# New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System
## 6 Conclusions  
As the number of internet users continues to grow annually, businesses are increasingly using Wi-Fi services to reach a wider audience, leading to an increasing demand for audience behavioral analysis. This study aimed to introduce a new RFI model designed to analyze the audience behaviors in the Wi-Fi advertising system. With the help of clustering algorithms in segmenting the audience, this model successfully summarized and presented behavioral characteristics and patterns in distinct groups based on audience recency, frequency, and interest metrics. Through experiments, the unique characteristics of each cluster were revealed and demonstrated. With the aid of clustering and the dynamic characteristic range table, the audience behavioral patterns in both normal case and special case could be successfully interpreted. Consequently, it could be inferred that the new RFI model could be broadly \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Campaign** & **Case** & **Cluster** &
\begin{tabular}{c} **Audience** \\ **Percentage** \\ \end{tabular} & \multicolumn{2}{c}{**Characteristics**} \\ \hline \multirow{9}{*}{Normal case} & \multirow{4}{*}{3} & \multirow{4}{*}{1} & 8.56\% & Audience with shortest gap time from the last engagement, highest frequency, and low interest in the advertisement \\  & & & 2 & 42.76\% \\  & & & & frequency, and no interest in the advertisement \\  & & & & **Audience with longest gap time from the last engagement, low frequency, and no interest in the advertisement** \\  & & & 4 & 0.62\% \\  & & & & **Audience with shortest gap time from the last engagement, highest frequency, and highest interest in the advertisement** \\  & & & 5 & 10.08\% \\ \cline{2-4}  & \multirow{4}{*}{5} & \multirow{4}{*}{1} & 8.92\% & Audience with shortest gap time from the last engagement, highest frequency, and low interest in the advertisement \\  & & & & & frequency, and low interest in the advertisement \\  & & & 2 & 42.77\% \\  & & & & & frequency, and no interest in the advertisement \\  & & & & & frequency, and no interest in the advertisement \\  & & & & & frequency, and low interest in the advertisement \\  & & & & & frequency, and low interest in the advertisement \\  & & & & & frequency, and medium interest in the advertisement \\ \hline \hline \multirow{9}{*}{Special case} & \multirow{4}{*}{3} & \multirow{4}{*}{3} & \multirow{4}{*}{37.99\%} & \multirow{4}{*}{**Audience with longest gap time from the last engagement, low frequency, and low interest in the advertisement**} \\  & & & & & frequency, and low interest in the advertisement \\  & & & & & frequency, and low interest in the advertisement \\  & & & & & frequency, and high interest in the advertisement \\  & & & & & frequency, and high interest in the advertisement \\  & & & & & frequency, and high interest in the advertisement \\  & & & & & frequency, and medium interest in the advertisement \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Audience behavioral characteristics in each cluster of the two most active campaigns. applied to different Wi-Fi advertising attributes that exhibited different advertising effectiveness in terms of audience engagement. Furthermore, the RFI values of each segmented audience group were interpreted into meaningful characteristics through the utilization of the dynamic characteristic range table. Therefore, it could be concluded that the dynamic characteristic range table offered a viable approach for audience behavioral segmentation based on their respective RFI values. With the knowledge of the audience's behavioral patterns, businesses gain a better insight into their engagement and thus leverage the characteristics for designing and implementing more effective marketing strategies to boost their sales. In the future, exploring the engagement of advertisements among different genders or age groups using the RFI model shows a promising direction of research.