Extremely Frequent Behavior in Consumer Research:

Theory and Empirical Evidence for Chronic Casino Gambling

Ralph Perfetto, University of Rhode Island

Arch G. Woodside, Boston College

January 2008

Send correspondence to Ralph Perfetto, University of Rhode Island, Ballentine Hall,

Suite 216, Kingston, RI, 02881 (Email: rperfetto@mail.uri.edu). Arch G. Woodside, Boston

College, Carroll School of Management, Department of Marketing, 140 Commonwealth Avenue,

Chestnut Hill, MA 02467 (Email: arch.woodside@bc.edu).

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ABSTRACT

The present study informs understanding of customer segmentation strategies by

extending Twedt’s heavy-half propositions to include a segment of users that represent less than

two percent of all households – consumers demonstrating extremely frequent behavior (EFB).

Extremely frequent behavior (EFB) theory provides testable propositions relating to the

observation that few (2%) consumers in many product and service categories constitute more

than 25 percent of the frequency of product or service use. Survey data are useful for exploring

the propositions that extremely frequent behavior exists and that EFB user subgroups differ in

their demographic profile, attitudes-interests-and-opinions (AIOs), and consumption

constellations. The approach that paper presents builds from property-space analysis (Barton

1955, Lasarsfeld 1965) and explanatory typological literature (George and Bennet 2005, Elman

2005). Using casino gambling as an example for testing EFB theory, an analysis of national

survey data shows that extremely frequent casino gamblers do exist and that less than 2 percent

of all casino gamblers are responsible for nearly 25 percent of all casino gambling usage.

Approximately 14 percent of extremely frequent casino users have very low-household income,

suggesting somewhat paradoxical consumption patterns (where do very low-income users find

the money to gamble so frequently?). The present report contributes to the customer portfolio

analysis literature in identifying and examining the antecedents of customers segmented by usage

levels and product subcategories. Understanding the differences light, heavy, and extreme users

and non-users can help marketers and policymakers identify and exploit “blue ocean”

opportunities (Kim and Mauborgne, 2005), for example, creating effective strategies to convert

extreme users into nonusers or nonusers into new users.

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Extremely Frequent Behavior in Consumer Research:

Theory and Empirical Evidence for Chronic Casino Gambling

INTRODUCTION

Marketing executives often use consumer demographics, psychographics, and behavioral

characteristics for crafting market segmentation strategies. Common demographic segmentation

categories include age, income, ethnicity, family life cycle, and gender. Income is particularly

attractive as a segmentation basis because income helps define consumers buying power. As

such, extremely frequent users are segmentable further into those with low, moderate, and high-

income levels. When a product category’s users include both high-income and low-income users,

their demographic profiles are unique, and their consumption profiles across many product

categories differ substantially.

With copious amounts of information now available through internet purchasing and

customer loyalty programs, volume segmentation has become an increasingly useful

segmentation strategy. Volume segmentation assigns people into groups via usage categories—

non-users, light users, and heavy users (Twedt, 1964). According to Twedt, the heavy users

represent the top 50 percent of the users in a product category after ordering users by their

amount of product consumption. Thus, if 40 percent of the adult U.S. population consumes beer

and 60 percent do not consume beer, a median split by beer consumption among the 40 percent

results in 50 of the users being identified as the light-half users and 50 percent of the heavy-half

users. The heavy-half users often account for nearly 90 percent of the total consumption for

many fast moving consumer goods (FMCGs) (Twedt, 1964). Twedt argues that firms should

consider these consumers to be most important (Twedt, 1964). Many segmentation studies

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support Twedt’s heavy-half propositions, although the number of actual user categories varies a

bit (Goldsmith and Litvin, 1999; Spotts and Mahoney, 1991).

In addition to demographic and volume differences among customers, individuals often

reveal insights into their personalities, social status, self-identity, and profession through the

products they consume (Belk, 1988; Belk, Mayer, and Bahn, 1982; Holman, 1980, 1981; Levy,

1964; Lowery, Englis, Shavitt, & Solomon, 2001; Shavitt & Nelson, 2000). Solomon (1988)

argues that most psychological treatments of product symbolism focus on the individual product

category level and that “social behavior is often accompanied by the joint consumption of many

disparate products and services that, when taken together, appear to define a social role.

Products are not consumed in a vacuum, but instead often play an integral part in consumers’

social lives” (Solomon, 1988, p. 244). Clusters of disparate products and services make up

what Solomon and Assael (1987) refer to as “consumption constellations.”

The present study informs understanding of customer segmentation strategies by

extending Twedt’s heavy-half propositions to include a segment of product or service users that

represents 1 to 2 percent of all potential customers – the small number of customers

demonstrating extremely frequent behavior (EFB). Extremely frequent behavior (EFB) theory in

consumer research builds and extends on prior work (Cook and Mindak, 1984; Twedt 1964) to

provide testable propositions relating to the observation that very few (2%) customers in most

product and service categories constitute 20+ percent of the usage frequency of the product or

service. More generally, the present report contributes to customer portfolio analysis literature

(Woodside and Soni, 1991; Woodside and Trappey, 1996) in identifying and examining the

antecedents of customers segmented by usage levels and categories of products consumed.

