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Analytical model to measure the effectiveness of content marketing on Twitter: the case of governorates in Colombia

Author

Listed:
  • Anabel Guzmán Ordóñez

    (University of Barcelona)

  • Francisco Javier Arroyo Cañada

    (University of Barcelona)

  • Emmanuel Lasso

    (University of Cauca)

  • Javier A. Sánchez-Torres

    (University of Medellín)

  • Manuela Escobar-Sierra

    (University of Medellín)

Abstract

Twitter as a marketing tool has led to a growing interest in measuring the effectiveness of content marketing on this platform. However, there has yet to be a comprehensive analytical model to measure the effectiveness of public content marketing (PCM) accurately and reliably. A literature review determined the gaps between preliminary studies and constructing a new model to measure the content effectiveness, considering variables related to interactivity and performance of digital content marketing (DCM) strategies. For this reason, this study aims to build an analytical model that determines which content characteristics improve the effectiveness of Twitter accounts, taking as a case study the governorates of Colombia. Within the methodology for data mining, CRISP-DM was used, which allowed the cleaning, processing and analysis of all data collected from the accounts of Colombian governments. The results allowed to establish factors that have yet to be considered to measure the Engagement Rate per Post (ERP) and have a critical load on users’ interactivity with the content, such as the tweet type, emojis, dates, the type of media, sentiment associated with the post and emotions. With the model, it was possible to identify the variables that improve the ERP and their impact on the effectiveness of the content.

Suggested Citation

  • Anabel Guzmán Ordóñez & Francisco Javier Arroyo Cañada & Emmanuel Lasso & Javier A. Sánchez-Torres & Manuela Escobar-Sierra, 2024. "Analytical model to measure the effectiveness of content marketing on Twitter: the case of governorates in Colombia," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 962-978, December.
    • Handle: RePEc:pal:jmarka:v:12:y:2024:i:4:d:10.1057_s41270-023-00243-5
    DOI: 10.1057/s41270-023-00243-5
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    References listed on IDEAS

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    1. Hollebeek, Linda D. & Macky, Keith, 2019. "Digital Content Marketing's Role in Fostering Consumer Engagement, Trust, and Value: Framework, Fundamental Propositions, and Implications," Journal of Interactive Marketing, Elsevier, vol. 45(C), pages 27-41.
    2. Muhammad Atif & Valentina Franzoni, 2022. "Tell Me More: Automating Emojis Classification for Better Accessibility and Emotional Context Recognition," Future Internet, MDPI, vol. 14(5), pages 1-13, May.
    3. Salminen, Joni & Yoganathan, Vignesh & Corporan, Juan & Jansen, Bernard J. & Jung, Soon-Gyo, 2019. "Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type," Journal of Business Research, Elsevier, vol. 101(C), pages 203-217.
    4. Hunt Allcott & Matthew Gentzkow & Chuan Yu, 2019. "Trends in the Diffusion of Misinformation on Social Media," NBER Working Papers 25500, National Bureau of Economic Research, Inc.
    5. Juan Pablo Ramirez-Madrid & Manuela Escobar-Sierra & Isaias Lans-Vargas & Juan Manuel Montes Hincapie, 2022. "Government influence on e-government adoption by citizens in Colombia: Empirical evidence in a Latin American context," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-22, February.
    6. Clemens Koob, 2021. "Determinants of content marketing effectiveness: Conceptual framework and empirical findings from a managerial perspective," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-25, April.
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