An Estimation Model for Hypertension Drug Demand in Retail Pharmacies with the Aid of Big Data Analytics

The unpredictability of consumer preference observed in the last few years has coincided with the global digital data explosion as consumers are increasingly relying on the internet information to guide their buying behaviour. The emergence of this trend has resulted in demand volatility and uncertainty in the retail industry, leading to negative consequences on inventory control and on shareholder profits in the long-run. This paper examines whether retail pharmacies in Abuja, Nigeria may exploit the increasing availability of relevant big data (structured, semi-structured and unstructured) from different sources to anticipate the changes on demand profiles for antihypertensive medication. In order to examine this, we consider a VARX model with non-structured data as exogenous to obtain the best estimation

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