Accessible and Actionable Marketing Data

Financial software supplier bexio AG (https://www.bexio.com/) enjoys a solid market position with an annually growing number of customers. Based in Rapperswil, Switzerland, the mid-sized company wishes to take advantage of the dynamic growth potential the market offers. Its long-term goal is to leverage marketing expenditure and expand its customer base exponentially.

To achieve this ambitious target, bexio needs to prevail over fierce and growing competition. Accordingly, it has restructured its internal marketing organization to increase efficiency across different channels.

The company approached FELD M for advanced analytics support in mastering challenges including eliminating data silos to achieve a single source of truth for data relevant to analysis and understanding the complexity of the interaction between online and offline marketing channels. External factors like seasonal business also needed to be considered.

Insights revealed through a single source of truth

To clarify the goal of the project, we carried out a data thinking workshop with bexio. As a result of the workshop, two projects – customer journey mapping and MMM – were identified to support the long-term goal. Together, we decided to first seek quick wins while laying the groundwork for MMM: data-based insights into the different marketing channels and an estimate of the contribution of each individual touchpoint. This knowledge would allow a data-driven budget shift and smoothen the path for further analysis processes like customer journey mapping.

We then proceeded to create a data landscape of all relevant marketing channels and other data sources. A data landscape provides a fundamental overview of all available data sources to prioritize channels used for MMM. In addition, we developed a concept for data storage and ETL (extract, transform, load) aimed at eliminating data silos and documenting existing data sources. We conducted a descriptive data analysis to increase the understanding of different channels. The system comprises correlation analysis to evaluate the effects between channels and conversion as well as the effects across channels, lag analysis to determine the time delay between marketing action and conversion and analysis of scatter loss between played and seen ads.

To make results easily accessible, we implemented the descriptive analysis in a Tableau dashboard. The insights gained here enabled us to determine that there was enough variance in marketing actions to apply MMM, with the outliers explainable by external factors. We computed the length of the time lag between marketing action and conversion in each channel to consider the time needed to acquire a conversion in the model.