1. Title: Relationship marketing, mind-set segmentation, optimized messaging for life
insurance, and typing customers into the segments.
Authors:
Gillie Gabay, Ph. D.
College of Management Academic Studies
Rishon Letzion, Israel
E-mail: gillie.gabay@gmail.com
Howard Moskowitz, Ph. D.*
Moskowitz Jacobs, Inc.
1025 Westchester Ave., 4th fl.
White Plains, New York 10604 USA
Phone: 914-421-7400
Fax: 914-428-8364
E-mail: mjihrm@sprynet.com
Hollis Ashman
Jacqueline Beckely
The Understanding and Insight Group, Inc.
Denville, New Jersey USA
E-mail: Hollis@theunderstandingandinsightgroup.com
E-mail: Jackie@theunderstandingandinsightgroup.com
*Corresponding author
Abstract
Due to the intense competition and the intangibility of life insurance services, many
insurers who previously viewed their products as a utility, now view their products, and
coincidentally themselves as well, as a complex service to be marketed. The dual goal is to
satisfy customers while at the same time increasing profitability. Relationship selling strategy
resolves the contradictory tension between satisfying consumer needs on the one hand, and
ensuring high levels of profitability on the other. In order to implement the relationship selling
strategy, the professional must understand the experience and needs of individual customers at
both a personal level and a product level. Recent studies recognize the need to understand
differences among customers and to use those differences as foundations for marketing and
communication strategies. Our study focuses on mind-set segmentation in the world of life
insurance as a way to improve relationship selling. Our findings show that life insurance is an
emotional experience. We identified three distinct mindsets with different preferences and
demands, respectively. We suggest the shaping of mainstream life insurance policies using
winning elements that will enhance acceptability and increase market share. To retain customers,
insurers should use appropriate messaging for each segment, to generate the relationship, repeat
sales and, in turn, long term profitability. This study extends the existing literature on
segmentation and relationship selling by developing a method for typing by which insurance
salespeople can identify a new prospect immediately, thus making the segmentation actionable at
the local sales level.
Key words:
Relationship Marketing, Life insurance, Mind-set Segmentation, Customization, Messaging,
Typing, Discriminant Factor Analysis .
Introduction – the marketing problem
1

2. In the world of insurance, only life insurance is considered by its very nature to be long
term. The insurance policy is, therefore, as valuable as securities (1). Furthermore, life
insurance is an important investment, promising significant profit to the insured or beneficiary.
The insurance company places a method for saving and investing into the hands of people with
modest means, even in times of economic uncertainty (2).
It should not come as a surprise that the world of life insurance is highly competitive as a
consequence of its very nature, namely being widely available, acting as investment, providing
protection. The U.S federal charter of insurance allows nationally licensed insurers to compete in
any state, as well as those insurers having only one license to serve widely separated clients in a
geographic sense by Internet, using one site. The result is good for consumer; they enjoy a vast
choice among services providers. Thus, competition among insurers is fierce, even within the
insurer’s home state (3). Finally, today’s internet capabilities empower a customer to find
competitors selling with better rates, or to be educated by competitors providing information on
the fine points of their insurance policies, all at the click of a computer mouse (4,5).
Beyond the competition is the reality that insurance is complex. Life insurance itself is an
abstract, complex service that by its very nature must focus on future benefits. Even after one
purchases a policy, it’s often quite difficult for the buyer to figure out what the policy covers, and
what the terms really are (6). Thus, inherent uncertainty and ambiguity are the hallmarks of
consumer consumption of life insurance.
The intense competition and the intangibility of the insurance service has changed the
approach of many insurers from viewing themselves as the financial equivalent of a utility to
viewing themselves a service/product to be marketed. The dual goal is to satisfy customers while
at the same time identifying new, relevant aspects of insurance which will increase profitability.
There is, however, the ever-present contradictory tension between satisfying consumer needs on
the one hand, and ensuring high levels of profitability on the other. Simply putting onerous
conditions in the ‘fine print’ doesn’t work, especially when the agent is asked to explain the
meaning of the terms, and cannot leave the client guessing.
One strategy to resolve this tension and ensure profitability, while responding to
consumer needs, is relationship selling (7). This strategy focuses on the one-on-one relationships
between the agent and consumers (8, 9). Insurers apply relationship selling as a way to build long
term bonds with customers (10), which also ends up increasing customer loyalty (7). In turn, the
increased customer loyalty generates higher levels of profitability. One example comes from the
common wisdom of service sector relationship selling, e.g., insurance. It has been suggested that
the cost of developing a new customer is about five times higher than the cost of maintaining
existing customers (11,12). Relationship selling is a recommended strategy for enhancing
customer retention and overcoming service intangibility (8,13). In order to implement the
relationship selling strategy, the professional must understand the mind-set and needs of the
individual customer to whom he is selling.
A number of recent studies have focused on relationship selling and segmentation
(14,15,16). When studying the nature of customer retention, Ansell et al (14) reported clear
differences in customers with different lifestyles. The data suggest greater opportunities for
retention with mature adults rather than with young adults. These results suggest that future
studies should assess differences among customers, thereby providing a basis for marketing and
communication strategies. Kim et al (17) also pointed out the need to create a measure of the true
value of customers, and with this measure target customers who are profitable. In a similar
vein, but focusing on the product rather than the customer, Wang and Guicheng (18), suggested
that more attention should to be paid to understanding the different preferences and demands of
each group of insurance customers. They claim that a deep understanding of the unique demands
and preferences of customers is vital in order to sustain profitability because loyal customer
relationship selling contributes a substantial portion of the total profits.
2

