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ICFAI BUSINESS SCHOOL, CHENNAI

2008

Consumer Preference for Mutual funds Vs Insurance at Annanagar

SUBMITTED TO,Prof. GOPICHANDER.

SUBMITTED BY,V.NIRANJAN 07BS4686

I B S , C O N R A N S m i t h R o a d , C h e n n a i

Table of Contents :

EXECUTIVE SUMMARY

ABSTRACT OF THE WORK TILL THAT DATE

INTRODUCTION

ABOUT MUTUAL FUNDS

HISTORY OF MUTUAL FUNDS

ABOUT INSURANCE

HISTORY OF INSURANCE

TABLE OF CONTENTS

INTRODUCTION

EXECUTIVE SUMMARY

ABSTRACT OF THE WORK TILL THAT DATE

ABOUT SBI LIFE INSURANCE COMPANY

DIFFERENCE BETWEEN MUTUAL FUNDS AND INSURANCE

ABOUT COMPETITORS

ABOUT ANNANAGAR(AREA OF PROJECT)

MAIN TEXT

Objectives

Sampling design

Research design

Data collection

SPSS Output

Analysis from SPSS Output

Data analysis

Findings

Limitations

Recommendations

BIBLIOGRAPHY

Acknowledgement

I would like to thank my faculty guide Gopi Chander who not only served as a supervisor but also guided and encouraged me in doing this project.

I would also like to thank my company guide Mr V.Sathish Kumar for giving me the directions and encouraging me in doing this project

I would also like to thank Mr Gopal Branch Manager of SBI Life Insurance company for giving their sincere support and guidance to me in doing this project

Date April 22, 2008

Place Annanagar (Chennai)

Executive Summary

This report was basically undertaken to find out the consumer preference for mutual funds(Reasons for them to prefer mutual funds) and insurance(Reasons for the people to prefer insurance) in anna nagar. Before doing this project I did a thorough study about mutual funds and insurance.

The basic objective of the research was to find: -

Consumer Preference for mutual fundso Advantages of investing in mutual fundso Disadvantages of investing in mutual fundso Consumer perception towards mutual funds

Consumer Preference for insurance

o Advantages of investing in insuranceo Disadvantage of investing in insuranceo Consumer perception towards insurance

Reasons for migrating from mutual funds to insurance and vice versa

ABSTRACT OF THE WORK TILL THAT DATE:

DATE WORK DONE

22nd feb -29th feb

Classes conducted regarding insurance products

Brief introduction about the SBI Life insurance company

Training given to me regarding selling

1st March – 8th March Lead generation done in annanagar , saidapet

Collecting details regarding the various other insurance companies and putting the details in a excel sheet to get the summarized form

Conducted field activities in Arihant flat, meeting

professionals in the marketing the insurance products

I also met my faculty guide Mr Gopi Chander for getting feedbacks about my work

8th March – 15th March Discussed about my project to my Unit Manager and submitted my Initial Information Report to my Faculty Guide

Sold one UNIT PLUS 2 policy for a premium of Rs 50,000

Got permission for conducting field activity near Odyssey Shop to target a bulk of people for insurance.

Lead generation done in T Nagar and I got a permission to present about the various insurance schemes available in SBI Life Insurance to the people in a MVS training institute

Also did a follow up of leads generated in the previous week and tried to fix up an appointment with the customers

On Friday I met my Faculty Guide for getting feebacks

15th March – 22nd March Project was given to me from SBI headquarters.

My project title “Consumer Preference of Mutual Funds Vs Insurance”

I also discussed about my project to faculty guide and I got few tips in preparing questionnaire for my project

Did field activities in

a) Valluvar kottam

b) T Nagar

c) Century Plaza

I also did a follow up of leads that I generated in the last weeks

Finished one UNIT PLUS 2 policy for 25000

22nd March to 29th March My questionnaire for the project is approved by Mr Gopi Chander

Did field work in Odyssey shop and I did the lead generation

I started working on my project and first I targeted people in Annanagar East

1st April to 8th April Survey for my project done in Annanagar East in all shopping complexes, consultancy, and a few residential areas

8th April to 15th April Survey for my project is done with the people in Annanagar West and Shenoy Nagar to do the market research

Started collected some secondary data for interim report

I also discussed about interim report both with my company guide and faculty guide for getting better quality

15th April to 30th April Did surveys regarding my project in Annanagar and collected primary data for secondary data analysis

1st May to 15th May Did data analysis of my primary data and started preparing my final report

INTRODUCTION:

This project was basically taken to find out the consumer preference of mutual funds (Reasons for them to prefer mutual funds) and insurance (Reasons for preferring insurance).

Before starting this project I did a thorough study about the mutual funds ,Insurance and their classification

Mutual funds:

Mutual funds is defined as a method of joint investment by pooling money from many investors and investing them in shares, funds, short term financial institutions etc. The value of mutual fund is calculated as NAV of the company. It is calculated on daily basis

NAV=Market Value of Investment + Current Assets-Current Liabilities/ Number of Outstanding Units

Mutual funds can be classified based on maturity value, investment, other equity related funds

Based on maturity mutual fund is classified as

a) Open ended funds(No maturity period)

b) Closed ended funds(Maturity period varies from 3 months to 15 yrs

Based on investment mutual fund is classified as

a) Equity or Growth fund

b) Debt fund

c) Balanced fund

d) Money market fund

Based on other equity funds mutual fund is classified as

a) Tax savings schemes

a. Earnings Linked Savings Scheme(ELSS)

b. Earnings Linked Pension Scheme(ELPS)

b) Sectoral Schemes

It includes investing in IT sector, Banks and other government / non-government institutions

c) Index schemes

HISTORY OF MUTUAL FUNDS :

The mutual fund industry in India started in 1963 with the formation of Unit Trust of India, at the initiative of the Government of India and Reserve Bank the. The history of mutual funds in India can be broadly divided into four distinct phases

First Phase – 1964-87

Unit Trust of India (UTI) was established on 1963 by an Act of Parliament. It was set up by the Reserve Bank of India and functioned under the Regulatory and administrative control of the Reserve Bank of India. In 1978 UTI was de-linked from the RBI and the Industrial Development Bank of India (IDBI) took over the regulatory and administrative control in place of RBI. The first scheme launched by UTI was Unit Scheme 1964. At the end of 1988 UTI had Rs.6,700 crores of assets under management.

Second Phase – 1987-1993 (Entry of Public Sector Funds)

1987 marked the entry of non- UTI, public sector mutual funds set up by public sector banks and Life Insurance Corporation of India (LIC) and General Insurance Corporation of India (GIC).

SBI Mutual Fund was the first non- UTI Mutual Fund established in June 1987 followed by Canbank Mutual Fund (Dec 87), Punjab National Bank Mutual Fund (Aug 89), Indian Bank Mutual Fund (Nov 89), Bank of India (Jun 90), Bank of Baroda Mutual Fund (Oct 92). LIC established its mutual fund in June 1989 while GIC had set up its mutual fund in December 1990.At the end of 1993, the mutual fund industry had assets under management of Rs.47,004 crores.

Third Phase – 1993-2003 (Entry of Private Sector Funds) With the entry of private sector funds in 1993, a new era started in the Indian mutual fund industry, giving the Indian investors a wider choice of fund families. Also, 1993 was the year in which the first Mutual Fund Regulations came into being, under which all mutual funds, except UTI were to be registered and governed. The erstwhile Kothari Pioneer (now merged with Franklin Templeton) was the first private sector mutual fund registered in July 1993. The 1993 SEBI (Mutual Fund) Regulations were substituted by a more comprehensive and revised Mutual Fund Regulations in 1996. The industry now functions under the SEBI (Mutual Fund) Regulations 1996. The number of mutual fund houses went on increasing, with many foreign mutual funds setting up funds in India and also the industry has witnessed several mergers and acquisitions. As at the end of January 2003, there were 33 mutual funds with total assets of Rs. 1,21,805 crores. The Unit Trust of India with Rs.44,541 crores of assets under management was way ahead of other mutual funds.

Fourth Phase – since February 2003

In February 2003, following the repeal of the Unit Trust of India Act 1963 UTI was bifurcated into two separate entities. One is the Specified Undertaking of the Unit Trust of India with assets under management of Rs.29,835 crores as at the end of January 2003, representing broadly, the assets of US 64 scheme, assured return and certain other schemes. The Specified Undertaking of Unit Trust of India, functioning under an administrator and under the rules framed by Government of India and does not come under the purview of the Mutual Fund Regulations.

The second is the UTI Mutual Fund Ltd, sponsored by SBI, PNB, BOB and LIC. It is registered

with SEBI and functions under the Mutual Fund Regulations. With the bifurcation of the erstwhile UTI which had in March 2000 more than Rs.76,000 crores of assets under management and with the setting up of a UTI Mutual Fund, conforming to the SEBI Mutual Fund Regulations, and with recent mergers taking place among different private sector funds, the mutual fund industry has entered its current phase of consolidation and growth. As at the end of September, 2004, there were 29 funds, which manage assets of Rs.153108 crores under 421 schemes.

The graph indicates the growth of assets over the years.

GROWTH IN ASSETS UNDER MANAGEMENT

Note:Erstwhile UTI was bifurcated into UTI Mutual Fund and the Specified Undertaking of the Unit Trust of India effective from February 2003. The Assets under management of the Specified Undertaking of the Unit Trust of India has therefore been excluded from the total assets of the industry as a whole from February 2003 onwards.

Brief History Of Insurance Sector In India The insurance sector in India has come a full circle from being an open competitive market to nationalization and back to a liberalized market again.

Tracing the developments in the Indian insurance sector reveals the 360-degree turn witnessed over a period of almost 190 years.

The business of life insurance in India in its existing form started in India in the year 1818 with the establishment of the Oriental Life Insurance Company in Calcutta.

Some of the important milestones in the life insurance business in India are:

1912 - The Indian Life Assurance Companies Act enacted as the first statute to regulate the life insurance business.

1928 - The Indian Insurance Companies Act enacted to enable the government to collect statistical information about both life and non-life insurance businesses.

1938 - Earlier legislation consolidated and amended to by the Insurance Act with the objective of protecting the interests of the insuring public.

1956 - 245 Indian and foreign insurers and provident societies taken over by the central government and nationalized. LIC formed by an Act of Parliament, viz. LIC Act, 1956, with a capital contribution of Rs. 5 crore from the Government of India.

The General insurance business in India, on the other hand, can trace its roots to the Triton Insurance Company Ltd., the first general insurance company established in the year 1850 in Calcutta by the British.

Some of the important milestones in the general insurance business in India are:

1907 - The Indian Mercantile Insurance Ltd. set up, the first company to transact all classes of general insurance business.

1957 - General Insurance Council, a wing of the Insurance Association of India, frames a code of conduct for ensuring fair conduct and sound business practices.

