article · Ain Shams Engineering Journal
Customer segmentation plays a critical role in designing profitable marketing and advertising campaigns by effectively targeting consumers. Traditional approaches often rely on unsupervised machine learning techniques, such as K-Means clustering, applied to the Recency, Frequency, and Monetary framework. However, this conventional model overlooks other relevant parameters depending on the specific application sector. To address this limitation, the model has been modified by introducing diversity as a fourth metric, creating the RFM-D framework. This parameter reflects the variety of different products an individual customer purchases. Applied within a retail market context, the RFM-D classification approach detects distinct patterns of consumer behaviour. Incorporating purchase diversity enhances the quality of customer behaviour predictions, enabling commercial organisations to anticipate which consumers are most likely to respond positively to marketing initiatives.
Effective marketing relies on accurately identifying the right audience for specific products. Standard segmentation tools often miss nuances in how varied a person's shopping habits are. By factoring in product diversity alongside how recently, frequently, and heavily someone spends, businesses can refine their outreach. This improves campaign success rates, enhances customer engagement, and reduces wasted expenditure on poorly targeted advertising.
The method is designed for retail businesses and commercial marketers looking to improve customer targeting and campaign returns. Having been applied and tested in a retail market setting, the model demonstrates practical utility for customer analytics. Its next step towards market adoption would involve integrating the RFM-D clustering process into existing commercial enterprise resource planning or customer relationship management software platforms.
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Customer segmentation is seen as one of the pillars of a successful advertising campaign. Marketers give great importance to this flagship phase in the process of marketing new products. Successful segmentation will involve successful “Customer Targeting” and therefore a profitable customer marketing campaign. Many works have dealt with customer segmentation using unsupervised Machine Learning algorithms such as K-Means by applying the famous Recency, Frequency and Monetary model. That model suffers from insufficiency by ignoring other important parameters according to the field of application. In this paper, we have modified the model by adding diversity “D” as a fourth parameter, referring to the diversification of products purchased by a given customer. The segmentation based on RFM-D is applied in a retail market in order to detect behavior patterns for a customer. The proposed model increases the quality of prediction of customer behavior; Companies could predict, customers who will respond positively.
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DOI: 10.1016/j.asej.2023.102254
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