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

Alves Gomes, Miguel; Meisen, Tobias Article — Published Version A review on customer segmentation methods for personalized customer targeting in e-commerce use cases Information Systems and e-Business Management Provided in Cooperation with: Springer Nature Suggested Citation: Alves Gomes, Miguel; Meisen, Tobias (2023) : A review on customer segmentation methods for personalized customer targeting in e-commerce use cases, Information Systems and e-Business Management, ISSN 1617-9854, Springer, Berlin, Heidelberg, Vol. 21, Iss. 3, pp. 527-570, https://doi.org/10.1007/s10257-023-00640-4 This Version is available at: https://hdl.handle.net/10419/311765 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Information Systems and e-Business Management (2023) 21:527–570 https://doi.org/10.1007/s10257-023-00640-4 1 3 ORIGINAL ARTICLE A review oncustomer segmentation methods forpersonalized customer targeting ine‑commerce use cases MiguelAlvesGomes1 · TobiasMeisen1 Received: 25 April 2022 / Revised: 16 February 2023 / Accepted: 9 May 2023 / Published online: 9 June 2023 © The Author(s) 2023 Abstract The importance of customer-oriented marketing has increased for companies in recent decades. With the advent of one-customer strategies, especially in e-com- merce, 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. Keywords Customer segmentation· Feature engineering· Customer targeting· Customer relationship management· RFM-analysis Extended author information available on the last page of the article 528 M.Alves Gomes, T.Meisen 1 3 1 Introduction “As the Internet emerges as a new marketing channel, analyzing and understanding the needs and expectations of their online users or customers are considered as prerequisites to activate the consumer-oriented electronic commerce. Thus, the mass marketing strategy cannot satisfy the needs and expectations of online customers. On the other hand, it is easier to extract knowledge out of the shopping process under the Internet environment. Market segmentation is one of the ways in which such knowledge can be represented and make it new business opportunities.” (Kim and Ahn 2004). Already in 2004, Kim and Ahn (2004) described an essential paradigm shift that online marketing was encountering in a time in which the world wide web was rising. The statement focused on the limitation of mass marketing in a period where data-driven technological possibilities arose to analyze web-users footprints and enable personalized-oriented marketing. About two decades later personalized-oriented marketing is still a key challenge that many researchers conduct in their work (Chen etal. 2018; Apichottanakul etal. 2021; de Marco etal. 2021; Nguyen 2021; Sokol and Holy 2021). Not only has it been shown that personalized customer targeting is more profitable for companies, but also that knowledge about customer behavior is a decisive factor for success and failure (Mulhern 1999; Zeithaml et al. 2001; Kumar et al. 2008). In this respect, it is essential to understand the customers and their needs, and to be aware of their behavioral changes over time (Liu etal. 2009; Ding etal. 2019; Griva etal. 2021; Apichottanakul etal. 2021). In addition to technological changes and increasing functional requirements, legal regulations are also subject to constant change. This results in further non-functional requirements, as these regulations firstly describe local conditions and secondly can counteract the functional objectives (Burri and Schär 2016; European-Parliament 2016). From a functional perspective, companies that want to analyze customer behavior need (1) the capacity to record customer data, (2) an algorithm to characterize similar user behavior, and (3) strategies or processes that use the extracted information to achieve the business goal. Regarding the first requirement, it is necessary to collect data that enable algorithm-based characterization of user behavior. Thereby, we distinguish between customer behavior data that is collected explicitly and implicitly. As the names suggest, explicit data collection is intentional to collect customers’ information. In implicit data collection, the main purpose is not to collect information about customers, but to collect information about the process in which the customer appears as the interactant, such as purchase information for accounting purposes. Explicitly collected data such as demographic information, on the other hand, is difficult to collect and maintain for several reasons. Not all customers are willing to share demographic data or they browse anonymously on the web. In addition, information collected in this way is subject to change over time and, accordingly, is always subject to uncertainty that is difficult to quantify (Chan et al. 2011; Chen etal. 2018). Accordingly, implicitly gathered data is easier to collect. This data can be tracked with every user interaction. E.g. information about products 529 1 3 A review oncustomer segmentation methods forpersonalized… that are purchased together or the amount of money spent for a purchase. Nonetheless, data collected in such an implicit manner requires deeper analytical skills to exploit. For the second requirement, the gathered interaction data is used. A frequently used approach for managing different customers with diverse preferences is segmentation (Hong and Kim 2012; Hsieh 2004; Chen etal. 2018). Customer segmentation is an unsupervised-learning process and utilizes different clustering approaches which have the goal to separate aforementioned customer data based on similarity. Hereby, similarity is measured by an objective function such as euclidean distance. It should be noted that customer behavior is a continuous process, with customer needs, wants and satisfaction changing over time. Accordingly, the processes and underlying procedures implemented in companies must be flexible in order to accommodate this high level of dynamism (Liu etal. 2009; Ding etal. 2019; Griva etal. 2021). The last requirement is to utilize the analyzed customer information. Domain experts like marketers can tailor appropriate marketing strategies for individual customer groups based on segmentation. As Birtolo etal. (2013) already stated and showed, instead of domain experts, more and more automated methods to extract and to learn underlying patterns in customer behavior allow to target customers in advance. The aforementioned dynamics are not only reflected in the respective target market but also can be observed in the underlying segmentation methods. Therefore, the goal of our survey is to provide an overview of digital and autonomous customer targeting processes for customer relationship management (CRM) based on historical data. The main objective of the literature research lies in the customer segmentation process for different e-commerce related use cases like retailing or services in the banking sector. Our study is structured by three guiding questions, to which we provide answers in this work. 1. Which clustering processes and methods are frequently used to understand customer behavior and targeting afterward? 