Applying Machine Learning to Study the Marketing Mix's Effectiveness in a Social Marketing Context: Fashion Brands' Twitter Activities in the Pandemic

Sibei Xia and Chuanlan Liu
Additional contact information
Sibei Xia: Louisiana State University, USA
Chuanlan Liu: Louisiana State University, USA

International Journal of Business Analytics (IJBAN), 2022, vol. 9, issue 6, 1-17

Abstract: This study examines the effectiveness of the marketing mix practiced on Twitter across high-end and low-end fashion brands and explores whether any four Ps activities have changed across the different pandemic stages. A quantitative research method was designed to analyze text data scraped from identified fashion brands' Twitter accounts. A classification instrument was developed to group tweets based on the four Ps marketing mix. Then the developed instrument was applied to a small set of 145 tweets randomly sampled from the collected data. Logistic regression models were then trained using the sample set to predict four Ps activities on all the collected 144k tweets. The numbers of likes per tweet and frequencies of being retweeted per tweet were used to measure engagement effectiveness across brands.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBAN.313416 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jban00:v:9:y:2022:i:6:p:1-17

Access Statistics for this article

International Journal of Business Analytics (IJBAN) is currently edited by John Wang

More articles in International Journal of Business Analytics (IJBAN) from IGI Global
Bibliographic data for series maintained by Journal Editor ().