studying the consumer buying behavior while buying a laptop
DESCRIPTION
Marketing research on buying laptops studying the consumer behavior.TRANSCRIPT
Studying the Consumer Buying Behavior
while buying a laptop at SIC
Marketing Research Project
SUBMITTED BY:
YATEESH HOBLIDAR – 2009A02
PURAV SHAH – 2009A04
TUSHAR ALVA– 2009A06
Contents Executive Summary ........................................................................................................................... 4
Background ....................................................................................................................................... 4
Objectives: ........................................................................................................................................ 5
Primary Research Objective: .......................................................................................................... 5
Secondary Research Objective: ...................................................................................................... 5
Research Approach............................................................................................................................ 6
Data collection method ................................................................................................................. 6
Measurement technique ............................................................................................................... 6
Sample .......................................................................................................................................... 6
Analytical approach ....................................................................................................................... 7
Cost and Time requirement: .......................................................................................................... 7
Exploratory Research – Questionnaire ............................................................................................... 9
Secondary Data ............................................................................................................................... 10
Primary Questionnaire .................................................................................................................... 12
Respondent Profile .......................................................................................................................... 17
Chi-Square ....................................................................................................................................... 19
ANOVA ............................................................................................................................................ 21
Factorial Design: .......................................................................................................................... 22
Factor Analysis ................................................................................................................................ 24
Cluster Analysis ............................................................................................................................... 27
Multidimensional Scaling ................................................................................................................. 30
Attribute Based Perceptual Mapping ............................................................................................... 32
Recommendations .......................................................................................................................... 36
Appendix A – Anova (Factorial Design)............................................................................................. 38
Appendix B - Hierarchical Clustering Data ........................................................................................ 39
ICICLE PLOT ................................................................................................................................. 41
Appendix C – K-Means Cluster Analysis............................................................................................ 42
Appendix D - Attribute based Perceptual Mapping .......................................................................... 44
References: ..................................................................................................................................... 47
Executive Summary
Laptops are an integral part of today’s management student’s study kit. And with a
plethora of brands inundating the market, each trying to differentiate itself from the other in
terms of features, style, etc and trying to entice the buyer with attractive offers, the decision
of zeroing on the laptop has become a gruesome exercise for the student. The very nature
of the product encourages the prospective buyer to go through all the stages of a typical
buying process. That is the reason that we have taken up to study the consumer buying
behavior for laptop buying.
The primary objective of the project can be satisfied progressively by satisfying the
secondary research objectives which are determining the factors, mapping the customer
profile & perceptual mapping. A primary research based on questionnaires and analytical
processing of the response will be conducted to fulfill the research objectives wherever the
secondary data is not available.
The student community of the IC campus will form the sample for the research and
the research is expected to take about 20 days and cost around Rs 210.
Background
The computers have come a long way from their birth as complex mechanical
calculating machines available only to a few large institutions to being ubiquitous complex
electronic systems capable of handling large amount of data and executing complex
calculation in the today’s world. Their power and performance has been growing at an
incredible pace which has made them a must-have for every business from a retail shop to
airlines. The ever decreasing price of the computer systems has only added to the trend. All
these factors have increased the importance of computers in the business world and
otherwise at a phenomenal rate especially over the past few years.
As the requirement for better, powerful, compact, faster & higher memory computer
systems grew, smaller & portable computer systems called laptops was introduced. These
wonderful machines are a great help to the user owing to their compactness and capabilities.
In the present age of information, laptops have proved to be an executive ’s best friend. Their
feature of compactness over desktop PCs gives the user the convenience to work, share
and stay connected while on the go.
The B-Schools of the day have taken cognizance of the developing situation and are
training the future managers to be well versed and comfortable in using the
computers/laptops. The B-Schools across the globe have been introducing computer
enabled analysis, reporting decision support, management, etc as an integral part of their
curriculum.
Following the trends taking shape in the business world, the three colleges, SCMHRD, SCIT
and SIIB at the Symbiosis Infotech Campus, Hinjewadi, Pune have made it compulsory for
the students to use laptops for academic purposes. Every year around 600 students enrol for
the regular MBA course offered by these institutions. The students coming to the institutes
belong to different social, cultural and economical background. Hence, the study of
preferences for a laptop at SIC can help understand what the youth sees while buying
laptop.
Objectives:
Primary Research Objective:
To study the consumer behaviour while buying a laptop at Symbiosis Infotech Campus.
Secondary Research Objective:
1. To study the influence of various variables like sex, educational background, family
annual income, work experience, etc on the choice of laptop brand.
2. To probe if the interactions between the above mentioned variables have any effect on
the choice of laptop bought.
3. To group the huge number of attributes into major factors (Factor Analysis).
4. To map the profile of the students in terms of lifestyle, attitude & perception
(Segmentation).
5. To evaluate the student preferences about laptops of different brands available in the
market. (Attribute based perceptual mapping).
Research Approach
Data collection method
For exploratory research, the following techniques were used:
a. Open-ended questionnaire – These questions were used to know what are the
different attributes which a student at SIC looks for while selecting a laptop.
b. Focused group discussions – Here, a discussion among a group of students was
arranged to bring out the attributes that are evaluated by the students while selecting
a laptop.
For secondary research, the following sources were used:
a. Websites of different laptop brands to know their unique selling propositions.
b. Other journals and tech magazines available online and at SCMHRD library.
Based on the attributes found out in the exploratory research and the secondary research,
the information gap was identified and hence it was decided to conduct primary research to
fill the gap. The research was conducted by administering questionnaire on the students of
the SIC campus. For primary data collection, Questionnaire administration was done
personally and through online questionnaires.
Measurement technique
Questionnaire was used to collect data from respondents. Questionnaire was given
to the respondents in hard copy or emailed to them. Direct Response Attitude Scales was
used to understand the perceptions of consumers about attributes of various products.
Suitable scale was used based on the number of options available for measuring responses
on multiple dimensions
Sample
A sample size of 104 students from SIC was used for the purpose of research.
Students from both the batches (08-10 and 09-11) were surveyed spanning both the boy and
girl populations to obtain a representative sample. The sampling technique used was area
sampling so that students from all the hostels – A, B and D are surveyed.
