BRM_Project_Final1

8/7/2019 BRM_Project_Final1

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BUSINESS RESEARCH METHODS

FINAL REPORT

|CONSUMER PROFILING BASED ONFACTORS AFFECTING CONSUMPTION

CHOICES OF SWEETS @ IBS}

SUBMITTED TO: PROF. RITESHTIWARI

SUBMITTED BY:

Karishma Subudhi 10BSPHH010180 Seat No. 19

MohitMadan 10BSPHH010418 Seat No. 6

Parul Kshatriya 10BSPHH010520 Seat No. 32

Sahil Malik 10BSPHH010667 Seat No. 45

Shifa Sharma 10BSPHH010723 Seat No. 56

Srinivas Reddy 10BSPHH010796 Seat No. 69


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ACKNOWLEDGEMENT

Through the completion of this project, we would like to acknowledge the invaluable guidance

of our faculty, Prof. Ritesh Tiwari without whose support and inspiration this project would not

have been possible. We would like to take this opportunity to thank our faculty for the genuine

pieces of advice which he has given from time to time for the completion of this project.

We would also like to thank all our project mates and fellow students who extended their helpand support whenever required. A special thanks to all who provided their knowledgeable insight

into things of complexity and made them simple and lucid for us.

Last but not the least, my warm heartfelt thanks go out to IBS, Hyderabad for providing us with

the facilities required to do the adequate research and give the project its final shape.


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ROADMAP TO ATTAIN OBJECTIVES

ESTABLISHING RESEARCH OBJECTIVE

STATING THE RESEARCH HYPOTHESIS

IDENTIFYING THE

STATISTICALTECHNIQUES TO BE USED

COLLECTING THE DATA

APPLYING THE STATISTICAL

TECHNIQUES(DESCRIPTIVE,FACTOR

AND CLUSTER ANALYSIS)

INTERPRETING THE RESULTS AND SUGGESTING

STRATEGIES


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RESEARCH OBJECTIVE

Consumer profiling based on the factors affecting consumption choice of sweets.

RESEARCH HYPOTHESIS

As per the scope of this project, we have defined the hypothesis as- consumers can be profiled

on the basis of factors influencing consumption choices.

FOCUS GROUP STUDY

Focus group study is an exploratory research tool where participants meet at a centralized

location, at a designated time.

The focus group allows people to discuss their true feelings and expresses the depth of their

convictions in their own words.

First of all we decided upon a focus group of10 students, comprising of 5 male and 5 female

candidates. The focus group was asked to discuss various factors affecting the consumptionpattern of various sweets.

The data collected from the focus group helped in deciding the questions for the initialquestionnaire. Those variables which had been left out by the focus group were included, making

the questionnaire a holistic one, comprising all essential points.

DISCUSSION GUIDE:

I. Preparing the discussion:

Questioning Route- We created a set of 5 questions in a loose running order to facilitate

participant understanding and encourage replies.

The questions asked were as follows:

1.Which sweets do you like?2.How many times do you consume sweets in a week?

3.What price you are ready to pay for the sweets?4.Which brand(s) of sweets do you generally go for?5.What considerations do you take into account (for e.g., freshness, hygiene, etc) while

buying sweets?

Moderator- The moderators of the focus group discussion were Srinivas Reddy and Shifa

Sharma.


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Selection of participants- As mentioned above, 10 candidates were selected for the discussion,which took place in the reading room on 6

thDecember.

II. Facilitating the discussion:

The facilitation of the discussion began with greetings to the participants by moderators,followed by a brief description of the purpose of the focus group study.

The discussion started with a warm up question of Which sweets do you like? and paced up as

the moderators started posing more specific follow up questions. The outcome of the discussion

was fruitful in analyzing the consumption choices and preparing the questionnaire.

III. Analyzing the discussion:

By analyzing the focus group discussion, we were able to bring out the following factors whichwere helpful in designing the questionnaire-

yHygiene/Freshness

yTaste

yFrequency of consumption

yVarieties

yBrand

yMode of payment

yLocation

yOperating hours

yPrice

INITIALQUESTIONNAIRE

The questionnaire has been designed to gather information relating to opinions, price sensitivity,tastes and preferences in order to study the consumption pattern of sweets at IBS.

