CHAPTER 6- Analysis 2-AGENT PROFILE, PRACTICES

CHAPTER - VI

ANALYSIS - 2 AGENT PROFILE AND PRACTICES

6.1 INTRODUCTION

The study has been conducted at Chennai and Tiruchirapalli covering both

customers and agents. This chapter aims to analyse the responses received from

agents. The principal objective behind the analysis is to understand the profile of agents

and their selling practices. According to Insurance Act 1965, “insurance agent means an

insurance agent licensed under Sec. 42 who receives or agrees to receive payment by

way of commission or other remuneration in consideration of his soliciting or procuring

insurance business including business relating to the continuance, renewal or revival of

policies of insurance”. Agents surveyed are from different life insurance companies

including public sector giant, Life Insurance Corporation of India.

6.2 DEMOGRAPHIC VARIABLES

Table 6.1: Age groups of agents Chennai Tiruchirapalli

Age group No. of Respondents Percentage No. of

Respondents Percentage

< 30 6 19 12 75

>30 26 81 4 25

Total 32 16

Source: Primary data It can be inferred from the above table that 81 percent of the agents in Chennai

and 25 percent in Tiruchirapalli are above 30 years of age. 19 percent of the agents in

Chennai and 75 percent in Tiruchirapalli are less than 30 years of age.

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Table 6.2: Sex of agents Chennai Tiruchirapalli

Sex No. of Respondents Percentage No. of


Male 21 66 14 88

Female 11 34 2 12

Total 32 16

Source: Primary data Of the agents surveyed in Chennai, 66 percent are male and the balance 34

percent are females. 88 percent of the agents surveyed in Tiruchirapalli are male and

the rest are female. It is interesting to note that in countries like Japan and Korea women

are more successful as life insurance agents.

Table 6.3: Educational qualifications of agents

Chennai Tiruchirapalli Qualification No. of

Respondents Percentage No. of Respondents Percentage

Higher Secondary (HSC) 4 12.50 4 25.00

Under graduate (UG) 16 50.00 10 62.50

Post graduate (PG) 7 21.88 1 6.25

Professional 5 15.63 1 6.25

Total 32 16

Source: Primary data

More than 85% of the agent respondents in Chennai and 75% in Tiruchirapalli

were graduates, postgraduates or holders of professional qualification. Only 12.50

percent in Chennai and 25 percent in Tiruchirapalli have qualification upto Higher

Secondary (HSC). Insurance Regulatory and Development Authority has prescribed

passing of Higher Secondary Examination as the minimum educational requirement to

become a life insurance agent.

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Table 6.4: Previous work experience of agents Chennai Tiruchirapalli Previous work

experience No. of Respondents Percentage No. of


No experience 3 9 5 31

<=10 16 50 9 56

>10 13 41 2 13

Total 32 16 Source: Primary data Previous work experience refers to the work experience of the agent prior to

becoming an agent for life insurance products. 9 percent of the respondents in

Chennai and 31 percent of the respondents in Tiruchirapalli have no work experience.

50 percent of the agents in Chennai and 56 percent in Tiruchirapalli have work

experience of less than 10 years. 41 percent of the agents in Chennai and 13 percent

in Tiruchirapalli have work experience exceeding 10 years.

Table 6.5: Domain of previous work experience of agents

Chennai Tiruchirapalli Previous work experience No. of


Finance field & Sales 8 28 5 45

Finance field but Non sales 8 28 1 9

Non finance but sales exp 7 24 4 37

Non finance and non sales 6 20 1 9

Total numbers of agents with previous work experience 29 11


For the purpose of analysis, finance field includes experience in Non Banking

Finance Companies, Bank, Insurance Companies, Mutual Funds, Financial Products

distribution and Stock broking. Respondents from Chennai are more or less evenly

197

distributed across all the four groupings on previous work experience. 45 percent of the

respondents in Tiruchirapalli have worked in finance field with sales experience and

another 36 percent of the respondents in Tiruchirapalli have experience in sales

although in non-finance field.

Table 6.6: Nature of life insurance agency of agents

Chennai Tiruchirapalli Nature of life insurance agency No. of


Full time 21 66 5 31

Part time 11 34 11 69

Total 32 16


Figure 6.1: Nature of life insurance agency of agents


66 percent of the respondents in Chennai and 31 percent of the respondents in

Tiruchirapalli are full time agents. A Full time agent is one, whose main occupation is

selling life insurance product as life insurance agent. In addition to their main business

or employment an individual can also take to insurance distribution as part time agent.

198

Table 6.7: Classification of occupation of part time agents Chennai Tiruchirapalli Occupation of part time

agents No. of Respondents Percentage No. of


Employed 6 55 3 27

Business 2 18 7 64

Financial products distributor 3 27 1 9

Total number of part time agents 11 11


Figure 6.2: Classification of occupation of part time agents

Source: Primary data Of the part time agents 54.55 percent in Chennai are employed and 63.64

percent in Tiruchirapalli are into business. 27.27 percent in Chennai and 9.09 percent in

Tiruchirapalli are engaged in the retailing of financial products as distributors.

Table 6.8: Number of years of work experience as agent Chennai Tiruchirapalli Number of years

experience as agent No. of Respondents Percentage No. of


<=6 yrs 22 69 9 56

> 6 yrs 10 31 7 44

Total 32 16 Source: Primary data

199

Figure 6.3: Number of years of work experience as agents


It can be observed from the above figure that 68.75 percent of the agent

respondents in Chennai and 56.25 percent in Tiruchirapalli have an agency experience

of 6 years are less. 31.25 percent in Chennai and 43.75 percent in Tiruchirapalli have

work experience exceeding 6 years.

In order to encourage agents for higher levels of performance life insurance

companies have developed a reward and recognition program, which is generally known

as club membership benefit. After an agent starts performing above the threshold limit,

he or she becomes eligible for exclusive perks by way of ‘Club Memberships’. Club

membership refers to the additional benefits given by life insurance companies to their

agents as a reward and recognition mechanism over and above the commission payable

to them and is aimed at meeting the aspirations of the agents.

