Customer Loyality Segmentation on Point of Sale System Using Recency-Frequency-Monetary (RFM) and K-Means

2020 ◽  
Vol 5 (2) ◽  
pp. 130
Author(s):  
Bayu Rizki ◽  
Nava Gia Ginasta ◽  
Muh Akbar Tamrin ◽  
Ali Rahman

It is no doubt that the development of the business world has been progressive. Point of sale is one of the many system used as a means of payment in various existing businesses, especially in heterogeneous markets. The activity of transactions between Point of Sale Systems and Customers occur in the business world. Keep in mind also that one of the main factors of business success, is from customers. There is the need of an attractive strategy and certainly it will be to increase the income and assets of a business. To know that, this research will explore the behavior of customer which is based marketing, through RFM Method (Recency, Frequency and Monetary). The case of this study is in Goldfinger Store. It will do segmentation and also use data mining technique to do clustering by using K-Means with result of loyal and potential customer. The results of segmentation using RFM (Recency, Frequency, Monetary) and K-Means methods have produced multiple clusters by dividing them into groups.

Author(s):  
SACHIN KAMBEY ◽  
R. S. THAKUR ◽  
SHAILESH JALORI

Stock market prediction with data mining technique is one of the most important issues to be investigated and it is one of the fascinating issues of stock market research over the past decade. Many attempts have been made to predict stock market data using statistical and traditional methods, but these methods are no longer adequate for analyzing this huge amount of data. Data mining is one of most important powerful information technology tool in today’s competitive business world, it is able to uncover hidden patterns and predict future trends and behavior in stock market. This paper also highlights the application of association rule in stock market and their future movement direction.


Author(s):  
Md. Sadeki Salman ◽  
Nazmun Naher Shila ◽  
Khalid Hasan ◽  
Piash Ahmed ◽  
Mumenunnessa Keya ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document