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Analysis Of Customer Activity, The Importance Of Timing For Effective Marketing Actions: Case Of Group Buying Site, Grouper

Author

Listed:
  • Angelovska, Nina

    (Macedonian E-commerce Association, Republic of North Macedonia)

Abstract
In order to achieve successful management of their sales and marketing activities companies need to monitor and analyse the activity of their customes. The goal of this study is twofold. First, an empirical investigation of customers’ activitiy is conducted by using the Customer Activity measures (Kumar and Reinartz 2012), and in addition a new measure is introduced to determine when a customer ceases to be a customer and the relationship with him ends, and when a customer becomes "currently inactive" before he reactivates again. Second, by having information on the status of the customer’s activity, the implementation of appropriate marketing actions is investigated. Information and results gained from this analysis can be a base for action, tools for rehabilitation of "currently inactive customers" are provided that can be used by e-shops and marketplaces. Each company, can use the Customer activitiy measures that are suitable, depending on the industry in which it operates, in order to create a comprehensive image of its customers’s activity, increase their activity and make appropriate marketing decisions.

Suggested Citation

  • Angelovska, Nina, 2021. "Analysis Of Customer Activity, The Importance Of Timing For Effective Marketing Actions: Case Of Group Buying Site, Grouper," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 12(2), pages 156-170.
  • Handle: RePEc:ris:utmsje:0313
    as

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    File URL: https://utmsjoe.mk/files/Vol.12.No.2/4.ANALYSIS_OF_CUSTOMER_ACTIVITYTHE_IMPORTANCE_OF_TIMING_FOR_EFFECTIVE_MARKETING_ACTIONS_CASE_OF_GROUP_BUYING_SITEGROUPER.pdf
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    References listed on IDEAS

    as
    1. Korkmaz, E. & Kuik, R. & Fok, D., 2013. ""Counting Your Customers": When will they buy next? An empirical validation of probabilistic customer base analysis models based on purchase timing," ERIM Report Series Research in Management ERS-2013-001-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Peter S. Fader & Bruce G. S. Hardie & Ka Lok Lee, 2005. "“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 24(2), pages 275-284, August.
    3. V. Kumar & Werner Reinartz, 2018. "Customer Relationship Management," Springer Texts in Business and Economics, Springer, edition 3, number 978-3-662-55381-7.
    4. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
    5. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    6. Ryan Dew & Asim Ansari, 2018. "Bayesian Nonparametric Customer Base Analysis with Model-Based Visualizations," Marketing Science, INFORMS, vol. 37(2), pages 216-235, March.
    7. Bijmolt, T.H.A. & Bl, 2010. "Should they stay or should they go? Reactivation and termination of low-tier customers," Research Report 10008, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    8. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    CRM strategy; customer-centric strategy; customer retention; lifetime duration; North Macedonia;
    All these keywords.

    JEL classification:

    • M30 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - General

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