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    STUDIA INFORMATICA - Issue no. Sp. Issue 1 / 2014  
         
  Article:   DIRECT MARKETING OPTIMIZATION USING CLIENT RELATIONAL GRAPHS.

Authors:  .
 
       
         
  Abstract:   In the present paper we give an introduction to some problems which occur at a bank and can be represented by graphs. We will demonstrate network building possibilities on speci fic data and apply them to improve prediction and/or replace the present methods of the sector.We give a detailed analysis of the corporate transaction graph and a retail client relational graph based on data of the OTP Bank. Our main result is the optimization of the response rates of Direct Marketing (DM) campaigns using the relational network (built on the known attributes such as common telephone number, same family name, etc.). According to our new approach - in contrast to the traditional banking methods - we use neither clients` personal data itself nor account behaviours, but the structure of networks to find a better segmentation. Networks can also give us forecasting models: we restrict to sending DM off ers only to certain clients who met with some graph theoretical requirements. This approach raised the DM o ffer acceptance rate by 1.5-2 times of the average of previous DM campaigns.

2010 Mathematics Subject Classi fication. 91D30, 05C82.1998 CR Categories and Descriptors. J.4 [Social and behavioural sciences]: Subtopic- Economics; E.1 [Data structures]: Subtopic - Graphs and networks.

Key words and phrases. Direct Marketing optimization, client relational graph, collaboration graph, banking methods, banking graphs.
 
         
     
         
         
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