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Practice paper

Using loyalty card records and machine learning to understand how self-medication purchasing behaviours vary seasonally in England, 2012–2014

Alec Davies, Mark A. Green, Dean Riddlesden and Alex D. Singleton
Applied Marketing Analytics: The Peer-Reviewed Journal, 5 (4), 354-370 (2020)
https://doi.org/10.69554/MJDF4395

Abstract

This paper examines objective purchasing information for inherently seasonal self-medication product groups using transaction-level loyalty card records. Predictive models are applied to predict future monthly self-medication purchasing. Analyses are undertaken at the lower super output area level, allowing the exploration of ˜300 retail, social, demographic and environmental predictors of purchasing. The study uses a tree ensemble predictive algorithm, applying XGBoost using one year of historical training data to predict future purchase patterns. The study compares static and dynamic retraining approaches. Feature importance rank comparison and accumulated local effects plots are used to ascertain insights of the influence of different features. Clear purchasing seasonality is observed for both outcomes, reflecting the climatic drivers of the associated minor ailments. Although dynamic models perform best, where previous year behaviour differs greatly, predictions had higher error rates. Important features are consistent across models (eg previous sales, temperature, seasonality). Feature importance ranking had the greatest difference where seasons changed. Accumulated local effects plots highlight specific ranges of predictors influencing self-medication purchasing. Loyalty card records offer promise for monitoring the prevalence of minor ailments and reveal insights about the seasonality and drivers of over-the-counter medicine purchasing in England.

Keywords: self-medication; over-the-counter medicines; minor ailments; machine learning; tree ensembles

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Author's Biography

Alec Davies is studying for a PhD in geographic data science at the University of Liverpool. His research focuses on the application of data science and machine-learning methods alongside new forms of data to generate new insights within health research.

Mark A. Green is a senior lecturer in health geography at the University of Liverpool. His research applies novel approaches for utilising consumer data sources to study the determinants of health and healthrelated behaviours.

Dean Riddlesden is a research fellow at the University of Liverpool. He specialises in the application of machine-learning methods to customer relationship management. He holds a PhD in geographic information science from the University of Liverpool, UK.

Alex D. Singleton is a professor of geographic information science at the University of Liverpool. He is Deputy Director of the ESRC Consumer Data Research Centre and Director of the ESRC Data Analytics & Society Centre for Doctoral Training. His research examines how the complexities of individual behaviours, attitudes and contexts manifest spatially and can be represented and understood through a framework of geographic data science.

Citation

Davies, Alec, Green, Mark A., Riddlesden, Dean and Singleton, Alex D. (2020, May 1). Using loyalty card records and machine learning to understand how self-medication purchasing behaviours vary seasonally in England, 2012–2014. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 5, Issue 4. https://doi.org/10.69554/MJDF4395.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 5 / Issue 4
© Henry Stewart
Publications LLP

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