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The Demographic Effect on thePerformance Level of PrivateClubsRaymond R. Ferreira PhD, MBA, BA a b c da Cecil B. Day School of Hospitality Administration ,Georgia State University, College of BusinessAdministration , University Plaza, Atlanta, GA,30303-3083, USAb University of Maryland , USAc University of Minnesota , USAd Rutgers University , USAPublished online: 20 Oct 2008.

To cite this article: Raymond R. Ferreira PhD, MBA, BA (1998) The DemographicEffect on the Performance Level of Private Clubs, Journal of Hospitality & LeisureMarketing, 5:4, 23-32, DOI: 10.1300/J150v05n04_03

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The ~ e m o ~ r s ~ h i c Effect on the Performance Level

of Private Clubs R a y m o n d R. Ferreira

ABSTRACT. This study explored which dcmographic variables had the largest intluence on five different financial perforn~ance mcasure- menis (total revenue, dues incomc, initiation feesideposits, average mcmber spending, and gross operating profits) and thc number of full- privilege members for private clubs (e.g., country clubs, city-dining clubs, yacht clubs, and athletic clubs). Data was collected from 58 private clubs in eleven major cities across the United States. The vari- ance in four of the six private club performance measurements could be explained from the demographic variables measured within a ten mile radius of the club. The percentage of variance explained by the demo- graphic variables for the following performance measurcrnents: initia- tion feesJdeposits, number of full-privilege members, total revenue, and average member spending, was between 49 and 61 peicent. [A~.licle copies uvuiluble for u fee from Tlte Huwo,?h Doc~ime~rf Delive~y Savice: 1-800-342-9678. E-muil udd~zss: ~e~i~~fofb@l~owor~lr~re~si~~c.co~n~

KEY WORDS. Private club, marketing, demographics, membership

INTRODUCTION

T h e location of a business can often be the key component in determining the success or failure of that business. Location, or place

Raymond R. Ferreira is Assistant Professor in the Cecil B. Day School of Hos- pitalit; Administration at Georgia State University, College of ~us;liess Administra- tion, University Plaza, Atlanta, GA 30303-3083. He holds a PhD from the University of Maryland, an MBA from the University of Minnesota, and a BA from Rutgers University and is a member of the Club Managers Association of America, the American Marketing Association, and the Council on Hotel, Restaurant, and Institu- t io~~al Education.

Journal of Hospitality Sr Leisure Marketing, Vol. 5(4) 1998 O 1998 by Tl~c Haworth Press, Inc. All rights reserved. 23

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24 .lOURNAL OF HOSPITALITY& LEISURE MARKETING

as it is referred to in the marketing mix, is also one of the key compo- nents in the development of a marketing strategy for a business or organization. The location of the business can be a strength, a weak- ness, or have no effect on that business. Many hospitality businesses are somewhat dependent on their locations. A hospitality business location could be considered a strength due to a number of reasons: its proximity to a customer's place of business, its proximity to a custom- er's place of residence, its convenience to or from one's residence, the physical beauty of the location, etc. (Lewis, Chambers, & Chacko, 1995; Kotler, Bowen, & Makens, 1996).

Another important factor for hospitality businesses, such as restau- rants, in regard to their location, is that many customers are only willing to travel a certain distance to purchase the businesses' prod- ucts. Often, if the market is not near the business' location, i t will not travel or travel as frequently to purchase the product. Therefore, in selecting a location, most businesses would prefer a location that is close to the market i t is serving. Determining if the market segment a business wants to capture is present in that location is a difficult procedure. Some hospitality businesses have been able to identify their market segment based on demographic descriptors: age, gender, profession, income level, etc. (Andreasen, 1988).

The hospitality industry's reliance on demographic description of its market segment has led many large hospitality businesses to make key decisions on acquisitions and development of properties based on the demographics of the location being considered. Some restaurant franchises mandate that any location being considered for acquisition or development first have a demographic profile developed before spending any additional resources or development time onthe project (Melaniphy, 1992). This typically involves acquiring the demograph- ics within a certain radius, such as 3 miles, to determine if there is a sufficient number of households within a certain income level, if there are enough businesses in the area, if there are enough people workitlg in the area, if there is enough traffic flow past the proposed locatio;, etc. The minimum demographic requirements are often derived from a company's records and demographic profiles of its successful and unsuccessful ventures. These demographic numbers are placed in the company's model to forecast potential sales for a proposed location (Melaniphy, 1992).