2. Are there methodological limits with regard to data dimensionality? 3. Do methodological trends exist that can be observed over a period of two decades? The main difference between our survey and former ones is that we focus on the process of customer targeting and behavior analysis in the e-commerce domain. The most recent literature review with a related topic is from 2016 (Sari etal. 2016). However, six years have passed since then, which makes an updated view necessary. Besides that in our study, we conduct a more extensive literature review that leads to a different classification of segmentation methods and more use case examples. In addition, we recognized a more extensive e-commerce process for customer targeting. Our contribution and main finding are: 530 M.Alves Gomes, T.Meisen 1 3 • We provide an overview with examples from the literature of how customer behavior analysis is used. • We determine a customer targeting process with four phases. • We could not identify a consensus in metrics to evaluate and compare the quality of the segmentation algorithm and therefore it cannot be said which of the methods is “best”. • Based on the frequency in publications and ability to handle large amounts of data, we recommend a process that uses RFM-analysis as a feature representation and k-means for segmentation. • We identify open questions and possible research gaps regarding embeddings for customer representation and deep learning-based segmentation for customer analysis and customer targeting strategies Our study is structured as follows: In Sect.2, we present and explain our research methodology. In Sect.3, we present a literature overview of the identified works. Hence, in this section, we address the first guiding question accordingly and provide an answer. Moreover, we present the survey literature more in-depth. Based on the identified process, we notice that feature selection (be it manual or computerized) is an essential preprocessing step of customer behavior segmentation. Therefore, we explain the different segmentation and feature selection methods that are used. Additionally, the methods in the surveyed literature are described regarding the applied use cases for customer targeting and data volume. Section3 ends with an overview of the publications’ evaluation metrics for customer segmentation. We analyze and discuss our findings in Sect.4 which is further divided into two subsections. The first subsection is about the feature selection. In terms of feature selection methods, we present an answer to guiding question two and three. Similarly, in the second subsection we analyzes, discusses, and answers guiding questions two and three regarding the reviewed segmentation methods. In each subsection, we state open research questions that are not covered by our survey but have future potential. Finally, we conclude the survey in Sect.5 with a brief summary of the findings and new open research questions and potential. 2 Literature research methodology As already encouraged in the introduction we want to scientifically investigate which processes exist for personalized customer marketing approaches. Especially, to get an overview of commonly used customer segmentation methods in the context of CRM in e-commerce, we have conducted an extensive literature review. Thereby, Vom Brocke etal. (2015) published a recommendation on how to conduct such a search in an effective and highly qualified way. Hence, we followed their recommendation for the most part. Figure1 illustrates our review process. We started our literature research by reading survey papers to derive an integrated and consolidated understanding of the conceptualization of the subject. Thereafter, we started the literature search. Therefore, we defined our search scope. Vom Brocke etal. (2015) 531 1 3 A review oncustomer segmentation methods forpersonalized… refer to Cooper (1988) which states four steps on how to define a search scope: (1) process, (2) sources, (3) coverage, and (4) technique. Leaning on these four steps we choose a sequential search process. As a publication source, we used the Web of Science1 (WoS) online research tool as it is one of the leading scientific citation and analytical platforms and provides scientific publications across a wide amount of knowledge domains (Li etal. 2018). To keep the focus on the customer segmentation methods we used the following search term: • “Customer segmentation” or “customer clustering” or “user segmentation” or “user clustering” Herein, we chose to use the word “user” as a synonym for “customer” and “clustering” for “segmentation”. We wanted the search to be as less restrictive as possible to not miss relevant publications. Therefore, we expected works that are not relevant to our research. After having a corpus of hundreds of publications, we started reading the title, abstract, and keywords of the publications. We filtered out all publications that did not deal with customer behavior in commerce, especially in the context of e-commerce. The next step was to read all remaining papers and excluded all publications that did not deal with customer segmentation in an e-commerce use case and it became apparent that customers were segmented based on their information and actions. We extracted all wanted information from publications we classified as relevant. Specifically, we retrieved bibliometric information, information about the use case, the used methods, information about the used data, and the results. 