3. Although lifestyle and marketing research has succeeded in solving the marketing
segmentation dilemma, the problem of linking segments with specific customers in the population
still lingers (19). By knowing to which segment a specific customer belongs, marketers could
shape a strong concept or selling message for that person. Typically the individual presented with
this message would respond positively because the message is relevant, focused and appropriate
to the individual’s segment. To date, there is no clear information by which to know which
segment a particular person belongs to based on a complex mindset, preference or attitude
segmentation. Despite the existence of data mining, it is difficult to assess the vast amount of
data by intricately combining customer characteristics with data. Requirements for approaching
individual customers in a targeted, more efficient manner have been partially satisfied by massive
customized direct marketing (19) but are yet to be fulfilled. Furthermore, it is not clear that there
exists a link between the information about a customer purchasable from databases and the mind-
set of that individual in terms of that to which he reacts at an emotional level. We deal here with
the problem of how to marry information about a person with the mind of the person, and suggest
that it will necessary to do an ‘intervention’ in medical terms. Rather than knowing about a
person from other data (so-called family history approach) it maybe necessary to type a person in
an active way (so-called blood test approach, the way modern medicine is practiced).
The contribution of this study
This study continues the steam of effort from recent studies, which aim to provide deeper
understanding of what aspects of insurance are important to consumers, in terms of their own
needs (14,17,18). The study makes three major contributions. First, it segments customers on the
basis of their true desires from life insurance, focusing on the experience of buying insurance and
dealing with insurance professionals, a topic that has been ignored since the focus has been on
what the insurance covers, rather than on the experience and ‘softer side’. Second, the study
focuses on providing an actionable framework for communicating with consumes, through the
method of conjoint analysis, where the test stimuli resemble actual selling concepts. The study
examines if the distinct segments desire different experiences from this service. The study
examines whether there can be a unified message for life insurance or a fractured message by the
radically different mindsets of segments. Third, this study extends the literature of segmentation
and relationship selling by presenting a straightforward approach to type prospective customers in
terms of mind-sets, allowing the sales effort to be more individualized and focused on what
stimulates the emotional triggers of the prospect.
Materials and Methodology
Participants
A total 158 respondents were recruited through Open Venue, Ltd. (an email list broker).
The respondent was invited to participate in a study on insurance by an email invitation that
promised a reward (sweepstakes), was led to a screen which showed the different insurance
studies, selected the study in which he was interested, and then participated immediately in the
study. Thus the respondents in this study represent those who were specifically interested in life
insurance. Figure 1 presents the 'wall of studies' from which respondents selected the topic of life
insurance.
Insert Figure 1 here.
Figure 1: The wall of studies, from which the respondent selected the topic preferred.
3

