A predictive analytics of physicians prescription and pharmacies sales correlation using data mining. Issue 3 (2nd September 2019)

• Record Type: Journal Article
• Authors: Sohrabi, Babak; Raeesi Vanani, Iman; Nikaein, Nastaran; Kakavand, Saeideh
• Is Part Of: International journal of pharmaceutical and healthcare marketing. Volume 13:Issue 3(2019)
• Page Start: 346
• Page End: 363
• Publication Date: 2019-09-02
• Subjects: Classification -- Data mining -- Pharmaceutical industry -- Decision tree -- RFM model; Pharmaceutical industry -- Marketing -- Periodicals; Medical care -- Marketing -- Periodicals; 338.4761510688
• DOI: 10.1108/IJPHM-11-2017-0066
• Languages: English
• ISSNs: 1750-6123

Abstract

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.