Mining Associations between Dynamics of Customers and Market Trends using Big Data Analytics

Document Type : Original Article

Authors

1 PhD Candidate of Information Technology Engineering, Faculty of Industrial Engineering, Tarbiat Modares University, Tehran, Iran.

2 Professor, Faculty of Industrial Engineering, Tarbiat Modares University, Tehran, Iran

3 Professor, Faculty of Industrial Engineering, Tarbiat Modares University, Tehran, Iran.

4 Assistant Professor, Faculty of Industrial Engineering, Tarbiat Modares University, Tehran, Iran.

Abstract

Customers are considered as a precious asset for companies. Marketing should pay attention to long-term exchanges rather than discrete transactions. It’s crucial for enterprises to find out which incentives encourage customers to know the real value of their customers.
Segmentation is a strategic tool for organizations to group customers by their common needs; but in the era of digital transformation and unstable markets, customer segments change rapidly. It’s important to understand the dynamics of these changes to predict and manage them efficiently. In the present study, the patterns of customers’ dynamics have been investigated using big-data analytics in the banking industry. The results revealed thirteen categories of associations between the dynamics of customers and market trends which can be used to predict future dynamics of customers and direct it in the favor of the organization.

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History

  • Receive Date: 09 October 2019
  • Accept Date: 16 January 2021

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