1968 - The Insurance Act amended to regulate investments and set minimum solvency margins and the Tariff Advisory Committee set up.

1972 - The General Insurance Business (Nationalization) Act, 1972 nationalized the general insurance business in India with effect from 1st January 1973.

107 insurers amalgamated and grouped into four companies viz. the National Insurance Company Ltd., the New India Assurance Company Ltd., the Oriental Insurance Company

Ltd. and the United India Insurance Company Ltd. GIC incorporated as a company.

Indian Life Insurance Industry Overview

All life insurance companies in India have to comply with the strict regulations laid out by Insurance Regulatory and Development Authority of India (IRDA). Therefore there is no risk in going in for private insurance players. In terms of being rated for financial strength like international players, only ICICI Prudential is rated by Fitch India at National Insurer Financial Strength Rating of AAA(Ind) with stable outlook indicating the highest claims paying ability rating.

Life Insurance Corporation of India (LIC), the state owned behemoth, remains by far the largest player in the market. Among the private sector players, ICICI Prudential Life Insurance(JV between ICICI Bank and Prudential PLC)is the largest followed by Bajaj Allianz Life Insurance Company Limited (JV between Bajaj Group and Allianz). The private companies are coming out with better products which are more beneficial to the customer. Among such products are the ULIPs or the Unit Linked Investment Plans which offer both life cover as well as scope for savings or investment options as the customer desires.Further, these type of plans are subject to a minimum lock-in period of three years to prevent misuse of the significant tax benefits offered to such plans under the Income Tax Act. Hence, comparison of such products with mutual funds would be erroneous.

Commission / Intermediation fees

The maximum commission limits as per statutory provisions are:

Agency commission for retail life insurance business:

35 - 40% for 1st year premium if the premium paying term is more than 20 years

25 - 30% for 1st year premium if the premium paying term is more than 15 years

10 - 15% for 1st year premium if the premium paying term is less than 10 years

7.5% - yr 2 and 3rd year and 5% - thereafter for all premium paying terms.

In case of Mutual fund related - Unit linked policies it varies between 1.5% to 60% on the premium paid.

o Agency commission for retail pension policies: 7.5% for 1st year premium and 2.5% thereafter

Maximum broker commission - 30%

Referral fees to banks – Max 55% for regular premium and 10% for single premium. However in any case this fee cannot be more than the agency commission as filed under the product.

However, the above commission may be further subject to the product wise limits specified by IRDA while approving the product.

TERM PLAN VS PREMIUM-GUARANTEED PLAN

DNA MoneyOctober 28, 2006

It makes more sense to go for a term plan with no survival benefitMUMBAI: God and devil, they say, is in the detail. And Shalin Saxena was just finding that out. A little bit of research had told him that premium-guaranteed term insurance plans just did not make sense.

Shalin was looking to opt for a term insurance plan. In a term insurance plan, if the policy holder expires during the policy period, his nominee will get the sum assured (the insurance amount). If the policy holder survives the policy period, he will not get anything.

The insurance advisor who had come to Shalin had told him, “Sir, why do you opt for a term insurance plan? If you survive the policy period, you won’t get the premium back. So, why don’t you opt for a premium- guaranteed term plan. Here, the premium is a little more, but if you survive the policy period, you will get all the premium paid back”.

Initially, he was very excited about the fact that he would get all the premium back. But soon he realised that there was more to it than what meets the eye. And some amount of Net surfing told him why.

ICICI Prudential Life Insurance offers a policy called LifeGuard with a level term assurance and a return of premium option. Under this policy, if a healthy male, aged 30, opts for a 20-year policy, with a sum assured of Rs 10 lakh, he needs to pay an annual premium of Rs 10,860.

If he survives the policy period, the company will return all the premium paid over 20 years, i.e., Rs 2,17, 200 (Rs 10,860 x 20). So far, so good.

Let’s see what happens if the individual, instead of opting for the return of premium option, opts for a level term assurance option, wherein he does not get the premium back, if he survives the policy period.

In this case, the premium to be paid for the sum assured of Rs 10 lakh for 20 years is Rs 2,751, almost one-fourth of the earlier case. Now let’s say the individual opts for a simple term assurance option. He obviously has to pay a premium of Rs 2,751 per year. The difference in premium between the two options works out to be Rs 8,109 (Rs 10,860 - Rs 2,751).

If the individual were to invest this difference of Rs 8,109 in public provident fund (PPF) for 20 years, he would get an amount of Rs 4,00,770.5.

The PPF account pays an interest of 8% per annum and matures 15 years after the end of the financial year in which the first investment was made. Upon maturity, the investor has the option of extending it by five years. So, by simply investing the difference between the premiums, in a PPF, the individual can ensure that he gets 85% more money than he would have got, if he had opted for the return of premium option.

There is always the risk of the interest paid on the PPF account coming down. But even if the interest rate were to fall to 3%, which is highly unlikely, the individual would get Rs 2,24,428.6 at the end of 20 years.

This is still more than the Rs 2,17, 200 he would have got in case he had opted for a return of premium option.

The story holds true even with other insurance companies. Let’s take the example of two policies — Swadhan and Shield-Level Cover - from SBI Life. Swadhan is a premium-guarantee term plan whereas Shield-Level Cover is a simple term plan. If a healthy male, aged 30, opts for a 10-year cover with a sum assured of Rs 20 lakh, in case of Swadhan, he has to pay a premium of Rs 27,631 per annum. If he opts for Shield-Level Cover, the premium drops to Rs 3,985 per annum. If the individual survives the 10 years of the cover, he gets Rs 2,76,310 in case of Swadhan and nothing in case of Shield-Level cover.

If the premium differential of Rs 23,646 were to be invested every year for 10 years, and even if the return earned is as low as 3%, he would have got Rs 2,79,207.1 at the end of 10 years.

This is more than what he will get in case of Swadhan, the policy which guarantees return of premium.

Having done his research, Shalin concluded that premium-guaranteed term plans do not make sense. He had seen both God and devil in the detail.

INSURANCE:

A promise of compensation for potential future losses in exchange of periodic payment is known as insurance

Types of insurance:

A) Health Care Insurance

B) Life Insurance

C) Home Insurance

D) Auto Insurance

E) Travel Insurance

Various types of Life Insurance Schemes: The various types of life insurance schemes are as follows

a) Investment Scheme

a. ULIPS

b. UNIT PLUS CHILD PLAN

b) Savings Scheme

a. Money Back Plan

b. Endowment Plan

c) Pension Scheme

d) Risk Plan

a. Swadhan Plan

Differences between Mutual funds and Insurance

MUTUAL FUNDS INSURANCE

PREMIUM AMOUNT CANNOT BE INCREASED/DECREASED

FLEXIBILITY IN PREMIUM

LOW ADMINISTRATIVE CHARGES ADMINISTRATIVE CHARGES ARE HIGH

NO LIFE COVER LIFE COVER

EASE OF PROCESS COMPLICATED

NO LOCKUP PERIOD LOCKUP PERIOD IS THERE

SWITCHING FROM ONE FUND TO OTHE OTHER FUND IS COSTLY

FREE ENTRY/EXIT LOAD

All mutual funds do not give tax benefits All insurance schemes provides tax benefits

ABOUT SBI LIFE INSURANCE COMPANY

Our Mission:"To emerge as the leading company offering a comprehensive range of life insurance and pension products at competitive prices, ensuring high standards of customer satisfaction and world class operating efficiency, and become a model life insurance company in India in the post liberalization period".

Our Values:

Trustworthiness Ambition Innovation Dynamism Excelllence

SBI Life Insurance is a joint venture between the State Bank of India and Cardif SA of France. SBI Life Insurance is registered with an authorized capital of Rs 1000 crore and a paid up capital of Rs 500 crores. SBI owns 74% of the total capital and Cardif the remaining 26%.

State Bank of India enjoys the largest banking franchise in India. Along with its 7 Associate Banks, SBI Group has the unrivalled strength of over 14,500 branches across the country, arguably the largest in the world. Cardif is a wholly owned subsidiary of BNP Paribas, which is the Euro Zone’s leading Bank. BNP Paribas is one of the oldest foreign banks with a presence in India dating back to 1860. Cardif is ranked 2nd worldwide in creditor’s insurance offering protection to over 35 million policyholders and net income in excess of Euro 1 billion. Cardif has also been a pioneer in the art of selling insurance products through commercial banks in France and in 35 more countries.

SBI Life Insurance’s mission is to emerge as the leading company offering a comprehensive range of Life Insurance and pension products at competitive prices, ensuring high standards of customer service and world class operating efficiency.

SBI Life has a unique multi-distribution model encompassing Bank assurance, Agency and Group Corporate.

SBI Life extensively leverages the SBI Group as a platform for cross-selling insurance products along with its numerous banking product packages such as housing loans and personal loans. SBI’s access to over 100 million accounts across the country provides a vibrant base for insurance penetration across every region and economic strata in the country ensuring true financial inclusion.

Agency Channel, comprising of the most productive force of more than 25,000 Insurance Advisors, offers door to door insurance solutions to customers.

http://www.cardif.com/pages/index_gb.html

http://www.statebankofindia.com/

SBI COMPETITORS:

LIC ICICI BAJAJ HDFC TATA AIG RELIANCE

ABOUT LIC MUTUAL FUNDS:

Life Insurance Corporation of India set up LIC Mutual Fund on 19th June 1989 and contributed Rs. 2 Crores towards the corpus of the Fund.

LIC Mutual Fund was constituted as a Trust in accordance with the provisions of the Indian Trust Act, 1882.

The settlor is not responsible for the management of the Trust.

The settlor is also not responsible or liable for any loss or shortfall resulting in any of the schemes of LIC Mutual Fund.

The Trustees of the LIC Mutual Fund have exclusive ownership of Trust Fund and are vested with general power of superintendence, discretion and management of the affairs of the Trust.

LIC Mutal Fund Asset Management Company Ltd. was formed on 20th April 1994 in compliance with the Securities and Exchange Board of India (Mutual Funds) Regulations, 1993.

The Company commenced business on 29th April 1994.

The Trustees of LIC Mutual Fund have appointed LIC Mutual Fund Asset Management Company Ltd. as the Investment Managers for LIC Mutual Fund.

The Trustees are responsible for appointing a Custodian.

The Trustees should also ensure that the activities of the Trust and the Asset Management Company are in accordance with the Trust Deed and the SEBI Mutual Fund Regulations as amended from time to time. The Trustees have also to report periodically to SEBI on the functioning of the Fund.

The investors under the schemes can obtain a copy of the Trust Deed, the text of the concerned Scheme as also a copy of the Annual Report, on a written request made to the LIC Mutual Fund Asset Management Company Ltd. at a nominal price of Rs. 10/-.

LIC INSURANCE PLANS

As individuals it is inherent to differ. Each individual�s insurance needs and requirements are different from that of the others.

