Application of Data Mining Techniques to Support Customer Relationship Management (Crm) for Ethiopian Shipping Lines (Esl)
No Thumbnail Available
Date
2006-07
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
Nowadays, the global marketing strategy is making business extremely competitive,
dynamic and subject to rapid change. Hence, businesses should be highly concerned to
needs and wants of their customers in order to respond accordingly. Customer
Relationship Management is the overall process of exploiting customer - related
information and using it to enhance the revenue flow from an existing customer. Data
mining techniques are used to extract important customer information from available data
bases.The major objective of this study is testing the application of data mining techniques to
support CRM activities for Ethiopian shipping Lines. The customer profile file of ESL
contains individual shipment activities of more than 20 ,000 records, out of which about
4,000 are unique customers. After the data is collected, the necessary processing steps
are conducted on it in order to make it applicable for the modeling process.K - Means clustering algorithm was used to segment individual customer records in to
clusters with similar behaviors. Different parameters were used to run the clustering
algorithm before arriving at customer segments that made business sense to domain
experts . After the clustering is made, decision tree classification techniques were
employed to generate rules that could be used to assign new customer record to the
segments.The results from this study were encouraging which strengthened the belief that applying
data mining techniques could in deed support CRM activities at Ethiopian shipping
Lines . In the future , more segmentation studies using demographic information and
employing other clustering algorithms could yield better results
Description
Keywords
Information Science