200

Table 6.9: Club membership recognition of agents Chennai Tiruchirapalli

Club membership No. of Respondents Percentage No. of


Club member 22 69 3 19

Not a member 10 31 13 81


69 percent of the respondents in Chennai and 19 percent in Tiruchirapalli are

club members. 81 percent of the respondents in Tiruchirapalli are non-club members.

Table 6.10: Distributing products other than life insurance by agents Chennai Tiruchirapalli Distributing products

other than life insurance No. of Respondents Percentage No. of


Sell other products 11 34 5 31

Do not sell 21 66 11 69

Total 32 100 16 100 Source: Primary data

66 percent of the agent respondents in Chennai and 69 percent in Tiruchirapalli

distribute only life insurance products and they do not sell any other product. Only 34

percent of respondents from Chennai and 31 percent from Tiruchirapalli distribute or sell

other financial products. According to a study by LIMRA, an established agent derives

over 15 percent of his or her income from products other than insurance. For today’s

agents, the product breadth has greatly expanded their market, while at the same time

has become quite daunting for them to master (Limra-Moss Adams, 2006).

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Table 6.11: List of products other than life insurance distributed by agents

Chennai Tiruchirapalli Other products distributed by agents No. of


Mutual Fund 7 64 0 0.00

RBI /Post office deposits 5 45 1 20.00

Equity shares 6 55 2 40.00

Company deposit 3 27 0 0.00

Other product 5 45 2 40.00

Total of agents distributing other products 11 5

Source: Primary data Note: Multiple entries possible and hence total may not add up to 100% or sample size.

Out of the segment of the agent respondents who sell other products in addition

to life insurance products, 64 percent of the agents in Chennai sell mutual fund products.

RBI Bonds, Post office deposits and Equity shares. An agent respondent in Chennai

sells on an average 2.6 products in addition to life insurance products. This diversified

product offering has not been noticed among agent respondents in Tiruchirapalli.

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Table 6.12: List of life insurance products distributed by agents Chennai Tiruchirapalli

Life insurance products No. of Respondents Percentage No. of


Term assurance 16 50 1 6

Unit Linked (UL) Single Premium products 17 53 5 31

UL Pension 20 66 10 63

Investment plans 21 66 5 31

UL Endowment 23 72 15 94

Money back 24 75 1 6

Children's plan 25 78 3 19

Pension Plan 25 78 7 44

Endowment 26 81 1 6 Source: Primary data Note: Multiple entries possible and hence total may not add up to 100% or sample size. Endowment products are the most distributed by agent respondents in Chennai

with more than 81 percent stating that they sell endowment products. Pension Plans

and Children’s plan are also distributed by most of the respondents in Chennai.

Interestingly 94 percent of the agent respondents in Tiruchirapalli stated that they

distributed Unit Linked endowment plans followed by Unit Linked pension plans. Term

assurance product, which offers pure risk cover, has the lowest score in both Chennai

and Tiruchirapalli.

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Figure 6.4: Ranking of insurance products sold by agents


Except for ranking of Term assurance and Pension plan, significant divergence

can be observed in the life insurance products distributed by agent respondents in

Chennai and Tiruchirapalli. The recent bull market condition, started in the year 2005,

has fuelled the growth and higher sales of unit-linked products.

Commission is a compensation for an insurance agent for the sale of a policy.

Most life insurance companies pay a high first year commission and lower commissions

in subsequent years. This commission structure is controversial since critics feel that it is

heavily weighted towards selling a new product at the expense of subsequent service as

the agents chase new business leaving their existing customers without service.

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Table 6.13: Monthly commission earnings by agents Chennai Tiruchirapalli Commission

earnings Commission

Band No. of Respondents Percentage No. of


Less than 5000 3 9 6 38

5000 to 10000 Lower Band

5 16 6 38

10000 to 15000 10 31 3 19

15000 to 20000 Mid Band

6 19 1 5

20,000 to 30000 5 16 0 0 Greater than

30,000 Upper Band

3 9 0 0


25 percent of the agent respondents in Chennai and 76 percent in Tiruchirapalli

are in the lower band of commission earnings band. 50 percent in Chennai and 24

percent in Tiruchirapalli are in the mid band. Only 25 percent in Chennai belongs to the

upper band of commission earnings. It is very important for life insurance companies to

support agents to earn higher commission month on month to ensure their continued

contribution to business. In the absence of substantial commission earnings there may

be a possibility of stoppage of business by agents.

6.3 REASONS FOR PRODUCT RECOMMENDATION

Table 6.14: Reasons for recommending a product to a Customer Mean Ranking

Reasons Chennai Tiruchirapalli

Needs of customer 1.50 2.56

Customers financial planning 2.06 2.56

Product feature 3.28 2.88

Income/Commission 4.06 3.19

Ease of sale 4.13 3.38 Spread between the least and most 2.63 0.82


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Figure 6.5: Reasons for recommending a life insurance product to a customer


Agent respondents from both Chennai and Tiruchirapalli have ranked the needs

of the customer as the top most reason for recommending to a customer. While

analyzing the spread between the least and the most mean scores for recommendation,

it can be observed that the difference is 2.63 for agents in Chennai as against 0.82 for

Tiruchirapalli. It denotes that the respondents from Chennai have given a ranking, which

has a positive skew to the ‘needs of the customer”. On the other hand the ranking by

agents in Tiruchirapalli is far more uniformly spread across different reasons for

recommending the product.

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Hypothesis No: 6.1 Null Hypothesis: There is no significant difference between mean ranks towards

reasons for recommendation by agent. Table: 6.15: Friedman test for significant difference between mean ranks towards

reasons for recommendation by agent

Reasons for recommendation Mean Ranking

Chi- Square value

P value

Product feature 3.16

Ease of selling through convincing customer 3.88

Commission or earnings by agent 3.80

Needs of customer 1.90 As per customer’s financial planning requirement 2.27

61.137 0.000 **

Source: Primary data Note: a) ** in a cell denotes significance at 1% level b) Analysis for consolidated data1

Since P value is less than 0.01, the null hypothesis is rejected at 1% level of

significance. Hence there is a significant difference between the mean ranks towards

reasons for recommendation. Agents recommend products based on the needs of the

customer followed by his financial planning requirement. Ease of selling and

commission earnings figure as the least priorities by agents while recommending

products.