The Questionnaire has been devised keeping the following points in mind:

yThe language is easy to understand and interpret.

yThere is no ambiguity in the questions or responses.

yWe have included open-ended, dichotomous, multiple choice questions.

yQuestions have been arranged in an order ranging from general questions to specificquestions.

yWe have used Likert scale as the statements would help us evaluate different aspects ofthe same attitude.


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PRETEST

After preparing the questionnaire we performed a pre-test to detect the problems with the

questions before conducting the final survey. Pretest is used to describe a procedure where thequestionnaire itself is tested on a small scale before it is put to use in a full scale study. It is

conducted to assure that the questionnaire and the actual study are designed properly to elicit thedesired information.

In our pretest, we found that the following questions, which were a part of the initial

questionnaire, were irrelevant:

yWhich brand(s) of sweets are you aware of?

Haldirams

Bikanerwala

Others__________________________________________________________________

The above question was found redundant because of Q.3 included in the final

questionnaire.

yHow would you like to pay for the sweets?

Advance

At the time of purchase

This question was irrelevant as it was found that the students would not like to pay in

advance.


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FINAL QUESTIONNAIRE

Dear Sir/Mam,

We are conducting a survey on the consumption pattern ofsweets in the campus. Kindly fill the

following questionnaire for the same.

PERSONAL DETAILS:

Name (Optional):

Gender: M F

Program/Work Department:

Phone No (Optional):

1) How often do you consume sweets?

Once a day

Twice a day

Thrice a week

Rarely


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2) Which sweet(s) do you generally go for?

Gulabjamun

Jalebi

Kajukatli

Rasgulla

Gajrela

Laddu

Any other, please specify_________________________________________________________

3) Are you brand conscious about sweets?

Yes

No

If yes, which brands do you prefer__________________________________________________


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4) How much do you intend to spend on indigenous sweets in a week?

Rs.150

5) When do you prefer to have sweets in a day?

Morning

Afternoon

Evening

Night


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6) What operating hours would you prefer for the sweets shop?

_____________________________________________________________________________

7) Where would you like the sweet shop to be placed in the campus?

Near mess 1

Near mess 2

Academic block

At campus gate

Any other area, please specify____________________________________________________


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8) Rate the factors which influence your purchase of sweets, on a scale of 1 to 5.(1- HighlyInfluential and 5- Least Influential)

Highly influentialLeast Influential

1 2

FACTORS 1 2 3 4 5

Price

Hygiene

Product variety

Brand

Operating hours

Packaging

Taste

FreshnessLocation

9) Please tick the appropriate choices for sweets-

SA- strongly agree (5), A- agree (4), N- neutral (3), DA- disagree (2), SDA- strongly disagree (1)

S.No SA A N DA SDA

1. I find them tasty.

2. Hygiene affects my purchase decision.

3. I find them as bringing back the tradition.4. They give me energy.

5. I prefer them alone.

6. I take them because I am used to it.

7. The location of the parlor affects my purchase

decision.

8. I look at the wide variety of sweets while

purchasing.

9. I prefer them in the morning.

10. I look at the freshness of the product while

purchasing.

11. Price rise affects my purchase decision.12. I find them as bringing back the tradition.

13. Others purchase affects my decisions.

14. I prefer them with friends.

15. I prefer it in the evening.

THANK YOU FOR YOUR TIME AND CO-OPERATION. HAVE A NICE DAY!


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SURVEY

Survey is a method used to collect, in systematic way, information from a sample of

individuals.There are several ways of administering a survey- telephone, mail, online, door to

door, etc.The choice between administration modes is influenced by several factors, including

costs, coverage of the target population, flexibility of asking questions, respondents willingness

to participate and response accuracy.

Our questionnaire will be circulated to the sample under study through Door-to-Doorand will be

a selfcompletion method of survey.

TARGET POPULATION:

Our target population is the IBS Students and college staff. The total population is 2228.

