A Low-Risk Experiments for Customer Segments of E-Mail Marketing Campgains: A Business Intelligence Approach
Research in e-mail marketing is divided into two broad areas spam and improving response rate. In this paper a methodology is proposed which allows companies to experiment with their e-mail campaigns to increase the campaigns’ response rate. This methodology is particularly suited for companies that are reluctant to experiment with their customer’s data which may lead to a drop of the response rate due to unsuccessful changes of the e-mail campaign. The objectives of this research have been achieved in applying a two-step approach. Firstly, homogeneous groups of customers are identified, eliminating largely any hindering heterogeneity. Secondly, customers that are not clicking and/or having a low click rate within their homogeneous groups are identified. The results are promising such that it allowed for making informed recommendations for low-risk experiments on customers having a non/low-click behaviour on a weekly newsletter e-mail.
Keywords- E-mail marketing campaign, response rate, click rate, cluster analysis, decision tree modelling