4. Lawsuit• Job Transition• Mortgage•
Credit• Broken Hearts• Warranties•
Long Term Care• Terminal Illness• Retirement Planning•
Elder Care• Valuables• Disability•
Identity Theft• Education Planning• Medical•
Self-Employed• Earthquake• Travel Medical•
Small to Mid-Size Business• Flood• Travel Property•
Fertility/Pregnancy• RV• Life•
Single Moms• Umbrella Liability• Homeowner’s•
Terrorism• Liability• Auto•
Table 1 presents the composition of the sample, gender, state of origin, income, spending
commitment on insurance and preferred insurer. Age was normally distributed. Income was
skewed to lower income but there were sufficient respondents at the higher income to get a sense
of what specific messaging was attractive to this group.
Insert Table 1 here.
Table 1: Panel composition as defined by the classification questionnaire (n=158).
N=Frequency %=Percentage
N %
Please tell us your gender
Male 56 35%
Female 102 65%
Please tell us your age
Under 21 2 1%
21-30 19 12%
31-40 47 30%
41-50 40 25%
51-60 33 21%
61-75 16 10%
Over 75 1 1%
For which of the following financial products do you own
an INDIVIDUAL policy plan? (check all that apply)
Life insurance 97 61%
Credit card 50 32%
Homeowner’s insurance 45 28%
Medical insurance 35 22%
Auto loan 33 21%
None of the above 33 21%
Other 29 18%
Stocks 25 16%
Mutual fund 23 15%
Standard Individual Retirement Account (IRA) 21 13%
4

5. Disability insurance 20 13%
Financial planner 20 13%
Dental insurance 18 11%
Pension plan 12 8%
401K 10 6%
Annuity 10 6%
Approximate household income.
Under $25,000 37 23%
$25,000 - $34,999 28 18%
$35,000 - $49,999 20 13%
$50,000 - $74,999 37 23%
$75,000 - $99,999 14 9%
$100,000 or over 11 7%
Prefer not to say 11 7%
Respondents were encouraged to self - select into a study. They could complete more
than one study but could complete each study only once. Fewer than 2% participated in two
studies. The placement of studies on the wall rotated, with the most popular studies on the top
left, and the least popular studies on the top right. This strategy of locating hard-to-fill studies at
the bottom right, together with the cash-incentive sweepstakes, increased participation in all of the
studies.
The interview via the internet lasted 15-20 minutes for each study. Figure 2 shows the
welcome screen. The screen provides some information about the study, but does not give the
respondent any detailed information about the logic behind the selection of elements, nor does the
screen give any sense of a ‘right’ or ‘wrong’ answer.
Insert Figure 2 here.
Figure 2: Welcome Screen: The experience of Life Insurance
Elements for the test concept:
5

6. The basic elements comprised four silos, each with nine elements, or 36 elements,
designed to cover a broad range of aspects about the experience of life insurance (see Table 2).
The study encompassed the experience of insurance by using the following four silos: Policy
Details and Access, Trusted Advisor (service, payment, claims, communication), Emotional
motivation, and Brands and Education (including other benefits).
Each respondent evaluated a unique set of 60 concepts, one concept per screen. The
concepts comprised 2-4 elements each, constructed with either one or no element from a silo in
each concept. Each element appeared three times in each concept, and was absent 57 times. This
strategy of complete designs for individuals, missing silos from some concepts, and systematic
permutation of the combinations, created the conditions to minimize bias. The models were
created on a person-by-person basis (within subjects design), could be analyzed by dummy
variable regression using ordinary least squares (permitting computation of absolute values of
utilities), and were independent of possible unsuspected interactions among pairs of elements.
Figure 3 shows an example of a test concept for the life insurance study
Insert Table 2 here.
Table 2: Nine elements each in four silos: Policy detail and access, Advisor and payments,
Emotional motivation, and brand or educational benefits.
Silo # 1: Policy Silo # 2: Advisor and Silo # 3: Silo # 4: Brand/
Detail and Access Payment Emotional/ Educational Benefit
Motivational
From a company
created to benefit the
community … owning
Protects against Available even a policy is like holding
financial loss in the Recommended by when your need stock … the company
event of a death in someone well respected is triggered by a does well, you get a
the family in your community specific event return on your premium
From a company that
offers package plans
Provides money to Select from an easy to tailored to the life stage
your family after understand, do-it- you're in …
you're deceased to yourself set of plans homeowners, auto, and
help pay for funeral that are tailored to your Rest assured, umbrella coverage all
costs and other needs … now and for your family will in one seamless
household expenses the future be taken care of program
Life-long
protection, lock in
low premiums by
starting your policy
when you're young
… insurance that Work with someone
accumulates a cash who has the experience Because it feels From a company
value that you can to understand your good to experienced not just in
borrow against as needs and knows how accumulate insurance, but in
you get older you think wealth over time financial planning, too.
6

