Knowledge acquisition of association rules from the customer-lifetime-value perspective

Weng, Cheng-Hsiung; Huang, Tony Cheng-Kui

doi:10.1108/k-03-2016-0042

Cited by 7 publications

(4 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, it still had some difficulties in its generalization. On the other hand, Weng and Huang (2018) developed a relationship that related CLV weights to the items' transactions. Their "frequent item-sets of CLV" model could be of interest to both researchers and practitioners.…”

Section: Customer Engagement Valuementioning

confidence: 99%

Customer engagement value: process, limitations and future research

ElHamd

Shamma

Saleh

et al. 2021

JM2

View full text Add to dashboard Cite

Purpose The purpose of this paper is to close the gap between the theoretical nature of existing contributions in customer engagement value (CEV) and its need to practically empower business decisions. This is done by proposing a framework that consists of three techniques, each of which combines the components of CEV to make it more comprehensive and applicable. The paper also reviews and analyzes the work that has been done so far in the area of CEV whether in business to business (B2B), business to consumer (B2C) or consumer to consumer (C2C) markets. Design/methodology/approach CEV is a comprehensive term that measures the total value of the customer through capturing his transactional and non-transactional behaviors. Hence, it is an essential term for measuring the value of the customer in direct marketing. This motivates researchers to compete in developing models to maximize CEV. Meanwhile, most of the existing models are conceptual and the majority of them lack applicability due to many reasons. First, these models relied on a linear version of the CEV model, hence double-counting the value of the customer; also they weighted the components of CEV equally, which is unrealistic. Finally, the effect of the environmental components in determining the engagement level of each customer was almost ignored. In this paper, two main contributions are presented. First, a summary and analysis of the contributions of the literature in the CEV field for different market types whether in B2C, B2B or C2C. Furthermore, three modifications are added to the existing models. The first model introduces a non-linear relationship of the components of CEV. The second model is a weighted linear model of these components. Finally, the third model adds the environmental factors to the CEV components. All the proposed models are theoretical in nature, however, these models are expected to show superiority when being applied to real data sets due to their ability to capture the complexity in the relationship between the firm and its customers in real-life situations. The proposed models are expected to attract the practitioners and other researchers and they both are encouraged to apply the proposed models on real-life data sets, test their performance, compare them against each other, to be able to apply each of them on the best suitable data set and business scenario. Findings Based on the review and analysis that has been done on about 87 papers, it is found that the majority of the contributions that have been done in the area of CEV are theoretical in nature, in spite of the effectiveness of CEV in empowering business decision. It is also found that few researchers proposed a set of theoretical comprehensive frameworks that combined CEV’s components together. Meanwhile, those frameworks are not practically applicable. Research limitations/implications Although the contribution of the proposed models expected to attract both researchers and practitioners, these are not applied to real-life case studies to prove their effectiveness. Practical implications The research in this paper has many industrial and managerial implications. First, it helps managers and decision takers to treat the customers as assets and cost-free resources who can work with the firm to achieve what’s both aims to (i.e. increase customer satisfaction and firm’s profitability). Second, it helps the firm to determine the total value of each customer and treat its customers accordingly. Third, it empowers the managers to do target marketing, based on grouping the customers upon their total engagement. This would save time and cost and for sure increase the profitability and customer satisfaction. Forth, the proposed models take into consideration not only the transactional behavior of the customers but also the non-transactional factors that play a significant role in formulating the relationship between the firm and its customers. Originality/value This is hereby to certify that the paper is original, neither the paper nor a part of it is under consideration for publication anywhere else. Also, this study has no conflicts of interest to disclose.

show abstract

Section: Customer Engagement Valuementioning

confidence: 99%

Customer engagement value: process, limitations and future research

ElHamd

Shamma

Saleh

et al. 2021

JM2

View full text Add to dashboard Cite

show abstract

“…Association rule mining can find interesting relations and dependencies between variables in large databases (Chen et al, 2019). Then, the identification of these significant associations is helpful for many decision-making processes in business contexts (Weng and Huang, 2018;Medina-Serrano et al, 2019;Nguyen et al, JEC 16,1 2021). The process of association rule mining generally includes two basic steps: strong rule generation and rule pattern evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Weng measured consumers' values by associating the frequency-monetary weight with a transaction (Weng, 2017). Moreover, Weng and Huang calculated the transaction weight by associating customer lifetime value weights with transactions to reflect the worth and intensity of customers' values (Weng and Huang, 2018). Obviously, for the first type of method, to obtain a transaction weight, it is necessary to know the weights of all the items contained in the transaction in advance.…”

Section: Introductionmentioning

confidence: 99%

A novel consumer preference mining method based on improved weclat algorithm

Xin

et al. 2021

JEC

View full text Add to dashboard Cite

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.

show abstract

“…Models of the customer lifetime value (CLV) primarily allow companies to better allocate resources and formulate strategies in marketing (Ferrentino et al, 2016). Companies use CLV mainly in the area of customer segmentation to build long-term relationships with customers and effectively manage investments in marketing activities (Weng and Huang, 2018). However, use of CLV can help answer a number of very different questions such as decisions related to retaining and acquiring customers, investments in product image, or what is the company's long-term value (Lin et al, 2017;Haenlein et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Predictive Performance of Customer Lifetime Value Models in E-Commerce and the Use of Non-Financial Data

Jasek¹,

Vrana²,

Sperkova³

et al. 2019

Prague Economic Papers

View full text Add to dashboard Cite

The article contributes to the knowledge of customer lifetime value (CLV) models, where extensive empirical analyses on large datasets from online stores are missing. Based on this knowledge, practitioners can decide about the deployment of a particular model in their business and academics can design or enhance CLV models. The article presents predictive performance of selected CLV models: the extended Pareto/NBD model, the Markov chain model, the vector autoregressive model and the status quo model. Six large datasets of medium and large-sized online stores in the Czech Republic and Slovakia are used for a comparison of the predictive performance of the models. Online stores have annual revenues in the order of tens of millions of euros and more than one million customers. The comparison of CLV models is based on selected evaluation metrics. The results of some of the models which use additional non-financial data on customer behaviour-the Markov chain model and the vector autoregressive model-do not justify the effort which is needed to collect such data. The advantages and disadvantages of the selected CLV models are discussed in the context of their deployment.

show abstract

Knowledge acquisition of association rules from the customer-lifetime-value perspective

Cited by 7 publications

References 42 publications

Customer engagement value: process, limitations and future research

Customer engagement value: process, limitations and future research

A novel consumer preference mining method based on improved weclat algorithm

Predictive Performance of Customer Lifetime Value Models in E-Commerce and the Use of Non-Financial Data

Contact Info

Product

Resources

About