A PROJECT REPORT

ONCONSUMER PURCHASE DECISION

KIRANA STORES VS SUPER MARKETS

SUBMITTED TO: - PROF. SUSHAMA MARATHE

BY :-

AMRITA KASHYAP 09BSHYD0082 (SEAT NO 5) SECTION –K

ANUROOP SINGH 09BSHYD0149 (SEAT NO 3) DATE: - 15/1/2010

GAURAV MADAN 09BSHYD0288 (SEAT NO 7) GROUP NO. 1

KHUSHBU BANSAL 09BSHYD0368 (SEAT NO 9)

PREETISH KR. SINGH 09BSHYD0585 (SEAT NO 1)

HAKAIM SUMAIR RIYAZ 09BSHYD0305 (SEAT NO 11)

ACKNOWLEDGEMENT

We wish to express our sincere gratitude to PROF.SUSHAMA MARATHE, Dept. of Operations

and Systems, IBS Hyderabad for providing us an opportunity to do this project work titled

“CONSUMER PURCHASE DECISION: KIRANA STORES VS SUPERMARKETS” .We

sincerely thank our project mentor for her guidance and encouragement in carrying out this project

work .

AMRITA KASHYAP

ANUROOP SINGH

GAURAV MADAN

KHUSHBU SINHA

PREETISH KR. SINGH

HAKAIM SUMAIR RIYAZ

Place: Hyderabad

Date: 15.01.10

TABLE OF CONTENTS

1. Acknowledgement

2. Executive summary

3. Introduction

4. Methodology

a. Research Design.

b. Sampling Design.

c. Data collection.

d. Sample questionnaire.

e. Coding of data.

5. Data analysis and Discussion

6. Conclusion.

7. References.

Executive Summary

The purpose of our study was to study and analyze the differentiating factors that affects the

consumer’s purchase decisions between local Kirana stores and Supermarkets. A Questionnaire was

designed to record the response of consumers with respect to 13 important factors that determine the

purchasing decisions of consumers.

Data was collected from different Supermarkets and Kirana stores of Hyderabad. The sample size was 150.

The data was coded and analyzed in SPSS ver. 13.0. To analyze we performed cluster analysis. The rationale

of using the cluster analysis was to group together those factors which influenced a person to purchase from

a Kirana store or a Supermarket. Upon observation of the result three clusters(each cluster grouped together

certain factors which explained the purchasing decisions of that particular cluster) came up. Cluster 3

reflected the purchasing behavior that of a person buying from Local Kirana store. Cluster 2 explained the

purchasing behavior of a person buying from Supermarket. And Cluster 1 represented people who didn’t

seem to follow a particular purchasing behavior as no factor significantly triggered their purchasing

decisions.

Introduction

Food and grocery(F&G) segment comprises 62% of the 270 billion Indian retail market. Only 0.8%

of this segment is in organized sector and the organized F&G sector witnessed a year-on-year

growth of 30% as against 2.2% growth of the total F&G retail market in recent years.

This indicates huge opportunity in organized retail sector. Although traditional retail currently

constitutes over 95% of the total sales in the country, smaller Kiranas(Indian version of a

combination of convenience and mom-and-pop stores with <500sq ft area) that are unable to

compete with new age retailers in terms of variety and scale have begun losing volume and share of

customer’s wallet in several parts of the country.

Methodology.

Objective: To study the differentiating factors involved that affects the consumer’s purchase decisions

between local kirana stores and Supermarkets.

Research question: What makes consumers buy from Kirana stores or Supermarkets?

Research Design

A research design is a framework or blueprint for conducting the marketing research project. It details the

procedures necessary for obtaining the information needed to structure or solve marketing research problems

Research design process comprises of

Planning

Selecting method

Define the information needed.

Design the exploratory, descriptive, and/or causal phases of the research

Specify the measurement and scaling procedures

Construct and pretest a questionnaire for data collection

Specify the sampling process and sample size

Conducting survey

o Survey will be used to collect quantitative information about factors from the population.

The survey will focus on collecting factual information as per the purpose.

Develop a plan of data analysis

Sampling Design

Sampling is process or technique of selecting a part of population for purpose of determining the

characteristics of whole population.