Analytical approach
The data collected from the exploratory research provided us with the different
attributes that a student looks for in a laptop. Based on these responses, another
questionnaire was used to do factor analysis to reduce the number of attributes handled into
fewer attributes, so that handling of factors becomes easier for subsequent analysis.
To determine the profile of various students at SIC so that we can know more about
their lifestyle, attitudes and preferences so as to gain an insight on what kind of laptop they
are likely to choose, we have used cluster analysis, a segmentation technique.
Finally to evaluate the student perceptions about laptops of different brands, we have
used attribute based perceptual mapping using Discriminant analysis and also Multi-
Dimensional Scaling.
Apart from using these three major techniques, we have also used chi square
analysis with cross tab to evaluate whether the preferences are different for boys and girls
and other such one dimensional analysis. We have also used ANOVA technique to analyze
if the effect of various independent variables on the choice of the brand of laptop and also
the interaction effect that these variables have on the laptop choice of the population.
Cost and Time requirement:
The cost involved with research was only the printing and photocopying cost of the
questionnaire. 40 questionnaires were photocopied for the exploratory research while
around 50 were done for the primary research. Rest of the responses was collected from
respondents online. In addition, the final report submission, assuming it to be of about 50
pages, will cost about Rs 50 more. Thus, the total cost for the research will be approximately
Rs. 140.
The project began on 21-12-09 and took about 40 days for completion. the duration
take by each task in the project, their start and end date are mentioned clearly in the Gantt
chart given below.
Exploratory Research – Questionnaire Name:
Age:
Annual Income:
1. Do you own a laptop? If YES which brand is it?
2. What are the various brands of laptops that you are aware of?
3. What are the various attributes that you look at while purchasing a laptop?
4. What is the purpose of you purchasing a laptop?
5. What are the various features that you would like to have in your laptop?
6. How much would you spend on purchasing the laptop of your choice?
Secondary Data
The secondary data has been obtained from a project done in the first semester whose Pro was to
determine the factors affecting sales position of the Notebook PC for Dell Corporation India.
The conclusions obtained from the same are as below:
Awareness is a very important factor that leads to increase in sales of the newly developed Netbook PC Segment.
People prefer to buy Notebooks from Authorised retailers more than that from any other form of outlet.
The existing Customer Service practices of Dell Corporation in India are not effective. There is scope for improvement in the delivery time and repair time.
Dell is equally competitive in features and benefits offered as its competitors.
Primary Questionnaire 1. Name : ______________________________________________
2. Gender : a. Male b. Female
3. Indicate the age group you fall into
a. < 20 years
b. 20-22 years
c. 22-25 years
d. > 25 years
4. Indicate your educational qualification
a. BE/BTech
b. MBBS/BDS
c. B.Com
d. BSc
e. BA
f. Other (specify): ______________
5. Indicate your work experience
a. 0-1 years
b. 1-2 years
c. 2-4 years
d. > 4 years
6. Which brand of laptop are you currently using
a. Dell
b. Lenovo
c. HP
d. Toshiba
e. Apple
f. Acer
g. Vaio
h. Other ________
7. Indicate your agreement to the below statements on a 7 point Likert scale where
1- Strongly Agree
2- Moderately Agree
3- Mildly Agree
4- Neither agree nor disagree
5- Mildly Disagree
6- Moderately Disagree
7- Strongly Disagree
I want a laptop which attracts attention of colleagues 1 2 3 4 5 6 7
I don’t mind buying products based on recent (not-proven,
yet) technologies.
1 2 3 4 5 6 7
I feel utility matters and not the looks of any product. 1 2 3 4 5 6 7
I feel family plays an important role in my purchasing
decision.
1 2 3 4 5 6 7
I prefer a foreign made product to Indian made products. 1 2 3 4 5 6 7
I use email as a major source of communication. 1 2 3 4 5 6 7
I use laptop for entertainment more than for any other
purpose.
1 2 3 4 5 6 7
Owning a computer in today's world is very important. 1 2 3 4 5 6 7
I think borrowing money even during times of need is not a
good idea.
1 2 3 4 5 6 7
I feel everyone should enjoy the present without worrying too
much about the future.
1 2 3 4 5 6 7
I am a very fashion conscious person. 1 2 3 4 5 6 7
I feel good quality means more price 1 2 3 4 5 6 7
I prefer branded products 1 2 3 4 5 6 7
8. Indicate your preferences for laptop based on the following attributes.
I bought my laptop because it had a great display. 1 2 3 4 5 6 7
Bigger memory in a laptop is a must. 1 2 3 4 5 6 7
My laptop works on the best available processor. 1 2 3 4 5 6 7
I use my laptop to make a style statement. 1 2 3 4 5 6 7
My friends are jealous of my laptop because it is exclusive. 1 2 3 4 5 6 7
I bought my laptop because it had a great texture. 1 2 3 4 5 6 7
I bought my laptop because it was affordable. 1 2 3 4 5 6 7
Wi-Fi connection is the growing trend in internet connection. 1 2 3 4 5 6 7
Laptops can be used as an alternative to digital cameras. 1 2 3 4 5 6 7
High quality audio is very important for watching movies on
laptops.
1 2 3 4 5 6 7
I feel that the data kept in my laptop is always secure. 1 2 3 4 5 6 7
Bulky laptops are an important reason for their owner’s back
aches.
1 2 3 4 5 6 7
The battery backup of a laptop depends on the usage and not
on the brand.
1 2 3 4 5 6 7
I am aware of the features and weaknesses of different OS
versions.
1 2 3 4 5 6 7
I like to show off my laptop’s features in front of thers. 1 2 3 4 5 6 7
I like to try newer and better versions (heavier) of computer
games.
1 2 3 4 5 6 7
I believe that heat dissipaters of the laptops have to be
complimented with extra devices for optimal operation.
1 2 3 4 5 6 7
Laptops can serve as an important storage medium for all the
data from the mobile devices.
1 2 3 4 5 6 7
I liked my friend’s laptop that had a larger keyboard to
accommodate the additional functional keys.