S.No. Description

% of Total

Population

1 2-Year MBA Program 77.332 PhD 2.02

3 1-Year Executive MBA 1.80

4

College Staff (including Faculty,

Administration, Library, Hostel) 5.39

5 BBA/B.Tech 13.46

The following pie chart explains the distribution of students in various courses:

116

3

38

20

2-Year MBA Program

PhD

1-Year Executive MBA

College Staff (including

Faculty, Administration, Li

brary, Hostel)

BBA/B.Tech


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DESCRIPTIVE ANALYSIS

Descriptive analysis describes the main features of a collection of data quantitatively.Descriptive statistics aims at summarizing a data set quantitatively without employing a

probabilistic formulation, rather than it uses the data to make inferences about the population.

CONSUMPTION TIME OF SWEETS

The statistics has shown mixed results for the consumption time of sweets. Most of people prefer

to eat sweets thrice a week and others are not far behind that gives us an ample of opportunity tocarry out our business.

PRODUCT PREFERENCE

The data shows that there is not much variation in preference of products, while gajrela andladdu comes out to be favorites but other products are also in demand.

20%

22%

32%

26%

SALES

Once a week

Twice a week

Thrice a week

Rarely

22%

20%

15%

16%

14%

13%

0

PRODUCT CHOICE

LADDU

GAJRELA

JALEBI

KAJU KATLI

RASGULLA

GULAB JAMUN


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WEEKLY SPENDINGS ON SWEETS

The spending pattern shows ample scope for earning good revenues and such kind of spending

also shows the stable demand.

PREFERED TIME OF CONSUMPTION

The statistical data clearly shows that about half of the respondents love having sweets in theevening and others in the night and afternoon while a few respondents chose to have it in the

morning which is negligible.

20%

34%28%

18%

WEEKLY SPENDING

Less than 50

50-100

100-150

>150

8%

21%

46%

25%

PREFERED TIME OF

CONSUMPTION

Morning

AfternoonEvening

Night


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BRAND CONCIOUSNESS

Most of the respondents showed that they are brand conscious with the preference of their shops

dependent on their region. The brands included Haldirams, Agrawal Sweets, Krishna Sweets etc.

LOCATION

This was not a dominant variable but most of the respondents gave mess 1 as the desired locationof sweet shop.

31%

69%

BRAND COUNCIOUSNESS

Yes

No

10%

46%27%

15%

2%

LOCATION

Near Academic

Block

Mess 1

Mess 2

Main Gate

Others


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FACTOR ANALYSIS

While conducting a survey, a researcher comes across several sets of variables that are important

in the study and the analysis of the research problem that he is investigating. For proper and

accurate analysis, it is necessary to interpret and understand the structure of a set of variables and

objects. And it is here that factor analysis comes in handy. Rather than trying to predict a

variable or a set of variables from independent variables, factor analysis helps in explaining the

variables at a more generalized level.

The basic task of factor analysis is to break-up or explain the information contained in a large

number of variables into smaller number of factors, thereby summarizing the underlying

dimensions of the variables. It helps a researcher to understand the complex linear relationship

among different sets of variables and interpret the results in terms of simple factors.

In our case, we have defined the research hypothesis as - Consumers can be profiled on the

basis of factors influencing consumption choices. Therefore, we have a set of variables that

tell us about the purchasing pattern of the consumers, we are applying the concept of factor

analysis to break-up the information contained in the large number of variables into smaller

factors, so that it helps us in proper investigation of the variables. Using factor analysis will help

us to reduce the large number of variables to certain exact dimensions that will help us to

summarize important information contained in the variables. It helps in the creation of new and

abstract variables called factors, which further aid us in analysis of the problem better.


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OUTPUT ANALYSIS

Correlation Matrix:

It shows the Correlations among the variables in the analysis. If correlation among the variables

is more than 0.9 then we should drop one of them since there is no use of having both the

variables. Also, we can drop the variables which have the correlation less than .005 and carry out

the analysis with the remaining ones.