Table 6.16: Reasons attributed by customers while buying insurance Mean Ranking

Reasons for buying life insurance Chennai Tiruchirap

alli Total

Life Insurance cover 2.28 3.06 2.54

Savings for Children’s education, Marriage etc 2.63 2.13 2.46

Availing Income tax benefit 2.41 3.44 2.75

Savings for Old age, Pension 4.28 3.31 3.96

Your recommendation 4.44 4.63 4.50

Recommendation from Friends/Relatives 5.66 4.88 5.40

Housing Loan Cover 6.13 6.38 6.21 Source: Primary data

1 Analysis for consolidated data refers to analysis of combined data collected from both Chennai and Tiruchirapalli

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Figure 6.6: Ranking of reasons attributed by customers while buying insurance


From the above figure it can be observed that in the agents’ perception the

primary reason for buying insurance by customers is for life cover purpose in the city of

Chennai. Availing income tax benefit and providing for children’s education/marriage

rank among the top 3 reasons attributed by the customers in Chennai. However agent

respondents in Tiruchirapalli observe that providing for children’s education/marriage, life

cover and provision for old age/pension are the top 3 reasons attributed by their

customers.

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6.4 SALES TOOLS USED BY AGENTS

Table 6.17: Sales tools used by agents No. of responses

Tools used for selling Chennai Tiruchirapalli

Pension requirement 26 13

Customer reference 25 7

Maturity value projection 24 13

Sales literature 24 12 Future cost of education, marriage 22 15

Competition comparison 18 12 Number of respondents surveyed 32 16

Source: Primary data Note: Multiple entries possible and hence total may not add up to 100% or sample size.

According to agent respondents in Chennai, pension requirement calculation is

the tool mostly used by them followed by customer reference, maturity value projection

and sales literatures. 15 of the total 16 respondents in Tiruchirapalli have stated that

future cost of education or marriage is the most often used sales tool by them and is

followed by maturity value projection and pension calculation. Incidentally agents in

Chennai are using 4.34 tools and those in Tiruchirapalli are using 4.5 tools implying that

more than one tool is being used by them.

Figure 6.7: Ranking of sales tools used by agents


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Projected future cost of education/marriage is the most preferred by agent

respondents in Tiruchirapalli but respondents in Chennai have ranked it at fifth position.

Similarly customer reference has been ranked at second position by respondents in

Chennai as against sixth position by respondents in Tiruchirapalli.

Table 6.18: Effectiveness of sales tools used Mean Rating Score

Sales tool used Chennai Tiruchirapalli

Maturity value projection 3.91 2.63

Sales literature 3.78 3.38

Competition comparison 3.03 3.06

Customer reference 3.59 3.13

Future cost of education, marriage 3.94 3.00

Pension requirement 3.78 2.75 Source: Primary data Note: Rating on a scale of 1 to 5 with 1 indicating least useful and 5 very useful

Figure 6.8: Ranking of effectiveness of sales tools used by agents


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Respondents from Chennai consider future cost of education/marriage projection

as the most effective tool in selling life insurance. Respondents from Chennai have

stated that competition comparison as the least effective sales tool used by them while

respondents from Tiruchirapalli have stated that maturity value projection is the least

effective one. According to respondents from Tiruchirapalli sales literature is the most

effective tool used by them followed by customer reference.

6.5 COMPARISON OF COMPETITION PRODUCTS

Table 6.19: Comparison with competition products by agents Chennai Tiruchirapalli

No. of Respondents Percentage No. of


Do compare 16 50.00 4 25.00

Do not compare 16 50.00 12 75.00


50 percent of the respondents in Chennai and 25 percent of the respondents in

Tiruchirapalli compare competition products and present to customers. It is significant to

note that the respondents in Chennai have ranked competition comparison as the least

effective sales tool.

Table 6.20: Factors considered while comparing competition products

Mean Ranking Factors in comparing competition products

Chennai Tiruchirapalli

Compare Premium 2.88 3.00

Compare product features 1.69 1.25

Information on product availability 3.44 3.50

To learn more about life insurance product, which suits customer’s requirement 2.63 3.00


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Figure 6.9: Factors considered while comparing competition products

(Mean Ranking)

1.69

2.632.88

3.44

1.25

3 3

3.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Compare productfeatures

To learn more aboutlife insurance product,

which suitscustomer’s requirement

Compare Premium Information onproduct availability

Mea

n sc

ore

of ra

nkin

gChennaiTiruchirapalli

Source: Primary data According to the respondents in both Chennai and Tiruchirapalli the top most

reason for comparing competition products is to understand the product features and is

followed by finding the suitability of the products to meet customers’ need.

Hypothesis No: 6.2 Null Hypothesis: There is no significant difference between mean ranks towards factors

considered while comparing competition products Table 6.21: Friedman test for significant difference between mean ranks towards

factors considered while comparing competition products Factors considered while comparing

competition products Mean Rank

Chi –Square value P value

Compare premium 2.90

Compare product features 1.60

Get information on types of products available 3.45 Learn about life insurance product, which suits customer need 2.70

Just for information 4.35

32.68 0.000 **

Source: Primary data Note: a) ** in a cell denotes significance at 1% level

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significance. Hence there is a significant difference between the mean ranks towards

factors considered while comparing competition products. Agents scan competition

products predominantly to compare product features and to find their suitability for

customers needs.

6.6 ACQUAINTANCE WITH CUSTOMER

Table 6.22: Acquaintance with customers

Mode of acquaintance Chennai Tiruchirapalli

Cold call. Just called on the customer 26 8

Friend or relative of the customer referred 23 15

Customer is a friend 21 16

Customer is a relative 17 16 Got the reference of the customer from the life insurance company 16 9

Auditor/ Advisor of customer referred 11 9

No of respondents surveyed 32 16 Source: Primary data

Note: Multiple entries possible and hence total may not add up to 100% or sample size.