1 2 3 4 5 6 7
My girlfriend/boyfriend/bestfriend uses the same model. 1 2 3 4 5 6 7
My laptop has attractive accessories. 1 2 3 4 5 6 7
Add-on software packages with laptops are a must. 1 2 3 4 5 6 7
I always shop when a new scheme or offer is on. 1 2 3 4 5 6 7
Extended service warranty is a redundant offer with a good
laptop.
1 2 3 4 5 6 7
I can travel any distance to buy my favourite laptop. 1 2 3 4 5 6 7
I believe that a good laptop (from a reliable brand) hardly
encounters any glitch.
1 2 3 4 5 6 7
I do not mind paying extra for a good service. 1 2 3 4 5 6 7
I need to have freedom of choosing the features in my laptop
even if that comes at a price.
1 2 3 4 5 6 7
9. Rank your current laptop on the following attributes
Rank on the scale of 1 to 5. Rank 1 if you find your laptop very bad on the attribute
and 5 if you find your laptop very good on the attribute and so on.
Brand Image 1 2 3 4 5
Features Configuration 1 2 3 4 5
Style 1 2 3 4 5
Audio/Video Quality 1 2 3 4 5
Price 1 2 3 4 5
Convenience of service 1 2 3 4 5
10. Please indicate the distance (1to 10) between the following brands in the cells below.
Brands which are similar are indicated as 1 and those far apart as 10.
DELL
DELL
DELL
DELL
DELL
DELL
LENOVO
HP
TOSHIBA
APPLE
ACER
VAIO
LENOVO
LENOVO
LENOVO
LENOVO
LENOVO
HP
TOSHIBA
APPLE
ACER
VAIO
HP
HP
HP
HP
TOSHIBA
APPLE
ACER
VAIO
TOSHIBA
TOSHIBA
TOSHIBA
APPLE
ACER
VAIO
APPLE
APPLE
ACER
VAIO
ACER
VAIO
11. Place the brands in the cells below in terms of their performance in the given
attributes
VERY GOOD
VERY BAD
BRAND IMAGE
FEATURES
CONFIGURATION
STYLE
AUDIO/VIDEO QUALITY
PRICE
CONVENIENCE OF SERVICE
Respondent Profile Primary research was conducted in online as well as personal interview form. The
questionnaire was created using Google forms and distributed to respondents in the SIC
campus. The main purpose behind doing this was to get data from respondent which varied
demographically and geographically. A total of 104 respondents filled in the questionnaire.
The internet link of the questionnaire is
https://spreadsheets.google.com/viewform?formkey=dFJ2YmcyVUdjWS1UY2liRXlHTkVtV2c6MA
The respondents profile can be seen in the following graphs:
64%
36%
Sex Ratio among respondents
Male
Female
36%
23%
39%
2%
Work Experience of respondents
0-1 years
1-2 years
2-4 years
> 4 years
43%
4%
37%
13%
3%
Educational Qualification break-up
BE/B.Tech
MBBS/BDS/B.Pharma
B.Com
B.Sc
B.A
6%
39%
31%
24%
Annual Family Income of respondents
< 2 Lacs
2-4 Lacs
4-7 Lacs
> 7 Lacs
33%
11%34%
7%
2% 8%5%
Laptops Owned by respondents
Dell
Lenovo
HP
Toshiba
Apple
Acer
Vaio
Chi-Square
1. To find out whether the choice of laptop depends on the gender of the individual, we
did a cross tabulation of the data and a chi-square analysis of the laptop choice w.r.t
gender at 95% confidence level.
Hypothesis:
H0 : There is no significant relationship/correlation between the laptop choice and gender at
95% confidence level.
H1: There exists a significant relationship between choice of laptop and the gender of the
person at 95% confidence level.
From the analysis, the following results were obtained:
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Laptop * Gender 104 100.0% 0 .0% 104 100.0%
Laptop * Gender Crosstabulation
Gender Total
Female Male Female
Laptop Lenovo Count 3 7 10
Expected Count 3.6 6.4 10.0
Dell Count 14 20 34
Expected Count 12.1 21.9 34.0
HP Count 11 25 36
Expected Count 12.8 23.2 36.0
Acer Count 5 3 8
Expected Count 2.8 5.2 8.0
Toshiba Count 2 5 7
Expected Count 2.5 4.5 7.0
Vaio Count 0 5 5
Expected Count 1.8 3.2 5.0
IBM Count 0 2 2
Expected Count .7 1.3 2.0
Apple Count 2 0 2
Expected Count .7 1.3 2.0
Total Count 37 67 104
Expected Count 37.0 67.0 104.0
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 11.164(a) 7 .132
Likelihood Ratio 13.833 7 .054 Linear-by-Linear Association
.034 1 .855
N of Valid Cases 104
The chi square value comes out to be 11.64 for 7 degree of freedom. The p value is
0.132. If we check at 95% confidence level since p observed > p critical, we cannot reject
the hypothesis. Thus, there is no significant relationship between the laptop choice and the
gender of the buyer.
2. To find whether there is a relationship between the graduation degree and the laptop
choice of students. For this too, we do a cross tabulation analysis using chi-square.
We test the laptop brand choice w.r.t the degree obtained by the respondent.
Hypothesis:
H0: There is no significant relation between the laptop choice and the graduation degree of
respondent at 95% confidence level.
H1 : There is a significant relationship between the laptop choice and the graduation degree
of respondent at 95% confidence level.
Chi-Square Tests
Value Df Asymp. Sig.
(2-sided)
Pearson Chi-Square 68.740(a) 49 .033
Likelihood Ratio 76.774 49 .007 Linear-by-Linear Association
2.260 1 .133
N of Valid Cases 103
From the table above, it can be seen that the chisquare value is very high while the p
value is low abolut 3.3%. At 95% confidence level, we will have to reject the null hypotheses
since the p value (observed) < p value critical. Thus, we reject the null hypothesis and
conclude that there is a significant relation between the degree and the kind of laptop
selected.
