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Factor based prediction model for customer behavior analysis

Author

Listed:
  • D. Kalaivani

    (Dr.SNS Rajalakshmi College of Arts and Science)

  • P. Sumathi

    (Government Arts College)

Abstract

Information Technology is nearing ubiquity stage in modern workplaces. The domain and applications of information technology is expanded abundantly. Any organization that wishes to improve their prospect in the market would definitely keep track their buyers’ perspective and emerging trends. In order to understand their aspirants, the companies are applying enormous technical ideas, tools and methodologies. Analysing more data and facts lead to better decision making. This is a strong perception of business intelligence experts. This work deals with a gradual transformation from instinct-driven approach to progressively data-driven approach. Understanding the expectations of the customers and improving their sales in particular to online trading. Therefore any business firm today have to access to unlimited amount of data. This include sales demographics, economic trends, competitive data and consumer behaviour, efficiency measures and financial calculations and more. Business Intelligence has a leading contribution in this venture. The empirical data are systematically gathered in order to analyse or test hypotheses and consequently make new observations and experiments that leads to gain new insights. The factor based principle component analysis method is used to select the important customer buying factors to analyze their behavior.

Suggested Citation

  • D. Kalaivani & P. Sumathi, 2019. "Factor based prediction model for customer behavior analysis," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 519-524, August.
    • Handle: RePEc:spr:ijsaem:v:10:y:2019:i:4:d:10.1007_s13198-018-0739-4
    DOI: 10.1007/s13198-018-0739-4
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    References listed on IDEAS

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    1. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
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    Cited by:

    1. Vajala Ravi & Richa Saini & Manoj Kumar Varshney & Gurprit Grover, 2021. "Modelling of survival time of life insurance policies in India: a comparative study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 164-175, February.
    2. Pratap Chandra Mandal, 2022. "Roles of Customer Databases and Database Marketing in Customer Relationship Management," International Journal of E-Business Research (IJEBR), IGI Global, vol. 18(1), pages 1-12, January.
    3. Gobinda Roy & Rajarshi Debnath & Partha Sarathi Mitra & Avinash K. Shrivastava, 2021. "Analytical study of low-income consumers’ purchase behaviour for developing marketing strategy," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(5), pages 895-909, October.
    4. Abedin, Mohammad Zoynul & Hajek, Petr & Sharif, Taimur & Satu, Md. Shahriare & Khan, Md. Imran, 2023. "Modelling bank customer behaviour using feature engineering and classification techniques," Research in International Business and Finance, Elsevier, vol. 65(C).

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