vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff44b52300000086ec050001000900 Customer relation management is a significant feature in the business development which assists for business peoples to get the knowledge about the customer’s opinions and can establish the profitable environment. Customer opinions were utilized to enhance the product, so that customer fulfilment and profit will rise desirably. So it is necessary to execute the functional design of customer opinions regarding the products which depends on the time duration. In the earlier work, Customer Knowledge Management (CKM) is established to raise the profit level of industries by tracking the entire regarding the product which is demanded by the customers. Nevertheless, creating and managing the CKM for the huge volume of the customer is a complex task. It results in inaccuracy, if in case it is done manually with less customer information. This issue is rectified in the proposed research methodology by bringing-in the methodology such as Neural Network based Social Customer Relation Management (NN-SCRM). This algorithm is utilized to examine the following factors by gathering the knowledge of the customer review information: “Predict the future, most profitable customers, maintaining quality of product development, customer life time value, identify customers and their products”. This is done according to the information of the customer review, which, in turn, obtained from the customers opinions disclosed by their comments regarding the product. This proposed research work is executed and examined in the MATLAB simulation environment from which it is confirmed that the proposed research framework tends to give the best output than the current CKM frameworks.
P Menaka1, K Thangadurai2
Manaonmaniam Sundaranar University, India1, Government Arts College, Karur, India2
Knowledge Management, Review Analysis, Pre-processing, Customer Opinions, Profit
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| Published By :
ICTACT
Published In :
ICTACT Journal on Management Studies
( Volume: 3 , Issue: 2 , Pages: 515-522 )
Date of Publication :
May 2017
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103
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