IDEAS home Printed from https://ideas.repec.org/a/ris/utmsje/0313.html
   My bibliography  Save this article

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

    Download full text from publisher

    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
    File Function: Full text
    Download Restriction: no
    ---><---

    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, June.
    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)

    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. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    2. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    3. Holtrop, Niels & Wieringa, Jaap E., 2023. "Timing customer reactivation initiatives," International Journal of Research in Marketing, Elsevier, vol. 40(3), pages 570-589.
    4. Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
    5. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.
    6. Park, Chang Hee & Yoon, Tae Jung, 2022. "The dark side of up-selling promotions: Evidence from an analysis of cross-brand purchase behavior☆," Journal of Retailing, Elsevier, vol. 98(4), pages 647-666.
    7. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
    8. Patrick Bachmann & Markus Meierer & Jeffrey Näf, 2021. "The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base Analysis," Marketing Science, INFORMS, vol. 40(4), pages 783-809, July.
    9. van Oest, Rutger & Knox, George, 2011. "Extending the BG/NBD: A simple model of purchases and complaints," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 30-37.
    10. Arun Gopalakrishnan & Zhenling Jiang & Yulia Nevskaya & Raphael Thomadsen, 2021. "Can Non-tiered Customer Loyalty Programs Be Profitable?," Marketing Science, INFORMS, vol. 40(3), pages 508-526, May.
    11. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
    12. Jerath, Kinshuk & Fader, Peter S. & Hardie, Bruce G.S., 2016. "Customer-base analysis using repeated cross-sectional summary (RCSS) data," European Journal of Operational Research, Elsevier, vol. 249(1), pages 340-350.
    13. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2014. "A multi-category customer base analysis," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 266-279.
    14. Brighton, Henry, 2020. "Statistical foundations of ecological rationality," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-32.
    15. Michael Haenlein, 2011. "A social network analysis of customer-level revenue distribution," Marketing Letters, Springer, vol. 22(1), pages 15-29, March.
    16. Donald R. Lehmann & Jeffrey R. Parker, 2017. "Disadoption," AMS Review, Springer;Academy of Marketing Science, vol. 7(1), pages 36-51, June.
    17. Romero, Jaime & van der Lans, Ralf & Wierenga, Berend, 2013. "A Partially Hidden Markov Model of Customer Dynamics for CLV Measurement," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 185-208.
    18. Chang, Chun-Wei & Zhang, Jonathan Z., 2016. "The Effects of Channel Experiences and Direct Marketing on Customer Retention in Multichannel Settings," Journal of Interactive Marketing, Elsevier, vol. 36(C), pages 77-90.
    19. Glady, Nicolas & Lemmens, Aurélie & Croux, Christophe, 2015. "Unveiling the relationship between the transaction timing, spending and dropout behavior of customers," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 78-93.
    20. Hoppe, Daniel & Wagner, Udo, 2014. "The role of lifetime activity cues in customer base analysis," Journal of Business Research, Elsevier, vol. 67(5), pages 983-989.

    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

    Statistics

    Access and download statistics

    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:ris:utmsje:0313. 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: Assistant Professor. Dejan Nakovski, PhD (email available below). General contact details of provider: https://edirc.repec.org/data/feutmmk.html .

    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.