7. Insures two lives …
pays the 'death
benefit' to the Speak to a professional
surviving person insurance consultant …
when the first one assess your needs and
passes away or build a customized
pays the 'death financial plan designed From a company that
benefit' only when around you, not based Protects your offers online quotes and
both individuals on one company's hard earned comparisons to other
have passed on offerings investment companies' pricing
Provides protection
to a single
individual for a
specified period of
time only …
renewable, From a company
convertible, or focused on the
variable coverage Talk to someone that individual and small
tailored to your you know is an Creates wealth business owner … no
personal financial advocate for you … not even when you're corporate lingo, just
needs the insurance company not looking basic common sense
Special options to
waive your
premium if
permanently
disabled ... double
indemnity
protection in case
of accidental death Makes it
... accelerated death Premiums payable in effortless to sign Straightforward access
benefits in case of monthly installments up for insurance to information on your
catastrophic illness … no interest for the first time . specific policy
Choose from a set
of plans pre- With flexible payment No confusing
selected by your scheduling … manage wording so you Offers educational
employer to meet your expenses and do can be sure of seminars on the ins and
your needs more with your dollars your decision outs of insurance
Receive financial
Offered at the time Simple claim forms … advice early … know
you join or start a online entry, fast Protection against just the right time to
company approval the unthinkable buy a policy
Receive quarterly
reports … by mail or Makes it easy to
email … highlighting find what you
your progress in need and sign up
Personalized access building equity and for just what you Offers tax breaks for
online achieving your goals want you and your family
Insert Figure 3 here.
7

8. Figure 3: Example of a test concept
The respondent read the concept, and rated the entire combination on a simple 9-point
scale: "How interested are you in this type of insurance" was asked with a scale of 1-9, (1 = Not at
all Interested; 9 = Extremely Interested).
Results
The nine-point rating scale was converted to a binary scale, with the ratings 1-6 converted
to 0, and the ratings 7-9 converted to 100. Ordinary least squares regression was used to relate the
presence/absence of the 36 elements to the dependent variable. The analysis used two dependent
variables. The first analysis, using the original 9-point rating as the dependent variable, generated
the persuasion model. The persuasion model, which shows the intensity of feeling, was used to
segment the respondents into groups of individuals showing similar patterns of insurance
elements that appeal to them.
The second analysis used the aforementioned binary transform, so that the independent
variables were still the 36 concept elements, whereas the dependent variable was either 0 (not
interested in the concept) or 100 (interested). The coefficients or utilities emerging from this
second analysis, the interest model, show the conditional probability of a person being interested
in the element, i.e., the conditional probability of switching from not interested to interested if the
element were to be inserted into the insurance concept. We interpret the interest utilities as
follows: Positive values indicate that the feature enhances consumer interest. Scores that are
near zero indicate that consumers selling are indifferent to inclusion of that feature in the life
insurance policy, and Negative values indicate that the feature detracts from consumer interest.
For all data reported here, we will use the second, i.e., the interest model. We will only
use the persuasion model (dependent variable = original 9-point rating) to develop the concept-
response clusters.
It is important to note that whereas the original assignment of the elements to concepts
was done within the framework of the categories, the regression analysis takes no account of the
categories when doing the model, nor does it need to do so. The experimental design makes the
category irrelevant for statistical analysis. The categories would be relevant when the design
requires one element from each category had to be present in the concept. By moving away from
this so-called effects model (one element from each category present) to true zero conditions for
categories (a category may be entirely absent), we avoid the problem faced by traditional
research. Certainly smaller concepts with some categories missing may generate incomplete
concepts, but the stratagem also produces databases whose utilities have ratio scale properties,
and can be compared across studies with different elements, done at different times. Thus the
8

