They are looking to optimize their overall planning process and build models that are best suited to their business mechanics and lead to accurate forecasts. The present bachelor thesis aims to build a framework that serves digital marketplaces like Homegate as a general guidance for the implementation of driver-based planning models. It is furthermore intended to highlight the related challenges and how they can be overcome.

The primary data collected consists of qualitative interviews with representative experts from within the Swiss Marketplace Group. The experts were all working in financial planning or controlling functions at the time of the interviews. For the secondary data collection, an extensive literature review on digital marketplaces, as well as multiple financial forecasting methods including driver-based forecasting was conducted. Key primary and secondary data was merged to create a framework that could help digital marketplaces in their planning process.

Derived from the results of the literature review and the conducted interviews, a customized framework was created that depicts the most important steps in the creation of a driver-based model, the resulting challenges, relevant entities, and necessary fundamental groundwork. This framework is furthermore intended to provide valuable methods for the optimization of existing driver-based models. Additionally, the framework has been applied to previously identified six types of digital marketplaces. Thereby, clear differences have been identified in the outlook marketplaces have to face depending on their business model. Based on the answers provided by the interview partners, clear conclusions could be drawn regarding the general limitations of driver-based models and alternative methods. The author recommends further research in the area of hybrid approaches between driver-based models and other types of predictive models and to what extent these could generate added value in the planning process.