LIC�s Insurance Plans are policies that talk to you individually and give you the most suitable options that can fit your requirement.

Jeevan Anurag Komal Jeevan

CDA Endowment Vesting At 21 Marriage Endowment Or

Educational Annuity Plan CDA Endowment Vesting At 18

Jeevan Kishore Jeevan Chhaya

Child Career Plan Child Future Plan

Jeevan Aadhar

Jeevan Vishwas

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The Endowment Assurance Policy

The Endowment Assurance Policy-Limited Payment

Jeevan Mitra(Double Cover Endowment Plan)

Jeevan Mitra(Triple Cover Endowment Plan)

Jeevan Anand

New Janaraksha Plan

Jeevan Amrit

Jeevan Shree-I

Jeevan Pramukh

The Money Back Policy-20 Years

The Money Back Policy-25 Years

Jeevan Surabhi-15 Years



Bima Bachat

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Jeevan Bharati

The Whole Life Policy

The Whole Life Policy- Limited Payment

The Whole Life Policy- Single Premium

Jeevan Anand

Jeevan Tarang

Two Year Temporary Assurance Policy

The Convertible Term Assurance Policy

Anmol Jeevan-I

Amulya Jeevan (Closed)

ICICI LIFE INSURANCE:

On the basis of which life stage you are in and the corresponding insurance needs, ICICI Prudential plans can be categorized into the following three types:

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Education Insurance Plans

Wealth Creation Plans

Premium Guarantee plans

Protection Plans

ABOUT ICICI MUTUAL FUNDS:

At ICICI Bank we will help you identify an appropriate mix of Mutual Fund schemes for your portfolio using asset allocation strategies.

Through ICICI Bank you can invest in various schemes of multiple mutual funds with decent performance record.

You can take the aid of our various research reports on mutual funds and their schemes before choosing a scheme for investment.

ICICI Bank offers investment in Mutual Funds through Multiple Channels.

With ICICI Bank, you can invest in Mutual Funds through following channels

I CICI Bank Branches ICICIdirect.com

Dedicated workforce to serve you

Before being deputed, our officers complete a comprehensive training program and, once deputed, they receive thorough instructions in financial planning skills and techniques. Throughout their careers officers also attend programs to update their skills.

All officers in charge of Mutual Funds are certified professionals by AMFI (Association of Mutual Funds in India)

Many of these officers also hold professional degrees like - MBA, CA, ICWA, CFA etc.

Anna Nagar

http://www.icicibank.com/pfsuser/icicibank/investments/mutualfund/mutualfundmainpage.htm

http://www.icicibank.com/pfsuser/inter_locator/inter_locator.htm

Anna Nagar is one of the best residential areas located in the city of Chennai. The region lies south of the Chennai central and features a regular and planned township. The region is as much a residential place as it is a commercial centre. The township is hooked with straight road within its premises and links to the major roads that connects it with other city areas. The region marks the boundary of the actual city area.

Anna Nagar area is spread out in small regular blocks with straight roads separating the adjacent ones. The blocks are named by single alphabets, like block 'A', as well combination of two alphabets, 'AD', 'AF', 'AG', 'AL', etc. There is a small locality in the east called Anna Nagar East.

The eastern side of 'Y' block, at Anna Nagar, is marked by popular Visweswararya Tower Park. The park features an imposing tower structure and the sprawling lawns. The park was rarely visited due to is deplorable conditions following negligence. But the renovations in the past few years has returned back the charm and beauty.

The region of Anna Nagar is provided with numerous enterprises that are responsible for various commercial activities. Hotels, restaurants, shopping stores and I.T.Companies are in abundance.

One of the best places for shopping at Anna Nagar is at 'Any Shopping Complex.'

Some renowned enterprises from the region are as: I.T.Companies: Avanor Systems Pvt. Ltd., Eco Tech Software Pvt. Ltd., Pegasus Software, Palpap Software, Launch Pad, Astra Infotech, L-Cube Innovation Solutions, Precision Software Communications

Hotels: Hotel Saravana, Hotel Soorya, Hotel Sky Park

Restaurants: Nawab's Restaurant, Moghul Feast, Blue Star Biryani, Prabhu Restaurant.Hospitals: Sridevi Hospital, Senthil Hospital, K.H.M. Hospital, Medical Foundation.

Accessibility

Anna Nagar lies precisely to the west of George Town, the city center. The two, as well as other suburbs, are connected by a network of roads and railways. The road distance between the two is approximately 6 kms.

Anna Nagar has a railway terminal located towards its northern edge. The railways thus, provide the accessibility.

Chennai Airport located at Meenambakkam is the nearest and about 10 kms far from Anna Nagar.

Areas Under Anna Nagar

Shenoy Nagar Anna Nagar East Anna Nagar West

MAIN TEXT:

The main text will include the following

OBJECTIVES

PLANNING RESEARCH DESIGN

RESEARCH METHOD

SAMPLING PROCEDURE

DATA COLLECTION

OBJECTIVES:

The objective of the project is to compare the consumer preference for mutual funds and insurance products

A) CONSUMER PREFERENCE FOR MUTUAL FUNDS

a. REASONS FOR CONSUMER PREFERENCE OF MUTUAL FUNDS

b. ADVANTAGES OF INVESTING IN MUTUAL FUND

c. DISADVANAGES OF INVESTING IN MUTUAL FUNDS

B) CONSUMER PREFERENCE FOR INSURANCE

a. REASONS FOR CONSUMER PREFERENCE FOR INSURANCE

b. ADVANTAGES OF INVESTING IN INSURANCE

c. DISADVANTAGES OF INVESTING IN INSURANCE

C) REASONS FOR CONSUMER MIGRATING FROM MUTUAL FUNDS TO INSURANCE AND VICE VERSA

RESEARCH METHOD : This research is basically undertaken to find the customer preference of mutual funds and insurance. After preparing a questionnaire a survey is

done to the people in anna nagar area to find their preference for mutual funds and insurance

The research design used was basically of exploratory in nature.

Information was gathered on all the issues pertaining to the research & a variety of techniques were used to analyze the findings of the research.

Questionnaire was the main tool taken into account for data collection & it was designed in such a way that it catered to the needs of the research being undertaken.

Both open & closed ended questions were used in the questionnaires involving both probing & in-depth analysis of the questions.

In the research both secondary & primary sources were taken into consideration for data collection. Questionnaire was the main tool for primary data & secondary sources were newspaper articles, magazines, internet etc.

The scales used in the study were basically all – nominal, ordinal, interval & ratio. Nominal & Ordinal was used in the findings related to the qualitative data . Interval & Ratio scales were used in the context of quantitative data,

The instruments used in the findings of our study were basically surveys & questionnaires. Surveys were done in shopping malls ,companies of Annanagar East and West.

The main strengths of our study were the data collection part. I did a lot of surveys in various parts of Annanagar & also a lot of respondents were taken into account for questionnaires.

The major weaknesses from our side were the time & money constraints which limited our research to a certain limit.

Sampling Design

Target Population: - 1. Age Group between 16-65 years.

Sampling Type: - Probability Sampling (Simple Random)

Sample Size: - 100 Respondents.

Level of Confidence: - 95% ( 5% error assumed)

DATA COLLECTION :

Time of data collection:

I started the research in the month of March 2008 & it is expected to be completed the final study by the month of May 2008

Field condition during data collection:

The march month was the main season for insurance and most of the people may prefer insurance in the month of march (Tax benefit)

In April month it was a festival season(Tamil New Year’s Day).So shop was more crowded and I could get more customers for survey.I took the surveys taking these factors into consideration

1) Age

2) Sex

3) Income

4) Occupation

I took equal no of surveys in all the interior variables of each

BEST FORM OF INVESTMENT

CrosstabsCase Processi ng Summary

100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%

G ENDER * SHAREM KTG ENDER * BANKG ENDER * PO STO FFG ENDER * G O LDG ENDER * M UTUALG ENDER * REALESTAG ENDER * I NSURANCAG E * SHAREM KTAG E * BANKAG E * PO STO FFAG E * G O LDAG E * M UTUALAG E * REALESTAAG E * I NSURANCO CCUPATI * SHAREM KTO CCUPATI * BANKO CCUPATI * PO STO FFO CCUPATI * G O LDO CCUPATI * M UTUALO CCUPATI * REALESTAO CCUPATI * I NSURANCI NCO M E * SHAREM KTI NCO M E * BANKI NCO M E * PO STO FFI NCO M E * G O LDI NCO M E * M UTUALI NCO M E * REALESTAI NCO M E * I NSURANC

N Per cent N Per cent N Per centValid M issing Tot al

Cases

GENDER * SHAREMKTCrosst ab

Count

3 5 18 13 5 447 14 17 13 5 56

10 19 35 26 10 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SHAREMKT

Tot al

Chi-Square Tests

4.517a 4 .3414.670 4 .323

100

Pearson Chi-SquareLikel ihood RatioN of Val id Cases

Value dfAsymp. Sig.

(2-s ided)

2 cel ls (20.0%) have expec ted count less than 5. Theminimum expec ted count is 4.40.

a.

GENDER * BANKCrosst ab

Count

12 7 23 1 1 4411 16 20 6 3 5623 23 43 7 4 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00BANK

Tot al

Chi-Square Tests

7.007a 4 .1367.436 4 .115

100


Value dfAsymp. Sig.

(2-s ided)

4 cel ls (40.0%) have expec ted c ount less than 5. Theminimum expec ted count is 1.76.

a.

GENDER * POSTOFFCrosst ab

Count

18 14 11 1 4434 7 10 4 1 5652 21 21 5 1 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00PO STO FF

Tot al

Chi-Square Tests

8.791a 4 .0679.300 4 .054

100


Value dfAsymp. Sig.

(2-s ided)

4 cel ls (40.0%) have expec ted c ount less than 5. Theminimum expec ted count is .44.

a.

GENDER * GOLDCrosst ab

Count

7 13 16 7 1 447 23 16 9 1 56

14 36 32 16 2 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00G O LD

Tot al

Chi-Square Tests

1.611a 4 .8071.622 4 .805

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * MUTUALCrosst ab

Count

4 10 16 12 2 448 9 15 13 11 56

12 19 31 25 13 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00MUTUAL

Tot al

Chi-Square Tests

6.340a 4 .1756.900 4 .141

100


Value dfAsymp. Sig.

(2-s ided)

0 cel ls (.0%) have expec ted count les s than 5. Theminimum expec ted count is 5.28.

a.