Some basic things

Population: - The entire aggregation of items from which samples can be drawn. N indicates the size of

Population

Sample: -Sample is part of total population. It is subset of population and it represents population. n

indicates the size of Sample

Data collection

Data was collected from customers visiting different Supermarkets(Food Bazaar and

More) and Kirana stores in Hyderabad. The size of the sample was 150.

Research Questionnaire

A research questionnaire was prepared to record the response of the sample size. Please refer to the

Annexure 1 to see the questionnaire.

CODING OF THE DATA

GENDER:

MALE 1

FEMALE 2

FAMILY INCOME:

LESS THAN 1 LAC 1

1 LAC TO LESS THAN 2 LAC 2

2 LACS TO LESS THAN 3 LACS 3

3 LACS TO LESS THAN 4 LACS 4

4 LACS AND ABOVE 5

DAILY NEEDS PURCHASED FROM:

SUPER MARKET 1

KIRANA STORE 2

PURCHASING PATTERN:

BULK BUYING 1

AS PER REQUIREMENTS 2

REGULAR VISITOR:

YES 1

NO 2

DURATION OF BEING A CUSTOMER:

LESS THAN 1 YEAR 1

1 YEAR TO LESS THAN 2 YEARS 2

2 YEARS TO LESS THAN 3 YEARS 3

3 YEARS AND ABOVE 4

SATISFACTION WITH THE REGULAR STORE:

VERY SATISFIED 5

SATISFIED 4

NEUTRAL 3

DISSATISFIED 2

EXTREMELY DISSATISFIED1

ON A SCALE OF 5 THE FOLLOWING FACTORS ARE WEIGHTED WHICH INFLUENCE THE

CHOICE OF PURCHASING OUTLET-

STRONGLY AGREE 5

AGREE 4

NEUTRAL 3

DISAGREE 2

STRONGLY DISAGREE 1

1.LOCATION

2.DISCOUNTS

3.TRUST

4.AVAILABILITY

5.AMBIENCE

6.HOME DELIVERY

7.INFLUENCE OF FRIENDS

8.LOYALTY

9.PROMOTIONAL SCHEMES

10.CREDIT AVAILABILITY

11.QUICK SERVICE

12.STAFF ASSISTANCE

13.CREDIT/DEBIT CARD PAYMENT

Analysis and DiscussionCluster analysis, also called segmentation analysis or taxonomy analysis, seeks to identify

homogeneous subgroups of cases in a population. That is, cluster analysis is used when the

researcher does not know the number of groups in advance but wishes to establish groups and then

analyze group membership. Cluster analysis implements this by seeking to identify a set of groups

which both minimize within-group variation and maximize between-group variation.

Key Concepts and Terms

o Cluster formation is the selection of the procedure for determining how clusters are created, and

how the calculations are done. In agglomerative hierarchical clustering every case is initially

considered a cluster, then the two cases with the lowest distance (or highest similarity) are

combined into a cluster. The case with the lowest distance to either of the first two is considered

next. If that third case is closer to a fourth case than it is to either of the first two, the third and

fourth cases become the second two-case cluster; if not, the third case is added to the first cluster.

The process is repeated, adding cases to existing clusters, creating new clusters, or combining

clusters to get to the desired final number of clusters.

o Cluster validity . The utility of clusters must be assessed by three criteria:

1. Size. All clusters should have enough cases to be meaningful. One or more very small clusters

indicates the researcher has requested too many clusters. Analysis resulting in a very large,

dominant cluster may indicate too few clusters have been requested.

2. Meaningfulness. As in factor analysis, ideally the meaning of each cluster should be readily

intuited from the constituent variables used to create the clusters. Variable importance plots,

discussed below, are one method of making this assessment.

3. Criterion validity. The crosstabulation of the cluster id numbers by other variables known from

theory or prior research to correlate with the concept which clustering is supposed to reflect, should

in fact reveal the expected level of association.

Failure to meet these criteria may indicate the researcher has requested too many or too few

clusters, or possibly that an inappropriate distance measure (discussed below) has been selected. It

is also possible that the hypothesized conceptual basis for clustering does not exist, resulting in

arbitrary clusters.

o Distance (proximities ). The first step in cluster analysis is establishment of the similarity or

distance matrix. This matrix is a table in which both the rows and columns are the units of analysis

and the cell entries are a measure of similarity or distance for any pair of cases.