Figure 6.10: Ranking of preferred mode of acquaintance of customers by agents


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Cold call is the most preferred way of knowing a customer by agent respondents

in Chennai. This may be due to the fact they have exhausted the inner circle of friends

and relatives and are looking out for newer customers. Reference by auditors is the

least preferred by agent respondents in Chennai. On the other hand friends and relatives

have chosen to be the most preferred way of knowing customers by agent respondents

and cold call is the least preferred by agents in Tiruchirapalli.

Table 6.23: Types of interaction while selling life insurance – Mean Scores of rankings

Mean ranking Types of interaction

Chennai Tiruchirapalli

Met the customer face to face 1.50 1.06

Sent the information on email, Post, courier 2.56 3.00

Spoke to the customer on phone 1.78 2.00


Agents prefer to meet the customer face to face for selling life insurance in both

Chennai and Tiruchirapalli. Speaking to the customer on phone is the second preferred

option of interaction with the customer. Sending the information by post or by email is

the least preferred option by the agent respondents in both Chennai and Tiruchirapalli.

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Table 6.24: Family members participating in life insurance decision-making

Chennai Tiruchirapalli Family members No. of


Only Customer 2 6.25 1 6.25

Spouse 9 28.13 5 31.25

Parent 2 6.25 2 12.50

Children 1 3.13 - 0.00

Friend 3 9.38 - 0.00

Spouse & Parent 9 28.13 4 25.00

Spouse & Children 2 6.25 - 0.00

Spouse & Friend 3 9.38 - 0.00

Parent & Children 1 3.13 1 6.25

All Spouse, parent & children 0 0.00 3 18.75

Total 32 16 Source: Primary data It can be observed that spouse of the customer participates in the decision

making either alone or with other members of the family. This participation can be

observed in both Chennai and Tiruchirapalli. It is important for the life insurance agent to

set up the meeting of the customer at the residence of the customer to ensure

participation by the spouse and other family members. Although recommendations

regarding agent or products are taken from friends, they are not part of the discussion

and decision making process.

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6.7 DIFFICULTY IN DECISION MAKING BY CUSTOMERS

Table 6.25: Difficulty in decision making by customers –Agents’ observation Mean Rating

Difficulty in decision making pertaining to Chennai Tiruchirapalli

Understanding the policy details, benefits 2.88 3.81

What type of Insurance to buy 3.25 3.44

Choice of the life insurance company 3.41 3.31

Sum assured 3.06 3.06


Figure 6.11 Ranking of difficulty in decision making by customers –Agents’ observation


As brought out by the above table and figure, agents have observed that

customers have least difficulty on deciding the life insurance company in Chennai and

understanding the policy details in Tiruchirapalli. Understanding the policy details and

benefits by customers in Chennai and deciding on the sum assured by those in

Tiruchirapalli have been observed to be most difficult.

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6.8 CUSTOMER INTERACTION BY AGENTS

Table 6.26: Agents having a customer base for servicing regularly

Chennai Tiruchirapalli Customer base of agents No. of


Have a customer base 31 97 15 94

Do not have a customer base 1 3 1 6

Total 32 16

Source: Primary data Almost all the agent respondents in both Chennai and Tiruchirapalli stated that

they have a customer base to service and upsell. In an industry observation, it is

claimed that after 3 years of selling a policy, a customer is ready for buying the next

policy. Secondly existing satisfied customers constitute a huge source of reference for

new customer acquisition. It is extremely important for agents to meet their customers

not only for the purpose of servicing but also for providing updated information on

products and industry.

Table 6.27: Frequency of meeting customers

Chennai Tiruchirapalli Frequency of meeting customers No. of


Once in 6 months 19 59 10 63

Once in a year 6 19 4 25

Once in 2 years 4 13 2 12 Only during new product launch 3 9 0 0

Total 32 16 `Source: Primary data

From the above table it can be inferred that 59 percent of the agent respondents

in Chennai and 63 percent in Tiruchirapalli meet their customers once in 6 months and a

further 19 percent in Chennai and 25 percent in Tiruchirapalli meet their customers once

217

in a year. Only 13 percent in Chennai and 12 percent in Tiruchirapalli state that they

meet their customers once in 2 years implying that they are not servicing the customers

in the first two years after the issuance of the policy.

Agents need to keep in touch with the customers. Most customers want some

form of contact on regular basis and this is the best opportunity for an agent to know

when the customers needs change and to be at the top of their mind in terms of whom to

turn to for advice (Terry, 2005).

6.9 FINANCIAL PLANNING AWARENESS OF AGENTS

Table 6.28: Agents’ awareness of personal financial planning

Chennai Tiruchirapalli Personal financial planning

awareness Frequency Percentage Frequency Percentage

Aware 30 94 13 81

Unaware 2 6 3 19

Total 32 16 Source: Primary data 94 percent of the respondents from Chennai and 81 percent from Tiruchirapalli

are aware of life stage personal financial planning and the different needs arising during

human life cycle.

Table 6.29: Helping customers in personal financial Planning

Chennai Tiruchirapalli Providing help to customers No. of


Provide help 30 94 13 81

Do not provide help 2 6 3 19

Total 32 16


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All those agents who are aware of life stages personal financial planning have

said that they help their customers do their personal financial planning.

Hypothesis No: 6.3

Null Hypothesis: There is no significant association between experience as agent and providing help in financial planning to customers

Table: 6.30: Chi –Square test for association between experience as agent and

providing help in financial planning to customers Experience as

agent Help in financial planning < 6 >=6

Total Chi-

square value

P value

27 16 Provide help in financial planning (62.8)

[87.1] (37.2) [94.1]

43

4 1 Do not provide help (80)

[12.9] (20) [5.9]

5

Total 31 17 48

0.57996 0.44633

Source: Primary data Note: Analysis for consolidated data

Since P value is greater than 0.05, the null hypothesis is accepted. Hence there

is no significant association between experience as agent and providing help in financial

planning to customers implying no value addition for customers in terms of financial

planning by an agent who has more years of experience.