GENDER * REALESTACrosst ab

Count

11 8 15 8 2 4417 10 21 5 3 5628 18 36 13 5 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00REALESTA

Tot al

Chi-Square Tests

1.989a 4 .7381.980 4 .740

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * INSURANCCrosst ab

Count

8 12 18 6 4412 12 17 12 3 5620 24 35 18 3 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00I NSURANC

Tot al

Chi-Square Tests

4.453a 4 .3485.588 4 .232

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * SHAREMKTCrosst ab

Count

2 1 4 7 1 153 10 14 10 6 434 3 11 4 2 24

1 11 3 4 3 1 12

1 2 2 510 19 35 26 10 100

45 and abovebet ween 22 and 34bet ween 35 and 44less 22less t han 22m or e t han 45

AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SHAREM KT

Tot al

Chi-Square Tests

15.231a 20 .76315.076 20 .772

100


Value dfAsymp. Sig.

(2-s ided)

24 c el ls (80.0%) have expec ted count less than 5. Theminimum expec ted count is .10.

a.

AGE * BANKCrosst ab

Count

7 4 3 1 155 14 17 5 2 438 5 8 2 1 241 12 10 12

5 523 23 43 7 4 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00BANK

Tot al

Chi-Square Tests

33.062a 20 .03338.226 20 .008

100


Value dfAsymp. Sig.

(2-s ided)


a.

Crosst ab

Count

3 6 6 1527 6 8 1 1 4313 6 3 2 241 15 3 3 1 123 1 1 5

52 21 21 5 1 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00PO STO FF

Tot al

AGE * POSTOFFChi-Square Tests

19.435a 20 .49421.052 20 .394

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * GOLDCrosst ab

Count

4 5 5 1 158 12 14 7 2 432 17 4 1 24

1 11 6 5 121 3 1 5

14 36 32 16 2 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00G O LD

Tot al

Chi-Square Tests

37.116a 20 .01137.317 20 .011

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * MUTUALCrosst ab

Count

1 7 4 3 151 6 12 15 9 432 5 9 6 2 24

1 14 1 5 1 1 124 1 5

12 19 31 25 13 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00M UTUAL

Tot al

Chi-Square Tests

53.702a 20 .00043.805 20 .002

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * REALESTACrosst ab

Count

6 2 7 1512 9 14 5 3 435 5 8 5 1 24

1 14 2 4 2 121 2 1 1 5

28 18 36 13 5 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00REALESTA

Tot al

Chi-Square Tests

12.767a 20 .88715.934 20 .721

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * INSURANCCrosst ab

Count

1 5 5 4 156 10 17 8 2 43

11 7 5 1 241 1

2 1 6 3 121 2 2 5

20 24 35 18 3 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00I NSURANC

Tot al

Chi-Square Tests

28.365a 20 .10131.862 20 .045

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * SHAREMKTCrosst ab

Count

5 10 20 14 7 561 2 5 7 1 163 3 6 3 1 161 3 4 2 1 11

1 110 19 35 26 10 100

pr of essionalr et ir edself em ployedst udentSt udent

O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SHAREM KT

Tot al

Chi-Square Tests

10.036a 16 .8658.660 16 .927

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * BANKCrosst ab

Count

12 15 21 6 2 567 3 6 161 5 7 1 2 162 9 111 1

23 23 43 7 4 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00BANK

Tot al

Chi-Square Tests

23.310a 16 .10626.524 16 .047

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * POSTOFFCrosst ab

Count

36 10 9 1 565 5 5 1 165 3 5 3 165 3 2 1 111 1

52 21 21 5 1 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00PO STO FF

Tot al

Chi-Square Tests

18.932a 16 .27219.961 16 .222

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * GOLDCrosst ab

Count

9 21 17 7 2 564 4 6 2 161 10 4 1 16

1 5 5 111 1

14 36 32 16 2 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00G O LD

Tot al

Chi-Square Tests

25.021a 16 .06924.336 16 .082

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * MUTUALCrosst ab

Count

10 16 20 10 563 7 3 3 165 1 8 1 1 164 1 4 1 1 11

1 112 19 31 25 13 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00M UTUAL

Tot al

Chi-Square Tests

44.354a 16 .00047.149 16 .000

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * REALESTACrosst ab

Count

15 14 16 8 3 566 1 8 1 163 2 7 2 2 164 1 4 2 11

1 128 18 36 13 5 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00REALESTA

Tot al

Chi-Square Tests

12.616a 16 .70114.140 16 .588

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * INSURANCCrosst ab

Count

15 11 21 7 2 566 7 3 16

4 6 1 4 1 161 1 6 3 11

1 120 24 35 18 3 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00I NSURANC

Tot al

Chi-Square Tests

22.562a 16 .12627.091 16 .040

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * SHAREMKTCr osst ab

Count

3 7 14 13 3 401 1

1 4 10 6 3 244 5 8 5 4 262 2 2 2 8

1 110 19 35 26 10 100

1. 5 t o 3 lakh1. 5 t o 3lakh3 t o 4. 5 lakhless t han 1. 5 lakhm or e t han 4. 5 lakhm or e t han 4. 5lakh

I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SHAREM KT

Tot al

Chi-Square Tests

13.750a 20 .84313.372 20 .861

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * BANKCr osst ab

Count

5 12 15 6 2 401 15 6 12 1 247 5 14 265 2 1 8

1 123 23 43 7 4 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00BANK

Tot al

Chi-Square Tests

49.536a 20 .00035.002 20 .020

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * POSTOFFCr osst ab

Count

21 9 9 1 401 1

13 6 3 2 2411 5 8 2 266 1 1 8

1 152 21 21 5 1 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00PO STO FF

Tot al

Chi-Square Tests

15.552a 20 .74418.838 20 .532

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * GOLDCr osst ab

Count

10 8 17 4 1 401 1

12 6 5 1 244 10 6 6 26

6 2 81 1

14 36 32 16 2 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00G O LD

Tot al

Chi-Square Tests

30.075a 20 .06933.869 20 .027

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * MUTUALCr osst ab

Count

8 10 15 7 401 1

5 5 9 2 3 246 6 7 5 2 261 4 3 8

1 112 19 31 25 13 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00M UTUAL

Tot al

Chi-Square Tests

29.856a 20 .07234.323 20 .024

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * REALESTACr osst ab

Count

13 8 13 3 3 401 1

6 5 8 4 1 244 5 11 6 264 3 1 81 1

28 18 36 13 5 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00REALESTA

Tot al

Chi-Square Tests

16.693a 20 .67320.142 20 .449

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * INSURANCCr osst ab

Count

6 10 15 7 2 401 1

6 7 8 3 247 5 9 5 261 2 3 1 1 8

1 120 24 35 18 3 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00I NSURANC

Tot al

Chi-Square Tests

16.404a 20 .69114.552 20 .801

100


Value dfAsymp. Sig.

(2-s ided)


a.

Factor AnalysisCom munalities

1.000 .8781.000 .6041.000 .7221.000 .5091.000 .7821.000 .7871.000 .669

SHAREMKTBANKPOSTOFFGOLDMUTUALREALESTAINSURANC

Initial Extraction

Extraction Method: Principal Component Analysis.

Tot al Vari ance Expl ai ned

1. 495 21. 352 21. 352 1. 495 21. 352 21. 3521. 316 18. 794 40. 146 1. 316 18. 794 40. 1461. 106 15. 800 55. 946 1. 106 15. 800 55. 9461. 036 14. 794 70. 739 1. 036 14. 794 70. 739. 828 11. 828 82. 567. 678 9. 689 92. 256. 542 7. 744 100. 000

Com ponent1234567

Tot al % of Var iance Cum ulat ive % Tot al % of Var iance Cum ulat ive %I nit ial Eigenvalues Ext r act ion Sum s of Squar ed Loadings

Ext r act ion M et hod: Pr incipal Com ponent Analysis.

Scree Plot

Co mp o n e n t Nu mb e r

7654321

Eig

en

va

lue

1. 6

1. 4

1. 2

1. 0

. 8

. 6

. 4

Component Matrixa

.145 .201 .589 .685

.646 -3.97E-03 -.368 .228

.188 .776 .150 -.248-.354 .563 8.341E-02 .244-.596 -.261 -.286 .526.346 -.530 .620 -5.22E-02.649 8.351E-02 -.357 .337

SHAREMKTBANKPOSTOFFGOLDMUTUALREALESTAINSURANC

1 2 3 4Component

Ex trac tion Method: Princ ipal Component Analy s is .4 c omponents ex trac ted.a.

ProximitiesCa s e Proc e s s ing Su m m a rya

1 0 0 9 9 .0 % 1 1 .0 % 1 0 1 1 0 0 .0 %N Pe rc e n t N Pe rc e n t N Pe rc e n t

Va l i d Mi s s i n g T o ta lCa s e s

Sq u a re d Eu c l i d e a n Di s ta n c e u s e da .

ClusterWard Linkage

Aggl omerati on Schedul e

83 98 . 398 0 0 358 73 . 795 0 0 33

49 91 1. 296 0 0 1085 89 1. 809 0 0 2054 62 2. 515 0 0 2493 94 3. 258 0 0 4337 39 4. 017 0 0 3060 63 4. 833 0 0 4124 55 5. 649 0 0 3949 92 6. 499 3 0 6695 97 7. 357 0 0 3511 51 8. 230 0 0 5086 88 9. 104 0 0 5614 16 10. 014 0 0 7744 45 11. 027 0 0 2634 41 12. 040 0 0 4928 52 13. 202 0 0 553 30 14. 363 0 0 30

53 59 15. 542 0 0 3785 87 16. 724 4 0 5450 80 17. 943 0 0 6320 76 19. 214 0 0 3138 40 20. 486 0 0 7054 99 21. 811 5 0 5823 31 23. 165 0 0 6844 47 24. 522 15 0 6946 96 25. 898 0 0 7129 67 27. 284 0 0 796 82 28. 717 0 0 623 37 30. 155 18 7 70

18 20 31. 598 0 22 6121 48 33. 044 0 0 618 75 34. 527 2 0 785 58 36. 086 0 0 50

83 95 37. 716 1 11 811 2 39. 349 0 0 77

53 71 41. 032 19 0 6568 70 42. 774 0 0 594 24 44. 705 0 9 85

13 72 46. 656 0 0 7360 64 48. 631 8 0 6526 57 50. 667 0 0 4822 93 52. 763 0 6 66

St age12345678910111213141516171819202122232425262728293031323334353637383940414243

Clust er 1 Clust er 2Clust er Combined

Coef f icient s Clust er 1 Clust er 2

St age Clust er FirstAppears

Next St age

V e rtic a l Ic ic le

X X X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X

N u m b e r o f c lu s te rs1234567891 0111 21 31 41 51 61 71 81 92 02 12 22 32 42 52 62 72 82 93 03 13 23 33 43 53 63 73 83 94 04 1

25

:ma

le

92

:fem

ale

91

:ma

le

49

:fem

ale

94

:ma

le

93

:ma

le

22

:ma

le

78

:ma

le

75

:fem

ale

73

:fem

ale

8:m

ale

79

:fem

ale

35

:ma

le

36

:ma

le

33

:fem

ale

C a s e

Dendrogram* * * * * * 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 Ward Method

