Jianfang Qi, Yue Li, Haibin Jin, Jianying Feng and Weisong Mu
The purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable…
Abstract
Purpose
The purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises.
Design/methodology/approach
In this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm.
Findings
The results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation.
Practical implications
This paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing.
Originality/value
This study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets.
Details
Keywords
Jianfang Qi, Xin Mou, Yue Li, Xiaoquan Chu and Weisong Mu
Conventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the…
Abstract
Purpose
Conventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the relevant applied studies, which leads to failure to obtain some association rules with lower support but from higher-value consumers. Because not all customers are financially attractive to firms, it is necessary that their values be determined and that transactions be weighted. The purpose of this study is to propose a novel consumer preference mining method based on conventional frequent itemsets mining, which can discover more rules from the high-value consumers.
Design/methodology/approach
In this study, the authors extend the conventional association rule problem by associating the “annual purchase amount” – “price preference” (AP) weight with a consumer to reflect the consumer’s contribution to a market. Furthermore, a novel consumer preference mining method, the AP-weclat algorithm, is proposed by introducing the AP weight into the weclat algorithm for discovering frequent itemsets with higher values.
Findings
The experimental results from the survey data revealed that compared with the weclat algorithm, the AP-weclat algorithm can make some association rules with low support but a large contribution to a market pass the screening by assigning different weights to consumers in the process of frequent itemsets generation. In addition, some valuable preference combinations can be provided for related practitioners to refer to.
Originality/value
This study is the first to introduce the AP-weclat algorithm for discovering frequent itemsets from transactions through considering AP weight. Moreover, the AP-weclat algorithm can be considered for application in other markets.