The Best of Two Worlds – Using Recent Advances from Uplift Modeling and Heterogeneous Treatment Effects to Optimize Targeting Policies

The design of targeting policies is fundamental to address a variety of practical problems across a broad spectrum of domains from e-commerce to politics and medicine. Recently, researchers and practitioners have begun to predict individual treatment effects to optimize targeting policies. Although different research streams, that is, uplift modeling and heterogeneous treatment effect propose numerous methods to predict individual treatment effects, current approaches suffer from various practical challenges, such as weak model performance and a lack of reliability. In this study, we propose a new, tree- based, algorithm that combines recent advances from both research streams and demonstrate how its use can improve predicting the individual treatment effect. We benchmark our method empirically against state-of-the-art strategies and show that the proposed algorithm achieves excellent results. We demonstrate that our approach performs particularly well when targeting few customers, which is of paramount interest when designing targeting policies in a marketing context

The Best of Two Worlds – Using Recent Advances from Uplift Modeling and Heterogeneous Treatment Effects to Optimize Targeting Policies

The design of targeting policies is fundamental to address a variety of practical problems across a broad spectrum of domains from e-commerce to politics and medicine. Recently, researchers and practitioners have begun to predict individual treatment effects to optimize targeting policies. Although different research streams, that is, uplift modeling and heterogeneous treatment effect propose numerous methods to predict individual treatment effects, current approaches suffer from various practical challenges, such as weak model performance and a lack of reliability. In this study, we propose a new, tree- based, algorithm that combines recent advances from both research streams and demonstrate how its use can improve predicting the individual treatment effect. We benchmark our method empirically against state-of-the-art strategies and show that the proposed algorithm achieves excellent results. We demonstrate that our approach performs particularly well when targeting few customers, which is of paramount interest when designing targeting policies in a marketing context

Sharing is Caring: Using Open Data To Improve Targeting Policies

When it comes to predictive power, companies in a variety of sectors depend on having sufficient data to develop and deploy business analytics applications, for example, to acquire new customers. While there is a vast literature on enriching internal data sets with external data sources, it is still largely unclear whether and how open data can be used to enrich internal data sets to improve business analytics. We choose a particular business analytics problem – designing targeting policies to acquire new customers – to investigate how an internal data set of a German grocery supplier can be enriched with open data to improve targeting policies. Using the enriched data set, we can improve the response rate of several well-established targeting policies by more than 30% in back-testing. Based on these results, we encourage firms and researchers to use, leverage, and share open data to enhance business analytics

To Treat, or Not to Treat: Reducing Volatility in Uplift Modeling Through Weighted Ensembles

When conducting direct marketing activities, companies strive to know whom to target with a marketing incentive to maximize the campaign effect. For example, which customer should receive churn prevention incentive to minimize overall churn rate? Uplift modeling is a promising approach to answer such a question. It allows to separate customers who would likely react positively to a treatment from those who would remain neutral or even react negatively. However, while different uplift approaches have been proposed, they usually suffer from high volatility and their performance often depends largely on data set and application context. Thus, it is difficult for practitioners and researchers to apply uplift modeling. To overcome these problems, we propose a weighted ensemble of different uplift modeling approaches to reduce volatility and improve robustness. We evaluate the novel approach against single uplift modeling approaches on multiple data sets and find that the ensemble is indeed more robust

Reducing Videoconferencing Fatigue through Facial Emotion Recognition

In the last 14 months, COVID-19 made face-to-face meetings impossible and this has led to rapid growth in videoconferencing. As highly social creatures, humans strive for direct interpersonal interaction, which means that in most of these video meetings the webcam is switched on and people are “looking each other in the eyes”. However, it is far from clear what the psychological consequences of this shift to virtual face-to-face communication are and if there are methods to alleviate “videoconferencing fatigue”. We have studied the influence of emotions of meeting participants on the perceived outcome of video meetings. Our experimental setting consisted of 35 participants collaborating in eight teams over Zoom in a one semester course on Collaborative Innovation Networks in bi-weekly video meetings, where each team presented its progress. Emotion was tracked through Zoom face video snapshots using facial emotion recognition that recognized six emotions (happy, sad, fear, anger, neutral, and surprise). Our dependent variable was a score given after each presentation by all participants except the presenter. We found that the happier the speaker is, the happier and less neutral the audience is. More importantly, we found that the presentations that triggered wide swings in “fear” and “joy” among the participants are correlated with a higher rating. Our findings provide valuable input for online video presenters on how to conduct better and less tiring meetings; this will lead to a decrease in “videoconferencing fatigue”