Laptop * Degree Cross tabulation
C
Degree
Total B.E/B.Tech BSc. MBBS/BDS B.Com B.A BBA M.Sc BBM
Laptop Lenovo 7 3 0 0 0 0 0 0 10
Dell 12 3 5 6 3 3 2 0 34
Vaio 5 0 0 0 0 0 0 0 5
HP 22 2 0 8 0 0 0 3 35
Acer 0 3 3 2 0 0 0 0 8
Toshiba 7 0 0 0 0 0 0 0 7
IBM 2 0 0 0 0 0 0 0 2
Apple 2 0 0 0 0 0 0 0 2
Total 57 11 8 16 3 3 2 3 103
It can be seen from the cross tabulation that while Dell is chosen by almost all
students, brands like Acer are popular among the MBBS students and B.E/B.Tech students
tend to use more of HP computers. The B.Com students use HP, Acer and Dell. Thus, there
is a relationship between the degree obtained by the respondent and the laptop brand used
by him.
ANOVA ANOVA stands for Analysis Of Variance, the generic name given to a set of
techniques for studying cause-and-effect of one or more factors (independent variables) on a
single dependant variable. ANOVA is used when the independent variable are of nominal
scale (categorical) and the dependant variable is metric (continuous). The application areas
in marketing research for experiments using ANOVA as the analytical method are wide.
Whenever a marketing mix variable such as price, a specific promotion or type of
distribution, even specific elements like shelf space, or color of packaging and so on is
change, we would want to know its effect. Under proper conditions, an experiment can tell us
the effects of specific variations in one or more elements of the marketing mix.
In our project ANOVA has been used to study the effect of multiple factors on the
choice of laptop simultaneously and also the interaction effect between the independent
factors (factorial design with 3 factors).
Factorial Design:
Null Hypothesis
There is no significance difference in the choice of the laptop brand between
respondents of different educational background.
There is no significance difference in the choice of the laptop brand between
respondents of with varied annual family income.
There is no significance difference in the choice of the laptop brand between
respondents of varied amount of work experience.
There is no significant impact on the choice of laptop brand when both
educational background and the family income interact with each other.
There is no significant impact on the choice of laptop brand when both
educational background and years of work experience interact with each other.
There is no significant impact on the choice of laptop brand when both annual
family income and years of work experience interact with each other.
There is no significant impact on the choice of laptop brand when annual family
income, work experience and educational background of the respondent interact
with each other.
Findings:
The significance of the F-test with respect to the variable educational qualification
is 0.037.
The significance of the F-test with respect to the variable annual family income is
0.004.
The significance of the F-test with respect to the variable years of work
experience is 0.126.
The significance of the F-test with respect to the interaction of the variables
educational qualification and annual family income is 0.000.
The significance of the F-test with respect to the interaction of the variables
educational qualification and work experience is 0.336.
The significance of the F-test with respect to the interaction of the variables
annual family income and tears of work experience is 0.038.
The significance of the F-test with respect to the interaction of the variables
educational qualification and annual family income and work experience is 0.605.
Inference:
As 0.037 < 0.05, we reject the null hypothesis and infer that educational
qualification has a significant impact on the choice of laptop brand.
As 0.004 < 0.05, we reject the null hypothesis and infer that annual family has a
significant impact on the choice of laptop brand.
As 0.126 > 0.05, we do not reject the null hypothesis and infer that educational
qualification does not have a significant impact on the choice of laptop brand.
As 0.000 < 0.05, we reject the null hypothesis and infer that the interaction of the
variables educational qualification and family income has a significant impact on
the choice of laptop brand.
As 0.336 > 0.05, we do not reject the null hypothesis and infer that the interaction
of the variables educational qualification and family income does not have a
significant impact on the choice of laptop brand.
As 0.038 < 0.05, we reject the null hypothesis and infer that the interaction of the
variables family income and work experience has a significant impact on the
choice of laptop brand.
As 0.605 > 0.05, we do not reject the null hypothesis and infer that the interaction
of the variables educational qualification, family income and work experience
does not have a significant impact on the choice of laptop brand.
Factor Analysis Factor Analysis is a general name denoting a class of procedures primarily used for
data reduction and summarization. In marketing Research, there may be a large number of
variables most of which are correlated and which must be reduced to a manageable level.
Relationships among sets of many interrelated variables are examined and represented in
terms of a few underlying factors. Factor Analysis is an independent technique in that an
entire set of independent relationships is examined.
Factor analysis is used in the following circumstances:
1. To identify underlying dimensions or factors that explains the correlation among a
set of variables. For ex, a set of lifestyle statements may be used to measure the
psychographic profiles of consumers. These statements may be factor analyzed
to identify the underlying psychographic factors.
2. To identify a new, smaller set of uncorrelated variables to replace the original set
of correlated variables in subsequent multivariate analyses.
3. To identify a smaller set of salient variables from a larger set for use in
subsequent multivariate analysis. For example, a few of the original lifestyle
statements that correlate highly with the identified factors may be used as
independent variables to explain the differences between the loyal and normal
users.
In the exploratory research, we obtained 28 attributes which respondents find important
while buying a laptop. Factor analysis was used to club similar attributes into factors so as to
know what exactly the consumers look for while choosing a laptop.
The total variance explained is shown in the table below along with the eigen value at each
stage. When the eigen value drops below 1, we stop the factor analysis process. Since at
the 6th stage, the eigen value became < 1, we stopped the process and concluded that
there are 6 factors as per the respondents.
We obtained the following 6 factors after doing factor analysis:
Factor 1 Convenience features
Factor 2 Heavy usage features
Factor 3 Style features
Factor 4 Gaming features
Factor 5 Easy movability features
Factor 6 Security features
By the main questionnaire, we tried to measure people’s attitude towards various attributes
that directly or indirectly affect the buying behaviors of people towards buying of snacks
food. Please refer to questionnaire in the appendix. Respondents were asked questions like
if they prefer foreign brands, if they watch TV during leisure time, If people around them
affect their buying decisions.
Respondents were asked to rate their attitude towards on a Likert scale of 1 to 7, where 1
stands for Strongly agree and 7 stands for strongly disagree.
The data collected was analysed using SPSS for identifying the significant factors. Factors
with eigen values more than 1 were considered and it explained 70% of the total variation.
Factors identified are Brand Image, taste, price, quality and advertisement & promotions.
