Customers' segmentation in pharmaceutical distribution industry based on the RFML model

2021 ◽  
Vol 37 (1) ◽  
pp. 29
Author(s):  
Nastaran Nikaein ◽  
Ehsan Abedin
Pharmacy ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Kah Seng Lee ◽  
Yaman Walid Kassab ◽  
Nur Akmar Taha ◽  
Zainol Akbar Zainal

Increasing prescription drug pricing often reflects additional work stress on medical professionals because they function as financial advisors for patients and help them manage out-of-pocket expenses. Providers or prescribers wish to help patients with prescription costs but often lack related information. Healthcare plan providers try to display prescription and drug cost information on their websites, but such data may not be linked to electronic prescription software. A mark-up is defined as the additional charges and costs that are applied to the price of a product for the purpose of covering overhead costs, distribution charges, and profit. Therefore, the policies implemented in the pharmaceutical distribution chain might include the regulation of wholesale and retails mark-ups and pharmaceutical remuneration. If mark-ups are regulated, countries are highly recommended to use regressive mark-ups rather than fixed percentage mark-ups. This narrative review provides insights into the framework of pharmaceutical mark-up systems by describing different factors impacting pharmaceutical prices and affordability. These include the interplay of medicine pricing and the supply chain, the impact of pertinent laws and regulation and out-of-pocket expenditure.


Author(s):  
Mariana Jacobo-Cabrera ◽  
Santiago-Omar Caballero-Morales ◽  
José-Luís Martínez-Flores ◽  
Patricia Cano-Olivos

Author(s):  
Jocelyn Poncelet ◽  
Pierre-Antoine Jean ◽  
François Trousset ◽  
Jacky Montmain

Author(s):  
Juhi Singh ◽  
Mandeep Mittal ◽  
Sarla Pareek

Due to the increased availability of individual customer data, it is possible to predict customer buying pattern. Customers can be segmented using clustering algorithms based on various parameters such as Frequency, Recency and Monetary values (RFM). The data can further be analyzed to infer rules among two or more purchases of the customer. In this chapter we will present a clustering algorithm, enhanced k- means algorithm, which is based on k- means algorithm to divide customers into various segments. After segmentation, each segment is mined with the help of a priori algorithm to infer rules so that the customer's purchase behavior can be predicted. From large number of association rules with sufficient coverage, the customer's purchasing pattern can be predicted. Experiment on real database is implemented to evaluate the performance on effectiveness and utility of the approach. The results show that the proposed approach can gain a well insight into customers' segmentation and thus their behavior can be predicted.


2021 ◽  
pp. 249-273
Author(s):  
Carine Baxerres ◽  
Adolphe Codjo Kpatchavi ◽  
Daniel Kojo Arhinful

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