Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan. To demonstrate the RFMTC marketing model (a modified version of RFM), this study adopted the donor database of Blood Transfusion Service Center in Hsin-Chu City in Taiwan. The center passes their blood transfusion service bus to one university in Hsin-Chu City to gather blood donated about every three months. To build a FRMTC model, we selected 748 donors at random from the donor database. These 748 donor data, each one included R (Recency - months since last donation), F (Frequency - total number of donation), M (Monetary - total blood donated in c.c.), T (Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood).
A data frame with 748 rows and 5 variables
The variables are as follows:
R. Recency - months since last donation
F. Frequency - total number of donations
M. Monetary - total blood donated in c.c. (mL)
T. Time - months since first donation
y. a binary variable representing whether he/she donated blood in March 2007 (1=yes; 0 =no)
Dataset downloaded from the UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center
Original Owner and Donor: Prof. I-Cheng Yeh Department of Information Management Chung-Hua University Hsin Chu, Taiwan 30067, R.O.C. e-mail: icyeh 'at' chu.edu.tw
Yeh, I-Cheng, Yang, King-Jang, and Ting, Tao-Ming, "Knowledge discovery on RFM model using Bernoulli sequence", Expert Systems with Applications, 2008. DOI: 10.1016/j.eswa.2008.07.018
Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.