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Hypothesis No: 6.4 Null Hypothesis: There is no significant association between nature of agency and

providing help in financial planning to customers

Table 6.31: Chi –Square test for association between nature of agency and providing help in financial planning to customers

Nature of agencyHelp in financial

planning Full Time

Part time

Total Chi-

square value

P value

24 19 Provide help in financial planning

(55.8) [92.3]

(44.2) [86.4]

43

2 3 Do not provide

help (40) [7.7]

(60) [13.6]

5

Total 26 22 48

0.4512 0.50177



is no significant association between nature of agency and providing help in financial

planning to customers implying no value addition for customers in terms of financial

planning by an agent irrespective of the fact that the agent is a full time agent or a part

time agent.

220

6.10 ANALYSIS FOR AGENCY RECRUITMENT

Agency channel comprising of individual agents, also known as tied agency

channel is the predominant distribution channel for life insurance products in India. For

the year 2005-06, business acquired by tied agency constituted about 98% for LIC of

India and about 60% for private life insurance companies and this indicates the

importance of individual agents to the life insurance company. (IRDA Annual report

2006). The cost of recruitment and training is very high for developing an individual

agent. It is a challenge for the life insurance companies to keep the recruited agents

active and make them productive year after year to benefit out of the huge initial

investment on recruitment and training.

In this section an agent’s ‘performance’ is measured in terms of the commission

earned and also the agent becoming a club member. ‘Commission’ in life insurance

industry parlance refers to the payment made to agents, brokers by the life insurance

company for selling the life insurance products to customers. Commission earnings are

linked to the new business premium sourced by the agent and is considered as

representing good performance in this analysis. Similarly ‘club membership’ refers to

additional benefit given by life insurance companies to their agents as a rewards and

recognition mechanism and is aimed at meeting the aspirations of the agent. An agent

will be eligible to become a club member on crossing a certain threshold of business

requirement as specified by the individual life insurance companies. Each of the life

insurance companies has their own set of club membership eligibility criteria and

benefits associated with it.

In this section the impact of age, sex, educational qualification, nature of agency,

agency work experience, previous work experience and selling products other than life

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insurance on the commission earnings and club membership are analysed. The

responses from agents from both Chennai and Tiruchirapalli are considered together for

this purpose.

Hypothesis No: 6.5 Null Hypothesis: There is no significant association between the age groups of agents

and their commission earnings. .

Table: 6.32: Chi –Square test for association between age groups of agents and

their commission earnings Age group Commission

earnings per month <= 30 > 30

Total Chi-

square value

P Value

15 5 Less than10000 (75)

[83.3] (25)

[16.7] 20

3 17 10000 to 20000 (15)

[16.7] (85)

[56.7] 20

8 20000 - (100)

[26.7] 8

Total 18 30 48

21.12 0.000**

Source: Primary data Note: a) ** in a cell denotes significance at 1% level b) Analysis for consolidated data


significance. Hence there is an association between age group and commission

earnings. Agents in the age group of more than 30 years earn more commission

implying they generate more business and possess greater business capability. Another

social feature in the Indian context is the considerable respect for age in our society and

a belief that an elderly person knows better. A very young agent who is a typical

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salesman may not appeal to a large segment of middle class, which is looking for a solid

trustworthy person from whom they can buy insurance (Lakshmikutty, 2003)

Hypothesis No: 6.6 Null Hypothesis: There is no significant association between the sex of agents and

their commission earnings. . Table: 6.33: Chi –Square test for association between the sex of agents and their

commission earnings Commission earnings per

month Sex

<1000010000

to 20000

>20000Total

Chi-square value

P Value

16 14 5 Male (45.7)

[80] (40) [70]

(14.3) [62.5]

35

4 6 3 Female (30.8)

[20] (46.2) [30]

(23.1) [37.5]

13

Total 20 20 8 48

1.03385 0.596



is no association between sex of the agents and commission earnings. It is interesting to

note that in countries like Japan and Korea women agents work in large numbers and

are more successful. Women constitute more than 80 percent of the individual agents in

Korea.

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Hypothesis No: 6.7 Null Hypothesis: There is no significant association between educational qualification of

agents and commission earnings by agents. Table: 6.34 Chi –Square test for association between educational qualification of

agents and commission earnings by agents Educational Qualification Commission

earnings per month HSC UG PG Others Total

Chi- Square value

P Value

4 12 3 1 < 10000 (20)

[50] (60)

[46.2] (15)

[37.5] (5)

[16.7] 20

3 9 4 4 10000 to 20000 (15)

[37.5] (45)

[34.6] (20) [50]

(20) [66.7]

20

1 5 1 1 >20000 (12.5)

[12.5] (62.5) [19.2]

(12.5) [12.5]

(12.5) [16.7]

8

Total 8 26 8 6 48

2.83846 0.82883



is no association between educational qualification of agents and commission earnings.

Agents’ educational qualification is not a factor influencing his commission earnings and

hence his business potential.

224

Hypothesis No: 6.8 Null Hypothesis: There is no significant association between number of years of

experience as agent and commission earnings. .

Table: 6.35: Chi –Square test for association between the number of years of experience as agent and commission earnings

Experience as agent (years) Commission earnings

per month < 6 >=6

Total Chi-square value P Value

12 8 < 10000 (60)

[38.7] (40)

[47.1] 20

15 5 10000 to 20000 (75)

[48.4] (25)

[29.4] 20

4 4 >20000 (50)

[12.9] (50)

[23.5] 8

Total 31 17 48

1.87528 0.39135



is no association between number of years of experience as agent and commission

earnings.

225

Hypothesis No: 6.9 Null Hypothesis: There is no significant association between nature of life insurance agency (full time or part time) and commission earnings by agents. . Table: 6.36: Chi –Square test for association between the nature of life insurance

agency and commission earnings by agents Nature of agency

Commission earnings per month Full Time Part time

Total Chi-

square value

P Value

7 13 < 10000 (35)

[26.9] (65)

[59.1] 20

14 6 10000 to 20000 (70)

[53.8] (30)

[27.3] 20

5 3 >20000 (62.5)

[19.2] (37.5) [13.6]

8

Total 26 22 48

5.20208 0.07417



is no association between nature of life insurance agency (fulltime or part time) and

commission earnings.

