MATEC Web Conf.
Volume 292, 201923rd International Conference on Circuits, Systems, Communications and Computers (CSCC 2019)
|Number of page(s)||5|
|Section||Circuits and Systems|
|Published online||24 September 2019|
Using Big Data Analytics to Develop Marketing Intelligence Systems for Commercial Banks in Egypt
Computer and Information Systems Department, Sadat Academy for Management Sciences, Maadi, Cairo, Egypt
* Corresponding author: email@example.com
Nowadays, Big Data (BD) Analytics is receiving great attention in banking industry, considering the worthy data that have been stored for several decades, to reach the main targets of marketing by increasing the bank’s efficiency of studying their clients, knowing their feedback, in addition to promoting active and passive security systems. This study focuses on utilizing BD analytics to develop marketing intelligence systems. It aims to explore the big data as a valuable resource for Egyptian commercial banks, to improve the customer experience, customer segmentation and profiling, selling products based on profiling, and describing customer behavior. In order to develop the proposed system, data were collected from several banks of transaction performed in 2016, including a report on customer satisfaction, a procedure of analyzing customer satisfaction data, consisting of about 39,000 records of transactions for customers and a collection of about 4,000 records of transaction data for cardholders. These data were analyzed using Apache Hadoop to perform many tasks such as profiling the bank's clients to groups, customer segmentation based on client’s history, interest and habits, predicting customer behavior based on profiling, designing a new marketing strategy, and presenting the right offers to the bank's clients as individuals or as groups. It was concluded that BD analytics were very beneficial for achieving Marketing Intelligence in Banks.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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