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This article uses casino gambling behavior in an application of the extremely frequent

behavior theory. Most chronic casino gambling (defined here as gambling most-to-all weeks

annually) likely represents one example of compulsive buying. Compulsions are "repetitive and

seemingly purposeful behaviors that are performed according to certain rules or in a stereotyped

fashion" (American Psychiatric Association 1985, p. 234). “Results indicate people who buy

compulsively are more likely to demonstrate compulsivity [n = 380 from a mail survey of

compulsive buyers living in California] as a personality trait, have lower self-esteem, and are

more prone to fantasy than more normal consumers [n = 250 from a mail survey of adults living

in 3 Illinois cities]. Their [compulsive buyers’] primary motivation appears to be the

psychological benefits derived from the buying process itself rather than from the possession of

purchase objects. The consequences of compulsive buying include extreme levels of debt,

anxiety and frustration, the subjective sense of loss of control, and domestic dissension”

(O’Guinn and Faber 1989, p. 147).

“Pathological gambling research, and chronic casino gambling research in particular, is

still in very early stages of development….Pathological gambling (PG) is a major psychiatric

disorder and public health issue that is just beginning to receive public and scientific attention.

For many individuals who gamble, the activity is an occasional form of entertainment that does

not negatively influence their lives, but PG is different. The American Psychiatric Association’s

Diagnostic and Statistical Manual of Mental Disorders, third edition (DSM-111) first recognized

PG as a psychiatric disorder in 1980. PG has been categorized as an impulse-control disorder not

elsewhere classified since that time (Hollander, Buchalter, and DeCaria 2000, p. 629).

The general population’s estimated rate of gambling [all types] is more than 80%.

Epidemiologic surveys distinguish between PG and problem gambling. Problem

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gambling refers to all individuals with gambling-related problems, including

mildly affected individuals who do not meet clinical diagnostic criteria for PG as

defined by the DSM-IV. The prevalence of PG is estimated to be 1% to 3% of the

population, and the male-to-female ratio traditionally has been reported as 2: 1.

In adolescent populations, the prevalence of PG has been reported to be as high as

5.7%. (Hollander et al., 2000, p. 630)

Three points worth emphasizing include the following propositions. First, not all

extremely frequent casino gamblers are necessarily PG individuals even though they are

classifiable as chronic (persistent, inveterate, unremitting) casino gamblers. Yet, chronic casino

gambling versus casual-to-no casino gambling is likely to associate positively to PG diagnosis.

Second, the available research on the psychology of gambling and profiles of casino gambling

and gamblers offer unclear and shallow profiles of such behavior and individuals (Hope and

Havir 2002; Kusyszyn, 1984). Third, representative national samples of households may be

useful in particular for identifying individuals with compulsive behaviors such as chronic casino

gamblers versus occasional and non casino gamblers.

The present report builds from these three points to provide in-depth profiles of three

distinct types of chronic casino gamblers and compares these types to each other and with

occasional and non casino gamblers. Rather than using a variable-oriented approach, the

approach presented in this paper is based on property-space methods (Barton 1955, Lasarsfeld

1965) and explanatory typological literature (George and Bennet 2005, Elman 2005). Property-

spaces and explanatory typologies allows for a case-based analysis of survey data while

capturing interaction effects in an simplier manner than large-n regression techniques (Bennett

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and Elman 2006). Furthermore, conjuntural conditions, whereby the same outcome may exist

for differing typologies, may be identified.

Extending the Heavy Half Proposition – Extremely Frequent Behavior (EFB)

Dik Twedt (1964) encourages the process of volume segmentation; Loudon and Della Bitta

(1993, p. 64) claim that:

Volume segmentation attempts to identify frequent users of a product category or

brand. Marketers often refer to the “20-80” thesis. That is, that 20 percent of the

market accounts for 80 percent of sales of their product. Although the exact

proportion may vary and the rule may not universally apply, it does indicate the

importance of a firm’s product or service.

Marketing researchers historically identify consumers dichotomously as either users or

non-users (Twedt 1964). Tests of marketing manipulations seldom focus on nonusers and such

tests often assume all users are equally important (Twedt, 1964). Some marketers weight light

users the same in importance as high-volume users. Twedt gives the following example to

emphasize the point of considering alternative customer importance weighting methods:

A household that consumes two six-packs of soft drinks in a given month would be

weighted twice as heavily as a household that consumers only one six-pack. And if

another household were to consume 30 six-packs during the same time period, then to the

manufacturer the opinions of that household should be 30 times as important as those

represented by the household that buys only one package. (Twedt, 1964, p. 71)

According to Twedt, high-volume consumers are more valuable than low-volume

consumers. In one experiment, Twedt studies a sample of Chicago households and finds that

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42 percent did not purchase lemon-lime beverages (0% of the volume purchased). The

remaining 58 percent of the sample (those households that did purchase lemon-lime beverages)

were ordered into two sub-groups, based on a median split of purchasing volume. Twedt calls

these the “light using-half” and the “heavy-using half” and notes that “one heavy-half household

is equal in purchase volume to nine households in the light-half” (Twedt, 1964). Extending this

analysis to other products, Twedt informs that in all product categories (except toilet paper), the

top 50 percent of the consumers (users of a product category) account for most of the

consumption. Twedt concludes his findings by suggesting that “what can be said is that the

heavy-using household buys more, buys more often, and buys more different brands” (Twedt,

1964, p. 71 ). Heavy users are an attractive segment for many firms, since they are a relatively

small group that accounts for a very large volume (Goldman & Litvin, 1999; Goldsmith, 2000;

Goldsmith & d’Hauteville, 1998; Woodside, Cook, & Mindak, 1987).

