Data-driven evaluation of individual customer journeys to optimize marketing campaigns

The project proposed in this thesis aims to bring analyses and tools to support ad vertising marketers in their daily decision-making process for advertising campaigns in a media mix context. Online advertising allows advertisers to reach users on multiple channels, who of ten get in touch with multiple advertisements. On the web, the users’ interactions can be tracked and an attribution model can achieve the issue of measuring the effectiveness of ads on each user assigning a percentage of the conversion value to each ad in the customer journey. The results of an attribution model represent meaningful information to evaluate and optimize campaigns. This thesis remarks the lacks of the rule-based attribution model and treats chronologically some data driven multi-touch attribution models proposed by academic researchers, who be lieved that models should not be based on assumption but data. In according with them, the analyses proposed in this work are based on data calculated by a data driven multi-touch attribution model. This project faces the budget allocation problem proposing an analysis using model driven approach and marginal analysis to maximize the expected revenue. KPIs for marketing campaigns and e-commerce performances have been calculated from different sources and reported in dashboards and reports for further analysis by marketers. The project is part of six months internship at Haensel AMS - Advanced Mathematical Solution GmbH located in Berlin