Application of RFM principles: What to do with 1–1–1 customers?

Analyzing Customers of South Khorasan Telecommunication Company with Expansion of RFM to LRFM Model

Telecommunication Companies use data mining techniques to maintain good relationships with their existing customers and attract new customers and identifying profitable/unprofitable customers. Clustering leads to better understanding of customer and its results can be used to definition and decision-making for promotional schemes. In this study, we used the 999-customer purchase records in Sout...

Molecular approaches to diagnosis of invasive aspergillosis what we know and what we do not know.

Invasive aspergillosis (IA) are a major complication in immunocompromized patients where can be serious and rapidly fatal. Early diagnosis and early appropriate antifungal treatment is important in reducing mortality and morbidity. But despite many efforts to develop detection methods, the diagnosis of IA still remains challenging and current conventional methods are limited for adequate diagno...

Diagnostic and therapeutic challenges for dermatologists: What shall we do when we don’t know what to do?

What shall we do when we have done everything we could for the diagnosis and treatment of a patient, but were not successful? What shall we do when there is no definite treatment for a patient? What shall we do when we have no diagnosis or treatment for a patient? Some useful suggestions are presented here to get rid of these situations.

Do We Know What We Do not Know? A Response to Celine Bonnet

Mining Customers Behavior Based on RFM Model to Improve the Customer Satisfaction

Organizations use data mining to improve their customer relationship management processes. Data mining is a new and well-known technique, which can be used to extract hidden knowledge and information about customers’ behaviors. In this paper, a model is proposed to enhance the premium calculation policies in an automobile insurance company. This method is based on customer clustering. K-means a...