FORECASTING THE NUMBER OF MEMBERS IN PRIVATE CLUBS Topic area: Marketing Topic Area #17 Investments Raymond R. Ferreira, Cecil B. Day School of Hospitality Administration, J. Mack Robinson College of Business, Georgia State University, 35 Broad Street, Suite 216, Atlanta, GA. 30303, Phone: 404-651-3671, Fax: 404-651-3670, [email protected] ABSTRACT The objective of this empirical study was to understand the influence that select demographic variables have on the number of members for private clubs (e.g.: country clubs, city-dining clubs, yacht clubs, and athletic clubs). Data was collected from private clubs in twenty cities across the United States. Information collected from each club included the number of members and different demographic variables within a ten mile radius around each club. The relationship between the dependent measurement (membership number) and the independent measurements (demographic variables and private club type) was explored using regression and other statistical methods. INTRODUCTION The location of a business can often be the key component in determining the success or failure of that business. Location, or place as it is referred to in the marketing mix, is also one of the key components in the development of a marketing strategy for a business or organization. The location of the business can be a strength, a weakness, 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 customer’s place of residence, its convenience to or from one’s residence, the physical beauty of the location, etc. (Lewis, Chambers, & Chacko 2004; Kotler, Bowen, & Makens, 2004). Another important factor for hospitality businesses, such as restaurants, in regards to their location, is that many customers are only willing to travel a certain distance to purchase the businesses’ products. Often, if the market is not near the business’ location, it 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 it 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 on the project (Melaniphy, 1992). This typically involves acquiring the demographics within a certain radius, such as 3 miles, to determine if there are a sufficient number of households within a certain income level, if there are enough businesses in the area, if there are enough people working in the area, if there is enough traffic flow past the proposed location, 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 clubs in the hospitality industry (Barrows, 1999). 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 competition from other hospitality businesses, etc. (Kaspriske, 2003). An analysis of the variables that could possibly influence the membership size 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 member-owned private club industry (Ferreira, 2003, Ferreira, 2002, and Ferreira, 1997). The purpose of this study was to address some of these important issues. METHODOLGY The intent of this study was to determine which demographic variables, if any, account for the variance in a club's membership size. The demographic variables were measured at a ten mile radius around each private club. The demographic variables collected from the clubs were the number of residents, owner occupied households, properties over $150,000 in value, households with over $100,000 income, businesses, CEOs/executives/professionals, and individuals with a college degree. The subjects were private clubs (country clubs, city clubs, city-athletic clubs, and yacht clubs) whose managers were members of the Club Managers Association of America and have been in operation for five or more years located in 20 greater metropolitan areas in the United States as listed in Table 1. [Table 1] The demographic information for each of the private club's location was provided by PCensus. The data collected from the private clubs and the demographic variables for each club was analyzed using the SPSS/PC+ statistical package. Significant relationships among the performance measurement and the demographic variables were explored through correlation coefficients and stepwise regression analysis. RESULTS Responses were received from 312 clubs for a response rate of 51 percent (600 clubs sent a survey). Table 2 displays the means and standard deviations for the dependent performance variable (number of full-privilege members) and the seven demographic or independent variables for the private clubs. There was no difference in the mean number of members based on the club’s city (location) or club type (country, city-dining, yacht, and athletic). [Table 2] Table 3 lists the correlation values among the variables. The following statistically significant correlations existed between the dependent performance variable (number of members) and the independent (demographic) variables. The number of full-privilege members significantly correlated with the number of businesses (.63), property values (.58), owner occupied households (.57), number of CEO’s/executives (.56), and household income levels (.55). [Table 3] The variance in the number of members in a private club could be explained from four of the demographic variables, measured within a ten mile radius of the clubs. In Table 4, the results of a stepwise regression analysis indicated that the dependent variable’s variance could be significantly explained by a select number of demographic variables. The variance in the number of full-privilege members a club had (R2 = .56) was related to the number of businesses, the number of property values over $150,000, the number of households having income levels over $100,000, and the number of CEOs/executives/professionals. This was significant at the .01 level. [Table 4] CONCLUSIONS This research project confirmed the findings from other hospitality businesses, especially restaurants, that the demographic makeup of a location has an impact on a business (Melaniphy, 1992). In this study, four demographic variables had an impact on the number of members a private club had. As the number of households with income levels of over $100,000, the number of properties valued at over $150,000, number of businesses and the number of CEO/executives increased, so did the club’s number of members. In other words, as the demographic variables that measure one’s wealth (income, property value, and professional positions) increased, so did the club’s ability to attract these individuals as members of a club. Obviously, when there are many individuals with high income residing near the club, there will also be a large pool of prospective candidates for membership there as well. Moreover, if a club is conveniently located to a member’s business, it becomes more attractive for membership. Members join a club typically that is conveniently located to their residence, their place of business, or both (Ferreira, 1996). Many club members are CEOs, executives, owners, or professionals. 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 information is imperative given the increased competitive nature within the private club industry (Kaspriske, 2003). 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 location is favorable. An analysis of the area can help to explain a club’s performance in attracting membership candidates. LIMITATIONS AND FUTURE RESEARCH The findings of this study are limited in their application. The first limitation is the sample size of 312 private clubs in 20 major metropolitan areas. The study should be replicated with clubs in smaller cities and towns. 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 both non-member and member owned clubs and profit and non-profit clubs to see if the variables that affect member owned clubs affect non-member owned clubs in the same manner. This proposed research project represents a significant contribution to the private club industry. For academicians it is the first attempt to demonstrate the relationship between performance and the demographic variables around a member-owned private club’s location. TABLES AND GRAPHS Table 1: US Metropolitan Areas • Atlanta, GA • Los Angeles, CA • Austin, TX • Kansas City, MO • Boston, MA • Minneapolis, MN • Charlotte, NC • New York, NY • Chicago, IL • Pittsburgh, PA • Cincinnati, OH • Phoenix, AZ • Dallas, TX • San Antonio, TX • Denver, CO • San Francisco, CA • Houston, TX • Seattle, WA • Las Vegas, NV • Tampa, FL Table 2: Private Club Variables Characteristic Performance Variable Number of Full-Privilege Members Demographic Variables Within Ten Mile Radius Residential Population Owner Occupied Households Property Values Household Income Levels Number of Businesses Number of CEO’s/Executives Number With College Degree n Mean Std. Dev. 312 914.66 984.99 312 312 312 312 312 312 312 787,789.45 356,787.25 $116,973.27 $51,678.95 125,860.77 136,114.21 178,851.34 361,058.73 186,682.28 $66,426.13 $36,899.69 66,670.43 75,485.85 81,875.93 Table 3: Correlation of Private Club Variables Variables 1 Number of FullPrivilege Members 2 Residential Population Owner Occupied Households 3 4 Property Values 5 Household Income Levels 6 Number of Businesses 7 Number of CEOs Executives/Mgrs. 8 Correlation Coefficients 1 - 2 .17 - .57 ** .58 ** .55 ** -.63 ** .56 ** .04 .39 - -.12 .47 - -.09 .43 .50 - .19 .16 .17 .08 - .09 .21 -.14 .04 .44 - -.11 .09 -.03 .07 .17 .51 Number With College Degree ** Significant at .001 level 3 4 5 6 7 8 - Table 4: Results from Stepwise Regression Analyses of the Relationship Among the Characteristics of Private Clubs and the Performance Variable Dependent Variable Number of FullPrivilege Members ** Significant at .01 level Independent Variables Number of businesses Property values Household income levels Number of CEOs/executives Constant B beta .0014 .0012 .0046 .0028 69.85 .98 .44 .38 .47 F 28.31* R Square .56 REFERENCES Andreasen, A. 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