0.8833 37.36


Slide 57

57

Benefit Clusters - Color

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

White

Red

Green

Fight CavitiesOral sensation

A la carte

While color is immaterial for `Fight Cavities’ cluster, `Oral Sensation’ cluster ahs a positive preference for green color.

White Red Green ImportanceFight Cavities 0.45 -0.1 -0.35 4.14Oral sensation 1.8 -2.525 0.725 19.88A la carte 1.6 -1.55 -0.05 15.38

Slide 58

58

Benefit Clusters - Brand

Colgate Aqua Fresh Crest ImportanceFight Cavities 0.6167 -2.3083 1.6917 20.70Oral sensation 0.8 -0.525 -0.275 6.09A la carte -0.2333 -1.7583 1.9917 18.32

Oral sensation cluster members have a positive preference for Colgate (this is consistent with earlier results – see slide ) while the other two clusters

prefer Crest.

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Colgate

Aqua Fresh

Crest

Fight Cavities

Oral sensation

A la carte


Slide 59

59

Benefit Clusters - Price

-4

-3

-2

-1

0

1

2

3

4

$2

$4

$6 Fight CavitiesOral sensation

A la carte

$2 $4 $6 ImportanceFight Cavities 2.7833 0.4833 -3.2667 31.31Oral sensation 3.1333 -0.4417 -2.6917 26.78A la carte 3.1 -0.925 -2.175 25.76

Price is an important factor for all four clusters

Slide 60

60

Male-Female Differences in Conjoint Utilities

While brand and price are more important for males, taste and color are more important for females.

Transparent Semi-Transparent Non-Transparent ImportanceMale -0.2667 0.7583 -0.4917 6.16Female 0.58333 0.4583 -1.0417 8.02 Mint Spicy Cafinated ImportanceMale 2.9 -2.7 -0.2 27.6Female 4.4167 -3.8333 -0.5833 40.7

White Red Green ImportanceMale 1.0667 -0.9083 -0.1583 9.73Female 2.0833 -2.1667 0.0833 20.99

Colgate Aqua Fresh Crest ImportanceMale 0.4 -1.825 1.425 16.01Female -0.5833 -0.3333 0.9167 7.41

$2 $4 $6 ImportanceMale 4.4 -0.575 -3.8250 40.52Female 2.25 0.125 -2.375 22.84


Slide 61

61

White –Nonwhite: Differences in Conjoint Utilities

Transparent Semi-Transparent Non-Transparent ImportanceWhite -0.6667 0.0333 0.0333 3.27Non White 1.0333 -0.0167 -0.0167 5.54 Mint Spicy Cafinated ImportanceWhite 3.9333 -3.4667 -0.4667 34.6Non White 4.3667 -2.9333 -1.4333 38.5

White Red Green ImportanceWhite 1.6 -1.55 -0.05 14.72Non White 2.0333 -1.5167 -0.5167 18.73

Colgate Aqua Fresh Crest ImportanceWhite 0.2667 -2.13333 1.8667 18.69Non White -0.3 -0.35 0.65 5.28

$2 $4 $6 ImportanceWhite 3.2667 -0.3833 -2.8883 28.76Non White 3.3667 -0.6833 -2.6833 31.93

Brand is more important for Whites

Slide 62

62

Age-wise Conjoint UtilitiesTransparent Semi-Transparent Non-Transparent Importance

Less Than 25 0.6333 -0.0667 -0.5667 5.7725 and Above -0.05 0.025 0.025 0.42 Mint Spicy Cafinated ImportanceLess Than 25 4.3 -3.9 -0.4 39.425 and Above 3.1167 -1.6833 -1.4333 26.7

White Red Green ImportanceLess Than 25 1.9667 -1.9833 0.0167 18.9925 and Above 1.45 -1.85 0.4 18.36

Colgate Aqua Fresh Crest ImportanceLess Than 25 -0.0333 -1.2333 1.2667 12.0225 and Above 0.2833 -1.1417 0.8583 11.13

$2 $4 $6 ImportanceLess Than 25 2.6333 -0.3167 -2.3167 23.8025 and Above 4.45 -1.1 -3.35 43.39

While taste is important for younger students, price is important for mature students.


