Enhanced Actionable Knowledge Mining for Effective Customer Relationship Management using Neural Networks
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Date
2010
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Abstract
Most Data Mining algorithms and tools stop at discovered customer models, producing distribution information on customer
profiles. Such techniques when applied to industrial problems such as CRM (Customer Relationship Management) are
useful in pointing out customers who are likely attrittors and customers who are loyal, but they require human experts to post
process the discovered knowledge manually. Most of the post processing techniques did not directly suggest actions that will
lead to increase in the objective function such as profit .A novel algorithm is proposed in this paper that suggest actions to change
the customers from undesired status to desired ones. This approach combines data mining with decision trees using neural
networks to realistic insurance application domain and UCI benchmark data.