Component
Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %
1 11.79 42.11 42.11 11.79 42.11 42.11 8.10 28.91 28.91
2 3.80 13.58 55.69 3.80 13.58 55.69 4.82 17.22 46.13
3 2.90 10.34 66.03 2.90 10.34 66.03 4.26 15.22 61.35
4 2.36 8.44 74.47 2.36 8.44 74.47 2.75 9.83 71.18
5 1.96 7.00 81.47 1.96 7.00 81.47 2.67 9.53 80.71
6 1.39 4.98 86.45 1.39 4.98 86.45 1.61 5.74 86.45
7 0.97 3.46 89.91
8 0.82 2.93 92.84
9 0.57 2.04 94.88
10 0.46 1.63 96.51
11 0.35 1.25 97.76
12 0.29 1.05 98.81
13 0.14 0.48 99.30
14 0.08 0.30 99.60
15 0.06 0.22 99.82
16 0.04 0.15 99.96
17 0.01 0.04 100.00
18 0.00 0.00 100.00
19 0.00 0.00 100.00
20 0.00 0.00 100.00
21 0.00 0.00 100.00
22 0.00 0.00 100.00
23 0.00 0.00 100.00
24 0.00 0.00 100.00
25 0.00 0.00 100.00
26 0.00 0.00 100.00
27 0.00 0.00 100.00
28 0.00 0.00 100.00
Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
After the factor analysis, perceptual maps were drawn using excel for graphically depicting
the relationship by showing the loadings of various attributes on factors identified. Every
possible combination leading to 6C2 i.e. total fifteen maps are drawn for the factor
combinations. One such perceptual map is as shown below:
Series 1 : F2 v/s F3 i.e Heavy usage v/s Style parameters
Rotated Component Matrix:
Factor No. 1 2 3 4 5 6
DisplayQuality 0.224 0.757 -0.033 0.515 0.031 0.083
Memory 0.208 0.763 -0.403 0.251 -0.047 0.112
Processor 0.438 0.859 -0.072 0.105 -0.091 0.054
Looks -0.171 -0.069 0.861 0.151 -0.232 -0.007
Brand 0.744 0.523 -0.208 0.041 0.131 0.101
Texture 0.135 0.048 0.916 -0.244 0.045 -0.007
Price -0.850 -0.115 0.272 -0.378 0.056 0.005
WiFi 0.248 0.164 -0.807 0.117 0.374 -0.123
Camera -0.534 -0.245 0.347 -0.058 0.477 -0.132
AudiospeakersMic 0.520 0.731 0.172 0.039 0.098 -0.061
FingerprintScan -0.076 -0.029 -0.235 0.327 -0.118 0.719
WeightSize -0.169 -0.129 0.354 0.199 -0.789 -0.059
BatteryLife -0.806 -0.309 0.054 -0.423 -0.008 0.083
Operatingsystempreloaded 0.722 0.253 -0.117 0.355 0.147 -0.195
Manyfeatures -0.155 -0.097 0.835 -0.336 0.334 -0.009
Graphicscard 0.219 0.275 -0.167 0.702 0.019 -0.081
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
-0.2 0 0.2 0.4 0.6 0.8 1 1.2
Series1
Texture
Audio
Display
Porcessor
Customized
Memory
Wi-Fi
Many features
Heatdissipationefficiency -0.793 -0.221 0.513 0.020 -0.117 -0.062
Bluetooth -0.805 -0.175 0.185 -0.260 0.197 -0.140
FunctionButtons 0.813 0.424 0.108 0.206 0.148 0.016
Othersopinion 0.272 0.098 -0.253 0.760 0.041 0.284
Accessories 0.907 0.192 0.158 0.221 -0.065 0.098
Addonsoftwares 0.421 0.164 0.269 0.532 0.498 0.298
SchemesandOffers -0.027 0.051 0.077 -0.169 -0.839 0.106
Extendedservice 0.185 0.108 0.239 -0.099 0.026 0.858
Easyavailabilityofdealers 0.668 0.559 -0.148 -0.129 -0.164 0.058
Closeaftersalesservice 0.907 0.028 -0.005 -0.146 0.223 0.108
Onsiteservice -0.510 0.422 0.341 0.109 0.553 -0.001
Customisedlaptops 0.121 0.956 -0.038 0.068 0.133 -0.016
The attributes important for each factors have a corelation value of > ±0.7. These have been
shown by the darkened cells in the table above.
Cluster Analysis (Please refer to Appendix B and C)
Cluster Analysis is a class of techniques used to classify objects or cases into relatively
homogenous groups called clusters. Objects in each cluster and tend to be similar to each
other and dissimilar to objects in the other clusters. Cluster analysis is also called
classification analysis or numerical taxonomy. Cluster Analysis is also used for the following:
1. Segmenting the market: For ex: Consumers may be clustered on the basis of
benefits sought from the purchase of a product. Each cluster would consist of
consumers who are relatively homogenous in terms of the benefits they seek. This
approach is called benefit segmentation.
2. Understanding Buyer Behaviors: Cluster Analysis can be used to identify
homogenous groups of buyers. Then the buying behavior of each group can be
examined separately.
3. Identifying new product opportunities: By clustering brands and products, competitive
sets within the market can be determined.
4. Selecting Test Markets:
5. Reducing Data: Clustering analysis can be used as general data reduction tool to
develop clusters or subgroups of data that are more manageable than individual
observations.
The hierarchical clustering was performed on the sample data using SPSS. The sample
consisted of data from 104 respondents on 13 variables. The agglomeration schedule gives
the stage wise cluster formation. Based on the quantum jump in the coefficients, it was
decided to have 5 clusters. After the subjective decision to have five clusters, K-means
cluster analysis was carried out with number of clusters as 5.
The clusters identified are as given below:
Cluster1 – Lazy Individuals:
They do not feel that quality comes with price.
They prefer branded products.
They are not interested in showing –off their laptops.
They are risk averse.
They value utility of a product more than its attractiveness.
They never involve their families in their purchasing decisions.
They are indifferent to Indian made and foreign products.
They always use email as a major source of communication.
They use laptops more for entertainment purposes than for any other purpose.
They feel computers are very important in today’s world.
They strongly feel that borrowing money even during times of need is improper.
They are cautious about their future.
They are fashion conscious.
Cluster2 – Adventurous:
They feel that quality comes with price.
They prefer branded products.