Some researchers characterize the heavy user in product categories such as fashionable

clothing (Goldsmith, 2000), wine consumption (Goldsmith & d’Hauteville, 1998), casino

gambling (Moufakkir, Singh, Moufakkir-van der Woud, & Holecek, 2004) and travel (Spotts &

Mahoney, 1991; Woodside et al., 1987). Twedt (1964) separates users into three usage

categories – non-users, light-users, and heavy-users. Other researchers have arbitrarily chosen to

use a different number of categories. Goldsmith and Litvin (1999) segment users into only two

segments; light users and heavy users. Spotts and Mahoney (1991) report that most users fall in

the middle of the frequency distribution of the usage variable and, as such, chose to segment

users into three user groups; light users, medium users, and heavy users. Spotts and Mahoney

(1991) argue that the researcher must select user categories in a manner that best allows the

characteristics of the market segments to be uniquely distinguishable from one another.

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Extremely Frequent Behavior Theory

Extremely frequent behavior (EFB) theory in consumer research extends from

typological theory and Twedt’s ‘heavy-half’ proposition (Cook & Mindak 1984; Twedt 1964) to

provide testable propositions relating to the observation that very few (2%) customers for many

product and service categories constitute more than 25 percent of the frequency of product or

service use. Typological theory provides propositions on possible groups related to what

Lazarsfeld (1965) refers to as “property-space” (see also Barton 1955). This approach “allows

researchers to translate a multi-dimensional attribute space into a handful of types” (Ragin 2000,

pp. 66) and helps illuminate ways in which the same outcome can arise from different

conjunctive paths. Furthermore, property-space analysis can help identify the existence of

extreme cases as well as paradoxical relationships that might exist.

Property-space analysis starts with identification of conjunctive variables that are likely

related to the frequency of a given behavior. In the case of extreme casino gambling, income is

particularly attractive variable because it helps define consumers buying power. Figure 1 shows

the property-space contingency table and identifies seven unique types of households by

gambling frequency and income level.

_ _ _ _ _ _ _ _ _ _ _ _

Figure 1 about here.

_ _ _ _ _ _ _ _ _ _ _ _

Extremely frequent behavior (EFB) theory includes the proposition that (P1) for FMCGs

and comparable services a very small share of potential customers constitute more than one-fifth

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of the total frequency of use. In casino gambling, for example, EFB users may gamble in a

casino at least once or more per week. The wealthiest (or high income) EFB users are often

sought after by casino owners, since their consumption habits associate with extraordinarily high

spending on gaming activities (Moufakkir et al., 2004). This segment of casino gamblers is so

important to revenues that casino owners fittingly refer to these consumers “whales” since their

wealth affords them an appetite for consumption that is so much larger than any other consumer

in the product category.

As Figure 1 demonstrates, not all EFB casino visitors have high income. Rather,

extremely frequent consumers vary greatly by demographic make-up and include not only high-

income users but also very low-income users as well. Much like the whales, these low-income

EFB casino users gamble just as often, but with much smaller pocketbooks. If the high-income

extremely frequent casino gamblers are considered whales, then perhaps very low-income casino

gamblers are more like “jumbo shrimp” since they represent high volume users within the group

of very small income levels. Those extremely frequent gamblers with low to moderate-income

fall some where in between – “big fish” that have more buying power than the jumbo shrimp,

but not as much as the whales.

Whales likely can afford to gamble in casinos, but how do the jumbo shrimp afford to

gamble at the same frequency? When both low-income and high-income EFB users exist in the

same product category, paradoxical consumption patterns for one or more EFB user segments

may exist. More formally, P2: EFB users include a seemingly paradoxical sub-segment of

extremely low-income consumers, for example, customers who visit gambling casinos weekly

with extremely low incomes (jumbo shrimp).

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Non-users are an important segment of consumers and are often overlooked by

marketers. Typically, marketers test advertising copy with users, not the non-users. As Twedt

(1964, p. 71) states,

Ordinarily, marketing researchers screen respondents for product usage before

proceeding with questions about attitudes, opinions, or behaviors. Thus, a non-

smoker would rarely be included in a test of cigarette advertising copy, nor would

a woman who never bakes cakes be asked to state her flavor preference for a cake

mix.

Given the potential importance of nonusers in crafting what Kim and Mauborgne (2005)

refer to as, “blue ocean strategies”, the total number of households represents the base in EFB

theory - not the total number of users. The aim of a blue ocean strategy is not to outperform the

competition by constantly fighting bloody battles over current product and brand user bases (i.e.,

“red ocean strategies”). Rather, a blue ocean strategy makes a firm’s competitors irrelevant by

going after the non-users and converting them with special offers and “lighter versions” of

product offerings (i.e., product designs offering a few exceptional benefits usually at a very low

price point). In essence, blue ocean strategy creates a whole new market space (Kim and

Mauborgne, 2005). Understanding the differences between users and non-users can help firms

identify and exploit blue ocean opportunities.

