A new framework for predicting customer behavior in terms of RFM by considering the temporal aspect based on time series techniques

Abbasimehr, Hossein; Shabani, Mostafa

doi:10.1007/s12652-020-02015-w

Cited by 21 publications

(13 citation statements)

References 57 publications

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“…The authors used a Machine Learning-based fusion technique that categorized the feedback as positive, negative, or neutral [ 101 ]. On the other hand, the methodology proposed by Abbasimehr and Shabani, used a time series forecasting component [ 102 ] that fuses the results of linear and non-linear models. This method provided the advantage of using the known information about the performance of the techniques in the forecasting, which was then used by the fusion component in order to assign the proper weights to the components included in the methodology, being the clustering and the forecasting.…”

Section: Resultsmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

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The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper’s goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.

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Section: Resultsmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…As "other" clustering, we define the clustering methods which don't fit the previous cluster definitions. For example, Abbasimehr and Shabani (2021) propose a time series segmentation approach to get knowledge from customer behavior or Chen et al (2018) proposed an segmentation an algorithm which they call PurTreeClust. Hsu and Chen, Y.-g.C.…”

Section: In-depth Customer Segmentation Methodsmentioning

confidence: 99%

“…In three publications the RFM-analysis is used as feature selection method. For example, Abbasimehr and Shabani (2021) propose a time series clustering approach to get knowledge from customer behavior. First, they split the dataset into predefined time intervals.…”

Section: Tablementioning

confidence: 99%

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

Gomes

Meisen

2023

Inf Syst E-Bus Manage

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The importance of customer-oriented marketing has increased for companies in recent decades. With the advent of one-customer strategies, especially in e-commerce, traditional mass marketing in this area is becoming increasingly obsolete as customer-specific targeting becomes realizable. Such a strategy makes it essential to develop an underlying understanding of the interests and motivations of the individual customer. One method frequently used for this purpose is segmentation, which has evolved steadily in recent years. The aim of this paper is to provide a structured overview of the different segmentation methods and their current state of the art. For this purpose, we conducted an extensive literature search in which 105 publications between the years 2000 and 2022 were identified that deal with the analysis of customer behavior using segmentation methods. Based on this paper corpus, we provide a comprehensive review of the used methods. In addition, we examine the applied methods for temporal trends and for their applicability to different data set dimensionalities. Based on this paper corpus, we identified a four-phase process consisting of information (data) collection, customer representation, customer analysis via segmentation and customer targeting. With respect to customer representation and customer analysis by segmentation, we provide a comprehensive overview of the methods used in these process steps. We also take a look at temporal trends and the applicability to different dataset dimensionalities. In summary, customer representation is mainly solved by manual feature selection or RFM analysis. The most commonly used segmentation method is k-means, regardless of the use case and the amount of data. It is interesting to note that it has been widely used in recent years.

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“…Finally, there are 5*5*5 groups (total segments are 125groups). The numbers of group sorting can be decided by different industries and depending on different situations such as the sample size of database or the budge of marketing (Abbasimehr & Shabani, 2021;Heldt et al,2021;Rahimac et al, 2021). This paper is demonstrated different numbers of groups segmented by the database and the attribution of industry.…”

Section: Monetary Valuementioning

confidence: 99%

Using RFM approach with PBL to course design

Huang

2022

J. Sci. Educ.

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Based on Problem-Based Learning (PBL) concept, this study creates an advertising script by conducting RFM (recency, frequency, monetary) approach from the data analysis in customer relationship management (CRM) course. In PBL process, the students themselves should use RFM method to make segment from the customer database of service industries and find out the characteristics of target segment based on the demographic variables and according to these traits to form their advertising scripts. Through finding the highest value of segment as the target customer, they should focus on the characteristic of their choice target and create an advertising script to attract this segment by their traits. These processes of steps can provide the course design of CRM or advertising project to make creation from customer targeting.

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A new framework for predicting customer behavior in terms of RFM by considering the temporal aspect based on time series techniques

Cited by 21 publications

References 57 publications

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

Using RFM approach with PBL to course design

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