They are not interested in showing –off their laptops.
They do not mind taking risks while buying products based on unproven
technologies.
They strongly value utility of a product more than its attractiveness.
They involve their families in their purchasing decisions.
They are indifferent to Indian made and foreign products.
They always use email as a major source of communication.
They use laptops equally for entertainment and other purposes.
They feel computers are very important in today’s world.
They strongly feel that borrowing money even during times of need is improper.
They are indifferent to saving for the future.
They are fashion conscious.
Cluster3 – Flexible:
They feel that quality comes with price.
They prefer branded products.
They are not interested in showing –off their laptops.
They are risk averse.
They are indifferent between utility of a product and its attractiveness.
They involve their families in their purchasing decisions.
They are indifferent to Indian made and foreign products.
They always use email as a major source of communication.
They use laptops equally for entertainment and other purposes.
They feel computers are very important in today’s world.
They feel that borrowing money during times of need is proper.
They are indifferent to saving for the future.
They are indifferent to fashion.
Cluster4 – Patriotic:
They feel that quality comes with price.
They prefer branded products.
They hate to show –off their laptops.
They are risk averse.
They value utility of a product more than its attractiveness.
They involve their families in their purchasing decisions.
They prefer Indian made to foreign products.
They always use email as a major source of communication.
They use laptops equally for entertainment and other purposes.
They feel computers are very important in today’s world.
They strongly feel that borrowing money even during times of need is improper.
They are indifferent to saving for the future.
They are indifferent to fashion.
Cluster5 – GenY:
They feel that quality comes with price.
They strongly believe in branded products.
They like to show –off their laptops.
They are indifferent to risk involved in buying a product based on unproven
technology.
They value utility of a product more than its attractiveness.
They involve their families in their purchasing decisions.
They prefer foreign made to Indian products.
They always use email as a major source of communication.
They use laptops for entertainment purposes.
They feel computers are very important in today’s world.
They are indifferent to borrowing money during times of need.
They are a carefree lot who believe in enjoying the present.
They are indifferent to fashion.
Multidimensional Scaling MDS is a class of procedures for representing perceptions and preferences of
respondents spatially by means of a visual display. Perceived or psychological relationships
along stimuli are represented as geometric relationships among points in a multidimensional
space. These geometric representations are often called spatial maps. The axes of the
spatial map are assumed to denote the psychological bases or underlying dimensions
respondents use to form perceptions and preferences for stimuli. MDS is used to identify:
1. The number and nature of dimensions consumers use to perceive different brands in
the market place
2. The positioning of current brands on these dimensions
3. The positioning of consumers’ ideal brand on these dimensions.
In our research we used multidimensional scaling technique for finding the number of
dimensions on which people perceive different leading brands that had come up during
exploratory research.
Out of 104 respondents, we filtered out non-serious responses in the distances we had
asked between brands. Users having given same distances between all the brands were not
considered. Also we applied our own perception to weed out arbitrary and casual responses.
Out of serious responses, we took averages of the distances between various brands given
by the users.
Below is the average distance matrix between top five brands.
Apple Viao Dell HP Toshiba Lenovo Acer
Apple 0 1.4 6.3 5.8 7 6 10.6
Viao 1.4 0 5 4.47 5.6 5.3 9.2
Dell 6.3 5 0 1.4 3.16 5 5.3
HP 5.8 4.47 1.4 0 2 3.6 5
Toshiba 7 5.6 3.16 2 0 3.16 3.6
Lenovo 6 5.3 5 3.6 3.16 0 5.09
Acer 10.6 9.2 5.3 5 3.6 5 0
We ran SPSS software for multidimensional scaling. We got that above distances between
brands can be explained using two dimensions, with Kruskal’s stress less than 2.75%.
Attached below is the SPSS output.
Stimulus Coordinates
Stimulus Number
Stimulus Name
Dimension
1 2
1 Apple 2.0575 -0.1803
2 Viao 1.5328 0.0732
3 Dell -0.2741 0.9531
4 HP -0.1932 0.3928
5 Toshiba -0.6936 -0.0418
6 Lenevo -0.3032 -1.0412
7 Acer -2.1263 -0.1559
Attribute Based Perceptual Mapping
In this case there are 7 brands of laptop and we are interested in finding out how customers
perceive these brands. In particular, we would like to know how the brand we are marketing
is perceived in relation to other brands.
Dell
HP
LenovoToshiba
Apple
Acer
Vaio
-6
-4
-2
0
2
4
6
-20 -10 0 10 20
Price,Service
Features,Configuration
Series1
Thus from the above graphs we can observe that the brands Dell and HP fall in the first
quadrant and hence have great features and are priced well as well. Then in the second
quadrant there is Apple and Vaio and in this it shows that Apple is costly but it is not as good
as Dell with respect to configuration. Here Vaio is also costly but the configuration is better
than Apple. In the third quadrant we have Lenovo and Toshiba. Thus we can see that they
are not as costly as Apple and Dell but their configuration is not that good as well and hence
it is in the negative. Finally in the fourth quadrant we have Acer which is very cheap and its
configuration is not good at all.
Dell
HP
LenovoToshiba
Apple
Acer
Vaio
-6
-4
-2
0
2
4
6
-15 -10 -5 0 5 10 15
Price,Service
Features,Configuration
Series1
Features
Price
Style
AudioVideoQuality
Configuration
Convenienceofservice
BrandImage
Perceptual mapping:
Features
PriceStyle
Audio Video Quality
Configuration
Conveinence of Service
Brand Image
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
-0.2 0 0.2 0.4 0.6 0.8
Factor 1-2
Series1
Features
Price
StyleAudio Video QualityConfiguration
Conveinence of Service
Brand Image-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
-0.2 0 0.2 0.4 0.6 0.8
Factor 2-3
Series1
There will be 21 such plots in all that can be plotted from the structure matrix table.
Thus from the structure matrix and the Eigen values and the Wilks lambda we can see that
the factors effecting the purchase of laptops are features and price and these are based on
the factor 1,factor 2,factor 3 and factor 4 as the Eigen values in this case are >1 and as wilks
lambda is <0.5.