3 Literature overview As aforementioned in Sect. 2, we started our literature review with reading related surveys. Plenty of research surveys in the field of segmentation prioritize the underlying methodology or class of methods but not their usage in specific domain (Gennari 1989; Rokach 2010; Hiziroglu 2013; Ben Ayed etal. 2014; Firdaus and Uddin 2015; Reddy and Vinzamuri 2018; Shi and Pun-Cheng Fig. 1 Flow chart of the literature research process 1 https:// www. webof scien ce. com/. 532 M.Alves Gomes, T.Meisen 1 3 2019). For example, Shi and Pun-Cheng (2019) review clustering methods for spatiotemporal data which are collected in diverse domains like social media, human mobility, or transportation analysis. Another survey example is brought by Hiziroglu (2013). The author reviews segmentation approaches for applications of soft computing techniques. Other surveys or studies focus on specific methods like k-means or RFM-analysis (Sarvari et al. 2016; Deng and Gao 2020). The most related literature review we found in our literature search is from Sari etal. (2016) which reviews customer and marketing segmentation methods and the necessary data. They identify different segmentation approaches and e-commerce process which coincides in some parts with our outcomes. However, as already mentioned before, six years have already passed and their paper corpus consist of less than 20 publications. From this, we deduce the need for an up-to-date and more detailed review in the area of customer segmentation in e-commerce. The WoS search from 2023/01/01 led to 852 publications, of which not all were related to our research as assumed. As described we excluded all publication that did not deal with customer behavior in e-commerce. The major domain that was not related to our research objective dealt with user segmentation in nonorthogonal multiple access (NOMA) techniques. Over half (66%) of the publications were not related to our research topic and we had 289 publications left that were somehow e-commerce related. From the 289 publications, we classified 149 publications as “not relevant” and 140 publications as “relevant” based on the title, abstract and keywords with the aforementioned criteria. Reading the remaining literature (140 publications), we paid particular attention to recurring processes. We identified a process that is constantly used to determine customer behavior with segmentation approaches. Figure2 illustrates the identified process that depicts the answer to our first question. It illustrates the customer targeting process and it can be divided into four steps: (1) customer information, (2) customer representation, (3) customer analysis, and (4) customer targeting. In the first step, the customer information is stored in form of data and is made available for further processing. In the literature, this step is usually given by Fig. 2 Process of customer targeting based on behavioral information gathered from data 533 1 3 A review oncustomer segmentation methods forpersonalized… provided datasets. Nevertheless, in some publications, the information is collected by the researcher. Especially, when data is collected explicitly which is for example done by Hong and Kim (2012), Nakano and Kondo (2018), and Wu (2011). Based on the collected information a customer representation is built as the second step. The customers are represented by their features which are selected manually or with a feature selection method. In nearly half of the cases (47.6%), features are selected manually and in the other half (52.4%) feature selection methods are used. Both feature selection approaches have their advantages and disadvantages. For example, feature selection methods are utilized to eliminate features with less information content or to aggregate and extract additional knowledge out of the customer data. The most used method in our literature review is the Recency, Frequency, Monetary (RFM) analysis that aggregates additional information about the customers’ behavior and value to a company (Hughes 1994) which we show in Sect.4. Manual feature selection usually is performed by extracting information like item view or click events, purchased items, and item information such as the associated category. In some other cases, mostly for recommendation, the authors additionally use ratings and reviews for the behavior analysis. Otherwise, demographic data is collected through membership or similar programs. Another approach to get demographic or psychographic information is by user surveys. The third step of the found process is customer analysis which is the key component of the process and is done by applying segmentation methods. Customers are split into more homogeneous groups of similar behavior. This is done by different segmentation approaches, like methods that compare the similarity between the customer representation or other methods that partition the customers by given thresholds. In Sect.3.4, we further explain the interaction of customer representation with feature selection methods and the customer analysis on found case studies. The fourth and last step, customer targeting, uses the behavioral information from the customer analysis to target the right user with the right CRM decision. In the literature, we identified different targeting approaches which includes recommendation, marketing campaigns, and pricing strategies. The main difference in the literature is that recommenders are evaluated against others with evaluation metrics like hit-rate, accuracy, etc, and marketing campaigns or pricing strategies focus on the plausibility of the customer segmentation and try to explain the outcomes over the performance. We decided to consider only literature that mostly adheres to this characteristic process because it fulfills all necessary conditions for personalized customer marketing which is our defined investigation scope. The work of Coussement et al. (2014) is an example of a scientific publication which we did not consider in our work because it is not in our scope. In their research, they investigated the impact of data quality on different segmentation methods and showed which methods are more robust to inaccuracies. Based on this aforementioned method, we further filtered our corpus to obtain a final corpus of 105 scientific papers. The literature is distributed between the years 2000 and 2022 over different use cases and journals. The reviewed publications are not equally distributed over the years. Figure3 illustrates the distribution of the paper’s publication year. We see that there are more publications over time in 534 M.Alves Gomes, T.Meisen 1 3 the field of e-commerce considering customer analysis with segmentation methods. Before 2010, we usually find one publication per year. In the period from 2001 to 2003, however, there is no publication in the paper corpus at all. In total, there are 16 publications in the period from 2000 to 2010. After 2010, there are at least three publications per year with an increasing tendency. 