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

Anabel Guzmán Ordóñez (), Francisco Javier Arroyo Cañada (), Emmanuel Lasso (), Javier A. Sánchez-Torres () and Manuela Escobar-Sierra ()
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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

Journal of Marketing Analytics, 2024, vol. 12, issue 4, No 15, 962-978

Abstract: 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.

Keywords: Social media; Twitter; Content marketing; Engagement; Machine learning; Government (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1057/s41270-023-00243-5

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