Rescaled Distance Cluster Combine

C A S E 0 5 10 15 20 25 Label Num +---------+---------+---------+---------+---------+

male 83 female 98 female 95 male 97 female 44 male 45 female 47 male 77 female 81 male 46 male 96 male 26 male 57 male 12 female 15 male 19 male 17 female 20 female 76 male 18 female 21 female 48 female 14 male 16 male 1 male 2 female 68 male 70 male 69 male 29

male 67 male 9 male 61 female 65 female 54 female 62 male 99 male 56 male 66 male 24 male 55 male 4 _

* * * * * * 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 * * * * * *

C A S E 0 5 10 15 20 25

Label Num +---------+---------+---------+---------+---------+

female 28 male 52 male 27 male 6 male 82 female 84 female 33 male 36

male 10 male 35 female 79 female 49 male 91 female 92 male 93 male 94 male 22 male 25 male 8 female 73 female 75 male 78 male 23 female 31 male 50 female 80 female 32 male 11 male 51 female 5 male 58 female 53 female 59 female 71 male 60 male 63 male 64 female 85 male 89 male 87 male 7 female 86 male 88 female 100 female 90

_

* * * * * * 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 * * * * * *

C A S E 0 5 10 15 20 25

Label Num +---------+---------+---------+---------+---------+

female 38 female 40 female 37 female 39 female 3 female 30 male 42 female 43 male 13 female 72 male 34 female 41 female 74

INTEPRETATION OF SPSS OUTPUT :

BEST INSURANCE COMPANY TO INVEST

Crosstabs

Case Processi ng Summary

100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%

G ENDER * SBIG ENDER * I CI CIG ENDER * LI CG ENDER * BAJAJG ENDER * HDFCG ENDER * RELI ANCEG ENDER * TATAAI GAG E * SBIAG E * I CI CIAG E * LI CAG E * BAJAJAG E * HDFCAG E * RELI ANCEAG E * TATAAI GO CCUPATI * SBIO CCUPATI * I CI CIO CCUPATI * LI CO CCUPATI * BAJAJO CCUPATI * HDFCO CCUPATI * RELI ANCEO CCUPATI * TATAAI GI NCO M E * SBII NCO M E * I CI CII NCO M E * LI CI NCO M E * BAJAJI NCO M E * HDFCI NCO M E * RELI ANCEI NCO M E * TATAAI G


Cases

GENDER * SBICrosst ab

Count

5 4 23 9 3 4411 12 18 11 4 5616 16 41 20 7 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SBI

Tot al

Chi-Square Tests

5.847a 4 .2116.003 4 .199

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * ICICICrosst ab

Count

10 7 23 3 1 4411 16 21 6 2 5621 23 44 9 3 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00I CI CI

Tot al

Chi-Square Tests

3.606a 4 .4623.672 4 .452

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * LICCrosst ab

Count

22 12 9 1 4437 6 8 4 1 5659 18 17 5 1 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI C

Tot al

Chi-Square Tests

7.338a 4 .1197.824 4 .098

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * BAJAJCrosst ab

Count

13 13 15 2 1 4410 23 16 6 1 5623 36 31 8 2 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00BAJAJ

Tot al

Chi-Square Tests

3.816a 4 .4313.889 4 .421

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * HDFCCrosst ab

Count

4 9 14 12 5 447 11 14 10 14 56

11 20 28 22 19 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00HDFC

Tot al

Chi-Square Tests

4.082a 4 .3954.206 4 .379

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * RELIANCECrosst ab

Count

11 8 14 8 3 4417 10 20 6 3 5628 18 34 14 6 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RELI ANCE

Tot al

Chi-Square Tests

1.433a 4 .8381.426 4 .840

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * TATAAIGCrosst ab

Count

8 10 15 7 4 4411 9 16 12 8 5619 19 31 19 12 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00TATAAI G

Tot al

Chi-Square Tests

1.794a 4 .7741.808 4 .771

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * SBI

Crosst ab

Count

1 1 8 5 159 6 16 7 5 435 4 11 3 1 24

1 11 3 4 3 1 12

1 2 2 516 16 41 20 7 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SBI

Tot al

Chi-Square Tests

17.399a 20 .62717.846 20 .598

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * ICICICrosst ab

Count

6 4 4 1 154 14 17 6 2 438 5 8 2 1 241 12 10 12

5 521 23 44 9 3 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00I CI CI

Tot al

Chi-Square Tests

31.680a 20 .04736.394 20 .014

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * LICCrosst ab

Count

3 4 6 1 1 1529 5 8 1 4315 6 2 1 241 17 3 1 1 124 1 5

59 18 17 5 1 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI C

Tot al

Chi-Square Tests

25.060a 20 .19924.716 20 .213

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * BAJAJCrosst ab

Count

9 3 3 158 14 15 4 2 432 17 4 1 24

1 14 1 6 1 12

1 3 1 523 36 31 8 2 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00BAJAJ

Tot al

Chi-Square Tests

47.349a 20 .00141.231 20 .003

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * HDFCCrosst ab

Count

1 7 4 3 151 7 11 12 12 431 5 7 6 5 24

1 14 1 5 1 1 124 1 5

11 20 28 22 19 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00HDFC

Tot al

Chi-Square Tests

53.326a 20 .00044.374 20 .001

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * RELIANCECrosst ab

Count

6 2 7 1511 9 12 7 4 436 5 8 4 1 24

1 14 2 4 2 121 2 1 1 5

28 18 34 14 6 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RELI ANCE

Tot al

Chi-Square Tests

12.497a 20 .89816.453 20 .688

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * TATAAIGCrosst ab

Count

1 5 5 4 156 9 15 8 5 43

10 4 3 1 6 241 1

2 1 6 3 122 2 1 5

19 19 31 19 12 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00TATAAI G

Tot al

Chi-Square Tests

32.898a 20 .03536.422 20 .014

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * SBICrosst ab

Count

12 7 22 10 5 562 10 4 16

3 3 5 4 1 161 3 4 2 1 11

1 116 16 41 20 7 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SBI

Tot al

Chi-Square Tests

15.064a 16 .52016.734 16 .403

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * ICICI

Crosst ab

Count

11 15 21 7 2 566 3 6 1 161 5 8 1 1 162 9 111 1

21 23 44 9 3 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00I CI CI

Tot al

Chi-Square Tests

18.916a 16 .27321.905 16 .146

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * LICCrosst ab

Count

39 9 8 565 4 5 2 167 2 4 2 1 167 3 1 111 1

59 18 17 5 1 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI C

Tot al

Chi-Square Tests

22.509a 16 .12824.712 16 .075

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * BAJAJ

Crosst ab

Count

9 23 18 4 2 569 2 4 1 161 10 4 1 164 1 5 1 11

1 123 36 31 8 2 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00BAJAJ

Tot al

Chi-Square Tests

34.232a 16 .00528.237 16 .030

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * HDFCCrosst ab

Count

11 15 17 13 563 7 3 3 164 1 6 1 4 164 1 4 1 1 11

1 111 20 28 22 19 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00HDFC

Tot al

Chi-Square Tests

37.500a 16 .00242.808 16 .000

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * RELIANCE

Crosst ab

Count

14 14 14 10 4 566 1 8 1 164 2 7 1 2 164 1 4 2 11

1 128 18 34 14 6 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RELI ANCE

Tot al

Chi-Square Tests

14.447a 16 .56516.578 16 .413

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * TATAAIGCrosst ab

Count

15 9 17 7 8 565 7 3 1 16

3 4 1 5 3 161 1 6 3 11

1 119 19 31 19 12 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00TATAAI G

Tot al

Chi-Square Tests

23.540a 16 .10027.809 16 .033

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * SBI

Cr osst ab

Count

7 7 15 8 3 401 1

4 2 12 5 1 243 4 12 4 3 262 2 2 2 8

1 116 16 41 20 7 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SBI

Tot al

Chi-Square Tests

14.652a 20 .79612.931 20 .880

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * ICICICr osst ab

Count

5 12 15 6 2 401 13 6 12 3 247 5 14 265 2 1 8

1 121 23 44 9 3 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00I CI CI

Tot al

Chi-Square Tests

28.363a 20 .10131.099 20 .054

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * LIC

Cr osst ab

Count

24 8 8 401 1

15 5 2 2 2413 5 6 2 266 1 1 8

1 159 18 17 5 1 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI C

Tot al

Chi-Square Tests

108.836a 20 .00023.586 20 .261

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * BAJAJCr osst ab

Count

10 10 17 2 1 401 1

1 12 7 3 1 248 10 6 2 264 4 8

1 123 36 31 8 2 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00BAJAJ

Tot al

Chi-Square Tests

31.578a 20 .04829.943 20 .071

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * HDFCCr osst ab

Count

10 9 11 10 401 1

5 4 9 3 3 246 6 7 5 2 26

2 3 3 81 1

11 20 28 22 19 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00HDFC

Tot al

Chi-Square Tests

28.251a 20 .10432.997 20 .034

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * RELIANCECr osst ab

Count

12 8 12 5 3 401 1

7 5 7 3 2 244 5 11 6 264 3 1 81 1

28 18 34 14 6 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RELI ANCE

Tot al

Chi-Square Tests

15.254a 20 .76219.139 20 .513

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * TATAAIG

Cr osst ab

Count

6 8 13 7 6 401 1

5 4 6 4 5 247 5 9 5 261 2 3 1 1 8

1 119 19 31 19 12 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00TATAAI G

Tot al

Chi-Square Tests

15.988a 20 .71716.895 20 .660

100


Value dfAsymp. Sig.

(2-s ided)


a.

Factor Analysis

Com munalities

1.000 .7941.000 .5911.000 .6301.000 .8741.000 .5821.000 .6091.000 .541

SBIICICILICBAJAJHDFCRELIANCETATAAIG

Initial Extraction



1. 297 18. 530 18. 530 1. 297 18. 530 18. 5301. 264 18. 051 36. 581 1. 264 18. 051 36. 5811. 055 15. 072 51. 653 1. 055 15. 072 51. 6531. 005 14. 352 66. 005 1. 005 14. 352 66. 005. 869 12. 412 78. 417. 817 11. 672 90. 089. 694 9. 911 100. 000

Com ponent1234567



Component Matrixa

-.333 .409 .714 8.290E-02.563 .466 .223 -8.27E-02

-.613 .496 8.268E-02 -4.25E-02.205 .166 -6.49E-02 .894

-.390 -.482 .165 .412.537 -.196 .531 2.015E-02.106 .578 -.419 .138

SBIICICILICBAJAJHDFCRELIANCETATAAIG

1 2 3 4Component

Ex trac tion Method: Princ ipal Component Analy s is .4 components ex trac ted.a.