226

Hypothesis No: 6.10 Null Hypothesis: There is no significant association between selling products other than

life insurance and commission earnings. .

Table: 6.37: Chi –Square test for association between the selling products other than life insurance and commission earnings

Commission earnings per month Selling other

products <1000010000

to 20000

>20000Total

Chi-square value

P Value

7 3 6 16 Sell other products (43.8)

[35] (18.8) [15]

(37.5) [75] [33.3]

13 17 2 32 Do not sell (40.6)

[65] (53.1) [85]

(6.3) [25] [66.7]

Total 20 20 8 48

9.3 0.00956**



significance. Hence there is an association between selling products other than life

insurance and commission earnings by agents. An agent selling other products in

addition to life insurance products will be more successful as he has more opportunity to

be in constant touch with the customer.

227

Hypothesis No: 6.11 Null Hypothesis: There is no significant association between the age groups of agents

and their club membership. . Table: 6.38 Chi –Square test for association between the age groups of agents and

their club membership Age group

Club membership <= 30 > 30

Total Chi-

square value

P Value

3 22 Club member (12)

[16.7] (88)

[73.3] 25

15 8 Not a club member (65.2)

[83.3] (34.8) [26.7]

23

Total 18 30 48

14.4751 0.000**



significance. Hence there is an association between age group and club membership.

Agents in the age group of more than 30 years of age are more successful in the life

insurance business.

228

Hypothesis No: 6.12 Null Hypothesis: There is no significant association between the sex of agents and their

club membership. . Table: 6.39: Chi –Square test for association between the sex of agents and club

membership

Club membership Sex

Yes No Total

Chi-square value

P Value

18 17

(51.4) (48.6) Male

[72] [73.9]

35

7 6 (53.8) (46.2) Female [28] [26.1]

13

Total 25 23 48

0.022 0.8816



is no association between sex of the agents and their club membership.

Hypothesis No: 6.13 Null Hypothesis: There is no significant association between educational qualification of

agents and club membership.

Table: 6.40: Chi –Square test for association between the educational qualifications of agents and club membership

Educational Qualification Club membership

HSC UG PG Others Total

Chi-square value

P Value

4 13 3 5

(16) (52) (12) (20) Club member

[50] [50] [37.5] [83.3]

25

4 13 5 1 Non club member (17.4)

[50] (56.5) [50]

(21.7) [62.5]

(4.3) [16.7]

23

Total 8 26 8 6 48

3.0887 0.37815


229


is no association between educational qualification and club membership. Agents’

educational qualification is not a factor for reckoning club membership and hence

business.

Hypothesis No: 6.14 Null Hypothesis: There is no significant association between number of years of

experience as agent and club membership.

Table: 6.41: Chi –Square test for association between the number of years of experience as agent and club membership

Age group Club membership

<6 >=6 Total Chi-square

value P Value

17 8 Club member (68)

[54.8] (32)

[47.1] 25

14 9 Not a club member (60.9)

[45.2] (39.1) [52.9]

23

Total 31 17 48

0.266 0.606



is no association between number of years of experience as agent and club

membership.

230

Hypothesis No: 6.15 Null Hypothesis: There is no significant association between nature of life insurance

agency (Full time or part time) and club membership. . Table: 6.42: Chi –Square test for association between the nature of life insurance

agency and club membership Nature of life insurance

agency Club membership Full Time Part time

Total Chi-

square value

P Value

17 8 25 Club member (68)

[65.4] (32) [6.4] [52.1]

9 14 23 Not a club member (39.1)

[34.6] (60.9) [63.6] [47.9]

Total 26 22 48

4.022 .0.045*

Source: Primary data Note: a) * in a cell denotes significance at 5% level b) Analysis for consolidated data


significance. Hence there is an association between nature of life insurance agency

(fulltime or part time) and club membership. Agents, who operate full time, are acquiring

club membership in larger numbers and this indicates higher level of business

performance from full time agents.

231

Hypothesis No: 6.16 Null Hypothesis: There is no significant association between selling products other than

life insurance and club membership of agents. .

Table: 6.43: Chi –Square test for association between the selling products other than life insurance and club membership

Club membership Selling other products

Yes No Total

Chi-square value

P Value

9 7 Sell other products (56.3)

[36] (43.8) [30.4]

16

16 16 Do not sell (50)

[64] (50)

[69.6] 32

Total 25 23 48

0.16696 0.68283



is no association between selling products other than life insurance and club

membership of agents.

232

6.10.1 SUMMARY OF FINDINGS ON AGENT RECRUITMENT

Results of all the above hypotheses are summarized in the following table and it

can be observed that age of the agents plays a critical role in their performance. Nature

of life insurance agency and selling other products also impact performance of agents.

Insurance company can formulate their agent recruitment guidelines based on the

personal factors of the prospective candidates.

Table 6.44: Association between commission earnings and club membership of agents with agents’ personal factors - Summary

Existence of association Personal factors

Commission earnings

Club membership

Age Yes Yes

Sex No No

Educational qualification No No

Agency work experience in years No No

Nature of life agency Part time or full time No Yes

Selling other products Yes No Source: Primary data

233

6.11 COMPARISON OF CUSTOMERS’ AND AGENTS’ PERCEPTION OF PURPOSE OF BUYING LIFE INSURANCE

Customers’ reasons for buying life insurance and the observations of agents in

respect of reasons adduced by customers while buying life insurance have been

analysed to understand the degree of divergence between customers and agents.

Hypothesis No: 6.17 Null Hypothesis: There is no significant difference between the observations of

customers and agents with regard to purpose of buying life insurance products.

. Table: 6.45: Mann-Whitney U-tests for difference between the observations of

customers and agents with regard to purpose of buying life insurance products Mean Rank

Reasons for buying insurance Customer Agent

Z value P value

Availing Income tax benefit 173.32 149.90 1.561 0.118

Life Insurance cover 168.95 212.94 3.055 0.002**

Savings for Children’s education, marriage 167.85 155.07 0.880 0.378

Savings for old age, pension 158.51 171.76 0.936 0.349

Agent’s recommendation 153.56 134.43 1.423 0.154

Recommendation from Friends /Relatives 147.29 161.00 1.038 0.299

Housing Loan cover 136.09 186.55 3.999 0.000**


Since the P value is less than 0.01, the null hypothesis is rejected with regard to

life insurance cover and housing loan cover as the reasons for buying life insurance.