P3: Non-users with moderate income represent the single largest segment in numbers of

customers (or households)—this segment provides a nonequivalent comparison group (see Cook

and Campbell, 1979) for examining the unique profiles of EFG groups.

With such different demographic profiles, high-income and low-income extremely

frequent customers are likely to see the world very differently not only from each other, but also

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from high-income product non-users, low-income product non-users, as well as everyone else.

P4: EFB users sub-segmented by income (extremely low versus low-to-moderate versus high)

differ from (a) each other, (b) as well as from other potential customer segments in their

demographic profiles (i.e., unique conjunctive streams made up of gender, race, household size,

education, and age combinations).

Consumption Constellation of EFB Users

Prior studies show that demographic variables alone are, in general, poor predictors of

consumer behavior and are less than optimal for segmentation strategies (see Halley, 1968;

Frank, 1967; Frank, Massy, & Harper, 1967). In an attempt to better understand consumer

behavior and decision making, market researchers often include behavioral and psychometric

variables that help to group consumers based on values, personalities, AIOs, and consumption

constellations.

Levy (1959) informs that products are consumed not only for functional reasons but also

for their symbolic meanings. “A consumer’s personality can be seen as the peculiar total of the

products he consumes” (Levy, 1964, p. 149). Consumers often divulge their individual

personalities, social status, gender, self-identity and profession through the products they

consume (Belk, 1988; Belk, Mayer & Bahn, 1982; Levy, 1964; Lowery et al., 2001; Holman,

1980, 1981; Shavitt & Nelson, 2000). Heavy versus light use of complementary products and

services are captured in what Solomon and Assael (1987) refer to consumption constellations -

“clusters of complementary products, specific brands, and/or consumption activities” (p.235) - to

define and communicate social roles. For example, the consumption constellation of the social

group called “Yuppies” may include products such as a Rolex watch, Gucci loafers, and a

Burberry trenchcoat (Solomon, 1988). Essentually, a consumption constellation constitutes a

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set of products and services which seem to “go together” with a particular stereotypical lifestyle

category (Englis and Solomon, 1995; Lowery et al., 2001;).

While past research on consumption constellations informs our understanding of lifestyle

categories, reference groups and the way in which consumers cognitively categorize objects

(Englis and Solomon, 1995), marketers often focus attention on product comparisons within a

product category (Sujan and Bettman 1989; Ward and Loken 1986), such as comparing three

brands of cars or three brands of cameras. Lowery et al. (2001) notes that academic researchers

mostly ignore joint product interdependencies, instead choosing to focus on single-brand choice

models. Advertisers and psychographic researchers, on the other hand, have been fully aware of

cross-category relationships and often report product interaction effects in their studies (Lowery

et al., 2001). Consumer “behavior is often accompanied by the joint consumption of many

disparate products and services that, when taken together; appear to define a social role.

Products are not consumed in a vacuum, but instead often play an integral part in consumers’

social lives.” (Solomon, 1988, p. 244). For example, one study reports that heavy consumers of

Kentucky Fried Chicken also consume large amounts of other products such as eye makeup, nail

polish, soft drinks, gum and TV dinners (Tigert, Lathrope, and Bleeg, 1971).

Consumption constellations help marketers to position their products into desirable

lifestyle categories - “constructed on the basis of the purchase behaviors and, to a lesser extent,

on psychographic information such as opinions, attitides, and personality” (Englis et al., 1995, p.

15). It follows that EFB users differ not only in demographic makeup, but also in their

consumption constellations.

More formally, consider the following propositions. P5: EFB users sub-segmented by

income differ substantially from (a) each other, (b) as well as other potential customer segments

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in their consumption constellation (Solomon, 2006) profiles—each segment EFB sub-segment

has unique aspects of nonuse, use, and extreme use of other products and services. P6: EFB

users sub-segmented by income differ substantially from (a) each other, (b) as well as other

potential customer segments in their psychographic profiles (e.g., they differ dramatically views

toward government, children, religion, gun use, abortions, and/or other attitudes and beliefs.

Mass media, in the form of newspapers, magazines, television and the Internet, plays an

important and influential role in the socialization of consumers by positioning products as

symbols of cultural lifestyles and connecting these symbols with reference groups (Bearden and

Etzel 1982; Cocanougher and Bruce 1971; Englis and Solomon, 1995; Stafford 1966). Differing

frequencies of media consumption by EFB users likely lead to differing consumption patterns.

P7: EFB users sub-segmented by income differ substantially from (a) each other, (b) as well as

other potential customer segments in their media use behavior; such as the frequency of reading

newspapers and magazines, watching daytime and evening television, and listening to country,

rock, classical, or other radio stations.

METHOD

The data analyzed in this study came from the annual DDB Needham Life Style Survey. Similar

versions of the questionnaire were administered annually during 1975 to 1999, consisting of 300-

400 questions that address respondents’ demographic profile, perceived personality traits,

shopping habits, political beliefs, media habits (e.g., newspapers, TV, radio), religious beliefs,

international affairs and overall satisfaction with life. The data set contains approximately 3000

respondents per year and over 87,000 respondents in total. Data for years more recent than 1999

has not been released publicly by DDB Needham.