REASONS FOR PEOPLE PREFERRING INSURANCE

Crosstabs


100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%

G ENDER * LI FECO VEG ENDER * TAXBENEFG ENDER * SWI TCHO VG ENDER * WI THDRWAG ENDER * RI DERSG ENDER * RETURNSAG E * LI FECO VEAG E * TAXBENEFAG E * SWI TCHO VAG E * WI THDRWAAG E * RI DERSAG E * RETURNSO CCUPATI * LI FECO VEO CCUPATI * TAXBENEFO CCUPATI * SWI TCHO VO CCUPATI * WI THDRWAO CCUPATI * RI DERSO CCUPATI * RETURNSI NCO M E * LI FECO VEI NCO M E * TAXBENEFI NCO M E * SWI TCHO VI NCO M E * WI THDRWAI NCO M E * RI DERSI NCO M E * RETURNSPREM FLEX * LI FECO VEPREM FLEX * TAXBENEFPREM FLEX * SWI TCHO VPREM FLEX * WI THDRWAPREM FLEX * RI DERSPREM FLEX * RETURNS


Cases

GENDER * LIFECOVE

Crosst ab

Count

15 5 22 1 1 4415 13 21 5 2 5630 18 43 6 3 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI FECO VE

Tot al

Chi-Square Tests

5.214a 4 .2665.514 4 .239

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * TAXBENEFCrosst ab

Count

18 14 11 1 4434 7 10 4 1 5652 21 21 5 1 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00TAXBENEF

Tot al

Chi-Square Tests

8.791a 4 .0679.300 4 .054

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * SWITCHOV

Crosst ab

Count

4 8 12 16 4 444 18 15 11 8 568 26 27 27 12 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SWI TCHO V

Tot al

Chi-Square Tests

5.072a 4 .2805.128 4 .274

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * WITHDRWACrosst ab

Count

4 10 16 12 2 448 9 14 14 11 56

12 19 30 26 13 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00WI THDRWA

Tot al

Chi-Square Tests

6.558a 4 .1617.115 4 .130

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * RIDERSCrosst ab

Count

11 6 13 5 9 4415 10 20 4 7 5626 16 33 9 16 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RI DERS

Tot al

Chi-Square Tests

2.051a 4 .7262.043 4 .728

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * RETURNSCrosst ab

Count

13 14 12 4 1 4419 11 14 9 3 5632 25 26 13 4 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RETURNS

Tot al

Chi-Square Tests

3.168a 4 .5303.223 4 .521

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * LIFECOVECrosst ab

Count

8 3 3 1 157 12 17 6 1 43

12 3 8 1 241 12 10 12

5 530 18 43 6 3 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI FECO VE

Tot al

Chi-Square Tests

39.061a 20 .00743.954 20 .002

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * TAXBENEFCrosst ab

Count

3 6 6 1527 6 8 1 1 4313 6 3 2 241 15 3 3 1 123 1 1 5

52 21 21 5 1 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00TAXBENEF

Tot al

Chi-Square Tests

19.435a 20 .49421.052 20 .394

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * SWITCHOVCrosst ab

Count

2 5 4 4 154 9 17 9 4 432 11 1 5 5 24

1 11 4 7 12

1 1 3 58 26 27 27 12 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SWI TCHO V

Tot al

Chi-Square Tests

39.246a 20 .00641.068 20 .004

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * WITHDRWACrosst ab

Count

1 7 3 4 151 6 12 15 9 432 5 9 6 2 24

1 14 1 5 1 1 124 1 5

12 19 30 26 13 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00WI THDRWA

Tot al

Chi-Square Tests

53.883a 20 .00044.164 20 .001

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * RIDERSCrosst ab

Count

6 2 7 1512 9 13 4 5 434 4 7 3 6 24

1 13 1 3 1 4 121 2 1 1 5

26 16 33 9 16 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RI DERS

Tot al

Chi-Square Tests

16.220a 20 .70320.005 20 .458

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * RETURNSCrosst ab

Count

2 4 3 5 1 1513 9 14 5 2 4311 7 5 1 24

1 16 3 2 1 12

1 2 2 532 25 26 13 4 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RETURNS

Tot al

Chi-Square Tests

23.340a 20 .27226.149 20 .161

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * LIFECOVECrosst ab

Count

16 12 21 6 1 567 3 6 164 3 7 2 162 9 111 1

30 18 43 6 3 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI FECO VE

Tot al

Chi-Square Tests

21.297a 16 .16723.401 16 .103

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * TAXBENEFCrosst ab

Count

36 10 9 1 565 5 5 1 165 3 5 3 165 3 2 1 111 1

52 21 21 5 1 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00TAXBENEF

Tot al

Chi-Square Tests

18.932a 16 .27219.961 16 .222

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * SWITCHOVCrosst ab

Count

5 13 18 12 8 562 4 3 5 2 161 8 3 2 2 16

1 3 7 111 1

8 26 27 27 12 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SWI TCHO V

Tot al

Chi-Square Tests

19.429a 16 .24720.024 16 .219

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * WITHDRWACrosst ab

Count

10 16 20 10 563 7 2 4 165 1 8 1 1 164 1 4 1 1 11

1 112 19 30 26 13 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00WI THDRWA

Tot al

Chi-Square Tests

44.924a 16 .00048.186 16 .000

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * RIDERSCrosst ab

Count

14 13 14 5 10 566 1 8 1 163 2 7 2 2 163 3 1 4 11

1 126 16 33 9 16 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RI DERS

Tot al

Chi-Square Tests

17.014a 16 .38520.629 16 .193

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * RETURNSCrosst ab

Count

22 9 18 5 2 561 5 5 4 1 164 7 1 3 1 165 3 2 1 11

1 132 25 26 13 4 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RETURNS

Tot al

Chi-Square Tests

19.955a 16 .22222.183 16 .137

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * LIFECOVECr osst ab

Count

7 11 15 6 1 401 19 3 12 248 4 14 265 2 1 8

1 130 18 43 6 3 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI FECO VE

Tot al

Chi-Square Tests

58.658a 20 .00036.644 20 .013

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * TAXBENEFCr osst ab

Count

21 9 9 1 401 1

13 6 3 2 2411 5 8 2 266 1 1 8

1 152 21 21 5 1 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00TAXBENEF

Tot al

Chi-Square Tests

15.552a 20 .74418.838 20 .532

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * SWITCHOVCr osst ab

Count

5 6 18 7 4 401 1

10 3 5 6 243 5 4 12 2 26

5 1 2 81 1

8 26 27 27 12 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00SWI TCHO V

Tot al

Chi-Square Tests

36.758a 20 .01337.679 20 .010

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * WITHDRWACr osst ab

Count

8 10 15 7 401 1

5 5 9 2 3 246 6 7 5 2 261 3 4 8

1 112 19 30 26 13 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00WI THDRWA

Tot al

Chi-Square Tests

30.250a 20 .06634.681 20 .022

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * RIDERSCr osst ab

Count

12 8 13 3 4 401 1

6 5 7 3 3 243 3 9 3 8 264 3 1 81 1

26 16 33 9 16 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RI DERS

Tot al

Chi-Square Tests

17.551a 20 .61719.078 20 .517

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * RETURNSCr osst ab

Count

11 8 13 6 2 401 1

8 6 6 3 1 2411 8 5 2 262 2 2 1 1 8

1 132 25 26 13 4 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RETURNS

Tot al

Chi-Square Tests

16.091a 20 .71113.796 20 .841

100


Value dfAsymp. Sig.

(2-s ided)


a.

Factor Analysis

Com munalities

1.000 .9461.000 .2381.000 .6161.000 .7241.000 .7861.000 .8491.000 .687

PREMFLEXLIFECOVETAXBENEFSWITCHOVWITHDRWARIDERSRETURNS

Initial Extraction



1. 476 21. 081 21. 081 1. 476 21. 081 21. 0811. 236 17. 660 38. 742 1. 236 17. 660 38. 7421. 122 16. 023 54. 765 1. 122 16. 023 54. 7651. 013 14. 468 69. 233 1. 013 14. 468 69. 233. 958 13. 688 82. 921. 701 10. 012 92. 933. 495 7. 067 100. 000

Com ponent1234567



Component Matrixa

.180 3.948E-02 .236 .925

.470 1.515E-02 -2.42E-02 .125

.631 -1.82E-02 .441 -.1492.851E-02 .635 .561 -7.59E-02

-.662 -.475 .263 .229-.128 .658 -.589 .230.606 -.415 -.374 8.572E-02

PREMFLEXLIFECOVETAXBENEFSWITCHOVWITHDRWARIDERSRETURNS

1 2 3 4Component

Ex trac tion Method: Princ ipal Component Analy s is .4 c omponents ex trac ted.a.

REASONS FOR PEOPLE PREFERRING MUTUAL FUNDS

CrosstabsCase Processi ng Summary

100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%

G ENDER * RETURNSG ENDER * LO WASSETG ENDER * LI Q UI DI TG ENDER * EASEPRO CG ENDER * LO WCHARGAG E * RETURNSAG E * LO WASSETAG E * LI Q UI DI TAG E * EASEPRO CAG E * LO WCHARGO CCUPATI * RETURNSO CCUPATI * LO WASSETO CCUPATI * LI Q UI DI TO CCUPATI * EASEPRO CO CCUPATI * LO WCHARGI NCO M E * RETURNSI NCO M E * LO WASSETI NCO M E * LI Q UI DI TI NCO M E * EASEPRO CI NCO M E * LO WCHARG


Cases

GENDER * RETURNS

Crosst ab

Count

9 6 17 10 2 4415 12 16 9 4 5624 18 33 19 6 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RETURNS

Tot al

Chi-Square Tests

2.851a 4 .5832.874 4 .579

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * LOWASSETCrosst ab

Count

3 8 18 3 12 443 14 12 8 19 566 22 30 11 31 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LO WASSET

Tot al

Chi-Square Tests

5.326a 4 .2555.375 4 .251

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * LIQUIDITCrosst ab

Count

18 14 11 1 4434 7 10 4 1 5652 21 21 5 1 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI Q UI DI T

Tot al

Chi-Square Tests

8.791a 4 .0679.300 4 .054

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * EASEPROCCrosst ab

Count

7 12 13 6 6 447 23 16 8 2 56

14 35 29 14 8 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00EASEPRO C

Tot al

Chi-Square Tests

4.681a 4 .3224.764 4 .312

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * LOWCHARGCrosst ab

Count

1 9 13 13 8 443 8 16 12 17 564 17 29 25 25 100

f emalemale

G ENDER

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LO WCHARG

Tot al

Chi-Square Tests

3.256a 4 .5163.327 4 .505

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * RETURNSCrosst ab

Count

6 1 3 5 159 10 14 7 3 436 3 10 3 2 241 12 3 4 2 1 12

1 2 2 524 18 33 19 6 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RETURNS