9. current approach produces data that can be used as the foundation for a science, rather than
simply relative numbers that have meaning only within the limited world of the single study.
How the elements perform
Each respondent generates an individual model. The model comprises two main portions; the
additive constant and the 36 terms, as the foregoing equation specifies. As shown in Table 3 the
additive constant for insurance is moderate (40). This means that without any additional
information, about 40% or two in five are interested. It is important to keep in mind that we are
beginning with a group of individuals who selected life insurance as the topic of their interview,
and so the sample is biased towards those who are at least interested in the topic.
Insert Table 3 here.
Table 3: Average utility values for the elements: Policy Details and Access, Trusted Advisor
(service, payment, claims, communication), Emotional motivation, and Brands and Education
(including other benefits), are based on total panel results. The utilities come from the ‘interest’
model, so the numbers reflect the proportion of respondents who would rate a concept as
interesting (7-9) if the element were present in the concept. The elements are sorted in
descending order by utility value.
Element Utility
Special options to waiver your premium if permanently disabled ... double indemnity
protection in case of accidental death ... accelerated death benefits in case of
catastrophic illness 13
Provides money to your family after you're deceased to help pay for funeral costs and
other household expenses 11
Protects against financial loss in the event of a death in the family 9
Life-long protection, lock in low premiums by starting your policy when you're young
… insurance that accumulates a cash value that you can borrow against as you get
older 7
From a company that offers package plans tailored to the life stage you're in …
umbrella coverage all in one seamless program 7
With flexible payment scheduling … manage your expenses and do more with your
dollars 6
Offers tax breaks for you and your family 6
Premiums payable in monthly installments … no interest 5
From a company focused on the individual and small business owner … no corporate
lingo, just basic common sense 5
Select from an easy to understand, do-it-yourself set of plans that are tailored to your
needs … now and for the future 4
Makes it effortless to sign up for insurance for the firs Jordan Stanley Tide t time 4
Insures two lives … pays the 'death benefit' to the surviving person when the first one
passes away or pays the 'death benefit' only when both individuals have passed on 3
From a company that offers online quotes and comparisons to other companies' pricing 3
Straightforward access to information on your specific policy 2
Receive quarterly reports … by mail or email … highlighting your progress in building
equity and achieving your goals 2
No confusing wording so you can be sure of your decision 2
Creates wealth even when you're not looking 2
Available even when your need is triggered by a specific event 2
Work with someone who has the experience to understand your needs and knows how
you think 1
Speak to a professional insurance consultant … assess your needs and build a 1
9

10. customized financial plan designed around you, not based on one company's offerings
Protects your hard earned investment 1
Protection against the unthinkable 1
Makes it easy to find what you need and sign up for just what you want 1
From a company created to benefit the community … owning a policy is like holding
stock … the company does well, you get a return on your premium 1
Simple claim forms … online entry, fast approval 0
Rest assured, your family will be taken care of 0
Receive financial advice early … know just the right time to buy a policy 0
From a company experienced not just in insurance, but in financial planning too 0
Because it feels good to accumulate wealth over time 0
Talk to someone that you know is an advocate for you … not the insurance company -1
Personalized access online -1
Offers educational seminars on the ins and outs of insurance -3
Offered at the time you join or start a company -4
Recommended by someone well respected in your community -5
Provides protection to a single individual for a specified period of time only …
renewable, convertible, or variable coverage tailored to your personal financial needs -11
Choose from a set of plans pre-selected by your employer to meet your needs -11
Segmentation marketing recognizes that buyers differ in their wants, purchasing power,
buying attitudes and buying habits (20). But do they also differ in their patterns of utilities? Do
owners of insurance policies have similar utilities patterns compared to those of people who do
not own insurance policies? How similar are owners and non owners in their pattern of utilities?
Figure 4 presents the patterns of utilities for each element for the two groups, owners of insurance
and non owners insurance. Each point in the scattergram shows one of the 36 interest elements.
The similarity of the pattern to a 45 degree line suggests, surprisingly, in contrast to assumptions
of classic segmentation, the mind-set of a non life insured person is similar to that of a life
insured. Thus, prospect customers, in terms of elements we examined, have the same utility
patterns as do existing customers.
Insert Figure 4 here.
Figure 4: Distribution of utilities for 36 elements for owners and non owners of insurance
policies
10

11. 20
Does not have life insurance
10
0
-10
-20
-20 -10 0 10 20
Has life insurance
.
The data suggest that where segmentation of people by the products bought, specifically
life insurance, may show some differences between groups, the segmentation does not reveal
different mind-sets. One would talk to both groups in the same way, recognizing of course that
one group already purchased life insurance whereas the other group did not. Yet, it is important
to talk to a prospect in terms of mind-sets, in terms of what is important to the prospect. The
condition of not having versus having insurance, in itself, does not affect the mind-set. In
contrast, when we segment on the basis of mindsets as we did here, the segmentation can be
translated into action towards existing customers and prospective customers.
Creating groups of homogenous minds through segmentation
One can differentiate people in many ways, such as geo-demographics (age, gender),
behaviors (type of insurance purchased, when purchased), and attitudes (risk averse, future
oriented, etc.). Traditionally, segmentation has been used to identify groups of individuals who
are presumed to be similar in mind-set. That is, the notion has been that ‘birds of a feather flock
together’, or people who are similar to each other probably will do the same thing. If one can
successfully segment a population, the presumption is that the individuals in the segment share
the same proclivity to buy a specific product. This notion is true for most marketing efforts,
insurance as well as other financial products.
There is no clear evidence, however, that traditional segmentation of data produces
populations with similar mind-sets. Typically, the segmentation is done on a host of variables,
some of which are tangentially related to the product or service being segments. We propose here
a more focused, more granular approach to segmentation, which deals immediately with the
granular specifics of life insurance, both in terms of the product and in terms of the softer aspects
such as emotions and nature of service. The segmentation works by dividing people according to
the pattern of their utility values for life insurance. That is, individuals who show similar patterns
of elements that drive them to say they are interested in life insurance are located in the same
cluster. Segmentation proceeds by standard statistical methods. The inputs are the 36 utility
values, based on the persuasion model where the dependent variable was the rating of the 9-point
scale, and the independent variables were the 36 elements.
11

