Perceptions of the 7p marketing mix of Islamic banks in Indonesia: What do twitter users say about it?

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3 Citations (Scopus)


Purpose: This study attempts to analyse the perceptions of social media users, particularly Twitter users, of social media marketing attempts by Indonesian Islamic banks. The perceptions of social media users are assessed based on the 7P marketing mix, namely: (i) product, (ii) promotion and education, (iii) price and other user outlays, (iv) process, (v) people, (vi) physical environment, and (vii) time. Design/methodology/approach: The research approach is descriptive quantitative, calculating the frequency of tweets based on their 7P marketing category and then interpreting the results. We collected more than 500,000 tweets by Twitter users in Indonesia, containing the phrase 'Islamic bank' between 2007 to 2018. The data was analysed using R software and managed using R-Python software. Findings: Results indicate that the 'people' factor gained the most responses from social media users, who were mainly customers of the Islamic banks, obtaining both positive and negative sentiments. The highest positive response given by customers were comments on staff's attitude, good appearance and courteous customer service, while they disliked long queues and convoluted service. These findings provide important insight for effective marketing strategies of Islamic banks. Originality/Value: This research is the first research in the world revealing the perception of consumers of Islamic banks through Twitter. It is also the first to reveal perceptions of Islamic banks through Twitter, using the 7Ps Marketing Mix analysis tool. This research was conducted using big data analysis (i.e. it used more than 500,000 Twitter tweets) analyse Islamic bank consumers.

Original languageEnglish
Pages (from-to)300-319
Number of pages20
JournalInternational Journal of Innovation, Creativity and Change
Issue number11
Publication statusPublished - 2020


  • 7Ps
  • Indonesia
  • Islamic bank
  • Marketing mix
  • R-Python
  • Twitter


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