In our case, variables correlate well with each other and none of the correlation coefficients are

large (>.9) or small (


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KMO & Bartletts Test:

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy..556

Bartlett's Test of

Sphericity

Approx. Chi-

Square103.020

df 36

Sig. .000

This table shows two tests which indicate the suitability of the data for factor analysis.

KMO: The Kaiser-Meyer-Olkin Measure of Sampling Adequacy is a statistic which indicates the

proportion of variance in variables which is common variance, i.e. which might be caused by

underlying factors. Its value lies between 0 & 1. If KMO is closer to 1, then our data is amenable

to conveniently factorized i.e. it can give distinct factors. If KMO is closer to 0, then we cannot

get distinct factors. According to standards, KMO > 0.5 is Acceptable.

0.5-0.7 = Good

0.7-0.8 = Very Good

0.8-0.9 = Excellent

Above 0.9 = Superb

In our case, KMO = .556, hence we can perform the factor analysis on the gathered data.


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Bartlett's Test of Sphericity: It indicates whether the correlation matrix is an identity matrix,

which would indicate that the variables are unrelated. Here, we test for the following Null

Hypothesis:

H0: R = I, i.e. the Correlation matrix is equal to Identity matrix means there is no correlation

between the variables, so each variable itself is a Factor and hence it is not possible to carry out

the factor analysis.

Since for factor analysis to work we should have some relationships among the variables, and if

matrix is Identity we would have all the correlation coefficients as zeroes. Hence, in order to do a

Factor analysis we should be able to reject the Null Hypothesis i.e. R-M

atrix should not be anIdentity matrix.

In our case, p-value (0.000)


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Total Variance Explained:

Total Variance Explained

Comp

onent

Initial Eigenvalues

Extraction Sums of

Squared Loadings

Rotation Sums of

Squared Loadings

Tota

l

% of

Varian

ce

Cumulative

%

Tota

l

% of

Varian

ce

Cumulative

%

Tota

l

% of

Varian

ce

Cumulative

%

1 1.85

320.591 20.591

1.85

320.591 20.591

1.78

619.841 19.841

2 1.35

014.998 35.588

1.35

014.998 35.588

1.37

415.272 35.112

3 1.22

213.581 49.169

1.22

213.581 49.169

1.20

613.405 48.517

4 1.05

011.664 60.833

1.05

011.664 60.833

1.10

812.315 60.833

5 .916 10.174 71.007

6 .843 9.372 80.378

7 .694 7.708 88.087

8 .573 6.371 94.458

9.499 5.542

100.00

0


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Extraction Method: Principal Component Analysis.

We know mathematically each factor has all the variables. Before extraction (pulling factors out

of the variables present) we take each variable as a separate factor. The method used by us to

extract is Principal Component Analysis. It is a way of identifying patterns in data, and

expressing the data in such a way as to highlight their similarities and differences. The Eigen

value before and after extraction is shown in the above table. The Eigen value associated with

each factor represents the variance explained by that particular component. In the Initial Eigen

value column (before Extraction) we can see SPSS shows us Eigen value in terms of variance

explained i.e. factor 1 explains 20.591% of total variance.

Now, since we have asked SPSS to extract factors with Eigen value >1, it pulled the first four

having 1.853, 1.350, 1.222, 1.050 as their Eigen values (>1), which gives us four factors.

The Eigen value associated with these four factors are again displayed in the after extraction

column as well dropping all other insignificant factors. But in our case we see the cumulative %

accounts to 60.833% i.e. the extracted factors can explain 60.833% variance

Communalities:

The following table shows communalities before and after extraction. Communality is the

percentage of variation in variable that is explained by the factors (in our case it is Four). The

common variance of each variable summarized by the factors, or the amount (percent) of each

variable that is explained by the factors.

For Example: We can say the four factors can define 61.7% variance in Price variable. Since

after extraction some factors are dropped, this value 61.7% has come down owing to loss of

information.


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Communalities

Initial Extraction

Price 1.000 .617

Hygiene 1.000 .590

Product Variety 1.000 .585

Brand 1.000 .299

Operating hours 1.000 .700

Packaging 1.000 .739

Taste 1.000 .593

Freshness 1.000 .697

Location 1.000 .655

ExtractionMethod: Principal Component Analysis.