There have been very few academic studies completed with private

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Ruynzorrd R. Ferreiru 25

clubs in the hospitality industry. Furthermore, because of the exclusive nature of private clubs, the topic of marketing is one that many clubs have been reluctant to address. Many private clubs, today, find that they do not have a waiting list for members and that revenues have declined because of the following factors: overbuilding of private clubs, poor economics, companies downsizing management positions, companies moving from downtown areas to the suburbs, more com- petition from other hospitality businesses, etc. (Coyne, 1992).

A recent survey conducted by the Club Managers Association of America (CMAA) indicated that the marketing and selling of member- ships was a high priority for club managers in that association. This interest in the marketing of private clubs to increase membership has occurred recently. The majority of clubs across the country are now seeking members, whereas a decade ago these same clubs had a waiting list of individuals wanting to be members. Less members result in a decrease in revenues, initiation fees, dues income, covers, etc., and possi- bly a decrease in gross operating profit for the club (Ferreira, 19971-3).

An analysis of the variables that could possibly influence the mem- bership size and financial performance of a private club is needed, given the state of the industry. Information on market penetration rates, market share and the impact of demographics is not available in the private club industry (Ferreira, 1997a). The purpose of this study was to address some of these important issues.

The intent of this project was to explore which demographic vari- ables had the largest influence on different performance measurements for private clubs (e.g., country clubs, city-dining clubs, yacht clubs, and athletic clubs). This study investigated if there were any relation- ships between select demographic variables and various financial per- formance nleasurements and the number of members at private clubs in eleven major cities across the United States.

RESEARCH OBJECTIVES

The intent of this study was to answer the following questions:

Which demographic variables, if any, account for the variance in a club's membership size? (These variables were measured within a ten mile radius of the private club.) The demographic variables collected at a ten mile radius from the clubs were the number of:

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26 JOURNAL OF HOSPITALITY& LEISURE MARKETING

Residents Owner occupied households Properties of over $150,000 in value Households with over $100,000 income Businesses CEOslexecutives/professionals Individuals with a college degree

Which demographic variables have any relationship with the fol- lowing financial performance measurements at private clubs?

Total revenue Total initiation fees/deposits Dues income Average member spending Gross operating profit

The general hypothesis for this study is that the different perfor- mance levels for private clubs will be influenced by certain demo- graphic variables within a ten mile radius of the club.

METHODOLOGY

The subjects were 58 proprietary private clubs (country clubs, city clubs, city-athletic clubs, and yacht clubs) that have been in operation for five or more years located in the following greater metropolitan areas:

Atlanta, GA Austin, TX Charlotte, NC Chicago, IL Cincinnati, OH Dallas, TX Houston, TX Los Angeles, CA Phoenix, AZ San k ~ t o n i o , TX Tampa, FL

The demographic information for each of the private club's location was provided by Equifax's National Decision System's (NDS) market research branch. The southwest regional headquarters of Club Corpo- ration of America (CCA) provided information on the six annual performance measurements for the 58 clubs they own or manage in those eleven cities.

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The data collected ,from the private clubs and the demographic variables for each club were entered in a computer data file. The data was analyzed using the SPSSIPCt statistical package (Norusis, 1992). Significant relationships among the performance measurements and the demographic variables were explored through correlation coeffi- cients and stepwise regression analysis.

RESULTS

Table 1 displays the means and standard deviations for the six performance variables and seven demographic variables for the 5 8 private clubs. Table 2 lists the correlation values among the variables. The following statistically significant correlations existed between the dependent (performance) variables and the independent (demograph- ic) variables. Initiation feesldeposit significantly correlated with household income levels (.71) and property values (.67). The tzumber of full-privilege members significantly correlated with the number of businesses (.63), property values (.S8), owner occupicd households (.57), number of CEOs/executives (.56), and household income levels

TABLE 1. Private Club Variables

Characteristic n Mean Std. Dev.

Performance Variables

Initiation FeesiDeposit 58 $282,946.14 $344,964.93 Dues Income 58 $1,628,263.40 $1,435,807.88

Number of Fuil-Privilege Members 58 91 4.66 984.99 Total Revenue 58 $1,741,971.70 $1,092,045.34

Average Member Spending 58 $1,612.62 $1,088.18 Gross Operating Profits 58 $1,087,914.10 $1,143,716.33

Demographic Variables Within Ten Mile Radius

Residential Population Owner Occupied Households

Property Values Household Income Levels

Number of Businesses Number of CEOslExecutives Number with College Degree

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TABLE 2. Correlation of Private Club Variables

Variables Correlation Coefficients

Performance Variables 1 Intiation FeaiDeposit - 2 Dues Income ,637 -

X

3 Number of Full- .21 .68 - Privilege Members **

4 Total Revenue .69 .77 .41 - X X

5 Average Member .22- .17 -29 .26 - Spending

6 Gross Operating 6 1 .93 5 0 .84 -.09 - Profits X X X X

Demographic Variables 7 Residential Population .14-.16 .17 .21 .19- .14 - 8 Owner Occupied .22 .18 .57 .61 .41 .19 .39 -