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Although the DDB Needham lifestyle survey data are rich, some survey questions

changed over the 25-year period of administration. The present study analyzes a subset of the

survey data for years 1993 to 1998. These years are chosen because survey responses were

available for demographic variables such as income, race, education level, household size,

gender, and casino gambling during these years. The age variable is segmented into five groups

consisting of: less than 35, 35 to 44, 44-54, 55-64, and those 65 and older. Table 1 shows

additional demographic variables recoded into meaningful groups.

_ _ _ _ _ _ _ _ _ _ _ _

Table 1 about here.

_ _ _ _ _ _ _ _ _ _ _ _

DDB survey data includes a question on the frequency of gambling in a casino over the

past 12 months. Respondents were asked to indicate the number of times they gambled in a

casino during the past year (none, 1-4 times, 5-8 times, 9-11 times, 12-24 times, 25-51 times,

52+ times). The seven response levels were transformed into values at the midpoints with the

value of 60 used for the highest level. The values used include 0, 2.5, 6.5, 10, 18, 38, and 60.

The use of alternative values of the highest level (52 and 56) does not substantively change the

findings. Attitudes, interests, and opinions were measured on a 6-point scale (definitely disagree,

generally disagree, moderately disagree, moderately agree, generally agree, and definitely agree).

This study employs a quasi-experimental design using a non-equivalent group design. In

the nonequivalent group design (NEGD) (Cook and Campbell, 1979; Reichardt, 1979), persons

or intact groups (e.g., non-gamblers, very-low income gamblers,) are arbitrarily assigned to

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either the program or comparison condition. Non-gamblers with low to moderately high income

level represents the largest group (n = 9,225). This group serves as the basis for the comparison

group. Comparing the mean of the EFB users to low to moderate-income non-users (the largest

consumer segment) helps effectively discriminate between patterns of group behavior.

Following the suggestions of Armstrong and Andress, 1970) and Bass, Tigert, and

Lonsdale (1968), the present study also uses tree analysis and cross-classification analysis with

variable stacking. Armstrong & Andress (1970, p. 491) suggest that tree-analysis is superior to

regression analysis “in situations where there are large sample sizes and are subject to

interaction, non-linearties or causal priorities”. Bass, Tigert, and Lonsdale (1968) show that a

cross-classification analysis is more useful than regression analysis when user segmentation

variables include socioeconomic characteristics.

FINDINGS

A total of 20,658 survey responders were analyzed for the period during 1993 to 1998. Table 2

shows a frequency distribution of casino gambling behaviors indicated by responders. The

largest share of households is the non-gambler group, representing 66 percent (13,756) of all

respondents. When income level is factored in, the largest user segment is the low to moderate-

income non-gambler group (n = 9,225), representing 67 percent of all non-gamblers and nearly

45 percent of all respondents (see Table 3). For all income levels, moderate gamblers account for

32.2 percent (6,658) of all respondents. The study includes a total of 244 extremely frequent

casino gamblers — 35 (14%) have very low income, 183 (75%) have low to moderate income,

and 26 (11%) indicate high-income levels.

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__________________

Table 2 about here.

__________________

Approximately 33% of DDB households gambled in a casino at least one time during the

prior 12 months and took an average 6.2 trips per year. These findings are consistent with AGA

findings that suggest 26% of U.S. households gambled in a casino and made 161 million trips to

casinos in 1999; an average of 5.6 trips annually (AGA 1999). Although actual household

spending data are not included in the DDB survey, the AGA report estimates that U.S.

households spent approximately $20 billion at commercial casinos in 1998; slightly less than

spending for basic cable ($23B) and slightly more than spending for coffee ($18B) and home

video rentals and sales ($15B) (AGA 1999). This implies that U.S. households spent

approximately $124 per visit ($20B divided by 161 million visits), or nearly $700 to $750 per

year gambling in casinos.

The following limitation is worth considering when examining the findings. The results

of this study may be limited by the lack of diversity in the survey data. For the years 1993 to

1998, nearly 82 percent of the households responding to the survey are white/Caucasian. This

fact may influence the findings with respect to the demographic profile of extremely frequent

users. Future studies should attempt to replicate these findings with a more diverse sample of

households.

Support for P1: Extreme Frequent Gamblers Exist

Consistent with proposition 1, extremely frequent gamblers (those that gamble in casinos

25 or more times annually) do exist and represent less than 2 percent (244) of all casino

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gamblers. Yet, their casino use accounts for 27 percent of all casino gambling visits (see Table

4). Table 3 shows the percent share of casino gambling relating with each gambling group.

_________________

Table 3 here.

________________

High-income extremely frequent casino gamblers account for the highest ratio of share-

of-gambling to share–of-people. High-income extremely frequent gamblers account for the

highest casino usage of all casino user groups. In general, the ratio of the share of extremely

frequent casino gamblers (all income levels) to the share of people is 10 times greater than

moderate gamblers. Although extremely frequent gamblers (all income levels) account for less

than 2 percent of the overall households, they visit casinos approximately 10 times more often

than moderate gamblers.

