A novel model for product bundling and direct marketing in e-commerce based on market segmentation

Abstract

Nowadays, companies offer product bundles with special discounts in order to sell more products. However, it is important to note that customers show different levels of loyalties to companies, and each segment of the market has unique features, which influences the customers' buying patterns. The primary purpose of this paper is to propose a novel model for product bundling in e-commerce websites by using market segmentation variables and customer loyalty analysis. RFM model is employed to calculate customer loyalty. Subsequently, the customers are grouped based on their loyalty levels. Each group is then divided into different segments based on market segmentation variables. The product bundles are determined for each market segment via clustering algorithms. Apriori algorithm is also used to determine the association rules for each product bundle. Classification models are applied in order to determine which product bundle should be recommended to each customer. The results demonstrate that the silhouette coefficient, support, confidence, and accuracy values are higher when both customer loyalty level and market segmentation variables are used in product bundling. Accordingly, the proposed model increases the chance of success in direct marketing and recommending product bundles to customers.

Product bundling and advertising strategy for a duopoly supply chain: a power-balance perspective

“…They can also be made of multiple units of the same products. Bundling can involve product bundling or price bundling (Beheshtian-Ardakani et al 2018). Product bundling defines integrated products that provides extra value to the customers and price bundling consists of a package sold at a discount rate, without any integration of the goods and/or services involved (Adomavicius et al 2015;Giri et al 2020;Jeitschko et al 2017;Vamosiu 2018).…”

Log-Based Session Profiling and Online Behavioral Prediction in E–Commerce Websites

“…The contents of these descriptions can vary depending on the intended use of the analysis results. Customer personal data [11], their RFM (Recency, Frequency and Monetary) values [12]- [16], their browsing behaviors [17], [18] or purchasing habits [19]- [21], or the products they have shown interest in [22]- [25] are typically used for the creation of such descriptions. The concept of session plays a relevant role in this characterization due to the fact that a description is calculated for each customer session.…”

“…Once customers' descriptions have been created, they are usually grouped using either clustering methods [13], [15], [16], [19], [23], [26] or classification methods [17], [18], [25]. As a result, the application of these techniques generates a set of clusters that must be subsequently interpreted to understand the particular behaviors of each class of customers.…”

Analysis on Market Segmentation in Advertising Companies

“…MTG is a multinational company headquartered in Sweden and is involved in different kinds of media such as print, radio, and TV. MTG has a digital department called MTGx, and within the MGTx, the runs several sub-departments that work on several types of digital products that include online radio, online games, and online video streaming [2]. The report particular interest is on the two video streaming products where one is a paid subscription service free of adverts while the other is free but with advertising products.…”