Tot al

Chi-Square Tests

15.391a 20 .75417.089 20 .647

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * LOWASSET

Crosst ab

Count

1 3 3 8 152 13 16 3 9 432 5 5 1 11 24

1 11 4 5 2 12

1 2 2 56 22 30 11 31 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LO WASSET

Tot al

Chi-Square Tests

34.390a 20 .02434.809 20 .021

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * LIQUIDITCrosst ab

Count

3 6 6 1527 6 8 1 1 4313 6 3 2 241 15 3 3 1 123 1 1 5

52 21 21 5 1 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI Q UI DI T

Tot al

Chi-Square Tests

19.435a 20 .49421.052 20 .394

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * EASEPROCCrosst ab

Count

4 5 5 1 158 12 14 7 2 432 16 4 1 1 24

1 11 3 3 5 121 3 1 5

14 35 29 14 8 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00EASEPRO C

Tot al

Chi-Square Tests

48.180a 20 .00041.367 20 .003

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * LOWCHARG

Crosst ab

Count

1 7 4 3 151 4 9 14 15 43

5 9 6 4 241 1

2 1 5 1 3 122 1 2 5

4 17 29 25 25 100


AG E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LO WCHARG

Tot al

Chi-Square Tests

31.921a 20 .04433.380 20 .031

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * RETURNSCrosst ab

Count

13 10 20 9 4 565 2 4 5 164 3 5 3 1 161 3 4 2 1 111 1

24 18 33 19 6 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RETURNS

Tot al

Chi-Square Tests

8.624a 16 .9289.320 16 .900

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * LOWASSETCrosst ab

Count

3 13 20 3 17 561 3 5 1 6 161 6 2 2 5 161 3 5 2 11

1 16 22 30 11 31 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LO WASSET

Tot al

Chi-Square Tests

23.665a 16 .09721.573 16 .158

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * LIQUIDITCrosst ab

Count

36 10 9 1 565 5 5 1 165 3 5 3 165 3 2 1 111 1

52 21 21 5 1 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI Q UI DI T

Tot al

Chi-Square Tests

18.932a 16 .27219.961 16 .222

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * EASEPROCCrosst ab

Count

9 20 16 7 4 564 4 6 2 161 10 4 1 16

1 3 3 4 111 1

14 35 29 14 8 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00EASEPRO C

Tot al

Chi-Square Tests

32.317a 16 .00929.065 16 .024

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * LOWCHARGCrosst ab

Count

8 13 19 16 561 7 3 3 2 161 1 9 2 3 162 1 4 1 3 11

1 14 17 29 25 25 100


O CCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LO WCHARG

Tot al

Chi-Square Tests

30.882a 16 .01428.524 16 .027

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * RETURNSCr osst ab

Count

11 6 13 8 2 401 13 4 10 5 2 246 6 8 4 2 263 2 1 2 8

1 124 18 33 19 6 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00RETURNS

Tot al

Chi-Square Tests

11.044a 20 .94511.787 20 .923

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * LOWASSETCr osst ab

Count

11 15 3 11 401 1

3 6 6 3 6 241 5 8 5 7 262 1 5 8

1 16 22 30 11 31 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LO WASSET

Tot al

Chi-Square Tests

23.939a 20 .24526.042 20 .164

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * LIQUIDITYCr osst ab

Count

21 9 9 1 401 1

13 6 3 2 2411 5 8 2 266 1 1 8

1 152 21 21 5 1 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LI Q UI DI T

Tot al

Chi-Square Tests

15.552a 20 .74418.838 20 .532

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * EASEPROCCr osst ab

Count

10 8 16 4 2 401 1

12 6 5 1 244 9 4 4 5 26

6 2 81 1

14 35 29 14 8 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00EASEPRO C

Tot al

Chi-Square Tests

35.887a 20 .01637.780 20 .009

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * LOWCHARGCr osst ab

Count

7 7 14 12 401 1

4 10 3 7 244 6 7 5 4 26

4 3 1 81 1

4 17 29 25 25 100


I NCO M E

Tot al

1. 00 2. 00 3. 00 4. 00 5. 00LO WCHARG

Tot al

Chi-Square Tests

29.067a 20 .08629.769 20 .074

100


Value dfAsymp. Sig.

(2-s ided)


a.

Factor Analysis

Com munalities

1.000 .7661.000 .6211.000 .7331.000 .6281.000 .746

RETURNSLOWASSETLIQUIDITEASEPROCLOWCHARG

Initial Extraction



1. 362 27. 240 27. 240 1. 362 27. 240 27. 2401. 107 22. 143 49. 383 1. 107 22. 143 49. 3831. 025 20. 499 69. 882 1. 025 20. 499 69. 882. 920 18. 394 88. 276. 586 11. 724 100. 000

Com ponent12345



Com ponent Matr ixa

.114 .204 .843-.101 .583 -.521.831 .196 -6.81E-02.164 .765 .130-.789 .321 .144

RE TURNSLOW A S SE TLIQUIDITEA S E P ROCLOW CHARG

1 2 3Component

Extraction Method: P rincipal Component A nalysis.3 components extracted.a.

REASONS FOR PEOPLE NOT PREFERING MUTUAL FUNDS

Crosstabs


100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%100 99. 0% 1 1. 0% 101 100. 0%

G ENDER * ABSRETURG ENDER * EXTRAFEEG ENDER * TAXO NPROAG E * ABSRETURAG E * EXTRAFEEAG E * TAXO NPROO CCUPATI * ABSRETURO CCUPATI * EXTRAFEEO CCUPATI * TAXO NPROI NCO M E * ABSRETURI NCO M E * EXTRAFEEI NCO M E * TAXO NPRO


Cases

GENDER * ABSRETURCrosstab

Count

14 13 17 4419 23 13 1 5633 36 30 1 100

femalemale

GENDER

Tot al

1. 00 2. 00 3. 00 4. 00ABSRETUR

Total

Chi-Square Tests

3.682a 3 .2984.053 3 .256

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * EXTRAFEECrosstab

Count

3 8 33 442 15 38 1 565 23 71 1 100

femalemale

GENDER

Tot al

1. 00 2. 00 3. 00 5. 00EXTRAFEE

Total

Chi-Square Tests

2.275a 3 .5172.661 3 .447

100


Value dfAsymp. Sig.

(2-s ided)


a.

GENDER * TAXONPROCros s ta b

Co u n t

1 9 1 9 6 4 43 5 1 4 7 5 65 4 3 3 1 3 1 0 0

fe ma l ema l e

GENDER

To ta l

1 .0 0 2 .0 0 3 .0 0TAXONPRO

To ta l

Chi-Square Tests

4.196a 2 .1234.207 2 .122

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * ABSRETURCrosst ab

Count

9 4 2 1514 18 11 437 6 10 1 241 12 5 5 12

3 2 533 36 30 1 100

45 and abovebet ween 22 and 34bet ween 35 and 44less 22less t han 22more t han 45

AGE

Tot al

1. 00 2. 00 3. 00 4. 00ABSRETUR

Tot al

Chi-Square Tests

17.186a 15 .30818.431 15 .241

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * EXTRAFEECrosst ab

Count

1 5 9 151 13 28 1 432 4 18 24

1 11 11 12

1 4 55 23 71 1 100

45 and abovebet ween 22 and 34bet ween 35 and 44less 22less t han 22more t han 45

AGE

Tot al

1. 00 2. 00 3. 00 5. 00EXTRAFEE

Tot al

Chi-Square Tests

9.643a 15 .84213.120 15 .593

100


Value dfAsymp. Sig.

(2-s ided)


a.

AGE * TAXONPROCrosstab

Count

3 8 4 1528 12 3 4313 8 3 241 15 5 2 124 1 5

54 33 13 100

45 and abovebetween 22 and 34between 35 and 44less 22less than 22more than 45

AGE

Total

1.00 2.00 3.00TAXONPRO

Total

Chi-Square Tests

13.825a 10 .18116.171 10 .095

100


Value dfAsymp. Sig.

(2-s ided)

11 cel ls (61.1%) hav e expec ted count less than 5. Theminimum expec ted count is .13.

a.

OCCUPATI * ABSRETURCrosst ab

Count

17 21 18 567 5 4 167 5 3 1 161 5 5 111 1

33 36 30 1 100

prof essionalret ir edself employedst udentSt udent

OCCUPATI

Tot al

1. 00 2. 00 3. 00 4. 00ABSRETUR

Tot al

Chi-Square Tests

12.693a 12 .39211.938 12 .451

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * EXTRAFEECrosst ab

Count

2 13 40 1 561 5 10 161 5 10 161 10 11

1 15 23 71 1 100

prof essionalret ir edself employedst udentSt udent

OCCUPATI

Tot al

1. 00 2. 00 3. 00 5. 00EXTRAFEE

Tot al

Chi-Square Tests

6.300a 12 .9009.283 12 .679

100


Value dfAsymp. Sig.

(2-s ided)


a.

OCCUPATI * TAXONPROCrosstab

Count

37 14 5 566 6 4 165 9 2 165 4 2 111 1

54 33 13 100

professionalret iredself employedstudentStudent

OCCUPATI

Total

1.00 2.00 3.00TAXONPRO

Total

Chi-Square Tests

11.411a 8 .17911.390 8 .181

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * ABSRETUR

Crosst ab

Count

12 16 12 401 16 8 9 1 24

10 8 8 263 4 1 81 1

33 36 30 1 100

1. 5 t o 3 lakh1. 5 t o 3lakh3 t o 4. 5 lakhless t han 1. 5 lakhmor e t han 4. 5 lakhmor e t han 4. 5lakh

I NCO ME

Tot al

1. 00 2. 00 3. 00 4. 00ABSRETUR

Tot al

Chi-Square Tests

10.196a 15 .80710.427 15 .792

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * EXTRAFEECrosst ab

Count

10 29 1 401 1

2 7 15 241 4 21 262 2 4 8

1 15 23 71 1 100

1. 5 t o 3 lakh1. 5 t o 3lakh3 t o 4. 5 lakhless t han 1. 5 lakhmor e t han 4. 5 lakhmor e t han 4. 5lakh

I NCO ME

Tot al

1. 00 2. 00 3. 00 5. 00EXTRAFEE

Tot al

Chi-Square Tests

13.539a 15 .56113.265 15 .582

100


Value dfAsymp. Sig.

(2-s ided)


a.

INCOME * TAXONPRO

Crosstab

Count

22 12 6 401 1

14 8 2 2411 11 4 266 1 1 8

1 154 33 13 100

1. 5 to 3 lakh1. 5 to 3lakh3 to 4.5 lakhless than 1.5 lakhmore than 4.5 lakhmore than 4.5lakh

INCOME

Total

1. 00 2.00 3.00TAXONPRO

Total

Chi-Square Tests

6.736a 10 .7507.597 10 .668

100


Value dfAsymp. Sig.