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Component Matrix:

The output presented below is the component matrix before rotation. This matrix contains the

loadings of each variable onto each factor.

Component Matrix (a)

Component

1 2 3 4

Price .712 -.161 .277 -.085

Hygiene .488 .444 .060 .388

Product Variety -.043 .329 .674 -.144

Brand .367 .042 -.377 -.142

Operating hours -.200 .136 .353 .719

Packaging .227 .800 .023 -.215

Taste .758 .009 -.028 -.134

Freshness .549 -.514 .115 .344

Location .056 .305 -.638 .390



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Scree Plot:

This is also used to take the decision as to how many factors to retain. Here, we find the place

where the smooth decrease of Eigen values appears to level off to the right of the plot.

According to this criterion, we can retain the 4 factors as after this a stable plateau is reached.

987654321

Component Number

2.0

1.5

1.0

0.5

Eigenvalue

Scree Plot


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Rotated Component Matrix:

The rotated component matrix helps to determine what the components represent i.e. which of

the variables come under the components.

In this, we consider the Absolute Values.

Rotated Component Matrix(a)

Component

1 2 3 4

Price .754 .062 -.186 -.099

Hygiene .409 .494 .237 .351

Product Variety -.006 .379 -.629 .212

Brand .243 .123 .333 -.338

Operating hours -.064 .002 -.016 .834

Packaging -.006 .854 .004 -.101

Taste .689 .218 .085 -.254

Freshness .709 -.392 .08

1 .186

Location -.101 .195 .771 .110


RotationMethod: Varimax with Kaiser Normalization.

A Rotation converged in 8 iterations.


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Component Score Coefficient Matrix

Component

1 2 3 4

Price .426 .014 -.187 -.056

Hygiene .221 .329 .192 .350

Product Variety .010 .292 -.524 .150

Brand .101 .074 .248 -.277

Operating hours .015 -.009 .036 .757

Packaging -.053 .628 -.025 -.107

Taste .363 .127 .029 -.189

Freshness .432 -.329 .066 .224

Location -.085 .125 .649 .135


RotationMethod: Varimax with Kaiser Normalization.

Component Scores.

CLUSTER ANALYSIS:

Cluster analysis is a multivariate data analysis tool which aims at sorting different objects into

groups in a way that the degree of association between two objects is maximal if they belong to

the same group and minimal otherwise. In our case, it will help us in identifying group of target

customers who are similar in buying habits. Here, we have identified following factors for the

following factors for the cluster analysis:yValue for money

yPackaging & Hygiene

yVariety & Location

yOperating hours


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Hierarchical Clustering:

We have used hierarchical method to get the number of clusters first. Then we have used the

output number of clusters as an input for the K-Means method of cluster analysis. In hierarchical

method, we do not specify the number of clusters initially.

Agglomeration Schedule:

This schedule helps us in identifying the number of clusters that we should go for. This table

shows how the cases are clustered together at each stage of the cluster analysis. Clusters are

formed by merging cases and cluster a step at a time, until all cases are joined in one big cluster.

At each stage, one case or cluster is joined with another case or cluster.

The Coefficients column indicates the distance between the two clusters (or cases) joined at each

stage. For a good cluster solution, we have seen the sudden jump in the distance coefficient, as

we read down the table. The stage before the sudden change indicates the optimal stopping point

for merging clusters.

Here, this schedule suggests us to go for 2-Cluster solution.

Vertical Icicle:

This plot gives a graphic representation of how the cases are joined at each stage of the analysis.

Each blank space represents a boundary between clusters. Within a row, each contiguous X's

band indicates cases grouped as a cluster. In our case, since Agglomeration schedule suggested

us to have 2-clusters, we saw the second row by using above method and found the members in

each of the 2- clusters


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Dendrogram:

The Dendrogram shows relative similarities between cases.C

ases or clusters that are joined by

lines "further down" the tree (near the left side of the dendrogram) are very similar. Cases or

clusters that are joined by lines "further up" the tree (near the right side) are dissimilar.

