Marketing campaign management using machine learning techniques: An uplift modeling approach

Abstract

In order to engage customers and increase sales, businesses in today's dynamic business environment devote a large amount of resources to a variety of marketing methods. Businesses utilize a variety of strategies to attract customers' attention and encourage them to make a purchase. However even with a wide range of marketing strategies and channels, a fundamental issue still remains: how can businesses effectively evaluate how their marketing campaigns influence consumer behavior? By focusing on the significance of uplift modeling in determining the true impact of marketing initiatives, this research directly addresses this issue. By identifying the incremental impact of marketing initiatives, uplift modeling provides a more sophisticated approach than common predictive analytics which only forecasts customer behavior. The purpose of this research is to explore the limitations of conventional predictive analytics in marketing, investigate the application of prescriptive analytics specifically uplift modeling and develop a framework for implementing uplift modeling in business-to-business (B2B) marketing instances by examining real-world data from the Turkish telecom industry. In this study, three uplift modeling methodologies (two model approach, class variable transformation and modeling uplift directly) performed on a real-world B2B marketing campaign dataset and shown that the marketing campaign can be optimized by predicting the incremental impact more precisely with uplift models than the conventional predictive models. The results revealed how uplift modeling, which enables for the targeting of customers whose behavior is most likely to be positively influenced by marketing efforts, is helpful in improving resource allocation. Out of all the uplift models, the model that used the class variable transformation approach was able to capture 46% of uplift while targeting only the half of the campaign audience. This finding confirms the earlier research in the uplift modeling literature and shows that uplift models are successful in forecasting the truly responsive customers for a direct marketing campaign. It has been demonstrated that conventional response models are inadequate to distinguish which customers are positively impacted by a marketing treatment whereas uplift models successfully identify which customers will make a purchase due to being influenced by the marketing treatment. It is also shown that in a direct marketing campaign, focusing on a larger target audience may not always yield the greatest or most effective outcomes. Instead, different uplift modeling strategies combined with machine learning algorithms can yield higher uplifts.This study makes significant contribution as being the first to introduce uplift modeling in Turkish literature and being one of the few studies to apply uplift modeling in B2B context in the world-wide academic literature that predominantly focused on researches in B2C. Further, it provides valuable managerial insights for marketers to gain deeper customer insights and foster stronger relationships with customers by leveraging uplift modeling.