Creating a marketing strategy in healthcare industry: a holistic data analytic approach
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This study aims to assist marketing managers in identifying locations in which to host peer-to-peer educational events for healthcare professionals (HCPs) throughout the country using data analytics. These events would allow physicians and other HCPs to engage with their peers and learn about the most up-to-date clinical data and research from worldwide known Key Opinion Leaders. Decision making power in the healthcare industry is beginning to grow and fragment into numerous drivers. There are increasingly more variables, which affect marketing initiatives, and hence marketing managers are challenged to find the right methodology to place large investments and resources in the correct market segment. 3400 observations were collected from several sources including: The National Institute of Infant Nutrition monthly survey, Nielsen Consumer Behavior Data Reports, Congressional Budget Office Core Based Statistical Areas, US Census 2010 SF2 File, ZCTA Population and account information from the sales force. There were 17 input variables considered in this current analysis. The variables included; Return on Investment rank, total dollars of distribution margin, hospital influence rate, mother’s decision rate, healthcare professional decision rate, total investment, and competitive market share. The results from the data analytic models indicate that the most accurate classifier was the support vector machines followed by artificial neural networks and decision trees respectively. Marketing managers can flexibly utilize the proposed data analytic methodology proposed here to assist in identifying their target market. With the deployment of data analytics, marketing managers may now begin to sort through the large and complex data they gather and enhance their analyses of key target markets.
KeywordsCustomer relationship management Marketing strategy Healthcare operations Data analytics Data mining
We are thankful to the guest editors of the Big Data Analytics in Operations and SCM special issue (Dr. Rameshwar Dubey, Dr. Angappa Gunasekaran, Dr. Eric W. T. Ngai, and Dr. Samuel Fosso Wamba) and the two anonymous referees for their invaluable comments, which improved this paper significantly.
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