Segmenting customers by transaction data with concept hierarchy

Hsu, Fang-Ming; Lu, Li-Pang; Lin, Chun-Min

doi:10.1016/j.eswa.2011.12.005

Cited by 36 publications

(13 citation statements)

References 26 publications

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“…Lu and Wu (2009) proposed a customer segmentation method based on customers' transaction patterns. Hsu et al (2012) used the idea of the hierarchy of the items consumed to segment customers. Dutta et al (2015) categorized the data mining techniques employed in market segmentation into thirteen methods, such as neural network, RFM analysis, hierarchical clustering, and K-means.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Beheshtian-Ardakani¹,

Fathian²,

Gholamian³

2018

10.5267/j.dsl

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Beheshtian-Ardakani¹,

Fathian²,

Gholamian³

2018

10.5267/j.dsl

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“…Motivated by [9], the so called Customer-Oriented Catalog Segmentation problem, which concerns the problem of segmenting customer based on transactions, has been discussed in [2] [6]. The issues related to segmenting customers by transaction data with concept hierarchy have been addressed in [7] [12]. As an important association study, clustering transactions has drawn increasing attention [14] [16].…”

Section: Preliminariesmentioning

confidence: 99%

Similarity of Transactions for Customer Segmentation

Lü¹,

Furukawa²

2012

Lecture Notes in Computer Science

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Abstract. Customer segmentation is usually the first step towards customer analysis and helps to make strategic plans for a company. Similarity between customers plays a key role in customer segmentation, and is usually evaluated by distance measures. While various distance measures have been proposed in data mining literature, the desirable distance measures for various data sources and given application domains are rarely known. One of the reasons lies in that semantic meaning of similarity and distance measures is usually ignored. This paper discusses several issues related to evaluating customer similarity based on their transaction data. Various set distance measures for customer segmentation are analyzed in several imaginary scenarios, and it is shown that each measure has different characteristics which make the measure useful for some application domains but not for others. We argue that no measure always performs better than other measures, and suitable measures should be adopted for specific purposes depending on applications.

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“…Framework of the proposed scheme 수는 크게 일반변수(general variable)와 트랜잭션 기반 변수(transaction based variable)로 구분할 수 있다 [1,2] . 일반변수를 사용한 시장세분화 연구는 고객의 인구통 계적 특성, 생활습관, 태도, 심리와 같은 기초적인 고객의 속성을 이용하여 고객을 구분하고 이들의 대상으로 마케 팅을 차별화하여 진행할 수 있도록 한다 [3][4][5][6] . 일반변수를 기반으로 하는 시장세분는 직관적이고 쉽게 적용 가능하 다는 장점이 있는 반면에 유사한 일반변수의 특성을 갖는 고객이 유사한 구매 행태를 나타내지 않을 수 있으며, 데 이터를 수집하기 어렵다는 단점이 존재한다 [1,7] .…”

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A Market Segmentation Scheme Based on Customer Information and QAP Correlation between Product Networks

Jeong¹,

Shin²,

Koo³

et al. 2015

Journal of the Korea Society for Simulation

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In recent, hybrid market segmentation techniques have been widely adopted, which conduct segmentation using both general variables and transaction based variables. However, the limitation of the techniques is to generate incorrect results for market segmentation even though its methodology and concept are easy to apply. In this paper, we propose a novel scheme to overcome this limitation of the hybrid techniques and to take an advantage of product information obtained by customer's transaction data. In this scheme, we first divide a whole market into several unit segments based on the general variables and then agglomerate the unit segments with higher QAP correlations. Each product network represents for purchasing patterns of its corresponding segment, thus, comparisons of QAP correlation between product networks of each segment can be a good measure to compare similarities between each segment. A case study has been conducted to validate the proposed scheme. The results show that our scheme effectively works for Internet shopping malls.

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Segmenting customers by transaction data with concept hierarchy

Cited by 36 publications

References 26 publications

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

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

Similarity of Transactions for Customer Segmentation

A Market Segmentation Scheme Based on Customer Information and QAP Correlation between Product Networks

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