12. The output of the segmentation generates groups of people with similar ways of reacting
to the array of different life-insurance messages. These mind-set segments are independent of age,
gender, income, etc., and of policy ownership, since they are based simply on the pattern of
responses to stimuli. The segmentation is done by working specifically with information about
life insurance, and by intervention, i.e., by forcing people to respond to the insurance-relvant
statements as we did in the experimental design. Table 4 shows the strongest performing elements
by the three mind-set segments (easy to use, customization, peace of mind). The segments are
named by virtue of the commonality among the specific elements which perform best in each
segment.
Insert Table 4 here.
Table 4: Best performing elements for each of the three mind-set segments
Segment 1: Easy to use and customization seekers (65 respondents, additive
constant = 38)
Select from an easy to understand, do-it-yourself set of plans that are 10
tailored to your needs … now and for the future
Premiums payable in monthly installments … no interest 11
Available even when your need is triggered by a specific event 14
Makes it effortless to sign up for insurance for the first time 10
Makes it effortless to sign up for insurance for the first time 19
From a company that offers package plans tailored to the life stage you're 10
in, an umbrella coverage all in one seamless program
From a company that offers online quotes and comparisons to other 15
companies' pricing
From a company focused on the individual and small business owner … no 12
corporate lingo, just basic common sense
Offers tax breaks for you and your family 15
Segment 2: Assurance seekers (38 respondents, additive constant = 43)
Special options to waiver your premium if permanently disabled ... double 11
indemnity protection in case of accidental death ... accelerated death
benefits in case of catastrophic illness
Creates wealth even when you're not looking 7
Segment 3: Peace of mind seekers (55 respondents, additive constant = 38)
Protects against financial loss in the event of a death in the family 12
Provides money to your family after you're deceased to help pay for funeral 18
costs and other household expenses
Life-long protection, lock in low premiums by starting your policy when 14
you're young … insurance that accumulates a cash value that you can
borrow against as you get older
Special options to waiver your premium if permanently disabled ... double
indemnity protection in case of accidental death ... accelerated death 21
benefits in case of catastrophic illness
With flexible payment scheduling … manage your expenses and do more 10
with your dollars
From a company created to benefit the community … owning a policy is 8
like holding stock … the company does well, you get a return on your
premium
From a company focused on the individual and small business owner … no 13
12