Slide 63

63

Income-wise Conjoint UtilitiesTransparent Semi-Transparent Non-Transparent Importance

Less Than $3000 0.85 -0.05 -0.8 7.51$30,000 - $80,000 -0.7667 0.2583 0.5083 7.38$80,001 - $125,000 1.35 -0.175 -1.175 11.90Above $125,000 0.2 0.4 -0.6 5.55 Mint Spicy Cafinated ImportanceLess Than $3000 4.1833 -3.4667 -1.4333 34.81$30,000 - $80,000 3.0667 0.4667 -0.7167 21.89$80,001 - $125,000 3.85 -4.05 -3.5333 37.22Above $125,000 3.8667 -4.1833 0.2 44.66

White Red Green ImportanceLess Than $3000 2.35 -2.425 0.075 21.73$30,000 - $80,000 -0.1 -0.825 0.925 10.13$80,001 - $125,000 1.85 -1.3 -0.55 14.84Above $125,000 1.2 -1.1 -0.1 12.76

Colgate Aqua Fresh Crest ImportanceLess Than $3000 0.0167 -1.2583 1.2417 11.38$30,000 - $80,000 0.5667 -2.9083 2.3417 30.38$80,001 - $125,000 0.0167 -1.0083 0.9917 9.42Above $125,000 0.0333 -0.1417 0.1083 1.39

$2 $4 $6 ImportanceLess Than $3000 2.85 -0.3 -2.5500 24.57$30,000 - $80,000 3.2333 -1.2417 -1.9917 30.23$80,001 - $125,000 3.1833 -0.7167 -2.4667 26.62Above $125,000 2.7 1.025 -3.725 35.64

While the middle income is sensitive to brand, high income is taste and price sensitive. Though they are willing to pay up to 4 dollars per tube, the utility drops drastically when the

price goes up to 6 dollars

Slide 64

64

Evaluation of New Product Ideas -Summary

• 13 new product ideas were rated for intention to buy and the following four got short-listed as best ideas.o Transparent tube to see how much is left insideo Toothpaste containing multi-vitamino Tube fitted with dispenser for right amounto Toothpaste containing colored beads and flavor crystals

• More males prefer Night-paste with sleep inducers more females prefer Toothpaste with weight control formulae

• Members of the oral sensation cluster have a higher preference for a paste with weight-control formulae

• Conjoint analysis indicates that taste and price are the two most important attributes

• Transparent tube appears to be the preferred choice of majority of respondents


Slide 65

65

Psycho-graphic Segmentation

Slide 66

66

Factor Analysis of Psychographics Variables

Component Rotation Sums of Squared LoadingsTotal % of VarianceCumulative %

1 2.41 10.06 10.062 2.41 10.03 20.093 2.15 8.94 29.034 1.99 8.27 37.305 1.92 7.99 45.296 1.89 7.88 53.177 1.37 5.71 58.888 1.23 5.13 64.00

Scree Plot

Component Number

2321191715131197531

Eig

enva

lue

4

3

2

1

0


Slide 67

670.730.010.190.030.000.03-0.010.21I'd say I'm rebelling against the way I was brought up

0.070.54-0.160.340.310.06-0.090.23I am sensitive to others feelings

-0.010.850.04-0.06-0.090.020.10-0.07I am very organized

0.010.030.52-0.12-0.050.29-0.02-0.34I love to have good food every day.

0.210.050.530.30-0.32-0.050.02-0.09My objective in life is to acquire wealth to the maximum extent

possible.

0.200.150.61-0.16-0.08-0.15-0.130.36I am a little fickle minded

0.02-0.170.710.220.01-0.030.200.05I'm a "spender" rather than a "saver."

