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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

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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.

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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.

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•Lawsuit•Credit

•Long Term Care•Elder Care

•Identity Theft•Self-Employed

•Small to Mid-Size Business•Fertility/Pregnancy

•Single Moms•Terrorism

•Mortgage•Warranties

•Retirement Planning•Disability

•Medical•Travel Medical•Travel Property

•Life•Homeowner’s

•Auto

•Job Transition•Broken Hearts

•Terminal Illness•Valuables

•Education Planning•Earthquake

•Flood•RV

•Umbrella Liability•Liability

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%

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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:

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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 Detail and Access

Silo # 2: Advisor and Payment

Silo # 3: Emotional/ Motivational

Silo # 4: Brand/ Educational Benefit

Protects against financial loss in the event of a death in the family

Recommended by someone well respected in your community

Available even when your need is triggered by a specific event

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

Provides money to your family after you're deceased to help pay for funeral costs and other household expenses

Select from an easy to understand, do-it-yourself set of plans that are tailored to your needs … now and for the future

Rest assured, your family will be taken care of

From a company that offers package plans tailored to the life stage you're in … homeowners, auto, and umbrella coverage all in one seamless program

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

Work with someone who has the experience to understand your needs and knows how you think

Because it feels good to accumulate wealth over time

From a company experienced not just in insurance, but in financial planning, too.

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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

Speak to a professional insurance consultant … assess your needs and build a customized financial plan designed around you, not based on one company's offerings

Protects your hard earned investment

From a company that offers online quotes and comparisons to other companies' pricing

Provides protection to a single individual for a specified period of time only … renewable, convertible, or variable coverage tailored to your personal financial needs

Talk to someone that you know is an advocate for you … not the insurance company

Creates wealth even when you're not looking

From a company focused on the individual and small business owner … no corporate lingo, just basic common sense

Special options to waive your premium if permanently disabled ... double indemnity protection in case of accidental death ... accelerated death benefits in case of catastrophic illness

Premiums payable in monthly installments … no interest

Makes it effortless to sign up for insurance for the first time .

Straightforward access to information on your specific policy

Choose from a set of plans pre-selected by your employer to meet your needs

With flexible payment scheduling … manage your expenses and do more with your dollars

No confusing wording so you can be sure of your decision

Offers educational seminars on the ins and outs of insurance

Offered at the time you join or start a company

Simple claim forms … online entry, fast approval

Protection against the unthinkable

Receive financial advice early … know just the right time to buy a policy

Personalized access online

Receive quarterly reports … by mail or email … highlighting your progress in building equity and achieving your goals

Makes it easy to find what you need and sign up for just what you want

Offers tax breaks for you and your family

Insert Figure 3 here.

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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

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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

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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

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-20 -10 0 10 20Has life insurance

-20

-10

0

10

20

Doe

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t hav

e lif

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sura

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.

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.

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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 tailored to your needs … now and for the future

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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 in, an umbrella coverage all in one seamless program

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From a company that offers online quotes and comparisons to other companies' pricing

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From a company focused on the individual and small business owner … no corporate lingo, just basic common sense

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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 indemnity protection in case of accidental death ... accelerated death benefits in case of catastrophic illness

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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 costs and other household expenses

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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

14

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

21

With flexible payment scheduling … manage your expenses and do more with your dollars

10

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

8

From a company focused on the individual and small business owner … no 13

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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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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 personal financial needs

-0.64

0.59

0.38

9

1

3

Personalized access online 0.32

-0.78

0.34

1

8

1

Makes it easy to find what you need and sign up for just what you want

0.32

1.72

-0.82

9

9 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.

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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.

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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.

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

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