International Journal of Management and Applied Science (IJMAS)
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current issues
Volume-5,Issue-8  ( Aug, 2019 )
Past issues
  1. Volume-5,Issue-8  ( Aug, 2019 )
  2. Volume-5,Issue-7  ( Jul, 2019 )
  3. Volume-5,Issue-6  ( Jun, 2019 )
  4. Volume-5,Issue-5  ( May, 2019 )
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  6. Volume-5,Issue-3  ( Mar, 2019 )
  7. Volume-5,Issue-2  ( Feb, 2019 )
  8. Volume-5,Issue-1  ( Jan, 2019 )
  9. Volume-4,Issue-12  ( Dec, 2018 )
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Statistics report
Oct. 2019
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 66
Paper Published : 4089
No. of Authors : 8401
  Journal Paper




Paper Title :
Success Factors in Database Marketing Models

Author :Ekrem Duman

Article Citation :Ekrem Duman , (2018 ) " Success Factors in Database Marketing Models " , International Journal of Management and Applied Science (IJMAS) , pp. 7-12, Volume-4,Issue-10

Abstract : Database marketing, i.e. the marketing of new or additional products to existing customers has become an important part of the marketing activities in general. This is a consequence of the advances in computer technologies and the development of powerful knowledge discovery algorithms. Such efforts of determining the customers who are more likely to buy the targeted products are known as cross sell and up sell modeling and it is a part of analytical customer relationship management. Many big companies have been developing these models for quite a long time now but due to competition issues no one is making the details of their methodology known to public. The studies available in the literature are mostly built on small examples which may be far from reflecting the dynamics of big real life problems. In this study, based on our experiences in banking sector of more than 12 years, we first describe the full modeling cycle in detail and then discuss some success factors which are very critical in developing good models. We provide some solid comparisons of alternative choices in modeling. Keywords - Database marketing, cross sell, up sell, data mining.

Type : Research paper


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