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A Methodological Approach for the Journey through Real-Time Marketing: From Customer Journey Analytics to Personalization Engines

Author

Listed:
  • Claudiu-Catalin Munteanu

    (Romanian Academy)

  • Adina Cristea

    (Bucharest University of Economic Studies)

Abstract

Many companies rely in practice on personalization engines and value co-creation in order to boost the efficacy of their one-to-one customer relationships. Real-time marketing (RTM) can increase the efficacy of marketing activities by taking advantage of technology, big data analysis, social media and constant connectivity. RTM fosters consumer engagement in value co-creation and in the personalization of products, offers and customer service. This paper suggests a methodology for implementing RTM by considering the main characteristics of customization strategies: novelty, serendipity and diversity. Based on the seven rules for RTM implementation, we propose and discuss the process of incorporating big data and customization strategies into a personalization engine driven by machine learning and big data analytics. We include in the personalization engine value co-creation drivers based on marketing drive value, customer lifetime value and strength and intensity of brand associations. The proposed process-based framework for RTM implementation integrates algorithms related to customer profile to calculate their future purchase probability, thus being a useful managerial tool for segmenting and targeting consumers in real-time. By adapting marketing communication patterns to these permanently ‘evolving’ segments, value co-creation and consumer satisfaction increase, while the AI algorithms also improve.

Suggested Citation

  • Claudiu-Catalin Munteanu & Adina Cristea, 2021. "A Methodological Approach for the Journey through Real-Time Marketing: From Customer Journey Analytics to Personalization Engines," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 849-855, December.
    • Handle: RePEc:ovi:oviste:v:xxi:y:2021:i:2:p:849-855
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    References listed on IDEAS

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    1. Matt, C. & Benlian, Alexander & Hess, Thomas & Weiß, Christian, 2014. "Escaping from the Filter Bubble? The Effects of Novelty and Serendipity on Users’ Evaluations of Online Recommendations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 66193, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Kumar Rakesh Ranjan & Stuart Read, 2016. "Value co-creation: concept and measurement," Journal of the Academy of Marketing Science, Springer, vol. 44(3), pages 290-315, May.
    3. Jiménez, Fernando R. & Mendoza, Norma A., 2013. "Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 226-235.
    4. Jennifer Stevens & Carol L. Esmark & Stephanie M. Noble & Na Young Lee, 2017. "Co-producing with consumers: how varying levels of control and co-production impact affect," Marketing Letters, Springer, vol. 28(2), pages 171-187, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    real-time marketing; personalization engines; big data marketing; customization strategiesm value co-creation;
    All these keywords.

    JEL classification:

    • M30 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - General
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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