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, Ferreira@gsu.edu
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
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