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Exploring challenges in data-driven marketing decision-making: Adoption and utilisation of marketing mix modelling (MMM) in Finland
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School of Business |
Master's thesis
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en
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51+15
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Marketing Mix Modelling (MMM) is a key tool for evaluating the effectiveness of marketing activities and optimising resource allocation based on data. However, in Finland, marketing executives face significant challenges in adopting the model and implementing the results in their strategic decision-making. The context of this research is the increasing need for data-driven decision-making in marketing, which is often hindered by technological and organisational barriers.
The aim of this thesis is to identify and explore the concrete challenges involved in adopting and utilising the MMM technique. Additionally, it investigates how these challenges affect data-driven decision-making and broader marketing strategies. The study employs a qualitative approach, drawing on semi-structured interviews with senior marketing executives and a comprehensive review of existing literature on MMM and related marketing concepts.
This research builds upon the Integrative Framework of the Factors that Determine the Success of Marketing Management Support Systems, as introduced by Wierenga et al. (1999), and incorporates theoretical perspectives from marketing decision-making scholars such as Van Bruggen, Wierenga, and Kahneman.
The findings reveal that the primary obstacles to MMM adoption and utilisation include a lack of in-house expertise, unsupportive organisational cultures, and insufficient management support. Additionally, high implementation costs and the complexity of the model act as significant barriers, limiting its broader adoption. These challenges prevent marketing teams from fully harnessing MMM to make data-driven decisions, often leading to suboptimal marketing expenditure and inefficiencies.
Moreover, the research highlights a tension between short-term Return on Investment (ROI) goals and long-term brand-building strategies, with MMM frequently emphasising the former at the expense of more strategic marketing efforts. Addressing these issues requires better organisational alignment, enhanced data literacy, and continuous training for marketing professionals.
The implications of this study suggest the need for advancements in MMM tools, more robust support for marketing teams, and further exploration of emerging technologies such as artificial intelligence to enhance MMM's effectiveness. These findings contribute to understanding MMM adoption in Finland and provide practical recommendations for improving data-driven marketing decision-making.