IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v10y2019i4d10.1007_s13198-018-0739-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-018-0739-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-018-0739-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    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. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcel Fafchamps & Julien Labonne, 2017. "Do Politicians’ Relatives Get Better Jobs? Evidence from Municipal Elections," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 33(2), pages 268-300.
    2. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    3. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022. "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, vol. 112(C).
    4. Abu Taher, Sheikh & Uddin, Md. Kama, 2018. "Use of big data in financial sector of Bangladesh – A review," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190348, International Telecommunications Society (ITS).
    5. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    6. Jin-Hyuk Kim & Tin Cheuk Leung, 2013. "Quantifying the Impacts of Digital Rights Management and E-Book Pricing on the E-Book Reader Market," Working Papers 13-03, NET Institute.
    7. Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).
    8. Vincze, János, 2017. "Információ és tudás. A big data egyes hatásai a közgazdaságtanra [Information and knowledge: some effects of big data on economics]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(11), pages 1148-1159.
    9. Justin Longo & Alan Rodney Dobell, 2018. "The Limits of Policy Analytics: Early Examples and the Emerging Boundary of Possibilities," Politics and Governance, Cogitatio Press, vol. 6(4), pages 5-17.
    10. Max Nathan & Anna Rosso, 2017. "Innovative events," Development Working Papers 429, Centro Studi Luca d'Agliano, University of Milano, revised 08 Apr 2019.
    11. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    12. Martin Browning & Thomas F. Crossley & Joachim Winter, 2014. "The Measurement of Household Consumption Expenditures," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 475-501, August.
    13. Shaoming Cheng & Sukumar Ganapati & Giri Narasimhan & Farzana Beente Yusuf, 2022. "A machine learning‐based analysis of 311 requests in the Miami‐Dade County," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1627-1645, December.
    14. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education,, Elsevier.
    15. Matteo Iacopini & Carlo R.M.A. Santagiustina, 2021. "Filtering the intensity of public concern from social media count data with jumps," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1283-1302, October.
    16. Laurent Bergé, 2018. "Efficient estimation of maximum likelihood models with multiple fixed-effects: the R package FENmlm," DEM Discussion Paper Series 18-13, Department of Economics at the University of Luxembourg.
    17. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    18. Jian Gao & Tao Zhou, 2017. "Quantifying China's Regional Economic Complexity," Papers 1703.01292, arXiv.org, revised Nov 2017.
    19. Jens Prüfer & Christoph Schottmüller, 2021. "Competing with Big Data," Journal of Industrial Economics, Wiley Blackwell, vol. 69(4), pages 967-1008, December.
    20. Serena Ng, 2017. "Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data," NBER Working Papers 23673, National Bureau of Economic Research, Inc.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:10:y:2019:i:4:d:10.1007_s13198-018-0739-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.