13. corporate lingo, just basic common sense
Straightforward access to information on your specific policy 9
Segment 1: Easy to use and customization seekers. This segment is moderately interested in
insurance, and interested in policy details, easy of use, simplicity, and wealth. Customers
belonging to this segment appear to want to control what the policy contains and have plenty of
suggestions to include in the policy.
Segment 2: Assurance seekers. This segment is moderately interested in insurance, but becomes
more interested as it assures premiums are waived in case of a permanent disability.
Segment 3: Peace of mind seekers. This segment is moderately interested in insurance.
Individuals belonging to this segment look for a trusted authority to guide them regard the
appropriateness of the insurance and the relevant options. This segment comprises individuals
who are emotionally involved and connected.
Typing individuals in the population into segments
At the end of mindset segmentations, we identified groups in the study with similar
mind-sets. These groups transcend conventional methods of classification. As a result the
segmentation is actionable only when we knew the segment to which a person in the population
belonged. The task is to find a way to identify the segment to which a specific individual belongs.
This requires an intervention, such as a ‘mind-typing’ exercise, where the ratings immediately put
a person into the segment. Thus, we created a classification rule by which we applied a method
for typing the customer into the specific segment in the population to which he belongs.
Creating the classification rule
We segmented people based on their pattern of responses to specific phrases. After
establishing meaningful segments, we created a system that can type a new individual. We
used the well accepted method of discriminant function analysis (DFA). DFA is a popular,
widely available method used to put objects into categories, by an assignment rule. DFA
begins with a set of stimuli that are known to belong to the different categories (our
participants in the study classified into segments), and then identifies the scoring rule that, in
a statistical sense, best classifies these ‘known segment members (21).
We clustered or segmented individuals based upon the pattern of utilities from the 36
elements. We synthesized a set of 36 ‘markers’ for each individual, by creating one-element
concepts, one marker for each of the 36 elements. The creation of the marker followed the
modeling, only in reverse. For every element and every respondent we summed the additive
constant for the respondent and the individual utility of the element (from the persuasion
model). The sum is the expected rating of this one-element concept on the 9-point scale. This
strategy of reconstructing one-element concepts generates the raw material for use in the
discriminate factor analysis, and also simulates the ultimate typing tool, which comprises one
element phrases.
Now that we had these 36 elements as concepts, and the estimated rating for each
element on a 9-point scale, the elements themselves became markers. We needed a specific
combination of elements by which to predict membership in a segment. We searched for a
maximum of six elements that would be highly significant in their combined ability to
classify new people. We were able to classify respondents with four phrases, rather than six.
Adding two more elements only marginally improved the assignability of individuals. Table 5
presented these elements from the discriminate factor analysis, including a worked example
with three individuals (Pr1 – Pr3) who are given the typing test and then assigned.
The steps used to predict membership in a segment are:
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14. 1. We begin with the ‘training sample’ from our study, the same study we used to create the
segments.
2. For each person in the study we can estimate how the person would have scored each element
on the 9-point scale, were that element to be presented as a one-element test concept. We
created that 9-point rating from the ‘model’ for that person that relates the presence/absence
of the elements to the 9-point rating scale.
3. For each person in the study we also know the segment into which the respondent was
classified
4. Use discriminate factor analysis, we now look for a small number of statistically significant
elements, which when rated on a 9-point scale, and inserted into the classification function,
best estimate the segment membership. Statistical significance is defined in terms of the
ability to the elements to predict segment membership.
5. Once we have completed the DFA for the study data, we have the classification function,
which we can apply to new people. Table 5 presents the classification rule and an example
assignment of three prospective customers into segments based upon the ratings on the four
elements, used in the classification rule.
Insert Table 5 here.
Table 5: Elements for classification, classification rule, and worked example
showing the assignment of three individuals to the segments (shaded) based on the
weighted ratings of the four phrases in the classification rule.
S1 S2 S3 Pr1 Pr2 Pr3
Additive Constant -5.23 -7.08 -6.00
Provides protection to a single
individual for a specified period of
time only … renewable, convertible,
or variable coverage tailored to your -0.64 0.59 0.38 9 1 3
personal financial needs
0.32 -0.78 0.34 1 8 1
Personalized access online
Makes it easy to find what you need 0.32 1.72 -0.82 9 9
and sign up for just what you want 9
From a company focused on the
individual and small business owner
… no corporate lingo, just basic
common sense
Value of classification function;
Segment 1 3.99 1.11 0.15
Value of classification function;
Segment 2 3.35 2.61 6.89
Value of classification function;
Segment 3 4.04 3.38 1.76
The mechanics of identifying the prospect
How does the customer representative identify the person? Or, how does the
prospect identify himself? The rows in Table 5 show the four phrases that we used. These
were the four phrases which, in combination, best discriminated prospects, based on the data
that we used to create the segmentation in the first place.
14

