Development of a data-driven marketing strategy for an online pharmacy

Abstract: The term electronic commerce (e-commerce) refers to a business model that allows companies and individuals to buy and sell goods and services over the internet. The focus of this thesis is on online pharmacies, a segment of the ecommerce market. Even though internet pharmacies are still subject to the same stringent rules imposed on pharmacies that limit the scope for their market growth, it has shown a notable increase in the past decades. The main goal of this thesis is to develop a data-driven marketing strategy based on a Swedish based online pharmacy’s daily sales data. The methodology of the data analysis includes exploratory data analysis (EDA) and market basket analysis (MBA) using the Apriori algorithm and the application of marketing frameworks and theories from a data-driven standpoint. In addition to the data analysis, this paper proposes a conceptual framework of a digital marketing strategy based on the RACE framework (reach, act, convert, and engage). The result of the analysis has led to the following data-driven marketing strategy: Special attention should be paid to association rules with a high lift ration value; high gross profit margin percentile (GPMP) products should have a volume-based marketing strategy that focuses on lower prices on subsequent items; and price bundling is the best marketing strategy for low GPMP products. Some of the practical ideas mentioned in this thesis paper include optimizing keyword search for a high GPMP product type and sending reminder emails and push alerts to avoid cart abandonment. The findings and recommendations presented in this thesis can be used by online pharmacies to extract knowledge that may support several decisions ranging from raising overall order size, marketing campaigns, to increasing the sales of products with a high gross profit margin.