New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System

Shueh-Ting Lim, Lee-Yeng Ong () and Meng-Chew Leow
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Shueh-Ting Lim: Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia
Lee-Yeng Ong: Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia
Meng-Chew Leow: Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia

Future Internet, 2023, vol. 15, issue 11, 1-16

Abstract: In this technological era, businesses tend to place advertisements via the medium of Wi-Fi advertising to expose their brands and products to the public. Wi-Fi advertising offers a platform for businesses to leverage their marketing strategies to achieve desired goals, provided they have a thorough understanding of their audience’s behaviors. This paper aims to formulate a new RFI (recency, frequency, and interest) model that is able to analyze the behavior of the audience towards the advertisement. The audience’s interest is measured based on the relationship between their total view duration on an advertisement and its corresponding overall click received. With the help of a clustering algorithm to perform the dynamic segmentation, the patterns of the audience behaviors are then being interpreted by segmenting the audience based on their engagement behaviors. In the experiments, two different Wi-Fi advertising attributes are tested to prove the new RFI model is applicable to effectively interpret the audience engagement behaviors with the proposed dynamic characteristics range table. The weak and strongly engaged behavioral characteristics of the segmented behavioral patterns of the audience, such as in a one-time audience, are interpreted successfully with the dynamic-characteristics range table.

Keywords: behavioral model; engagement; behavioral audience segmentation; clustering algorithms; behavioral characteristics and patterns (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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