43 out of 105 publications (about 41%) are published in 2020, 2021 and 2022. Table4 gives an overview of the 105 publications containing title, author, and year.2 Fig. 3 Distribution of surveyed publications from 2000 until 2022 Fig. 4 Distribution of the surveyed feature selection methods over the years 2 Table4 can be found in the Appendix1. 541 1 3 A review oncustomer segmentation methods forpersonalized… Table 2 Segmentation and used feature selection methods with corresponding references of the survey literature Segmentation method Feature selection Count References Rule-based None 2 Boettcher etal. (2009), Hjort etal. (2013) RFM-analysis 7 Jonker etal. (2004), Chang and Tsai (2011), Hiziroglu etal. (2018), Wong and Wei (2018), Stormi etal. (2020), Hsu and Huang (2020), Sokol and Holy (2021) K-means None 16 Zhang etal. (2014), Abdolvand etal. (2015), Brito etal. (2015), Liu etal. (2015), Hafshejani etal. (2018), Bai etal. (2019), Deng and Gao (2020), Griva etal. (2021), Zhang etal. (2020), Alghamdi (2022b), Araujo etal. (2022), Chalupa and Petricek (2022), Zhang and Huang (2022), Gautam and Kumar (2022), Griva (2022), Tabianan etal. (2022) RFM-analysis 21 Chan etal. (2011), Peker etal. (2017), Akhondzadeh-Noughabi and Albadvi (2015), Ravasan and Mansouri (2015), Sarvari etal. (2016), Dogan etal. (2018), Alberto Carrasco etal. (2019), Christy etal. (2018), Guney etal. (2020), Lam etal. (2021); Pratama etal. (2020), Sivaguru and Punniyamoorthy (2021), Rahim etal. (2021), Wu etal. (2020), Wu etal. (2021), Zhao etal. (2021), Bellini etal. (2022), Mensouri etal. (2022), Mosa etal. (2022), Wu etal. (2022), Kanchanapoom and Chongwatpol (2022) PCA 3 Nie etal. (2021), Tsai etal. (2015), Umuhoza etal. (2020) Graph 1 Ding etal. (2019) Fuzzy C-means None 1 Ozer (2001) RFM-analysis 3 Wang (2010), Safari etal. (2016), Munusamy and Murugesan (2020) Customer lifetime value 1 Nemati etal. (2018) PCA 1 Alghamdi (2022a) Hierarchical clustering None 3 Li etal. (2009), Hsu etal. (2012), Wang and Zhang (2021) Discrete wavelet transform 1 Aghabozorgi etal. (2012) RFM-analysis 1 Zhou etal. (2021) Rough Set Theory None 2 Song and Shepperd (2006), Wu (2011) RFM-analysis 1 Dhandayudam and Krishnamurthi (2014) Latent class None 4 Teichert etal. (2008), Goto etal. (2015), Nakano and Kondo (2018), Valentini etal. (2020) RFM-analysis 2 Wu and Chou (2011), Apichottanakul etal. (2021) 542 M.Alves Gomes, T.Meisen 1 3 Table 2 (continued) Segmentation method Feature selection Count References Expectation-maximization RFM-analysis 1 Rezaeinia and Rahmani (2016) Spectral clustering Purchase Tree 1 Chen etal. (2019) Evolutionary Algorithm None 3 Wan etal. (2010), Sivaramakrishnan etal. (2020), Krishna and Ravi (2021) RFM-analysis 2 Chan (2008), Chan etal. (2016) Self-organizing map None 2 Nilashi etal. (2021); Verdu etal. (2006) RFM-analysis 3 Hsieh (2004), Liu etal. (2009), Liao etal. (2022) Deep learning-based None 1 Nguyen (2021) Hybrid clustering None 10 Wang and Shao (2004), Kang etal. (2012), Bian etal. (2013), Hong and Kim (2012), Ma etal. (2016), Ramadas and Abraham (2018), Logesh etal. (2020), Wang etal. (2020), Barman and Chowdhury (2019), Griva etal. (2022) CHAID 1 Kim and Ahn (2004) MCA 1 Jadwal etal. (2022) Other approaches None 6 Hsu and Chen, Y.-g.C. (2007), Jiang and Tuzhilin (2009), Rapecka and Dzemyda (2015), An etal. (2018), Madzik and Shahin (2021), Dogan etal. (2022) Purchase Tree 1 Chen etal. (2018) RFM-analysis 3 Hu and Yeh (2014), Abbasimehr and Shabani (2021), Simoes and Nogueira (2021) 543 1 3 A review oncustomer segmentation methods forpersonalized… the feature information, they assign each customer to one of four groups. The groups are based on the buying and returning habits of the customers. The authors conclude from the customer analysis, that customers who tend to return goods are also the more valuable for the company. In 16 publications the authors decide to not use a feature selection method but select features by hand before applying k-means clustering to the customer data. Authors of 21 publications use the value of RFM-analysis for the segmentation with k-means. Three research groups use a principal component analysis (PCA) for feature selection before clustering with k-means. Only Ding etal. (2019) use a graph representation before segmentation. The graph is built based on user-item interactions. Griva (2022) analysis the customer of 140 e-commerce stores in European countries with k-means and hand crafted features. The features are extracted from 270,000 responses from a customer satisfaction survey and 1 million orders from 800,000 customers. They propose a framework which is capable to build automated marketing actions based on the created customer satisfaction segments. Example for such marketing actions are social media sharing strategies for the satisfied segments or discounts for the less satisfied customer segments. Guney etal. (2020) are looking for the best campaign in movie rental use case (video on demand). In a first step they apply an modified RFM-approach which extract two additional features from the data. The two features are the number of days between the first and last rental and the standard deviation of the days between two rented movies. These five features are clustered via a k-means algorithm. The clustering results in four customer groups. An apriori algorithm namely an association rule mining approach is than used to assign the best marketing campaign to the customer segment. In our selected literature six publications utilize an FCM approach. Ozer (2001) collects the data from customers of an online music service via a customer survey and doesn’t use a feature selection method before applying FCM on the features. Nemati et al. (2018) search for the most appropriated marketing strategy for the customers of a telecommunication industry use case. First, they compute the customer lifetime value (CLV) for each customer and group them with FCM. To