Marketing improvement in a Chilean Retail Company using Uplift Modeling with neural networks

Marketing is a strategy that every company must implement today within its global plan both due to the need for external projection and for the achievement of commercial objectives. Currently, personalized marketing is key to the development of a company since it allows a better interaction with potential customers and the margin of loss or error in the direction of promotional campaigns is greatly reduced. One possibility to improve personalized marketing is the prediction of the effectiveness of campaigns to transform users into customers using artilicial intelligence, so it is necessary to develop models that allow identifying profiles or segments of people who are more willing to answer positively to a campaign. This task corresponds to uplift modeling that predicts the incremental impact of the application of treatments on a population, which is typically performed using classical models such as the one-model approach, the class transformation approach, and the two-model approach. In this work, the use of multilayer neural networks is proposed to perform uplift modeling in a Chilean online retail company. The results of the proposed model as well as classical uplift modeling techniques are presented. These results indicate that the neuronal model allows an increase of more than 30 % in relation to the area of the Qini curve, while it is competitive in other metrics. As future work, it is planned to model a Siamese neural network with the cost function of uplift modeling directly.