Adapting marketing mix modelling for the retail marketing environment – A road map for development

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto. fi Author Antti Heliste Title Adapting marketing mix modelling for the retail marketing environment – A road map for development School School of Science Master’s programme Industrial Engineering and Management Major Operations and Service Management Code SCI3049 Supervisor Timo Seppälä, D.Sc. (Tech.) Advisors Hannu Laine, D.Sc. (Tech.); Mikko Ervasti, D.Sc. (Tech.) Level Master’s thesis Date 28 September 2019 Pages 78 + 10 Language English Abstract Measuring the impact of marketing is essential for improving its performance and justifying marketing decisions to top management. However, marketers often struggle with it, even though various methods are available to them in the literature. A good starting point for marketers is the most popular method, marketing mix modelling (MMM), that is a linear regression fitted in sales and marketing data. Yet, it often suffers from various downsides, such as lack of data, deficient model forms and biases. Researchers have consequently suggested improving it through better data, better models and model validation. However, researchers mainly discuss these areas as a way to improve model accuracy rather than to widen the scope of analysis. Improving modelling granularity would enable marketers to analyse performance on lower levels and broaden their discussion on improvements. Higher granularity could particularly support the retail industry, where marketing is a complex operation because of wide product ranges, geographical reaches and customer bases. Consequently, the goal of the thesis was to analyse how the typical MMM is limited, how model developers could adjust it to meet the needs of the retail marketing environment and what impacts such adjustments would have. We conducted the study through a combination of a literature review and a simulation. The literature review discovered that the typical MMM is limited in use in the retail environment, mainly due to its low granularity that hides the information about the structure of performance. Other flaws include, e.g., the lack of modelling in retail-specific effects, such as stock-up, and the lack of model validation. The most significant opportunity arises from increasing granularity in at least three dimensions: frequency, geography and product hierarchy. Other improvements, in turn, arise from improving accuracy through comprehensive modelling and model validation through simulation. The simulation studied the impact of granularity on available improvement opportunities in the retail environment. A product-level model was able to reach a significant 33.1% increase in total profit compared to the unoptimised baseline. The traditional model, in turn, was only able to reach a meagre 1.7% improvement. The result supports the hypothesis that higher modelling granularity leads to more detailed and effective improvement opportunities in retail marketing. Based on the literature review and the simulation, we formed a road map for the development of MMM in the retail environment.Measuring the impact of marketing is essential for improving its performance and justifying marketing decisions to top management. However, marketers often struggle with it, even though various methods are available to them in the literature. A good starting point for marketers is the most popular method, marketing mix modelling (MMM), that is a linear regression fitted in sales and marketing data. Yet, it often suffers from various downsides, such as lack of data, deficient model forms and biases. Researchers have consequently suggested improving it through better data, better models and model validation. However, researchers mainly discuss these areas as a way to improve model accuracy rather than to widen the scope of analysis. Improving modelling granularity would enable marketers to analyse performance on lower levels and broaden their discussion on improvements. Higher granularity could particularly support the retail industry, where marketing is a complex operation because of wide product ranges, geographical reaches and customer bases. Consequently, the goal of the thesis was to analyse how the typical MMM is limited, how model developers could adjust it to meet the needs of the retail marketing environment and what impacts such adjustments would have. We conducted the study through a combination of a literature review and a simulation. The literature review discovered that the typical MMM is limited in use in the retail environment, mainly due to its low granularity that hides the information about the structure of performance. Other flaws include, e.g., the lack of modelling in retail-specific effects, such as stock-up, and the lack of model validation. The most significant opportunity arises from increasing granularity in at least three dimensions: frequency, geography and product hierarchy. Other improvements, in turn, arise from improving accuracy through comprehensive modelling and model validation through simulation. The simulation studied the impact of granularity on available improvement opportunities in the retail environment. A product-level model was able to reach a significant 33.1% increase in total profit compared to the unoptimised baseline. The traditional model, in turn, was only able to reach a meagre 1.7% improvement. The result supports the hypothesis that higher modelling granularity leads to more detailed and effective improvement opportunities in retail marketing. Based on the literature review and the simulation, we formed a road map for the development of MMM in the retail environment.