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A predictive analytics of physicians prescription and pharmacies sales correlation using data mining

Babak Sohrabi (Department of Information Technology Management, Faculty of Management, University of Tehran, Tehran, Iran)
Iman Raeesi Vanani (Department of Industrial Management, Faculty of Management, Allameh Tabatabai University, Tehran, Iran)
Nastaran Nikaein (Graduate of Information Technology Management, Faculty of Management, University of Tehran, Tehran, Iran)
Saeideh Kakavand (Department of Industrial Engineering, Islamic Azad University, Tehran, Iran)

International Journal of Pharmaceutical and Healthcare Marketing

ISSN: 1750-6123

Article publication date: 19 June 2019

Issue publication date: 12 August 2019

Abstract

Purpose

In the pharmaceutical industry, marketing and sales managers often deal with massive amounts of marketing and sales data. One of their biggest concerns is to recognize the impact of actions taken on sold-out products. Data mining discovers and extracts useful patterns from such large data sets to find hidden and worthy patterns for the decision-making. This paper, too, aims to demonstrate the ability of data-mining process in improving the decision-making quality in the pharmaceutical industry.

Design/methodology/approach

This research is descriptive in terms of the method applied, as well as the investigation of the existing situation and the use of real data and their description. In fact, the study is quantitative and descriptive, from the point of view of its data type and method. This research is also applicable in terms of purpose. The target population of this research is the data of a pharmaceutical company in Iran. Here, the cross-industry standard process for data mining methodology was used for data mining and data modeling.

Findings

With the help of different data-mining techniques, the authors could examine the effect of the visit of doctors overlooking the pharmacies and the target was set for medical representatives on the pharmaceutical sales. For that matter, the authors used two types of classification rules: decision tree and neural network. After the modeling of algorithms, it was determined that the two aforementioned rules can perform the classification with high precision. The results of the tree ID3 were analyzed to identify the variables and path of this relationship.

Originality/value

To the best of the authors’ knowledge, this is one of the first studies to provide the real-world direct empirical evidence of “Analytics of Physicians Prescription and Pharmacies Sales Correlation Using Data Mining.” The results showed that the most influential variables of “the relationship between doctors and their visits to pharmacies,” “the length of customer relationship” and “the relationship between the sale of pharmacies and the target set for medical representatives” were “deviation from the implementation plan.” Therefore, marketing and sales managers must pay special attention to these factors while planning and targeting for representatives. The authors could focus only on a small part of this study.

Keywords

Acknowledgements

The authors are grateful to Mohammad Sadegh Dibaji for his valuable advice and suggestions. They also thank the marketing and sales managers of the major pharmaceutical companies of Iran for their contributions, suggestions and help.

Citation

Sohrabi, B., Raeesi Vanani, I., Nikaein, N. and Kakavand, S. (2019), "A predictive analytics of physicians prescription and pharmacies sales correlation using data mining", International Journal of Pharmaceutical and Healthcare Marketing, Vol. 13 No. 3, pp. 346-363. https://doi.org/10.1108/IJPHM-11-2017-0066

Publisher

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Emerald Publishing Limited

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