assign the right marketing strategy to the right segment they utilize a fuzzy TOPSIS technique. For hotel businesses, customers’ satisfaction is crucial. Alghamdi (2022a) investigate customers’ satisfaction of hotel visitors in Mecca and Medina (Saudi Arabia). Therefore, they apply PCA on data collected from TripAdvisor and segment the resulting features via FCM. Hierarchical clustering is used in five publications. Three authors handcraft their features. Aghabozorgi etal. (2012) calculate the necessary features by applying a discrete wavelet transformation (DWT) on customer data of a bank use case. In their research, DWT is an appropriate approach because they consider customer activities as a time series which is not the norm. After using DWT on the data, the data is initially segmented with a hierarchical clustering method. The cluster is updated incrementally in a given period with new data. Zhou etal. (2021) combines hierarchical clustering with an extended the RFM-analysis for a retail use case. The 544 M.Alves Gomes, T.Meisen 1 3 RFM-analysis is extended by the interpurchase time which results in four different features. The interpurchase time is defined as the time gap between two consecutive purchases in the same location (same website). Afterwards, the customers are clustered by the calculated features. In our research, we have one publication from Dhandayudam and Krishnamurthi (2014) that combines RFM-analysis for feature selection with rough sets for clustering. In addition, they add another feature to the RFM-values that describes the average time between purchase and payment. They categorize all four features in their 20%-quantiles and then utilize a slightly modified rough set theory approach for the clustering. Song and Shepperd (2006), Wu (2011) don’t use feature selection methods before segmenting the customers with a rough set approach. Clustering based on latent class models is used six times in the surveyed literature. Four of them manually select the features and therefore, don’t use feature selection methods. Nakano and Kondo (2018) use psychographic, demographic, online store, social media, and device touchpoint data. The information is clustered with a latent class analysis approach which results in seven segments. Goto etal. (2015) propose a method based on latent class analysis that clusters items and customers. They assume valuable users purchase more often only browsing and valuable products are bought more often. They use the latent class model to cluster the customers into “good users” and “other users”. To analyse the resulting segments they use the Classification and Regression Tree (CART) Algorithmus. Wu and Chou (2011), Apichottanakul et al. (2021) use RFM-analysis for the feature selection and apply a latent class approach for the clustering. Apichottanakul etal. (2021) use the proposed GRFM approach from Chang and Tsai (2011) to analyse the customers of a pork processing use case. First, the RFM scores are calculated for nine product categories and each feature is categorized in one of five categories based on the 20%-quantile. The features are clustered with a probabilistic latent class model. Apriori the optimal number of k is unknown therefore, a suitable number of clusters is determined with the Akaike Information Criterion (Akaike 1974). In the last step, the clusters are analyzed with the help of the RFM-values. The only publication that uses the EM algorithm for clustering is from Rezaeinia and Rahmani (2016). The goal of their work is to recommend products in a retail use case. Therefore, they first compute the features via RFM-analysis and cluster them with an EM approach for customer targeting. Spectral clustering is used by Chen etal. (2019) to segment customers buying behavior. Therefore, they use a Purchase Tree representation for customers transactions which was proposed earlier by Chen etal. (2018). For the customer segmentation, they propose a two-level subspace weighting spectral clustering algorithm. Spectral clustering approaches are used only once in our literature. Our survey contains five publications that utilize EAs for customer clustering of which two use RFM-analysis and three don’t use a feature selection method on the available data. Both publications using RFM-analysis are published by or with Chu Chai Henry Chan. In his publication from 2007, the task is to determine an appropriated strategy for each customer of an Nissan automobile retailer. Therefore, Chan (2008) computes the features from the RFM-analysis and categorizes the values in one of five 20%-quantiles. Then the features are binary 545 1 3 A review oncustomer segmentation methods forpersonalized… encoded with four bits. Based on the binary features a GA is used with the customer lifetime value (CLTV) as the fitness function. In 2016, Chan etal. (2016) apply the same feature preprocessing and PSO with CLTV as fitness function on a similar use case but with more data. SOMs are used in five publications in total. Verdu etal. (2006); Nilashi etal. (2021) utilize handcrafted features to represent the customers. In the remaining three publications customers are represented by their RFM-values. For example, Hsieh (2004); Liu etal. (2009) combine an RFM-analysis feature extraction with a SOM clustering to segment the customers in their case study. A recent example of an SOM approach is proposed by Liao etal. (2022). They develop different marketing strategies for each segment for a retail use case. Therefore, they use an extended RFM-analysis approach to represent the customers. The extension is not only using RFM-analysis on customer purchase information but also on other behavioral information like clicks, add-to-cart, or add-to-favorite. For this, they utilize 2 million customer interaction records. The SOM approach is than applied on the different RFM-values of the customers to segment them in similar behavioral groups. Nguyen (2021) presents a deep learning-based clustering approach named Deep Embedding Clustering that combines a deep neural network and a self-supervised probabilistic clustering technique. They state that their approach produces explainable customer segments. In the first step, they determine the optimal number of clusters with a spectral clustering approach and the elbow method. Then they encode their manually selected variables and apply the deep embedding clustering which is a deep autoencoder that is trained with the mean squared error (MSE) loss. In our literature review, we found twelve research papers that use hybrid clustering methods. In ten publications no feature selecteion method is used. For example, Kang etal. (2012) don’t utilize a feature selection. They split the dataset into two sets of answering customers and not answering customers. The data points are clustered with a k-means and CSI Algorithm with different criteria. Kim and Ahn (2004) use (CHAID) as a feature preprocessing. The clustering is performed by a GA based on k-means clustering. Jadwal etal. (2022) use MCA as feature preprocessing and segment the customers of a bank use case with an segmentation approach based on k-means and hierarchical clustering. In our survey, we classified ten publications as “other clustering”. Six authors have manually selected features. 