Features
Price
Style
Audio Video Quality
Configuration
Convenience of Service
Brand Image
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
-0.6 -0.4 -0.2 0 0.2 0.4 0.6
Factor 3-4
Series1
Recommendations
1. The secondary research on the laptops makes us aware that people prefer to buy laptops from authorized retailers more than that from any other form of outlet.
2. From the Chi-square tests it is found that there is no significant relationship between
the laptop choice and the gender of the buyer.
3. Also from Chi-square test we found that there is a significant relation between the
degree and the kind of laptop selected.
4. From the ANOVA test we could recommend that
o educational qualification has a significant impact on the choice of laptop
brand.
o annual family has a significant impact on the choice of laptop brand.
o annual family income has a significant impact on the choice of laptop brand.
o the interaction of the variables educational qualification and family income
has a significant impact on the choice of laptop brand.
o the interaction of the variables family income and work experience has a
significant impact on the choice of laptop brand.
o the interaction of the variables educational qualification, family income and
work experience does not have a significant impact on the choice of laptop
brand.
5. The major factors form factor analysis are as follows:
Factor 1 Convenience features
Factor 2 Heavy usage features
Factor 3 Style features
Factor 4 Gaming features
Factor 5 Easy movability features
Factor 6 Security features
6. The clusters obtained from the peoples lifestyle are as follows:
Cluster1 – Lazy Individuals:
Cluster2 – Adventurous:
Cluster3 – Flexible:
Cluster4 – Patriotic:
Cluster5 – GenY
Appendix A – Anova (Factorial Design)
***************************************************************************************************************** UNIANOVA Laptop BY EdnQual FmlyIncome WorkEx /METHOD = SSTYPE(3) /INTERCEPT = INCLUDE /CRITERIA = ALPHA(.05) /DESIGN = EdnQual FmlyIncome WorkEx EdnQual*FmlyIncome EdnQual*WorkEx FmlyIncome*WorkEx EdnQual*FmlyIncome*WorkEx .
Between-Subjects Factors
Value Label N
Edn Qual
1 BE/B.Tech 45
2 MBBS/BDS 4
3 B.Com 39
4 B.Sc 13
5 B.A 3
Fmly Income
1 < 2Lacs 6
2 2-4Lacs 41
3 4-7Lacs 32
4 >7Lacs 25
Work Ex 1 0-1 years 37
2 1-2years 24
3 2-4years 41
4 >4years 2
Tests of Between-Subjects Effects
Dependent Variable: Laptop
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model 149.709(a) 25 5.988 2.933 .000
Intercept 187.463 1 187.463 91.801 .000
EdnQual 21.989 4 5.497 2.692 .037
FmlyIncome 29.362 3 9.787 4.793 .004
WorkEx 12.055 3 4.018 1.968 .126
EdnQual * FmlyIncome 67.526 4 16.881 8.267 .000
EdnQual * WorkEx 7.020 3 2.340 1.146 .336
FmlyIncome * WorkEx 21.781 4 5.445 2.667 .038
EdnQual * FmlyIncome * WorkEx 2.064 2 1.032 .505 .605
Error 159.281 78 2.042
Total 1101.000 104
Corrected Total 308.990 103
a R Squared = .485 (Adjusted R Squared = .319)
Appendix B - Hierarchical Clustering Data
*************************************************************************
CLUSTER Qlty_Price BrandedPdts Attracts_Attn Risk_Loving Utility_Loving
Fmly_involved_prch Frgn_Pdts Email Laptop_Ent Comp_Imp Not_borrow
Enjoy_Present Fashion_Consc
/METHOD BAVERAGE
/MEASURE= SEUCLID
/PRINT SCHEDULE
/PLOT DENDROGRAM VICICLE.
**************************************************************************
Case Processing Summary (a,b)
Cases
Valid Missing Total
N Percent N Percent N Percent
104 100.0 0 .0 104 100.0
a Squared Euclidean Distance used b Average Linkage (Between Groups)
* * * * * * H I E R A R C H I C A L C L U S T E R A N A L Y S I S * * * * * *
Dendrogram using Average Linkage (Between Groups)
Rescaled Distance Cluster Combine
C A S E 0 5 10 15 20 25
Label Num +---------+---------+---------+---------+---------+
15
99
57
1
85
43
11
95
53
17
101
59
16
100
58
3
87
45
10
94
52
41
83
40
82
39
81
42
84
38
80
8
92
50
2
86
44
6
90
48
7
91
49
5
89
47
12
96
54
27
69
21
63
26
68
23
65
24
66
18
102
60
22
64
20
104
62
25
67
19
103
61
14
98
56
9
93
51
4
88
46
13
97
55
29
71
35
77
32
74
36
78
34
76
31
73
37
79
28
70
30
72
33
75
ICICLE PLOT
Appendix C – K-Means Cluster Analysis **************************************************************************************************************** QUICK CLUSTER Qlty_Price BrandedPdts Attracts_Attn Risk_Loving Utility_Loving
Fmly_involved_prch Frgn_Pdts Email Laptop_Ent Comp_Imp Not_borrow
Enjoy_Present Fashion_Consc
/MISSING=LISTWISE
/CRITERIA= CLUSTER(5) MXITER(10) CONVERGE(0)
/METHOD=KMEANS(NOUPDATE)
/PRINT ANOVA CLUSTER DISTAN.
*************************************************************************
Iteration History (a)
Iteration
Change in Cluster Centres
1 2 3 4 5
1 4.010 3.561 3.843 3.285 3.669
2 .191 .582 .402 .728 .958
3 .000 .231 .445 .173 .665
4 .000 .000 .000 .000 .000
a Convergence achieved due to no or small change in cluster centres. The maximum absolute coordinate change for any centre is .000. The current iteration is 4. The minimum distance between initial centres is 8.062.
