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

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

Sibei Xia, Chuanlan Liu
Copyright: © 2022 |Pages: 17
DOI: 10.4018/IJBAN.313416
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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.
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Introduction

The ongoing “Fourth Industrial Revolution”, powered by advancing digital technologies, is reshaping the fashion industry in identifying, creating, delivering, and communicating values (Bertola & Teunissen, 2018; Nobile et al., 2021). More specifically, digital transformation, or digitalization, not only facilitates online sales for all types of fashion brands (Perry et al., 2019) but also helps fashion companies adapt operation systems by reducing costs and making each step of the value chain operate faster and cheaper with more efficiency and accuracy. For instance, digitalization allows customers to enjoy omnichannel shopping experiences and enables companies to improve customer relationships through two-way communications (González Romo et al., 2017; Siddiqui et al., 2019). Digitalization transforms fashion design and production, providing designers with tools for automation, and hence enhanced design process (K. Liu et al., 2019; Särmäkari & Vänskä, 2021). Digitalization also transforms decision-making in planning and implements production with more effective and efficient workflows (Ludbrook et al., 2019). Moreover, digitalization transforms fashion culture and society, enabling creative and sustainable solutions to the preservation of artworks (Luchev et al., 2013), fashion education (Lenoir, 2019), and virtual product consumption (Segran, 2020).

Social media platforms play an essential role in fashion digitalization. Before social media, consumers adopted trends much slower and more passively. Because of social media's wide diffusion and adoption, consumers instantly access real-time content and can actively participate in fashion creation, consumption, and communication. The broad customer participation via social media platforms and social networks brings new challenges and opportunities for fashion retailers, marketers, and suppliers (Bendoni, 2017). A company’s social media marketing (SMM) activities influence its brand equities and customer engagement (Bi̇lgi̇n, 2018; Godey et al., 2016). Some commonly employed SMM strategies include storytelling, electronic word-of-mouth (eWOM), customization, and celebrity endorsement for better engaging customers. Storytelling is marketer-created content that takes customers on a journey to forge stronger connections (Bendoni, 2017). eWOM is consumer-created and spread content that tends to have higher credibility, empathy, and relevance for the target audience than marketer-created sources of information (Gruen et al., 2006). Instead of broadcasting messages, the customization strategy sends individualized messages built based on consumers’ profiles, better fitting the consumer’s needs and increasing consumer loyalty (Zhu & Chen, 2015). Celebrity endorsement, especially for young consumers such as millennials, creates a product’s immediate identity or persona and positively affects consumer attitudes and purchase intention (McCormick, 2016). Brands with different profiles and price points may use different marketing strategies to best fit their consumers’ interests.

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