Support for P2 and P3

A cross tabulation of casino gambling frequency and income level reveals that extremely

frequent casino gamblers includes very low, low to moderate, and high income households (see

Table 4). Consistent with proposition 2, some very low-income households demonstrate

extremely frequent behavior. Of the 244 extremely frequent casino gamblers, 35 (14%) are from

very low-income households. Non-gamblers with low to moderate-income levels represent the

largest segment of households. Consistent with proposition 3, 9,225 (67%) households with low-

to-moderately high income indicate that they do not gamble in casinos at all. This group serves

here as the nonequivalent comparison group (see Cook and Campbell, 1979; Kusyszn, 1984;

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Reichardt, 1979).

__________________

Table 4 here.

_________________

Demographic Tree Analysis: Support for P4

To further explore the demographic differences among very low, low-to-moderate, and

very high income extremely frequent gamblers, the study includes a tree analysis using five

demographic variables including gender, race, household size, education level, and age. Figures

2 and 3 show demographic profiles of two extremely frequent casino gamblers groups. In

support of proposition 4, extremely frequent casino gamblers differ significantly in their

demographic profiles based on gender, age, race, education, household size and age. Extremely

frequent casino gamblers with very low-income (Figure 2) are predominantly white, females,

with a high school education or less. One third of all low-income extremely frequent gamblers

are over the age of 65, while nearly another third (28%) are less than 35 years old.

Overall, household size for very low income and extremely frequently casino visiting

females is small, typically less than two people. However, non-white females tend to have larger

households (3+) than white females. Non-white females account for 23 percent of all females

reporting. Although the largest female age group is 65 and older, it represents only 33 percent of

the total female group (extremely low income casino gamblers). Men are also predominantly

white, with a much smaller percentage of non-whites (11%) than the female group. Unlike the

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female group, white males tend to have more education, with nearly 63 percent reporting more

than a high school education.

________________

Figure 2 about here.

_______________

Extremely frequent casino gamblers with extremely high-income (Figure 3) are

predominantly white, male, with college experience. Unlike the very low-income group, only 11

percent of the high-income extremely frequent gamblers are over the age of 65 and only 13

percent are under the age of 35. Most of the high-income extremely frequent gamblers are

between 35 and 55 years old (60%). High-income extremely frequent casino visiting white males

also differ from their very low-income counterparts in household size. Females represent a small

percentage of high-income extremely frequent casino gamblers (27%). Most report ages

between 35 and 64 years of age (75%). The number of high-income extremely frequent casino

gamblers over the age of 64 is very small (less than 12%). Large households (3+) represent a

higher percentage of the high-income group (57%) than their low-income counterparts (35%).

Finally, no non-white females are found in the group of high-income extremely frequent casino

gamblers.

_________________

Figure 3 about here.

________________

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Those individuals reporting low to moderate-income levels represent the largest group of

extremely frequent casino gamblers (see Table 3). Females represent only slightly more than

males (53% females, 47% males). More than 60 percent report having more than a high school

education and more than half are less than 45 years of age. Non-whites represent about 17% of

this group, which is slightly more than the high-income group, but less than the low-income

group. More than half of this group have households larger than 3.

Consumption Constellations: Support for P5 and P6

Figures 4 and 5 shows consumption constellations for the seven groups. Consistent with

propositions 5, extremely frequent casino gamblers (EFCGs) differ in their consumption

activities, interests, and opinions not only from each other but also from the non-gamblers (see

Figure 4). Note that very low-income non-gamblers attend church nearly twice as often as low

income EFCGs (extremely frequent gamblers). Similarly, high income EFCGs cook outdoors

and go out for breakfast nearly two to four times more often than non-gamblers.

_________________

Figure 4 here.

________________

Figure 5 show attitudes, opinions and interests of EFCGs and other groups. In support of

proposition 6, EFCGs agree more favorably with the attitude that a drink or two at the end of the

day is a great way to relax, as compared with other non-EFG groups. Unlike the other groups,

22

low income non-gamblers are less likely to favor legalized abortion. Low income EFCGs enjoy

parties, games, shows and anything for fun more than all other groups. This group also feels

more strongly that guns should be in every home, as compared with all other groups.

________________

Figure 5 here.

_______________

Media Usage: Support for P7

In support of proposition 7, EFCGs also differ in their media usage (Figure 6). Seventy

three percent of high income EFCGs report reading most or all of the business section of the

newspaper; an amount ten percent greater than high income non-gamblers (62.4%). Fifty three

percent of high income EFCGs report that television is their primary source for entertainment, as

compared to only thirty nine percent of the high income non-gambling group. For the low-

income EFCGs, sixty percent report that TV is their primary source of entertainment (the highest

percentage of all groups) and twenty three percent report using the newspaper as their main

source of daily news (the lowest of all groups). These finding suggest that groups differ in their

media usage and would likely respond more favorably to targeted rather than mass media

campaigns.

CONCLUSIONS AND MARKETING STRATEGY IMPLICATIONS

Volume segmentation can be an effective tool to help firms focus their marketing efforts toward

customers that use their products or service the most. The present study falls in the category of a

23

rather delayed response to Twedt’s prediction that, “It seems most likely that volume of product

usage will eventually replace standardized demographic breaks in marketing survey research.”

(Twedt, 1964). While Twedt’s (1964) heavy-half proposition informs us that 50 percent of the

customers are responsible for most product usage, the theory of extremely frequent behavior

extends these findings to the heaviest users – the top 2 percent of the customers that are often

responsible for more than one-fifth of all product usage.