Hierarchical Cluster Analysis

o Hierarchical clustering is appropriate for smaller samples (typically < 250). When n is large, the

algorithm will be very slow to reach a solution. To accomplish hierarchical clustering, the

researcher must specify how similarity or distance is defined and how clusters are aggregated (or

divided). Hierarchical clustering generates all possible clusters of sizes 1...K, but is used only for

relatively small samples. In hierarchical clustering, the clusters are nested rather than being

mutually exclusive, as is the usual case. That is, in hierarchical clustering, larger clusters created at

later stages may contain smaller clusters created at earlier stages of agglomeration.

The basic criterion for any clustering is distance. Objects that are near each other should belong to

the same cluster, and objects that are far from each other should belong to different clusters. For a

given set of data, the clusters that are constructed depend on your specification of the following

parameters:

Cluster method defines the rules for cluster formation. For example, when calculating the

distance between two clusters, you can use the pair of nearest objects between clusters or the pair of

furthest objects, or a compromise between these methods.

Measure defines the formula for calculating distance. For example, the Euclidean distance

measure calculates the distance as a "straight line" between two clusters. Interval measures assume

that the variables are scale; count measures assume that they are discrete numeric; and binary

measures assume that they take only two values.

Standardization allows you to equalize the effect of variables measured on different scales.

Case Processing Summary(a,b)

Cases

Valid Missing Total

N Percent N Percent N Percent

150 100.0 0 .0 150 100.0

a Squared Euclidean Distance used

b Average Linkage (Between Groups)

Squared Euclidean distance removes the sign and also places greater emphasis on objects further

apart, thus increasing the effect of outliers. This is the default for interval data.

Between-groups linkage, also called UPGMA linkage (unweighted pair-group method using

averages), is the default. The distance between two clusters is the average distance between all

inter-cluster pairs. This method is generally preferred over nearest or furthest neighbor methods

since it is based on information about all inter-cluster pairs, not just the nearest or furthest ones.

This method works well for both elongated chain-type and with clumpy type clusters.

Agglomeration Schedule. Agglomeration schedule is a choice under the Statistics button for

Hierarchical Cluster in the SPSS Cluster dialog. In this table, the rows are stages of clustering,

numbered from 1 to (n - 1). The (n - 1)th stage includes all the cases in one cluster. There are two

"Cluster Combined" columns, giving the case or cluster numbers for combination at each stage. In

agglomerative clustering using a distance measure like Euclidean distance, stage 1 combines the

two cases which have lowest proximity (distance) score. The cluster number goes by the lower of

the cases or clusters combined, where cases are initially numbered 1 to n.

The proximity/distance/agglomeration coefficient in the "Coefficients" column is an indicator of

how far the agglomeration algorithm has to reach to combine an existing cluster with the next

closest cluster or variable (judge). For this example one can see that there is a large jump between

stages 5 and 6, corresponding to combining cluster 1 (judges 2,5,7, and 1) with cluster 2 (judges 2,

4, and 6) from stage 5. A large agglomeration coefficient will correspond with a long distance in the

dendogram discussed below. When there are relatively few cases, icicle plots or dendograms

provide the same linkage information in an easier format.

K-means Cluster Analysis

o K-means cluster analysis . Large datasets are possible with K-means clustering, unlike

hierarchical clustering, because K-means clustering does not require prior computation of a

proximity matrix of the distance/similarity of every case with every other case. Because cases may

be shifted from one cluster to another during the iterative process of converging on a solution, k-

means clustering is a type of "relocation clustering method

o K-means cluster analysis uses Euclidean distance. We must specify in advance the desired

number of clusters, K. Initial cluster centers are chosen randomly in a first pass of the data, then

each additional iteration groups observations based on nearest Euclidean distance to the mean of the

cluster. Cluster centers change at each pass. The process continues until cluster means do not shift

more than a given cut-off value or the iteration limit is reached.