Households ** **

9 Property Values .67 .12 .58 .47 .62 .09- .12 .47 - +, +, +,

10 Household Income .71 - .O1 .55 .41 .62 .17- .09 .43 .50 - Levels * ... ...

11 Number of Businesses .09 .I1 -.63 .66 - . I0 .18 .19 .16 .17 .08 - ... *

12NumberofCEOs -.07-.04 .56 .32 .14 .13 .09 .21-.14 .04 .44 - Executive/Mgrs. ft

13 Numberwithcollege .15 .09 .04 .19 .18 .08-. l l '.09-.03 .07 .17 .51 - Degree

** Significant at ,001 level

(.55). To~ul revenue was significantly related to the number of busi- nesses (.66) and owner occupied households (.61). Average member sperzdirzg highly correlated with property values (.62) and household income levels (.62).

Dues income only had significant correlation values with other performance variables and not with any demographic variables: gross operating profit (.93), total revenue (.77), number of full-privilege members (.68), and initiation feestdeposit (.67). Gross operating profil had significant correlation values only with performance variables as

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well: dues income (.93), total revenue (.84), number of full-privilege members (.60), and initiation feesldeposit (.61).

The variance in four of the six private club performance measure- ments could be explained from the demographic variables, measured within a ten mile radius of the club. The percentage of variance ex- plained by the demographic variables for the following performance measurements: initiation fees/deposits, number of full-privilege mem- bers, total revenue, and average member spending, ranged between 49 and 61 percent.

In Table 3, the results of a stepwise regression analysis indicated that four of the six dependent variables' variance could be significant- ly explained by a select number of demographic variables. The vari- ance in initiation fees and deposits ( R ~ = .59) that a private club collected was explained by the-number of households having income levels over $100,000, the number of property values over $150,000, and the number of owner occupied households. The number of full- privilege members a club had ( R ~ = .56) was related to the number of businesses, the number of property values over $150,000, the number

TABLE 3. Results from Stepwise Regression Analyses of the Relationship Among the Characteristics of Private Clubs and the Performance Variables

Dependent Independent 6 beta F R Variable Variable Square

Initiation Household income levels .21 .47 FeesIDeposit Property values 36 1.12

Owner occupied households .77 - 39 Constant - 54,749.03 21.91* .59

Number of Full- Number of businesses ,0014 .98 Privilege Members Property values ,0012 .44

Household income levels ,0046 .38 Number of CEOslexecutives ,0028 .47

Constant 69.85 28.31* .56

Total Revenue Number of businesses 6.97 .87 Owner occupied households 4.06 .35

Constant -383,821 40.61' .61

Member Properly values ,0091 1.13 Spending Household income levels ,0172 - 1.04

Owner occupied households ,0003 .27 Constant -236.78 18.Off .49

' Significant at .O1 level

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of households having income levels over $100,000, and the number of CEOs/executives/professionals. The variance in total revenue ( R ~ = .61) in a private club could be explained by the number of businesses and the number of owner occupied households. Finally, the amount of member spending (R* = .49) was related to the number of property values over $150,000, the number of households having income levels over $100,000, and the number of owner occupied households, which were the same demographic variables as those related to the initiation fees and deposits performance variable.

The variance for three of the performance measurements was ex- plained by these demographic variables: the number of households with income over $100,000, the number of property values over $150,000, and the number of owner occupied households. The number of businesses within a ten mile radius influenced two performance measurements, while the number of CEOs/executives/professionals influenced one performance variable. Two of the demographic vari- ables investigated, the number of residents and the number of individ- uals with a college degree, did not significantly affect the variance of any performance variables investigated.

CONCLUSIONS

This research project confirmed the findings from other hospitality businesses, especially restaurants, that the dimographic makeup of a location has an impact on a business (Melaniphy, 1992). In this study, three demographic variables had an impact on the financial perfor- mance of private clubs. As the number of households with income levels of over $100,000, the number of properties valued at over $150,000, and the number of owner occupied households increased, so did the club's ability to charge and collect higher initiation fees, as well as attract members who spent higher amounts at the club. In other words, as the demographic variables that measure one's wealth (in- come, property value, and property ownership) increased, so did the club's ability to charge higher initiation fees and have members that spent more at the club.

