Communication Strategy Development Using Machine Learning and Uplift Modeling

This research considers the theoretical application of the uplift modeling approach based on real data to identify the effectiveness of the approach. The data used in this research is a real historical observation on purchases and views of sports broadcasts from an online channel. In the first part, the theoretical part of uplift modeling is analyzed as well as methods for evaluating the quality of results, tools for work, and practices for applying this approach are considered. The second part is devoted to modeling and choosing the best approach for the task. This includes processing and preliminary analysis of available data, modeling uplift using multiple methods, evaluating the result of each model, and inferring and evaluating the cost-effectiveness of the approach The third part contains recommendations for conducting a communication strategy using the method studied. The following tasks are set: 1. Study of the theory of uplift modeling 2. Research of application practices 3. Collection, processing and analysis of data 4. Building the best quality model 5. Evaluation of the results obtained 6. Recommendations for an appropriate communication strategy The results showed that this approach can be a powerful tool for improving the effectiveness of communication with clients. However, to use the approach correctly, a number of requirements to input data should be considered, as well as test the approach to determine its effectiveness in a specific business area.