(2-s ided)

11 cel ls (61.1%) hav e expec ted count less than 5. Theminimum expec ted count is .13.

a.

Factor Analysis

Com munalities

1.000 .8751.000 .5821.000 .688

ABSRETUREXTRAFEETAXONPRO

Initial Extraction



1. 140 37. 986 37. 986 1. 140 37. 986 37. 9861. 006 33. 542 71. 528 1. 006 33. 542 71. 528. 854 28. 472 100. 000

Com ponent123



Com ponent Matr ixa

.370 .859

.763 2.324E-02

.649 -.517

ABSRETUREXTRAFEETAXONPRO

1 2Component

Extraction Method: Principal Component Analysis.2 components extracted.a.

ANALYSIS FROM SPSS OUTPUT

CROSS TAB: The cross tab gives a relationship between the independent variables(male and females) and their preference towards dependent variables .

The independent variables taken into account are

Gender : Male , Female

Age: Less than 22, Between 22 and 34, Between 35 and 44, 45 and above

Occupation: Self employed, Professional, Retired, Students

Annual Income: Up to 1.5 lakh, 1.5 to 3 lakh, 3 to 4.5 lakh , more than 4.5 lakh

The dependent variables taken into account are

Best form of investment: Share market, Bank, Post Office Savings ,Gold/Ornaments, Mutual funds, Real estate,Insurance

Best insurance company: LIC ,ICICI, SBI,HDFC,BAJAJ,RELIANCE,TATA AIG

Reasons for insurance: Flexibility in premium, life cover ,tax benefit, flexibility in withdrawals, riders

Reasons for people preferring mutual funds: Quick return, Low cost of asset management, Liquidity, Ease of process, Low administrative charges

Reasons for people not preferring mutual funds: Absence of guarantee on returns, Extra fees and commission, tax on profit made

Chi square test: The Chi-Square output shows the relationship between statistics and degrees of freedom. The 1st column shows a significant level of 4.517 for 3 degrees of freedom. In the 1st test the value of .21 shows that value of .457 or greater occurs only for 21% of sample. The chi square test takes the independent variable in the column and dependent variable in the row and finds the responses of independent variable over the dependent variable

FACTOR ANALYSIS: Suppose if the company wants to identify specific factors which makes the people to prefer insurance rather than mutual funds factor analysis could be used .The factors that favours insurance are listed out and depending upon the people responses factor analysis is done.

The various reasons for people to go for insurance are as follows

Flexibility in premium

Life cover

Tax benefit

No entry/exit load

Flexibility in withdrawals

Additional riders

The various reasons for people to go for mutual funds are as follows

Quick return

Low cost of Asset Management

Liquidity

Ease of process

Low administrative charges

The various reasons for the people to avoid mutual funds are as follows

Absence of guarantee on returns

Extra fees and commission made

Tax on profit made

CORRELATION MATRIX: Correlation matrix is a matrix that shows the relationship between various variables like age, income , occupation, gender etc.

In this matrix you will observe that relationship between two similar variables such as (age – age, income-income, occupation-occupation, gender-gender) (i.e) In all n*n cells the value is found to be 1 and in other cells the value is found to vary depending upon the result of the research

KMO AND BARTLETT’S TEST: From the result of KMO and Bartlett’s test it is very clear that we have to accept the null Hypothesis since the significance level is 0. It is lesser than 0.5

Communalities: In communalities there are column .The first column initial gives the initial correlation of the variable over the buying behavior of western wear. The second column extract gives the influence of these variables over the western wear after the research being done. After the research it is found that there is .518 extraction of income over buying behavior of insurance. The extraction method could be explained by the principal of component analysis.

TOTAL VARIANCE Explained: In total variance explained there are 3 columns namely initial eigen values, extraction of sum of of squared loadings and rotation sum of squared loadings. It gives the percentage of variance and cumulative percentage of variance. The eigen values whose variance is greater than 1 is taken to the next column of being extracted and taking the 2nd column as reference rotation sum of squared loadings is calculated in the third column

SCREE PLOT: The scree plot is the graphical representation showing the relationship between eigen values and component number

COMPONENT MATRIX: The component matrix shows the classification of variables into 3 components and shows the extraction level of variables in each component

Reproduced correlations: The reproduced correlations are correlations that is made after the final research being done and is computed on the basis of final decision made by the research

Residual correlations: Residual correlations are those correlations that is made between the observed and reproduced correlations80% of non redundant residuals have an absolute value of greater than 0.05

Component transformation matrix: At last we have the component transformation matrix which shows the loading of variables in 3 extracted factors. Loading less than 0.5 are not shown as the “surpress loading less than 0.5” value was entered in factor analysis options dialog box

CLUSTER ANAYSIS: Suppose if we want to analyze the group of people who go for mutual funds or insurance cluster analysis could be done. The clusters could be made among the people of the same kind and be done .For eg analysis could be done by all male professionals within the age group from 22 to 34

AGGLOMERATION SCHEDULE: The Agglomeration schedule defines the order in which the variable combines with each other. It tells the maximum difference between co-efficient at each stage.

DENDOGRAM: The dendogram shows clusters in graphical way. It resembles a fork that sub divides in different way. It splits the different types of clusters for easy analysis.

DATA ANALYSIS:

1) Reasons for people preferring insurance

PARTICULARS No of respondents rated the best

% OF TOTAL SAMPLE

FLEXIBILITY IN PREMIUM

6 6%

LIFE COVER 48 48%

NO ENTRY/EXIT LOAD 6 6%

TAX BENEFIT 34 34%

FLEXIBILITY IN WITHDRAWALS

3 3%

PROTECTION AGAINST CRITICAL ILLNESS

3 3%

2) Reasons for people preferring mutual funds


% OF TOTAL SAMPLE

Quick return 52 52%

Low cost of asset management

14 14%

Liquidity 14 14.%

Low administrative charges

20 20%

3) Best form of investment


% OF TOTAL SAMPLE

SHARE MARKET 6 6%

BANK 14 14%

POST OFFICE SAVINGS

40 40%

GOLD/ORNAMENTS 9 9%

MUTUAL FUNDS 14 14%

REAL ESTATE 5 6%

INSURANCE 12 12%

FINDINGS :

When taking survey I found that 60% of self employed people prefer real estate

I also found that 75% of the male between the age group 16 to 34 prefer mutual funds than insurance

I found that 60% of female between age group 16 to 34 prefer mutual funds than insurance

I found that 20% of male of age group greater than 35 prefer insurance

I found that 74% of the people with annual income greater than 3 lakhs prefer insurance than mutual funds

I found that 15% of the people with income greater than 3 lakhs prefer mutual funds than insurance

LIMITATIONS :

Surveys restricted to only those people who know about insurance and mutual funds

Surveys taken only from the people who are willing to disclose their annual income and age

Surveys are taken only from the people who are interested

Surveys are not taken from the people in slum areas and to the people below the poverty line

RECOMMENDATIONS:

SBI Life insurance should give a lot of training to their advisors and encourage the advisors to work as an team to increase their productivity.

SBI Life insurance company should recruit a lot of youngsters for promoting their products

SBI Life insurance company should do an blue ocean strategy (Targeting those who were unaware about insurance industry ).By this method mass marketing could be done and it will help SBI to increase its customers.

Should increase the no of advertisements in T .V, News papers etc and increase people awareness about the additional benefits given by SBI life insurance products

SBI Life insurance company should make a best use of this report and the company must analyze the people to be targeted for business and advisors in improving its productivity

QUESTIONNAIRE:

I V.NIRANJAN , a student pursuing MBA programme at IBS Chennai is conducting a study about “ CONSUMER PREFERENCE OF MUTUAL FUNDS VS INSURANCE”

The following questions seeks connection with this project.Please fill up this questionnaire with your responses. Any information provided by you will be treated in strict confidence and will not be treated for any other purpose other than the above study

V.NIRANJAN

1) Name :

2) ADDRESS:

3) GENDER : Male Female

4) Age

5) Occupation:

Less than 22

Between 22 and 34

Between 35 and 44

45 and above

Self employed

Professional

Retired

Students

Any other

6) MOBILE NO:

7) E-mail id:

8) Annual income:Self Upto 1.5 lakh 1.5-3 lakh 3-4.5lakh more than

4.5

Spouse

Children

Total

9) According to you which one is the best for investment? Rate them? 1-5 (1-highest 5 lowest)

A) Share market

B) Bank

C) Post office savings

D) Gold/Ornaments

E) Mutual funds

F) Real Estate

G) Insurance

10) Please state the percentage of savings investments in the following?

A. Share market

B. Bank

C. Post office savings

D. Gold/Ornaments

E. Mutual funds

F. Real Estate

G. Insurance

TOTAL 100%

11) In insurance companies which company do you prefer? Rate them? 1-5 (1-highest 5 lowest)

A. LIC

B. ICICI

C. SBI

D. HDFC

E. BAJAJ

F. Any other

12) Why do you prefer insurance? Rate them? 1-5 (1-highest 5 lowest)

A. Flexibility in premium

B. Life cover

C. No entry/ exit load

D. Tax Benefit

E. Flexibility in withdrawals

F. Protection against critical illness/accidental benefit

13) Are you aware of mutual funds? Yes/No

14) If you prefer mutual funds why do you prefer? Rate the reasons? 1-5 (1-highest 5 lowest)

A. Quick return

B. Low cost of Asset management

C. Liquidity

D. Ease of process (By internet, phone )

E. Low administrative charges

15) If you don’t prefer mutual funds why? Rate the reasons? 1-5 (1-highest 5 lowest)

A. Absence of guarantee on returns

B. Extra fees and commission (Entry / exit loads )

C. Tax on profit made

16) Please state your investment for the next one year?

A. Share market

B. Bank

C. Post office savings

D. Gold/Ornaments

E. Mutual funds

F. Real Estate

G. Insurance

17) Reasons for the above investment plans? Rate them from (1-5)(1 highest 5 lowest)

A) FLEXIBILITY IN PREMIUM

B) LIFE COVER

C) TAX BENEFITS

D) PROTECTION AGAINST CRITICAL ILLNESS /ACCIDENTAL DEATH

E) QUICK RETURN

F) LIQUIDITY

G)FREE ENTRY/EXIT LOAD

h) LOW ADMINISTRATIVE CHARGES

BIBLIOGRAPHY

www.google.com

www.progressive.com

www.wikepedia.org/wiki/insurance

www.mutualfundsindia.com

Business Research Methods, ICFAI

Naresh K.Malhotra .Marketing Research An Applied Orientation

http://www.wikepedia.org/wiki/insurance

http://www.progressive.com/

http://www.google.com/

Insurance for Practice: Moorthy

Business Research Methodology, K.Ashwathapa

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