15. The prospect rates each of these four phrases using a 9-point scale. The answers
are ‘weighted’ by four different classification functions. Each classification function
generated a value. The classification function with the highest numeric value shows the
segment to which the prospect is expected to belong. Of course, there is error, since the
original modeling predicted only about 2/3 of the people correctly. However, this 2/3 correct
is far higher than random guessing.
We show the ratings of four new prospects, about whom we only know their rating
of the four phrases. We show these ratings in Table 5, as well as the expected values from
each of the three discriminant functions. Logically, one of the four, or even two of the four
functions will come up with the highest or two highest values. We then use those numbers to
assign the customer prospect to a segment. The segment assignment also appears in Table 5.
Each individual who participated in the typing exercise thus generated four
identification numbers, corresponding to the segment to which he belongs. At the same time
all other information about this individual except for the way to reach him (e.g., e-mail
address) need not be kept. One might wish to keep the age and gender, in order to send
relevant information. Age and gender help to focus the offers that are sent.
Discussion
Our findings suggested three distinct mindsets. These findings mean that there are at least
three sets of offerings and styles to offer to customer, in order to add value. Selling to mind-sets
increases the likelihood that one can create a long-term relation with a customer. The customer’s
preferences are relatively homogenous in a mind-set, making it easy to customize as well as to
standardize the interaction with the customer. Furthermore, the experimental design allows the
insurance company to better understand the emotional aspects of the selling experience, and the
specific emotional needs of the segments. The introduction of systematized exploration of
customer needs, including emotion as a need, is the unique contribution of these results in
particular, and the approach in general.
We show that insurers selling can increase customer loyalty by identifying the segment to
which a customer belongs, and stressing the winning elements related to that mindset in the
messaging. Thus, once insurers begin to segment customers based on the customer’s mind-set, in
addition to other variables such as specific needs, the insurer will be in a better position to gain
the customer trust. In turn, this trust should translate into repeat sales and greater spending
commitment on insurance.
At a specific level our data suggest that despite the trend of customization, not all groups of
customers are interested in customization. Only 30% of our study population seeks a customized
life insurance policy. Furthermore, economic interests are not of primary concern to policy
holders. Insurers should not place too much emphasis on the cold, hard calculations of actuaries
in place of the emotions that are involved. Insurance is indeed an emotional experience. One out
of seven, or 15% of our study population, is looking for a trusted authority who will determine the
options and appropriateness of the insurance for them. These prospects buy on emotions as well
as on facts.
It is clear that we now have a tool by which a person can be classified as belonging to a
segment. Simply by having the prospect rate the four phrases, one can assign the prospect to the
segment. Furthermore, the segmentation moves beyond identifying the segment to which a
person belongs, and suggests what specific messages, at the very granular level, will interest the
prospect.
15

16. It’s important to keep in mind that these methods are not perfect. Typically we can get
between 50% correct on the low end and about 75% correct on the high end. It’s rare to do much
better. It’s possible to do worse, especially if we use the incorrect elements as predictors in the
discriminate factor analysis.
Practical Implications for Shaping the Messaging
The element 'waiving the premium in case of permanent disability' performed strong in all
three segments. This raises the possibility of a general message across segments, for mainstream
policies, that the company can use as a foundation on which to build specific offerings targeted to
the different segments.
After gaining a core market share the company may enlarge its share by targeting prospects
who belong to the other mindsets. To shape one policy with an acceptance level of 70%, the
insurance company may add options different options, such as:
1. Waive the premium if the customer is permanently disabled (Add 7% to the 40% basic
acceptance)
2. Accelerate death benefits in case of catastrophic illness (Add 13% to basic acceptance)
3. Allow flexible payment scheduling (Add 6% to basic acceptance)
4. Make it effortless to sign up for insurance for the first time (Add 4% to basic acceptance)
5. Offer package plans tailored to the life stage of the customer (Add 7% to basic acceptance).
Conclusions
The best performing elements across segments provide insights regarding the experience of
life insurance. We have reaffirmed the intuitive feeling that customer experience with insurance
is emotional. The prospective customer needs to be assured that the family will be taken care of
upon death, that the customer is protecting the hard earned investments, that the procedure is
effortless, that the customer can trust there is no confusing wording, and finally that the customer
is protected against the unthinkable.
The recognition of emotionality coupled with pragmatic issues about the features of
insurance strengthens the real fact that life insurance itself is an abstract, complex, intangible
service. Life insurance creates anxiety among customers. Life insurance sales thus may benefit
from a knowledgeable, sensitive, emotionally-aware professional insurer offering life stage
adjusted policies (6).
Using the innovative typing method suggested here, insurers need to have prospects interact
in an ‘intervention’, requiring the prospect to rate only four phrases. These four phrases suffice to
type the prospect into one of the three segments. Typing allows the messaging to be targeted
more efficiently than before, and allows the experience to be customized to the prospects needs,
even if the prospect is encountered for the first time. With the winning elements identified here,
insurers may shape the messaging to gain new customers. Understanding and using refined
messaging through mind-set segments, targeted correctly, should allow insurers to gain new
customers through the proper engineering of the sales experience. Furthermore, the knowledge of
segment membership for current customers should allow the company to create an program of
ongoing, appropriate communications, and offer both relevant and, in turn, emotionally
meaningful benefits for individuals in each segment.
Acknowledgments:
The authors would like to thank Linda Lieberman. Editorial Coordinator, Moskowitz Jacobs Inc.,
for preparing and submitting this manuscript.
16

17. All data presented were provided by It Ventures, Ltd., by permission.
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