The present study uses survey data and property-space theory to explore the propositions

that extremely frequent behavior exists and that EFB users differ in their demographic makeup,

attitudes, interests and consumption constellations. Using casino gambling as an example, we

show that extremely frequent casino gamblers do exist and that less than 2 percent of all casino

gamblers are responsible for nearly 25 percent of all casino gambling usage. Tree analysis and

cross classification analysis (Armstrong & Andress, 1970; Bass, Tigert & Lonsdale, 1968) is

useful for showing that the EFB users differ significantly in their demographic configurations.

The segment of EFB users consist of very low, low-to-moderate, and high income users. These

demographic differences lead to paradoxical consumption patterns. For example, low-income

extremely frequent casino gamblers are not supposed to have money to afford this type of

behavior. Yet, very-low income extremely frequent casino gamblers not only visit casinos more

often, but also eat out in restaurants and take airplane trips for personal reasons more often than

other low-income non-gamblers. The following question left for future research: where do they

find the resources for this type of consumption?

Extremely frequent behavior theory also proposes that EFB users differ in their attitudes,

interests, and consumption constellations. Using a nonequivalent group design (see Cook and

Campbell, 1979; Kusyszn, 1984; Reichardt, 1979), the present study compares the means of the

24

EFB users to the low-to-moderately high income non-users (the largest segment of households),

which helps to effectively discriminate between patterns of alternative group behaviors. The

findings suggest that EFB users indeed differ from non-users in their consumption activities,

interests and opinions. Identifying such differences increases understanding of EFB users and

also the non-users of their products or services. Understanding the differences between users and

non-users can help firms identify and exploit blue ocean opportunities (see Kim and Mauborgne,

2005) and inform industry, government, and near government organizations’ efforts in designing

effective social reform programs that attempt to control anti-social and self-abusive behavior

(Campbell 1969; Palmgreen, Lorch, Donohew, Harrington, Dsilva, & Helm 1995)

Chronic casino gamblers vary in demographics, consumption constellations, and AIOs.

However, even though no one profile fits all chronic casino gamblers, a limited number of

profiles are identifiable and most chronic casino gamblers are classifiable meaningfully into one

of these profiles. Jumbo shrimp, big fish, and whales are category names that include different

chronic casino gamblers with unique demographic, AIO, and consumption constellation/media

usage patterns. Members of these three unique chronic casino categories will most likely differ

in their responses to specific pathological-gambling-control intervention programs. The

effectiveness (i.e., rate of success and specific impact metrics) of alternative treatment programs

for pathological casino gambling addictions will likely vary substantially across the substantially

different sub-segments of chronic casino gamblers.

The necessity of targeting treatment programs—government and near government

organizations’ “demarketing” programs aiming to enable extremely frequent users to reduce or

eliminate their consumption dependencies—to reach extreme users does receive a modest

amount of attention in the behavioral science literature (e.g., Palmgreen et al. 1995; Wolfgang

25

1988). For example, Wolfgang considers undergraduate college students responses to a

sensation seeking scale by gender and the students’ expectations of future participation in four

leisure activities that usually involve betting money. However, the present article is unique in

developing and applying an extremely frequent consumption theory; applying the theory

empirically using national survey data enriches understanding of subcategories of chronically

extreme users.

Such knowledge confirms the proposition that considering sub-segments of extremely

frequent casino gamblers is likely necessary—one demographic, lifestyle, and media-use profile

does not fit all extremely-frequent casino gamblers. Chronic casino gamblers are segmentable

meaningfully into a few unique sub-segments and thick demographic and lifestyle descriptions of

each segment are possible. Designing a few, unique, treatment programs to control/eliminate

chronic dependencies—with each program separately targeting one sub-segment of extremely

frequent casino gamblers—is likely to be a more effective strategy than designing one program

that targets all chronic casino gamblers. Creating and testing such multiple-treatment program

designs reflects Campbell’s (1969) wisdom and proposals for viewing reforms as experiments.

26

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Table 1. Demographic Variables

Demographic Category 1 Category 2 Category 3

Gender Male Female

Race White Black Hispanic & Other

Household Size 1 2 3 or more

Education High school diploma

or less

Some college or

college degree

Some post-college

or Graduate Degree

Income Very low

(<$20,000/yr)

Low to moderate

($20k-79,999/yr)

High

($80,000+/yr)

33

Table 2. Casino Gambling – Annual Frequency Gambling

Group

Annual

Frequency Count Percent

Cumulative

Percent

Non-Gamblers 0 13,756 66.6 66.6

2-4 4,713 22.8

89.4

Moderate Gamblers 5-8 1,069 5.2 94.6

9-11 522 2.5 97.1

12-24 354 1.7 98.8

Extremely Frequent 25-51 146 .7 99.5

Gamblers 52+ 98 .5

Total 20,658 100.0

100.0

34

Table 3. Share of People and Share of People and Share of Casino Gambling

Casino Gambling Group

Income group

Share of People (%)

Share of Frequency (%)

Ratio of ‘Share of Frequency %’ to

‘Share of People %’ Non-Gamblers Low 15.4 0

Moderate 44.7 0

High

6.5 0

Moderate Gamblers All groups

32.2 72.6 2.25

Extremely Frequent Low 0.2 4.0 20.0

Gamblers Moderate 0.9 20.4 22.6

High 0.1 3.0

30.0

Note. Confirming the core extremely frequent behavior proposition, 1.2% of the users represent 27.4% of all casino

gambling visits. A total of 70.9% of the population report never gambling is a casino each year.

