A Study of Swimming Member Churn Model Using Data Mining Classification Techniques

Author: Chan-ping Lin(Hsin-pu Junior High School)、Chih-pin Shih(Department of Physical Education,National Taiwan Normal University)

Vol.&No.：Vol. 50, No.2
Date：October 2005
Pages：89-106
DOI：10.6210/JNTNULL.2005.50(2).05

Abstract：

The purpose of this study was to construct a model for analyzing the turnover rate among those who become members of the NTNU main campus swimming pool, in order to facilitate the diminishing, on the part of the swimming pool administrative staff, of member dropout and turnover. This model was constructed by using data mining classification technology including discrimination analysis, artificial neural networks, multivariate adaptive regression splines, and multivariate adaptive regression splines. The first step was of course to establish the salient characteristics of swimming pool member turnover.

After reorganizing the data for all swimmers in the NTNU main campus swimming pool, 2,707 records were chosen as our initial data. After deleting the unreasonable data, a total of 2,380 records were discussed in this study. The research results were as follows:

1. As for the over-all constitutive structure of this group: (1) the number of male members (49.87%) and female members (50.13%) was almost equal; (2) 55.63% were "free" members directly connected to NTNU; (3) most members were residents of Da-an and Zhongzheng districts (79.12%); (4) the average age of members was 32.15 years, and there was a roughly even age distribution; (5) the average participation period of members was 0.69 years and most joined for less than 2 years (93.66%); (6) the vast majority were non-discount members (95.80%); (7) the most frequently levied membership fee was 4,500 NT dollars (42.86%); (8) the summer season was easily the most popular for enrollment (49.66%); (9) no limitation on times when the pool could be used was the most popular choice (56.64%).

2. Combining artificial neural networks and multivariate adaptive regression splines generated a classification rate of 84.03%. The integrated approach successfully constructed a member churn model for the NTNU main campus swimming pool.

3. The optimal characteristics of an NTNU main campus swimming pool member were: participation period below 1 year, member fee of 2,500 NT dollars, membership enrollment in summer, and general members.

It is hoped that the NTNU Swimming Pool Administration Department can use these results in order to decrease member dropout and turnover

Keywords：Data Mining, Member turnover, Discrimination Analysis, Artificial Neural Networks, Multivariate Adaptive Regression Splines

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