TY - GEN
T1 - Predicting Customer Profitability Dynamically over Time
T2 - 24th Iberoamerican Congress on Pattern Recognition, CIARP 2019
AU - Chen, Daqing
AU - Guo, Kun
AU - Li, Bo
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - In this paper a comparative study is presented on dynamic prediction of customer profitability over time. Customer profitability is measured by Recency, Frequency, and Monetary (RFM) model. A real transactional data set collected from a UK-based retail is examined in the analysis, and a monthly RFM time series for each customer of the business has been generated accordingly. At each time point, the customers can be segmented by using the k-means clustering into high, medium, or low groups based on their RFM values. Twelve different models of three types have been utilized to predict how a customer’s membership in terms of profitability group would evolve over time, including regression, multilayer perceptron, and Naïve Bayesian models in open-loop and closed-loop modes. The experimental results have demonstrated a good, consistent and interpretable predictability of the RFM time series of interest.
AB - In this paper a comparative study is presented on dynamic prediction of customer profitability over time. Customer profitability is measured by Recency, Frequency, and Monetary (RFM) model. A real transactional data set collected from a UK-based retail is examined in the analysis, and a monthly RFM time series for each customer of the business has been generated accordingly. At each time point, the customers can be segmented by using the k-means clustering into high, medium, or low groups based on their RFM values. Twelve different models of three types have been utilized to predict how a customer’s membership in terms of profitability group would evolve over time, including regression, multilayer perceptron, and Naïve Bayesian models in open-loop and closed-loop modes. The experimental results have demonstrated a good, consistent and interpretable predictability of the RFM time series of interest.
KW - CRM
KW - Predictive modelling
KW - RFM model
KW - Time series analysis
UR - https://www.scopus.com/pages/publications/85075676097
U2 - 10.1007/978-3-030-33904-3_16
DO - 10.1007/978-3-030-33904-3_16
M3 - 会议稿件
AN - SCOPUS:85075676097
SN - 9783030339036
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 174
EP - 183
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 24th Iberoamerican Congress, CIARP 2019, Proceedings
A2 - Nyström, Ingela
A2 - Hernández Heredia, Yanio
A2 - Milián Núñez, Vladimir
PB - Springer
Y2 - 28 October 2019 through 31 October 2019
ER -