Customers and agents have different observations with regard to these two reasons.

They exhibit similar rankings for all other reasons.

234

6.12 COMPARISON OF CUSTOMERS’ AND AGENTS’ OBSERVATIONS ON

DIFFICULTY IN DECISION MAKING

Difficulties experienced by customers and also the observations of agents on the

difficulties of customers have been analysed to understand the difference in perceptions

of customers and agents.

Hypothesis No: 6.18 Null Hypothesis: There is no significant difference between the observations of

customers and agents with regard to difficulty experienced in decision-making by customers.

Table 6.46: t test for significant difference between the observations of customers and agents with regard to difficulty experienced in decision making by customers

Customer Agent Difficulty in decision making

Mean S D Mean SD t value P value

Decide on what type of insurance to buy 2.590 1.24 3.312 1.58 3.64 0.000**

Decide on the sum assured 2.944 1.13 3.062 1.27 0.67 0.505

Decide on the life insurance company 2.969 1.15 3.37 1.52 2.19 0.029*

Understanding the policy details, benefit 3.000 1.30 3.187 1.62 0.91 0.365

Source: Primary data Note: a) * in a cell denotes significance at 5% level b) ** in a cell denotes significance at 1% level c) Analysis for consolidated data

Since the P value is less than 0.01, the null hypothesis is rejected at 1% level of

significance with regard to the difficulty ‘deciding on what type of insurance to buy’.

There is a significant difference between customer and agent with regard to the difficulty

‘deciding on what type of insurance to buy’. Similarly since the P value is less than

0.05, the null hypothesis is rejected at 5% level of significance with regard to the

difficulty ‘deciding on the life insurance company’. There is a difference between

customers and agents with regard to the difficulty ‘deciding on the life insurance

company’ as both customers and agents have different degrees of perception on this

235

difficulty. In respect of the other two difficulties both the groups have expressed similar

difficulty ranking.

6.13 COMPARISON OF CUSTOMERS’ AND AGENTS’ OBSERVATIONS ON EFFECTIVENESS OF SALES TOOL

Agents while selling life insurance products use sales tools. Both customers and

agents have observed the effectiveness of these tools in helping to close the sale.

Hypothesis No: 6.19 Null Hypothesis: There is no significant difference between the views of customers

and agents with regard to effectiveness of sales tools used by agents. Table: 6.47: t test for significant difference between the views of customers and

agents with regard to effectiveness of sales tools used by agents

Customer Agent Effectiveness of sales tool

Mean S D Mean SD

t value P value

Comparison with products of competition 3.572 1.01 3.041 1.11 3.18 0.002**

Pension requirement and planning 3.841 1.01 3.437 1.55 2.14 0.034*

Explanation of sales literature 3.479 1.02 3.645 1.34 0.93 0.355

Assessment of future cost of education, marriage 3.731 1.04 3.625 1.56 0.55 0.583

Maturity value projection 3.568 1.34 3.479 1.58 0.41 0.686 Reference of other customers, who bought the product

3.462 1.09 3.437 1.33 0.13 0.896



significance with regard to effectiveness of comparison with competition products as a

236

sales tool. There is a significant difference between customer and agent with regard to

effectiveness of comparison with competition products as a sales tool. Similarly since

the P value is less than 0.05, the null hypothesis is rejected at 5% level of significance

with regard to effectiveness of pension requirement and planning as a sales tool.

Figure 6.12: Ranking of effectiveness of sales tools – Observations of customers

and agents in Chennai


From the above figure 6.12, it can be observed that there are divergences in

ranking by customers and agents on the effectiveness of sales tool used. While

customers rank ‘pension calculation” as very effective, agents have rated ‘future cost of

education or marriage’ as most effective. Customers have rated ‘Sales literature’ as the

least effective sales tool.

237

Figure 6.13: Ranking of effectiveness of sales tools – Observations of customers

and agents in Tiruchirapalli


Customers of Tiruchirapalli have rated ‘pension requirement’ as the most

effective and ‘competition comparison’ as least effective sales tool. On the other hand

agents in Tiruchirapalli have ranked ‘sales literature’ as the most effective and ‘maturity

value projection’ as least effective.

238

6.14 ASKING FOR REFERRALS FROM CUSTOMERS Taking reference from existing customers is a method of expanding the customer

base and business by an agent. The observations of both customers and agents on this

subject have been analysed to understand divergence if any in these observations.

Hypothesis No: 6.20 Null Hypothesis: There is no significant association between respondents and asking for

reference by agents. Table: 6.48 Chi –Square test for association between respondents and asking for

reference by agents Asking reference by

agents Respondents Asking

and Giving

Asking but not giving

No request

Total Chi-

square value

P Value

133 80 146 359 Customer (37)

[77.3] (22.3) [93]

(40.7) [98]

39 6 3 48 Agent (81.3)

[22.7] (12.5)

[7] (6.3) [2]

Total 172 86 149 407

35.193 0.000**



significance. There is a significant association between customer and agent with regard

to asking for reference by agents. Only 37 percent of the customers state that they

provide reference to agents when asked while more than 81 percent of agents’ state that

they ask for reference and get from customers. Similarly 40 percent of the customers’

state that agents do not ask for reference but only 6 percent of the agents say that they

do not ask for reference. Agents need to be trained on when to ask for reference, how

to ask and what details to be asked for.

239

6.15 AQUAINTANCE WITH AGENT / CUSTOMER One of the key factor to become successful as life insurance agent is to develop

a prospect list. Normally any prospect list starts with friends and relatives of an agent

and extends beyond. Prospects will also include reference from others like auditors, Life

Insurance Company or even by doing a cold call. In order to understand the method of

acquaintance with the customer or agent, the feedbacks received from both the

customers and agents have been analysed for each of the identified ways of

acquaintance.

