Data Mining Techniques to Improve the Response Rate of E-mail campaigns and Customer Loyalty

Abstract

The efficiency of e-mail campaigns is a big challenge for any e-commerce venture in terms of the response rate of e-mail campaigns and customer seg-mentation based on loyalty. Data mining techniques are useful tools to ex-tract customer information related to response rate from e-mail campaigns data. This study aims at predicting customer loyalty and improving the re-sponse rate of e-mail campaigns, specifically open rate and click through rate, using data mining techniques such as logistic regression and clustering. The models are trained using chi square and logistic regression techniques to detect the effect of customers’ loyalty based on their demographic and be-havioural characteristics. Furthermore, a clustering technique is used to seg-ment customers based on their behavioural characteristics . The models re-ported satisfactory results in predicting customer loyalty based on open rate and click through rate values. In addition, the clustering of customers suggest that companies will have a better understanding of their customers in terms of their demographic and behavioural characteristics. The response rates also increase at the preferred moment at which e-mails should be send to custom-ers in email campaigns.