A good cluster solution is one with small within-cluster distances, but large between-cluster

distances.

In our case, it has helped in knowing the respondents which lie in all their respective clusters.


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K-MEANS CLUSTER:

After we have got the right number of clusters to work on from the hierarchical cluster method,

we take that as the input for the K-M

eans cluster analysis. Here, we have taken the number ofclusters as 2 and performed the analysis.

Final Cluster Centers:

Final Cluster Centers

Cluster

1 2

REGR factor score 1 for

analysis 1-.26453 .28657


analysis 1.58516 -.63392


analysis 1.06472 -.07011


analysis 1-.47062 .50984

From the ANOVA table we observe the factor score 2( Hygiene & Packaging), factor score

4(Operating hours) are significant. But when we try to understand the importance our clusters

give to these factors it has been observed that the clusters share a similarity in importance.


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ANOVA

Cluster Error

F Sig.

Mean

Square df

Mean

Square df

REGR factor

score 1 for

analysis 1

11.371 1 .930 148 12.228 .001

REGR factor

score 2 for

analysis 1

55.641 1 .631 148 88.207 .000

REGR factor

score 3 for

analysis 1

.681 1 1.002 148 .679 .411

REGR factor

score 4 for

analysis 1

35.991 1 .764 148 47.135 .000

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.

As a result we have taken the variables in order to analyze the cluster so that we can comment on

the importance each cluster gives to the variables.


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Final Cluster centers:

This table shows the values for the final cluster centers. The values in the table are the means for

each variable within each final cluster. This shows that how much influence each factor has on

all the clusters. We have given the name to our clusters on the basis of this table.

Final Cluster Centers

Cluster

1 2

Price 2 2

Hygiene 1 1

Product Variety 2 2

Brand 2 3

Operating hours 3 3

Packaging 2 3

Taste 1 2

Freshness 1 1

Location 3 4

ANOVA

Cluster Error

F Sig.Mean

Square dfMean

Square df

Price .343 1 .667 148 .514 .475

Hygiene .788 1 .210 148 3.751 .055

Product 5.005 1 .662 148 7.564 .007


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Variety

Brand 21.970 1 .971 148 22.629 .000

Operating

hours13.530 1 1.055 148 12.819 .000

Packaging 27.514 1 .849 148 32.405 .000

Taste 7.649 1 .414 148 18.463 .000

Freshness .075 1 .293 148 .257 .613

Location 54.729 1 .758 148 72.172 .000

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 clustermeans are equal.

Now we understand from the ANOVA table that the following variables are significant i.e

Brand, Operating Hours, Packaging, Taste and Location.

Number of Cases in each Cluster:

This table shows how many cases are assigned to each cluster.

Number of Cases in each Cluster

Cluster 1 98.000

2 52.000

Valid 150.000

Missing .000


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CONCLUSION:

FACTOR ANALYSIS:

From the factor analysis, we got 4 factors which influence the consumption choice of IBS

students/staff. We need to focus on these factors to understand the consumption behavior of

Population

CLUSTER ANALYSIS:

From the analysis, we have got 2 clusters and we have named them for the sake of convenience

and identification.

CLUSTER 1 Not conscious

CLUSTER2 Highly conscious

1.NOT CONSCIOUS:

Cluster 1 comprises of respondents whose consumption and purchases are not governed

by any factors such as price, freshness, hygiene, etc. These are the people whose

purchases are random and this cluster might comprise ofimpulsivebuyers who do not

think much before spending.

2.HIGHLY CONSCIOUS:

Cluster 2 consists of respondents whose consumption and purchases are governed by all

the factors under study. These factors are Brand, Operating Hours, Packaging, Taste and

Location. Since their purchases depend on certain factors, appropriate marketing

strategies can be devised in order to capture this segment of respondents.


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REFERENCES

1.William G. Zikmund2.http://www.statsoft.com/textbook/cluster-analysis/3.http://www.norusis.com/pdf/SPC_v13.pdf

4.Questionnaire design by Ian Brace

Documents

BRM_Project_Final1