Retailers who deliver on – a shopping experience that is hassle-free, across all channels, and all products – are building customer loyal. Creating long-term customer loyalty requires monitoring and adapting retailing operations, which provide measurements of past and current shopping experiences, namely, customer behaviors and attitudes. To optimize long-term customer loyalty, a retail marketing model based on the customer behaviors and attitudes is required. The model’s output provides the necessary information to develop effective retail communications for optimal marketing efforts. The purpose of this article is to build a retail marketing model using the traditional statistical approach, and a newer machine learning approach – the GenIQ Model
© – for creating long-term customer loyalty. The GenIQ Model has the potential of outperforming the traditional approach, as its signature data mining muscle
would uncover undetected customer-loyalty predictive relationships, not possible with the traditional method.