Knowledge discovery on RFM model using Bernoulli sequence

By I-Cheng Yeh, King-Jang Yang, Tao-Ming Ting. Expert systems with applications, 2009

357 citations recorded by the source corpus.

The objective of this paper is to introduce a comprehensive methodology to discover the knowledge for selecting targets for direct marketing from a database. This study expanded RFM model by including two parameters, time since first purchase and churn probability. Using Bernoulli sequence in probability theory, we derive out the formula that can estimate the probability that one customer will buy at the next time, and the expected value of the total number of times that the customer will buy in the future. This study also proposed the methodology to estimate the unknown parameters in the formula. This methodology leads to more efficient and accurate selection procedures than the existing ones. In the empirical part we examine a case study, blood transfusion service, to show that our methodology has greater predictive accuracy than traditional RFM approaches.

Subfields: Information Systems, Marketing

View Paper: https://www.semanticscholar.org/paper/c8dc14c5758d3d2fdd28bdb9c833f88b939d8dff

Citations in the corpus

• TabLLM: Few-shot Classification of Tabular Data with Large Language Models (2022, neutral): “• Blood (Kadra et al., 2021) consists of data of a blood transfusion service from Taiwan (Yeh et al., 2009).”