Uncovering Competitor Pricing Patterns in the Danish Pharmaceutical Market via Subsequence Time Series Clustering: A Case Study

Adopting data-driven decision-making approaches can significantly enhance profitability and foster growth in economic situations through quantitative analysis of market dynamics. One intriguing market that warrants examination is the price competition observed within the Danish pharmaceutical sector, where numerous companies are vying for a larger market share through the offering of diverse pharmaceutical products. This paper aims to shed light on this market by employing subsequence time series clustering techniques to identify pricing patterns among the players involved in the Danish pharmaceutical industry. The data analysis pipeline performed in this study allows for the identification of price patterns for clustering and discovering different agent groups, as well as providing a foundation for expanding the current agent-based model of the European pharmaceutical parallel trade market by analyzing the pricing behavior and patterns of players, facilitating the utilization of historical data to model agent behavior and advancing research in this area.