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. As a second step, they apply RFM-analysis on each interval and use the monetary value of the customer for the time series. On the resulting time series, a time series clustering approach is applied. Also, Hu and Yeh (2014) utilize RFM-analysis based features for the clustering. Therefore, they propose an RFM-pattern-tree to represent customers which also is used to approximate customers with less information. They can use this to detect similar customers with similar behavior. Simoes and Nogueira (2021) uses RFM-features and segment the customers with an ABC curve segmentation. Chen etal. (2018). represent the data as a Purchase Tree and propose for the segmentation 546 M.Alves Gomes, T.Meisen 1 3 an algorithm which they call PurTreeClust and is based on a partitional clustering algorithm. 3.5 An overview ofthedata dimensionality inthepublications’ experiments An essential component for the behavior analysis and customer targeting process is the information that is collected by the companies. In this section, we describe which methods are used for which data in respect to the order of magnitude. We distinguish between two different types of data amount. The first is the number of data points e.g. transactions and it describes the amount of data an algorithm can handle at least. The second type is the number of customers in a dataset. The number of customers may indicate how much data an algorithm can process because customers and not data points are segmented. Therefore, it is important to consider the number of customers when analyzing the data dimensionality. Depending on the number of customers the number of data points can be reduced after a feature selection method. For example, in RFM-analysis the information of a user is aggregated for one period which leads to fewer data points the clustering algorithm needs to process. Other feature selection approaches like PCA doesn’t affect the number of data points or user but the number of features. Table 3 shows which feature selection methods and clustering algorithms are used with which data dimensionality regarding the number of data points and the number of customers in the use case. The number of data points is described by six columns of which each has a different order of magnitude. We choose a similar representation for the number of customers in a dataset but only have five columns. We annotate the methods that deal with this amount of data with checkmarks. Note, that not all publications describe the data in a way it is possible to extract the information of the data dimensionality. In some cases, only the number of data points are given, in others, we only know about the number of customers, and sometimes we don’t have information at all. How often a method is used, is indicated by the number of checkmarks. In some publications, different datasets with different sizes are utilized. If two datasets have different orders of magnitude, we indicated it by using checkmarks in the appropriated cells. However, if the datasets in the same publication have the same order of magnitude, we indicated it only once per publication. In terms of feature selection, we see that RFM-analysis is applied up to 108 data points, but above this number of data points it is not used anymore. For example, Akhondzadeh-Noughabi and Albadvi (2015) apply RFM-analysis on 35,537,276 customer activities from 14,772 customers. Based on the survey literature, PCA and DWT can be applied to data with up to 1 million data points. The graph approach utilized by Ding etal. (2019) is used on around 50,000 user activities. Chen etal. (2018, 2019) propose a purchase Tree approach which is tested on several datasets with different sizes in a range of a few thousand and 350 million transactions with customer numbers between 800 and 300,000. The clustering method rows only refer to the clustering algorithms where no feature selection methods are applied. Regarding the number of users in datasets, we 547 1 3 A review oncustomer segmentation methods forpersonalized… Table 3 Data dimensionality that the feature selection methods and clustering approaches handle in the experiments of the surveyed literature Methods Data points Number of customers < 10 3 103 – 104 104 – 105 105 – 106 106 – 108 > 10 8 < 10 3 103 – 104 104 – 105 105 – 106 >106 Feature selection RFM-analysis ✓ ✓ ✓✓✓ ✓✓✓ ✓✓✓ ✓✓✓ ✓ ✓✓ ✓✓✓ ✓✓ ✓✓✓ ✓✓✓ ✓ PCA ✓ ✓ ✓ ✓ ✓ MCA ✓ Graph ✓ Purchase Tree ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Discrete wavelet trasformation ✓ ✓ Clustering method Rule-based ✓ ✓ ✓ K-means clustering ✓ ✓✓ ✓✓ ✓✓ ✓✓✓ ✓ ✓✓ ✓ Hierarchical clustering ✓ ✓ Rough Set Theory ✓ ✓ Latent class ✓ ✓✓ ✓ Evolutionary Algorithm ✓ ✓ ✓ ✓ ✓ Self-organizing map ✓ ✓ Deep learning-based ✓ Hybrid clustering ✓ ✓✓✓ ✓ ✓ ✓✓ ✓ ✓ Other clustering ✓ ✓ 548 M.Alves Gomes, T.Meisen 1 3 see that usually, their number doesn’t exceed 10,000. An expectation is provided by Kang etal. (2012). They test their hybrid approach on two datasets in which one dataset contains information about 101,532 customers. Another one comes from Goto etal. (2015) where they apply a latent class model on 37,278,907 browsing actions from 99,924 users. Abdolvand etal. (2015) apply k-means on 25,000 bank customers. Investigating which clustering methods are used for data with at least one million entries, we identify that it is k-means clustering, latent class models, and hybrid clustering approaches. K-means is used twice on over a million data points by Liu etal. (2015); Zhang etal. (2014). Liu etal. (2015) have access to 3 million transaction data from taoboa.com. Zhang etal. (2014) use the MovieLens datasets in which one has 100,000 movie ratings of 1682 different movies rated by 943 different users and the other has 1 million ratings for 3952 movies made by 6040 users. 