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Abstract
Customer’s behavior and satisfaction are always play important role to increase organization’s growth and market value. Customers are on top priority for the growing organization to build up their businesses. In this paper presents the architecture of Decision Support Systems (DSS) in connection to deal with the customer’s enquiries and requests. Main purpose behind the proposed model is to enhance the customer’s satisfaction and behavior using DSS. We proposed model by extension in traditional DSS concepts with integration of Data Mining (DM) abstract. The model presented in this paper shows the comprehensive architecture to work on the customer requests using DSS and knowledge management (KM) for improving the customer’s behavior and satisfaction. Furthermore, DM abstract provides more methods and techniques; to understand the contacted customer’s data, to classify the replied answers in number of classes, and to generate association between the same type of queries, and finally to maintain the KM for future correspondence.
Highlights
Customers are regularly in contact with the organizations through telephone lines, online website portal, or through customer care centers directly
In this paper we presented the model of automated decision support systems for dealing with the customer’s queries for increase the customer’s satisfaction ration
We integrated the model with Data Mining (DM) abstract to build a knowledge management (KM) database in connection with automated Decision Support Systems (DSS) database
Summary
Customers are regularly in contact with the organizations through telephone lines, online website portal, or through customer care centers directly. In this paper we presented the model of automated decision support systems for dealing with the customer’s queries for increase the customer’s satisfaction ration. Classification, clustering and rule association mining are most common techniques use for predictive and descriptive analysis [10]. As Zaine [5] stated in his book chapter about major techniques of DM as follows: a) Classification: Classification analysis is the organization of data in given classes. Classification consider as an important task of DM Using this approach data must be already defined a class label (target) attribute. Clustering is one of the major task has been applying for DM, work on unsupervised data (no predefined classes) [12].
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