Final Cluster Centres
Cluster
1 2 3 4 5
Qlty_Price 5.8333 2.3571 3.3333 3.8696 2.6316
Branded Pdts 3.2917 2.2143 3.5000 2.4348 1.8421
Attracts_Attn 4.5417 5.0000 4.4583 6.4348 3.4737
Risk_Loving 4.9167 3.1429 5.0833 6.5652 4.1053
Utility_Loving 3.6250 1.9286 3.9167 2.0000 2.6316
Fmly_involved_prch 6.0000 2.4286 2.9583 2.0435 3.0000
Frgn_Pdts 3.9167 3.7143 3.8750 5.2174 2.4211
Email 1.7083 1.7857 2.2917 1.4783 1.7895
Laptop_Ent 1.8750 4.1429 4.0417 4.3913 3.0000
Comp_Imp 1.6250 1.2857 1.9167 1.1304 1.5263
Not_borrow 1.5833 1.8571 4.6250 3.5652 3.5263
Enjoy_Present 4.8750 4.0714 4.1250 4.2174 1.3684
Fashion_Consc 3.0833 3.2857 3.5833 4.3913 3.5263
Distances between Final Cluster Centres
Cluster 1 2 3 4 5
1 6.189 5.554 6.601 6.628
2 6.189 4.398 4.781 4.181
3 5.554 4.398 4.126 4.386
4 6.601 4.781 4.126 6.074
5 6.628 4.181 4.386 6.074
ANOVA
Cluster Error
F Sig. Mean Square
df Mean
Square df
Qlty_Price 39.8877 4.0 1.1405 99.000 34.97351 0.0000
Branded Pdts 10.6241 4.0 1.1666 99.000 9.106851 0.0000
Attracts_Attn 24.962 4.0 0.791 99.000 31.55891 0.0000
Risk_Loving 29.9266 4.0 1.2406 99.000 24.12202 0.0000
Utility_Loving 17.6442 4.0 1.2405 99.000 14.22361 0.0000
Fmly_involved_prch 56.3733 4.0 0.9429 99.000 59.78948 0.0000
Frgn_Pdts 20.4941 4.0 0.6653 99.000 30.80647 0.0000
Email 2.0895 4.0 0.3351 99.000 6.236225 0.0002
Laptop_Ent 24.7195 4.0 0.8159 99.000 30.29654 0.0000
Comp_Imp 2.0751 4.0 0.3602 99.000 5.760856 0.0003
Not_borrow 35.2634 4.0 0.7026 99.000 50.18686 0.0000
Enjoy_Present 36.9103 4.0 0.9749 99.000 37.86155 0.0000
Fashion_Consc 5.5749 4.0 1.058 99.000 5.269425 0.0007
The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal.
Number of Cases in each Cluster
Cluster 1 24.000
2 14.000
3 24.000
4 23.000
5 19.000
Valid 104.000
Missing .000
Appendix D - Attribute based Perceptual
Mapping
Eigenvalues
Function Eigenvalue % of Variance Cumulative % Canonical Correlation
1 25.839(a) 63.5 63.5 .981
2 11.567(a) 28.4 92.0 .959
3 1.946(a) 4.8 96.7 .813
4 1.283(a) 3.2 99.9 .750
5 .039(a) .1 100.0 .194
6 .002(a) .0 100.0 .049
a First 6 canonical discriminant functions were used in the analysis.
Wilks' Lambda
Test of Function(s) Wilks'
Lambda Chi-square df Sig.
1 through 6 .000 108.748 42 .000
2 through 6 .011 62.689 30 .000
3 through 6 .143 27.254 20 .128
4 through 6 .420 12.129 12 .435
5 through 6 .960 .571 6 .997
6 .998 .034 2 .983
Structure Matrix
Function
1 2 3 4 5 6
Features .624(*) .093 -.397 .568 -.175 .286
Price .038 .750(*) .494 -.225 -.093 -.323
Style .131 .175 .507 .738(*) .323 .204
AudioVideoQuality .030 .145 -.438 .197 .735(*) -.088
Configuration .110 .096 .319 -.146 -.458(*) .386
Convenienceofservice -.032 .521 -.142 -.049 -.349 .755(*)
BrandImage .275 -.140 .249 -.475 .473 .623(*)
Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. * Largest absolute correlation between each variable and any discriminant function
Functions at Group Centroids
Brands
Function
1 2 3 4 5 6
Dell 13.066 4.192 .007 -.245 .493 -.017
HP 4.742 1.882 -.139 .593 -.910 .032
Lenovo -.998 -2.382 2.394 -.420 -.131 -.111
Toshiba -.976 -2.953 1.013 .952 .388 .122
Apple -11.240 3.498 .064 -1.019 -.004 .077
Acer 3.715 -5.394 -1.918 -.782 -.047 .007
Vaio -8.308 1.157 -1.421 .921 .211 -.110
Unstandardized canonical discriminant functions evaluated at group means
Function 1
50-5-10
Func
tion
2
5.0
2.5
0.0
-2.5
-5.0
Vaio
Acer
Apple
Toshiba
Lenovo
HP
Dell
Canonical Discriminant Functions
Group Centroid
Vaio
Acer
Apple
Toshiba
Lenovo
HP
Dell
Brands
Classification Results(a)
a 97.14 % of original grouped cases correctly classified.
Brands
Predicted Group Membership Total
Dell HP Lenovo Toshiba Apple Acer Vaio Dell
Original Count Dell 17 0 0 0 0 0 0 17
HP 0 15 1 1 0 0 0 17
Lenovo 0 0 16 1 0 0 0 17
Toshiba 0 0 0 17 0 0 0 17
Apple 0 0 0 0 17 0 0 17
Acer 0 0 0 0 0 17 0 17
Vaio 0 0 0 0 0 0 17 17
% Dell 100.0 .0 .0 .0 .0 .0 .0 100.0
HP .0 88.24 5.88 5.88 .0 .0 .0 100.0
Lenovo .0 .0 93.75 6.25 .0 .0 .0 100.0
Toshiba .0 .0 .0 100.0 .0 .0 .0 100.0
Apple .0 .0 .0 .0 100.0 .0 .0 100.0
Acer .0 .0 .0 .0 .0 100.0 .0 100.0
Vaio .0 .0 .0 .0 .0 .0 100.0 100.0
References:
Market Research-Text and Cases- Rajendra Nargundkar.
Marketing Research-Measurement and Methods- Tull and Hawkins.
www.dell.co.in
www.hplaptopsindia.com
www.sonyviaolaptopsindia.com
https://www.toshiba-india.com
www.lenovo.com/in/en
www.acer.co.in
www.apple.com