3.6 Evaluation metrics Usually, a clustering model learns in an unsupervised manner and the ground truth is unknown. Therefore different criteria need to be used to evaluate their performance. In the following the frequently used evaluation measures are described and briefly analyzed. Clustering evaluation or cluster validation is an essential step in verifying the discovered groups in a data set. The fundamental challenge of evaluation lies in the missing ground truth, which can be a reason that we have not found a consensus between the evaluation methods in our literature research. Figure 6 presents the Fig. 6 Distribution of the segmentation methods used evaluation methods 549 1 3 A review oncustomer segmentation methods forpersonalized… distribution of used evaluation methods for segmentation methods in the literature that also shows the missing consensus. We classified the evaluation criteria into seven different groups which are indicated by a color. In the following, we briefly introduce the evaluation criteria and give some examples. It should be noted, that in some publications the authors don’t apply evaluation metrics. Instead, they analyze the segments based on their plausibility. Chan etal. (2011) for example, measures the performance of the proposed method by comparing the company’s sails before and after the using the approach. Guney etal. (2020), Nie etal. (2021), Wu etal. (2020) evaluate the segments with the help of the RFM-values. Statistical significance test The underlying concept of a Statistical significance test is to determine whether the data points are randomly distributed or not. Krishna and Ravi (2021) have used a statistical t-test to evaluate their genetic algorithm approach on five different datasets. Another approach is the Kendall coefficient (Kendall 1938) that is used by An etal. (2018). Analysis of variance The basic idea behind the analysis of variance (ANOVA) is to analyze whether the expected values of variables differ in distinct groups. By testing, if the variance of a variable is larger or smaller between the groups than within the groups, a statement about the meaningfulness of the group can be determined. ANOVA tests are used by Li etal. (2009), Hong and Kim (2012), Hjort etal. (2013), Hiziroglu etal. (2018). Silhouette analysis The silhouette analysis is a (visual) validation method that is independent of the number of clusters and determines the consistency within a cluster. In addition to validation, this method can also be used to find the optimal number of clusters (Rousseeuw 1987). For example, the silhouette analysis is used by Akhondzadeh-Noughabi and Albadvi (2015), Peker etal. (2017), Christy etal. (2018). Indices As shown in Fig.6 many different index metrics were used to validate the clustering performance. The most used indices in our literature review are Davies- Bouldin (DB) index, Calinski-Harabasz (CH) index, and Xie-Beni (XB) index. The DB index describes the average similarity of each cluster with its most similar cluster. The DB index is to be interpreted in such a way that the lower the value is, the better the clustering (Davies and Bouldin 1979). The CH index, is the ratio of intra-cluster dispersion and inter-cluster dispersion (Caliński and Harabasz 1974). The XB index is used for fuzzy segmentation approaches and describes the separation and compactness of the clusters. The optimal number of clusters has the lowest XB value (Xie and Beni 1991). Chan etal. (2016) evaluate their proposed EA clustering with the DB index. Munusamy and Murugesan (2020) evaluate their fuzzy c-means clustering approach with XB index but also with the Kwon index, and the Tang index. They also use error measures for the cluster evaluation. Information criteria These measures are used to select the models that fit the given data best but also take the number of parameters into account to prevent overfitting. One popular information criterion is the Akaike information criterion (AIC) which describes the model’s information based on the number of parameters and the model’s log-likelihood (Akaike 1974). Apichottanakul etal. (2021) utilize the AIC for evaluation to determine the optimal number of clusters in their latent class model. 550 M.Alves Gomes, T.Meisen 1 3 Error measures Another evaluation method that is used in the surveyed literature is based on error measures like the mean absolute error (MAE), sum of squared error (SSE), root mean squared error (RMSE), or symmetric mean absolute percentage error (SMAPE). Abbasimehr and Shabani (2021) measure the cluster performance with SMAPE. Aghabozorgi etal. (2012) evaluate their proposed hierarchical clustering with SSE. Also, Lam etal. (2021) evaluate their clustering approach with SSE. Others Some authors combine several evaluation metrics to express the usefulness and quality of their clustering models or use methods which donot fit in the six categories above. The mostly used “other” metric is cluster distance. We classify all inter and intra-cluster distance metrics as cluster distance if they are not further explained by the authors. For example, Wan etal. (2010) utilize an inter and intracluster distance to show that their CAS clustering approach has better distances and is more stable than k-means. Sivaguru and Punniyamoorthy (2021) apply a within/ total clustering error index (which we consider as a cluster distance metric) to evaluate their k-means approach. In addition, they utilize DB index and t-test too. Umuhoza etal. (2020) utilize the elbow method, silhouette score, and CH index to determine the optimal number of segments. Another metric is the concordance (C) statistic (C-index) also known as receiver operating characteristic (ROC) and associated area under curve (AUC) score is for example used by Hsu etal. (2012) (also use SVM, isolation, and AVG index) or Barman and Chowdhury (2019). Dhandayudam and Krishnamurthi (2014) uses cohesion and coupling to evaluate the cluster quality for their rough set theory approach. Griva etal. (2021) use cohesion, inter and intracluster distance, similarity, and separation for cluster validation and gap statistic plus silhouette analysis to determine the optimal number for their latent class model clustering. Ramadas and Abraham (2018) validate the hybrid clustering which combines GA and fuzzy c-means with a partition coefficient (degree of intersection of clusters), classification entropy (the fuzziness of clusters), XB index, separation index, and partition index. Abdolvand etal. (2015) utilize the DB index to determine the optimal number of segments for their k-means approach and data envelopment analysis (DEA) for the evaluation. 