Hypothesis No: 6.21 Null Hypothesis: There is no significant association between the respondents and the

agent or customer being a friend.

Table: 6.49 Chi –Square test for association between the respondents and the agent or customer being a friend

Friend Respondents

Yes No Total

Chi-square value

P Value

140 138 Customer (50.4)

[79.1] (49.6) [92.6]

278

37 11 Agent (77.1)

[20.9] (22.9) [7.4]

48

Total 177 149 326

11.779 0.001**



significance. There is a significant association between the respondents with regard to

a friend being a customer or agent. 50 percent of the customers and 77 percent of the

agents state that the agent/customer is a friend. Customers and agents have different

observation on a friend being a customer or agent.

240


agent or customer being a relative.

Table 6.50: Chi –Square test for association between the respondents and the agent or customer being a relative

Relative Respondents

Yes No Total

Chi-square value

P Value

52 226 278 Customer (18.7)

[61.2] (81.3) [93.8]

33 15 48 Agent (68.8)

[38.8] (31.3) [6.2]

Total 85 241 326

53.18491 0.000**



significance. There is a significant association between the respondents with regard to

a relative being a customer or agent. Only 18.7 percent of the customers, but more than

68 percent of the agents state that the agent/customer is a relative.

Very few customers state that they buy life insurance from an agent who is a relative.

This situation can arise due to tentativeness on the part of the agent to contact his

relative or hesitancy by the customer to share personal details to an agent who is a

relative.

241


customer or agent being referred to by a friend or relative.

Table: 6.51: Chi –Square test for association between the respondents and the customer or agent being referred to by a friend or relative

Referred by a friend or relative Respondents

Yes No Total

Chi-square value

P Value

91 187 Customer (32.7)

[70.5] (67.3) [94.9]

278

38 10 Agent (79.2)

[29.5] (20.8) [5.1]

48

Total 129 197 326

36.906 0.000**



significance. There is a significant association between respondents and the customer

or agent being referred to by a friend or relative. 32.7 percent of the customers and 79.2

percent of the agents state that a friend or relative has referred the agent/customer.

There is a significant variation between the feedbacks taken from customers and agents.

242


customer or agent being referred to by auditor or advisor.

Table 6.52: Chi –Square test for association between the respondents and the customer or agent being referred to by auditor or advisor

Referred by auditor / advisor Respondents

Yes No Total Chi-square

value P Value

34 244 Customer (12.2)

[63] (87.8) [89.7]

278

20 28 Agent (41.7)

[37] (58.3) [10.3]

48

Total 54 272 326

25.663 0.000**



significance. There is a significant association between the respondents and the

customer or agent being referred to by auditor or advisor. 12.2 percent of the customers

and 41.7 percent of the agents state that auditor or advisor has referred the

agent/customer.


customer or agent being referred to by the life insurance company.

Table 6.53: Chi –Square test for association between the respondents and the customer or agent being referred to by the life insurance company

Reference of life insurance company Respondents Yes No

Total Chi-

square value

P Value

23 255 Customer (8.3)

[47.9] (91.7) [91.7]

278

25 23 Agent (52.1)

[52.1] (47.9) [8.3]48

Total 48 278 326

62.569 0.000**

Source: Primary data Note: a) ** in a cell denotes significance at 1% level

243



customer or agent being referred to by the Life Insurance Company. 8.3 percent of the

customers and 52.1 percent of the agents state that the life insurance company has

referred the agent/customer. Agents depend on life insurance company for prospect list.


customer or agent being unknown.

Table 6.54: Chi –Square test for association between the respondents and the customer or agent being unknown

Agent unknown, called on Respondents

Yes No Total

Chi-square value

P Value

45 233 Customer (16.2)

[57] (83.8) [94.3]

278

34 14 Agent (70.8)

[43] (29.2) [5.7]

48

Total 79 247 326

66.573 0.000**

Source: Primary data Note: a) ** in a cell denotes significance at 1% level Since the P value is less than 0.01, the null hypothesis is rejected at 1% level of


customer or agent not known earlier. 16.2 percent of the customers and 70.8 percent of

the agents state that the agents/customers are not known earlier. Cold calling by agents

ranks higher than other modes of acquaintances like knowing through auditor or

company.

244

6.15.1 SUMMARY OF AQUAINTANCE WITH AGENT / CUSTOMER

Table 6.55: Acquaintance with agent or customer – Summary Customer Agent

Acquaintance with agent / customer Percentage of positive respondents

Friend 50.0 77.0

Relative 18.0 69.0

Referred by a friend or relative 32.7 79.2

Referred by auditor or advisor 12.2 41.7

Reference from life insurance company 8.3 52.1

Cold call 16.2 70.8

From the above table (6.55) it can be observed that customers are comfortable in

buying life insurance from a friend or an agent referred by a friend or relative. Very few

have bought life insurance through reference from life insurance company and which

confirms the fact that life insurance is not bought but sold. Agents depend on life

insurance company for leads, which is evident from the fact that 52 percent of the agents

stating that the customer has been referred by the life insurance company. It means that

they are missing out on the huge opportunity of business of their friends or relatives, who

also equally need life insurance.

245

REFERENCES:

1. Lakshmikutty, Sreedevi and Baskar, Sridharan, “ Insurance Distribution in India -

a perspective”, DCG, Infosys Technologies Ltd, 2003.

2. LIMRA,-Moss Adams “New Game, New Rules, New Reality The Economics of

Growing Distribution”, Limra, 2006.

3. Mandal SS, “A study on Advisor’s profile”, Insurance Chronicle 2006

4. Srinivasan, Vasanthi, Prakah, P, Sitharamu. S, “Selection of agents: A challenge

for the Indian insurance industry”, Centre for Insurance Research and Education,

Indian Institute of Management, Bangalore, 2001.

5. Terry, Karen, “ After the sale- what new buyers want from their insurers/agents”,

LIMRA, 2005

CHAPTER 6- Analysis 2-AGENT PROFILE, PRACTICES

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Transcript of CHAPTER 6- Analysis 2-AGENT PROFILE, PRACTICES