35

Table 4. Cross Tabulation of Casino Gambling Groups by Income Level

Income Groups*

Casino Gambling Groups Very Low

Low to

Moderate High Total

Non Gamblers

% within Row

3,180

(23.1)

9,225

(67.1)

1,351

(9.8)

13,756

(100.0)

Moderate Gambler

% within Row

858

(12.9)

4,723

(70.9)

1,077

(16.2)

6,658

(100.0)

Extremely Frequent Gamblers

% within Row

35

(14.3)

183

(75.0)

26

(10.7)

244

(100.0)

Total 4,073

(19.7)

14,131

(68.4)

2,454

(11.9)

20,658

(100.0)

* Low Income <$20,000/yr, Moderate Income $20,000 to $79,999/yr, High Income >= $80,000/yr

Figure 1 Property Space Configuration for “Extremely Frequent Casino Visitors”

(Percent distribution of U.S Households based on 1993-1998 BBD survey data, n=20658)

37

Figure 2 Demographic Tree Analysis for Low Income Extremely Frequent Gamblers (Jumbo Shrimp)

35-4

4

HS

1

65+

Grad

45-5

4

35-4

4

45-5

4

55-6

4

65+

<35

65+

<35

45-5

4

65+

65+

<35

35-4

4

65+

55-6

4

45-5

4

45-5

4

55-6

4

65+

65+

<35

35-4

4

<35

35-4

4

3+

HS Col

2

HS

White

Col

13+

Black

HS

Others

HS

2 3+

HS

White

1

HSCol

HS

3+

Grad

2

HS Col

65+

HS

Others

2

Female Male

Very Low Income Extremely FrequentGamblers

<35

1 1 1 1 1 1 1 1 1 1 111 11 1 1 1 1 1 1 1 12 2 2 22

Note. Key: H = high school or less education; C = some college to college graduate; P = postgraduate courses to postgraduate degree; <35, 35-44, 55-64, and 65+ = age categories. Numbers 1, 2, and 3+ represent household size. Total numbers of low income extremely frequent gamblers appear in boxes and represent 94% of all low income extremely frequent casino gamblers.

38

Figure 3 Demographic Tree Analysis for High Income Extremely Frequent Gamblers (Whales)

55-6

4

HS

2

Col

45-5

4

65+

45-5

4

45-5

4

<35

55-6

4

55-6

5

65+

65+

35-4

4

35-4

4

45-5

4

55-6

4

35-4

4

45-5

4

HSCol

3+

White

White

2

HS

Col

HSGrad

3+

Col

45-5

4

Col

Black

3+

Female Male

High Income ExtremelyFrequent Gamblers

45-5

5

Grad Grad

35-4

4

55-6

4

Grad

1 1 1 1 1 1 1 1 1 1 1 1 1 12 2 2 2 2 2 2

Note. Key: H = high school or less education; C = some college to college graduate; P = postgraduate courses to postgraduate degree; <35, 35-44, 55-64, and 65+ = age categories. Numbers 1, 2, and 3+ represent household size. Total numbers of low income extremely frequent gamblers appear in boxes and represent 94% of all low income extremely frequent casino gamblers.

39

Figure 4 Activities and Consumption Constellations for EFCGs and Other Groups

(Average frequency across groups statistically significant at p<.001.)

Extremely Low Income

No Casino Visits

Moderate Income

High Income

Extremely Low Income

Moderate Income

High Income

Extreme Casino Visits

Sometimes visit Casinos and All Income Levels

1 2 3 4 5 6 7

Average Frequency of Behavior

Attended church or other place of worship

Purchased from a mail order catalog

Went out to breakfast at a restaurant

Went to a bar or tav

5

10

20

15

25

30

Cooked outdoors

Groups

40

Figure 5 Attitudes, Opinions and Interests for EFCGs and Other Groups

Average agreement across groups statistically significant by analysis of variance means (p<.001)

Extremely Low Income

Moderate Income

High Income

Extremely Low Income

Moderate Income

High Income

Sometimes visit Casinos and All Income Levels

1 2 3 4 5 6 7

-2

-1

0

+1

+2 Average Agreement with AIO

I favor of legalized abortion

There should be a gun in every household

Dread the future

A drink or two at the end of the day is a great way to relax

I enjoy parties, games, shows anything for fun

No Casino Visits Extreme Casino Visits

Groups

41Figure 6 Media Habits of EFCGs and Other Groups

Average agreement across groups statistically significant by analysis of variance means (p<.001)

TV is primary source ofentertainment (phi= .162)

ExtremelyLowIncome

No Casino Visits

ModerateIncome

HighIncome

ExtremelyLowIncome

ModerateIncome

HighIncome

Extreme Casino Visits

Sometimes visitCasinos and AllIncome Levels

1 2 3 4 5 6 7

PercentAgreeing

Groups

Read most or all of thebusiness section of thenewspaper (phi=.154)

Subscribe to basiccable (phi= .095)

20

30

50

40

60

70

10

TV is main source ofdaily news (phi= .122)

Newspaper is mainsource of daily news(phi= .122)