Application of Data Mining Techniques to Support Customer Relationship Management (Crm) for Ethiopian Shipping Lines (Esl)
| dc.contributor.advisor | Rao B.R.K. (Professor) | |
| dc.contributor.author | Fekrie Kumneger | |
| dc.date.accessioned | 2020-06-09T10:25:06Z | |
| dc.date.accessioned | 2023-11-18T12:45:33Z | |
| dc.date.available | 2020-06-09T10:25:06Z | |
| dc.date.available | 2023-11-18T12:45:33Z | |
| dc.date.issued | 2006-07 | |
| dc.description.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 | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21486 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Information Science | en_US |
| dc.title | Application of Data Mining Techniques to Support Customer Relationship Management (Crm) for Ethiopian Shipping Lines (Esl) | en_US |
| dc.type | Thesis | en_US |