Total revenue was highest for clubs located in areas that have a high number of businesses and owner occupied households within ten miles of the club. Revenues at the clubs (dues, food & beverage purchases, banquet business, athletic fees, etc.) were affected by the

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number of businesses in the area. This finding was expected because the number of members, business meetings, banquets, etc., that a club has, is predicated by businesses who often pay for executive member- ships and business entertainment. Moreover, the number of owner occupied households is one indicator of wealth and a large number of wealthy individuals near the club provide a good source of potential revenue in terms of membership dues and frequent use of the club by members who live near the club.

The number of full-privilege members was highly related to the two demographic wealth indicators mentioned earlier: households with income levels over $100,000 and the number of properties valued over $150,000. Obviously, when there are many individuals with high in- come residing near the club, there will also be a large pool of prospec- tive candidates for membership there as well. Moreover, if a club is conveniently located to a member's business, it becomes more attrac- tive for membership. Members join a club typically that is convenient- ly located to their residence, their place of business, o r both (Ferreira, 1996). Many club members are CEOs, executives, owners, or profes- sionals. The larger the pool of individuals that fit this profile, the larger the number of prospective candidates for club membership (Ferreira, 1995).

This research project was one of the first of its type in the private club industry. Other hospitality businesses (hotels and restaurants) have completed similar extensive market research studies. The infor- mation is imperative given the increased competitive nature within the private club industry (Foutch, 1996).

Based on the results of this study, private clubs should analyze the demographic make-up of the area around the club to see if their loca- tion is favorable. An analysis of the area can help to explain a club's performance.

LIMITATIONS AND FUTURE RESEARCH

The findings of this study are limited in their application. The first limitation is that the sample size of 5 8 private clubs in eleven major cities is small. The reason for the stnall sample size was CCA's interest in providing data only for clubs in markets that they had three or more clubs in. Another limitation is that all the clubs in the study were owned or managed by CCA and did not include any member-owned,

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32 JOURNAL OF HOSPITALITY& LEISURE MARKETING

non-profit private clubs. Because of the small sample size, differences among the types of clubs could not be explored ( i t . , the performance of downtown city clubs may be influenced more by the demographics within a radius of 1 or 3 miles, while yacht clubs may be affected more by the demographics farther out than 10 miles).

Future studies should explore different radii lengths from the club's location and other demographic or psycho-graphic variables. The additional variables at various radii lengths may better explain the variance in a club's performance level. Moreover, future studies should be expanded to include non-proprietary (member-owned or non-profit) clubs to see if the variables that affect proprietary clubs affect non-proprietary clubs in the same manner. Another variable that should be explored is the club type to see if there are differences among city clubs, country clubs, yacht clubs, etc.

REFERENCES

Andreasen, A. R. (1988). Clteap hut good tnnrketi~tg research. Homewood, 1L: Irwin. Coyne, R. (1992, September-October). Membership marketing: Getting with the

prugram for the 90's. C l ~ t b Mattagm?lettt, pp. 70-93. Ferreira, R. R. (19973). How large is your market for potential members. Proceed-

itzg.5 of the Club Ma~zagers Associati011 of A~nerica $ Co~tferertce, in Orlando, FL, 107-111.

Ferreira, R. R. (1997b). Market Trends in Private Clubs. Proceedi~~gs of rlir Privale Cllib Marketit~g Associatiot7 5: Co~~Jeretzce, in Los Angeles, CA, 67-81.

Ferreira, R. R. (1996). The effect of private club members' characteristics on the identification level of members. Jorrrnul ofHospitality & Leisure Marketir~g, 4(3), 41-62.

Ferreira, R. R. (1995). The relationship of select performance variables among pri- vate clubs. Poster pr.esetttutiott at llte I~tter.tta/io~zal Cotrrtcil otr Hotel, Restaarutzt atrd I~rstit~rtiu~tul Educatiort Co~tferettce, at Nashville, TN.

Foutch, T. (1996, January). Strategic planning in private clubs. Privale Club Man- age~~zettt, 119-123.

Kotler, P., J. Bowen, & Makens, J. C. (1996). MarkdLtg for Hospitality ottd Tortrisnt. New York: Prentice Hall.

Lewis, R. C., R. E. Chambers, & H. E. Chacko (1995). Marketittg Leaderslrip it1 Hospitality: Forrtldaliorn a t ~ d Practices, 2nd Edition. New York: Van Nostrand Reinhold.

Melaniphy, J. C. (1992). Res/a~trortt and Fast Food Site Selectio~~. New York: John Wiley & Sons, Inc.

Norusis, M. J. (1992). Srarisrical Package for Sociul Scier~ceslPersotzal Cornpuler Pl~is (SPS,S/PC+) 4.0 Rase Syslenz Matl~ial. Chicago, 1L: SPSS Inc.

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