Towards data-driven marketing organization

https://drive.google.com/open?id=1BdLE_ECYPKqi2lyFgI-Rl3jYtHV9Oc0i&usp=drive_fs

This study delves into the challenges and solutions associated with implementing marketing-mix modeling (MMM) within organizations, especially under the complexities of restrictive privacy laws affecting current marketing effectiveness measurement approaches. Using a comprehensive mixed-method approach encompassing an interviewmodeling-interview sequence, the research examines the challenges encountered in a prior MMM implementation attempt by a private Finnish healthcare company. This approach allowed for the extraction of valuable insights through initial interviews, the application of these insights within a practical MMM framework, and the subsequent validation of findings through follow-up interviews. The study uncovers critical organizational challenges, such as the need for strong project commitment, adequate resource allocation, and clear goal setting, alongside technical challenges, including data availability and quality. The findings from this methodological approach offer pragmatic recommendations for effectively integrating MMM into organizational decision-making processes. These recommendations address human and technical dimensions, ensuring a comprehensive strategy for implementing complex analytical tools. This study makes theoretical contributions by emphasizing the importance of organizational qualities, such as skilled personnel, data-driven culture, and well-defined processes, for successfully adopting advanced analytical methods like MMM. It also provides empirical validation of MMM's effectiveness in real-world marketing scenarios. By bridging the theoretical and practical realms, the research enhances understanding of MMM's potential in marketing data analysis and offers valuable insights for academia and practitioners. Filling a notable gap in academic literature, this research presents empirical evidence on the practical implementation of MMM, contributing substantially to academic research and practical applications in data analytics and marketing.