-0.20-0.220.200.520.060.46-0.060.14I act on my hunches

-0.080.190.03-0.550.520.410.050.13I try to understand deeply about anything that I study

0.290.06-0.270.590.090.150.27-0.04I dislike being left alone.

-0.090.130.290.720.06-0.080.080.11Love and sex are great distractions to achieving ones objectives.

0.050.04-0.110.040.750.110.27-0.05I like to be different in whatever I do.

-0.02-0.03-0.050.070.830.08-0.09-0.02I love whatever work I do.

-0.190.070.31-0.120.080.580.03-0.26I like to try new and different things

-0.01-0.03-0.050.300.220.650.17-0.06I am always playful

0.170.08-0.16-0.110.050.750.13-0.02I am very inquisitive like a child.

-0.160.040.020.02-0.03-0.32-0.470.31I am more conventional than experimental

-0.310.10-0.200.13-0.070.330.510.29I keep thinking about my future.

-0.200.380.040.050.270.070.610.06I believe in keeping myself physically fit.

-0.150.040.100.090.030.180.76-0.05I take a lot of care about the dress I wear

0.26-0.100.050.030.00-0.120.810.06I am very concerned about my looks

0.27-0.020.03-0.07-0.180.02-0.160.72I like to follow what others do.

-0.18-0.080.050.040.23-0.070.030.75I always worry about my past failures.

0.210.12-0.060.12-0.07-0.090.140.82I am very sensitive to what others think about me.

87654321Variables

Factor Loading Matrix for Psychographics data

Slide 68

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Factor Labeling

Factor – 1 (External Locus of Control)o I am very sensitive to what others think

about me.o I always worry about my past failures.o I like to follow what others do.

Factor - 2 (Internal Locus of Control)o I am very concerned about my lookso I take a lot of care about the dress I wearo I believe in keeping myself physically fit.o I keep thinking about my future.o I am more conventional than experimental

Factor – 3 (Creative)o I am very inquisitive like a child.o I am always playfulo I like to try new and different things

Factor – 4 (Unique)o I love whatever work I do.o I like to be different in whatever I do.

Factor – 5 (goal-oriented)o Love and sex are great distractions to achieving

ones objectives.o I dislike being left alone.o I try to understand deeply about anything that I

studyo I act on my hunches

Factor 6 (Fun Loving)o I'm a "spender" rather than a "saver."o I am a little fickle mindedo My objective in life is to acquire wealth to the

maximum extent possible.o I love to have good food every day.

Factor 7 (Rule Bound)o I am very organizedo I am sensitive to others feelings

Factor8 (Rebel)o I'd say I'm rebelling against the way I was

brought up


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Psychographics Segments Using Factor Scores

1 2 3 4 5External Locus of Control -0.171 0.371 0.079 -0.897 0.461Internal Locus of Control -0.228 -0.030 -0.735 0.397 0.564Creative -0.159 0.806 -0.516 -0.384 -0.267Unique -0.695 0.544 0.074 1.027 -0.540Goal oriented -0.707 0.323 0.707 -0.802 0.175Fun Loving -0.057 -0.291 -0.411 -0.046 0.567Rule Bound -0.437 -0.311 -0.214 0.527 0.481Rebel -1.085 -0.333 1.018 0.234 0.459

Cluster 1 - Middle of the Road

Cluster 2 - Creative /Unique

Cluster 3 - Goal Directed Rebels

Cluster 4 - Systematic Achiever

Cluster 5 - Inner Directed individual

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Age Vs Psycho-graphic Segments

7 15 11 6 15 5413.0% 27.8% 20.4% 11.1% 27.8% 100.0%

5 2 0 3 1 1145.5% 18.2% .0% 27.3% 9.1% 100.0%

12 17 11 9 16 6518.5% 26.2% 16.9% 13.8% 24.6% 100.0%

Count% within AgeCount% within AgeCount% within Age

18-24 years

25-29 years

Age

Total

Middle ofthe Road

Creative/Unique

Goal DirectedRebels

SystematicAchiever

Inner DirectedIndividual


Total

?2 value of 10.8 at 4 degrees of freedom has a significance level of 0.029

Close to half the mature student population falls in the middle of the road category and also have a large representation in the systematic achiever category.

