# New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System
## 2 Related Works
### Behavioral Analysis
This can be explained by the extended interval between customer purchases, which suggests that the customer is not particularly drawn to it and does not have a strong wish to make another purchase [16]. The general rule that almost all market sectors follow stated that the more recently a customer transacts or interacts with a product, the more likely it is that they will react to the next promotion offer [17]. The frequency component of the RFM is typically determined by the total number of transactions customers have made or the average amount of time between their most recent interaction or transaction and their most recent transaction [16]. The frequency score can be rated higher to show the longer times of transactions that a client makes. The frequency score is used to quantify customer steadfastness with a brand. Customer loyalty, product demand, and market size all improve with frequency score, which, consequently, increases with product repurchases [16]. It is important to note that first-time customers can be viewed as potential customers because they represent a good target market for the company to follow-up with and entice with different products in order to turn them into a group of regular customers [18]. There are two methods to investigate the monetary component in the RFM model. The former indicates the average transaction amount made by the customer over the course of a specified period, whereas the latter indicates the overall accumulated transaction amount made by the customer over the course of a defined period. The former approach of average transaction amount is strongly advised over the latter approach for decreasing the collinearity of the frequency and monetary components [16]. This monetary component will portray the customer's identity as a light or hefty spender to the business. The more the customer spends, the more likely is the customer to respond to the new deal, and the more likely they are to become a repeat customer and make purchases [18]. However, the RFM model is not suitable for interpreting all the customer behaviors. Therefore, an improvement or variation is performed to the RFM model in the existing works. From Table 1, there is a new proposed RFMT model for the e-Commerce domain [5] and banking domain [9]. The RFM of the RFMT still represents the recency, frequency, and monetary value. The only difference is the addition of another component to the RFM model. The alphabet T of the RFMT model represents the time component, which is another significant component that should be considered for achieving the model's purpose. In [5], the time component refers to the inter-purchase time, which is calculated by the period between two continuous customer transactions made in the same location or on the same website, to understand their pattern of online shopping. While, in [9], the time component represents the transaction time, which is measured by the median of the gap time between the current and subsequent transactions, for discovering the customer's transaction pattern in the banking domain. However, the RFM or RFMT model is only suitable for interpreting the audience behaviors that are related with monetary, particularly in the Wi-Fi advertising industry. This can be explained by the main purpose of Wi-Fi advertising, which is to advertise and implant the brand and the products into the subconscious mind of the public as much as possible (involving engagement) instead of promoting the products to let the audience purchase (involving monetary) directly. Up to 95% of customer purchasing decisions are influenced by the subconscious impression generated from advertising impact [19]. When compared to other brands, customers are more likely to purchase from brands that exist subconsciously. Therefore, the customer's interest that evaluates the impression and action is a significant factor to customer's subconscious perceptions of a product or brands. To the best of the authors' knowledge, customer behavioral models are yet to be created for the Wi-Fi advertising industry.