Fanpage KPI analytics: “Determining the impact of KPI metrics on growth rate and user base“

Rahman, Z. and Kumaran, S. and Zanuddin, H. and Moghavvemi, S. and Nasir, M.H.N.M. (2016) Fanpage KPI analytics: “Determining the impact of KPI metrics on growth rate and user base“. International Journal of Applied Engineering Research, 11 (14). pp. 8098-8103. ISSN 0973-4562

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Official URL: https://www.ripublication.com/ijaer16/ijaerv11n14_...

Abstract

Facebook with its incredible audience is becoming immensely attractive channel to Brands and Marketers around the world. By this time, we are observing explosive growth in brand pages, variations in advertising and other innovative ways to monetize this audience. Now E-marketers are determined to know measurement procedures and related issues of Social media’s Key Performance Indicators (KPI). In the Fanpages companies are frequently and periodically posting different contents, but they also have to measure whether user engagement in this contents are valuable in maintain user base or page growth rate. E-marketers now also need to know which types of user engagement are more impactful on page performance. In this study we explored the effect of each KPI (Key performance Indicator) Metrics on page growth rate and Page likes (no of user). We have explored the KPI metrics data from the Fanpages of different industry (Consumer electronic, Electronics-phones, Health, beauty, and telecom). We investigated total 108 observations collected from18 Global Brand pages and data was composed from the month of February 2015 to July 2015. We recorded monthly data and conducted Panel Data analysis through E-views 9 software to show the individual impact of each KPI Metrics.

Item Type: Article
Uncontrolled Keywords: Fanpage; Social Media; Social Media Analytics; Fanpage KPI
Subjects: H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Arts and Social Sciences
Faculty of Computer Science & Information Technology > Dept of Software Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 04 Oct 2017 03:04
Last Modified: 04 Oct 2017 03:04
URI: http://eprints.um.edu.my/id/eprint/17858

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