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Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models
This article provides a descriptive analysis of how methodological factors contribute to the accuracy of customer churn predictive models. The study is based on a tournament in which both academics and practitioners downloaded data from a publicly available Web site, estimated a model, and made predictions on two validation databases.
The results suggest several important findings. First, methods do matter. The differences observed in predictive accuracy across submissions could change the profitability of a churn management campaign by hundreds of thousands of dollars. Second, models have staying power.
They suffer very little decrease in performance if they are used to predict churn for a database compiled three months after the calibration data. Third, researchers use a variety of modeling "approaches," characterized by variables such as estimation technique, variable selection procedure, number of variables included, and time allocated to steps in the model-building process.
The authors find important differences in performance among these approaches and discuss implications for both researchers and practitioners.
Ketersediaan
| Call Number | Location | Available |
|---|---|---|
| JM4306 | PSB lt.dasar - Pascasarjana | 1 |
| Penerbit | Chicago: American Marketing Association 2006 |
|---|---|
| Edisi | Vol. 43, No. 2 (May, 2006), pp. 204-211 |
| Subjek | Customer Retention Marketing analytics statistical modeling Customer Behavior Analysis |
| ISBN/ISSN | 0022-2437 |
| Klasifikasi | NONE |
| Deskripsi Fisik | 8 p. |
| Info Detail Spesifik | Journal of Marketing |
| Other Version/Related | Tidak tersedia versi lain |
| Lampiran Berkas |