Abstract :
We develop a F-measure performance evaluation system by combining with RFM criteria and Bayesian classification, decision tree induction, Gini index, Neuro and by analyzing the factors with recall related and precision. By using this system, more profitable customers can be discovered and less unprofitable customers will be missed. The research collects and analyzes references on promoting buying rate; moreover, it introduces R-F-M (recency, frequency, monetary amount) criteria, brings up the idea of identify each individual customer to promote both the marketing profit and the customer´s lifetime value. The result shows that marketing performance derive from Neuro-weighted RFM model has the advantage over traditional RFM model by 27.80%.
Keywords :
Bayes methods; decision trees; marketing; profitability; Bayesian classification; F-measure performance evaluation system; Gini index; Neuro-weighted RFM model; RFM criteria; customer lifetime value; decision tree induction; marketing performance; marketing profit; recency-frequency-monetary amount criteria; Bayesian methods; Classification tree analysis; Cybernetics; Data mining; Decision trees; Frequency; Machine learning; Performance analysis; Predictive models; Testing; Bayesian classification; Data mining; Decision tree induction; Gini; Neuro; Rfm;