Iteration History

This table shows the progress of the estimation process at each iteration. At each iteration, as cases

are reassigned to different cluster, cluster centers change. Each number indicates how far the new

cluster center is from the cluster center at the previous iteration. When the change is small enough

for all clusters, iteration stops and the final solution is reached.

Iteration History(a)

Iteration

Change in Cluster Centers

1 2 3

1 4.089 4.926 4.878

2 .591 .679 1.399

3 .624 .471 .565

4 .461 .291 .176

5 .256 .126 .116

6 .116 .073 .000

7 .000 .000 .000

a Convergence achieved due to no or small change in cluster centers. The maximum absolute coordinate change for any center

is .000. The current iteration is 7. The minimum distance between initial centers is 9.055.

Here each no. indicates how far the new cluster center is from the cluster center at the previous iteration e.g 0.591 indicates that new

cluster center is 0.591 units away from the cluster center of previous iteration.

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. The final clusters centers reflect the attributes of the prototypical case for each cluster

Cluster

1 2 3

Location 1.54 4.39 4.71

Discounts 3.39 3.92 1.44

Trust with the shopkeeper 1.63 3.80 4.73

availability of products 2.17 4.28 3.16

Ambiance 3.15 3.75 1.60

Home delivery 2.63 3.41 3.13

Influence of friends/ family 3.29 3.19 2.87

loyalty to the outlet 2.93 3.58 3.27

Promotional schemes 3.17 3.89 2.96

credit availability 2.61 3.52 4.53

Quick Service 2.93 3.16 4.16

staff assistance 3.90 4.22 1.71

Credit/debit card payment 3.54 4.03 1.31

According to our analysis the different points were observed with respect to different clusters. Viz.

Cluster 3. In this cluster following factors primarily play an important role in determining the

purchasing behavior: Location, Trust with shopkeeper, Credit availability and Quick service.

This group of people are those who are conventional buyers. These kind of people base their

purchasing decisions on the ease of procuring. Ease of procurement here defines both the nearness

of distance of the store and quickness of purchasing . Relationship with the retailer also plays an

important in their case. The usually rely on the advice of the retailer. Whether or not the retailer

allows credit purchases also is an important factor here.

Cluster 2. In this cluster following factors primarily play an important role in determining the

purchasing behavior: Discounts, Availability of Product, promotional schemes, Ambience, Staff

assistance and Credit/Debit card payment.

This group represents a set of people who are active shoppers. Active shoppers here mean those

people who gather a lot of information about the products and their prices they want to buy. This

behavior is likely to be that of an educated buyer who takes informed decisions. They also take into

account the place from where they buy. Also these people want convenience in the way they pay.

Cluster 1. In this cluster, there is not any clarity about the factors that play a role in determining

the purchasing behavior, however the factor that scores highest here is Influence of

Family/Friends.

Also the factors that can be secondarily associated here are: discount, promotional schemes, staff

assistance and credit/debit card payments

This group doesn’t seem to follow a particular purchasing behavior as no factor significantly

triggers their purchasing decisions. Influence of peers scores highest, thus we can say these buyers

keep changing their purchasing pattern.

Conclusion.

We performed cluster analysis of the data provided. The result gave 3 distinct clusters explaining

the purchasing behaviors of various people. On the basis of the results we are able to conclude the

following:

The people that buy from Kirana store look for convenience of buying in terms of distance of the

stores from their homes and the quickness of purchasing. They also rely on the relationship factor.

The people that purchase from Supermarket look for different discounts and promotional schemes.

They also take into account the ambience and the staff assistance at the store. These people also

want convenience in the way they pay.

References

1. http://www.ibef.org/industry/retail.aspx

2. http://www.indianground.com/articles/Miscellaneous-articles/organised-retail-in-

india.aspx

3. http://www.statsoft.com/textbook/cluster-analysis/ important

4.

5. http://faculty.chass.ncsu.edu/garson/PA765/cluster.htm

6. http://en.wikipedia.org/wiki/Category:Supermarkets_of_India

7. Goswami Paromita, Mishra Mridula S., “Would Indian Consumersmove from Kirana

stores to organized retailers when shopping for groceries”, Emerald Insight Publications.

8. Zikmund William G., “Business Research Methods”, 7th Edition, South-Western College

Publications.