The younger people are spread across other segments.


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Income Vs Psycho-graphic Segments

4 11 5 5 9 3411.8% 32.4% 14.7% 14.7% 26.5% 100.0%

5 0 1 2 1 955.6% .0% 11.1% 22.2% 11.1% 100.0%

1 4 1 1 3 1010.0% 40.0% 10.0% 10.0% 30.0% 100.0%

2 2 4 1 3 1216.7% 16.7% 33.3% 8.3% 25.0% 100.0%

12 17 11 9 16 6518.5% 26.2% 16.9% 13.8% 24.6% 100.0%

Count% within INCOMECount% within INCOMECount% within INCOMECount% within INCOMECount% within INCOME

Less than $30,000

$30,000 to $80,000

$80,001 to $125,000

Above $125,000

INCOME

Total

Middle ofthe Road

Creative/Unique

Goal DirectedRebels

SystematicAchiever



Total

More middle income people fall in the Middle of the Road category while larger proportion of high income people fall in the goal directed rebels category.

Due to the small sample size the level of significance of ?2 value of 16 at 12 degrees of freedom has only significance level of 0.187

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Gender Vs Psycho-graphic segments

9 7 6 3 8 3327.3% 21.2% 18.2% 9.1% 24.2% 100.0%

3 10 5 6 8 329.4% 31.3% 15.6% 18.8% 25.0% 100.0%

12 17 11 9 16 6518.5% 26.2% 16.9% 13.8% 24.6% 100.0%

Count% within GenderCount% within GenderCount% within Gender

Male

Female

Gender

Total

Middle ofthe Road

Creative/Unique

Goal DirectedRebels

SystematicAchiever



Total

?2 value of 4.6 at 4 degrees of freedom has significance level of 0.33

Thought the chi-square value is not significant some patterns are evident.

The Middle of the Road category has a larger proportion of males and the systematic achievers category has more females.


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Brand Used Vs Psycho-graphic Segments

The relationship between brand used and the psycho graphic segments is not statistically significant. Large sample sizes will be needed to establish firm relationships.

2 3 4 3 12

16.7% 25.0% 33.3% 25.0% 100.0%

1 5 7 4 17

5.9% 29.4% 41.2% 23.5% 100.0%

3 1 4 3 11

27.3% 9.1% 36.4% 27.3% 100.0%

0 4 3 2 9

.0% 44.4% 33.3% 22.2% 100.0%

3 5 6 2 16

18.8% 31.3% 37.5% 12.5% 100.0%

9 18 24 14 65

13.8% 27.7% 36.9% 21.5% 100.0%

Count% within ClusterNumber of CaseCount% within ClusterNumber of CaseCount% within ClusterNumber of CaseCount% within ClusterNumber of CaseCount% within ClusterNumber of CaseCount% within ClusterNumber of Case

Middle of the Road

Creative/Unique

Goal Directed Rebels

Systematic Achiever

Inner Directed Individual

ClusterNumberof Case

Total

Aqua-Fresh Colgate Crest OthersCURRENT

Total

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Psycho-graphic Segmentation -Summary

• Factor analysis of the ratings of 24 statements yielded eight factors.

• These eight factor scores were clustered to arrive at the following five segments– Cluster 1 - Middle of the Road– Cluster 2 - Creative /Unique – Cluster 3 - Goal Directed Rebels– Cluster 4 - Systematic Achiever– Cluster 5 - Inner Directed individual

• The relationship between brand used and the psycho graphic segments is not statistically significant. Large sample sizes will be needed to establish firm relationships.

M J XAVIER - Cengagecollege.cengage.com/business/parasuraman/marketing... · Use of regression...

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