4 Analysis anddiscussion As previously shown in Fig.3, the reviewed publications were not equally distributed over the years. An upward trend in the number of publications can be recognized which indicates the importance of customer behavior analysis and therefore, their segmentation even after twenty decades of research. Especially, in the years 2020, 2021, and 2022, we have found more publications than the years before. There may be several reasons for this. The first reason that comes to mind is the current covid pandemic. This has increased the growth in e-commerce services. This could have prompted less digitalized companies to digitalize more and offer their services online. In many publications the company remains unknown. However, in some other publications the companies are named. Two examples are taobao.com or nelly.com that are established online companies which is an indication against our 557 1 3 A review oncustomer segmentation methods forpersonalized… needs and behavior. This is necessary for marketing and domain expert to tailor personalized marketing strategies for the customers. However, with the advent of deep learning-based approaches personalized customer targeting can be done fully automated e.g. end-to-end model and therefore, the customer analysis step which includes customer segmentation can be omitted. This development can be seen for example in deep learning-based recommendation systems which make personalized recommendation without the need of the customer analysis. This leads to the question; How customizable are the individual phases of this process and can individual steps be omitted to increase efficiency or are all steps so fundamental that a deviation from these procedures would have a negative impact on the goal, customer targeting? • Manual feature selection is still frequently used. The feature quality is thereby highly depended on the underlying expertise to select or define important features for clustering. Progressive digitization is leading to growing challenges, especially in dealing with data volumes and data diversity. To meet these challenges, manual feature selection is reaching its limits as it is not able to tap the insight potential within this data. Hence, the question arises if approaches exist that can help experts to create meaningful and representative features for customer representation? In this regard a look outside the box to other e-commerce research, e.g. clickthrough rates prediction can yield new approaches. There researchers and professionals have started using feature embeddings on manual selected features with the underlying assumption that the learning models will learn meaningful representations from the data. This would simplify the manual feature selection process. However, these learning models are usually based on deep neural networks which are unfortunately black boxes and not interpretable. The question rises, if segmentation methods can be used as a post-processing to provide interpretability for the embedded features and therefore, an insight over the customers? (Which got lost by not using the customer analysis step). • In our research, we identified many different evaluation metrics to evaluate the performance of segmentation methods. Nevertheless, we could not find a consensus on evaluation metrics as in other domains. The reason is the missing ground-truth. This circumstance makes it difficult to determine the effectiveness and transferability of a segmentation approach from one use case to another. The open question that remains is, is it necessary, to develop evaluation metrics with semantic meaning and is it possible to transfer such metrics to different experiments to enable comparision of the segmentation methods? In our literature review, we covered the usage of feature selection and segmentation method for personalized customer targeting. E-commerce is a dynamic environment with ever new challenges and therefore, new research opportunities. A Table ofreviewed literature 558 M.Alves Gomes, T.Meisen 1 3 Table 4 Literature with title, author and date which are the object of investigation sorted by year of publication/acceptance Title References Years User segmentation of online music services using fuzzy clustering Ozer (2001) 2000 An integrated data mining and behavioral scoring model for analyzing bank customers Hsieh (2004) 2004 Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems Kim and Ahn (2004) 2004 Joint optimimization of customer segmentation and marketing policy to maximize long-term profitability Jonker etal. (2004) 2004 Effective personalized recommendation based on time-framed navigation clustering and association mining Wang and Shao (2004) 2004 Mining web browsing patterns for E-commerce Song and Shepperd (2006) 2005 Classification, filtering, and identification of electrical customer load patterns through the use of self-organiz- ing maps Verdu etal. (2006) 2006 Mining of mixed data with application to catalog marketing Hsu and Chen, Y.-g.C. (2007) 2007 Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer Chan (2008) 2007 Customer segmentation revisited: the case of the airline industry Teichert etal. (2008) 2007 Chameleon based on clustering feature tree and its application in customer segmentation Li etal. (2009) 2008 Improving personalization solutions through optimal segmentation of customer bases Jiang and Tuzhilin (2009) 2009 Mining changing customer segments in dynamic markets Boettcher etal. (2009) 2009 A hybrid of sequential rules and collaborative filtering for product recommendation Liu etal. (2009) 2009 Customer segmentation of multiple category data in e-commerce using a soft-clustering approach Wu and Chou (2011) 2010 CAS based clustering algorithm for Web users Wan etal. (2010) 2010 Segmenting and mining the ERP users’ perceived benefits using the rough set approach Wu (2011) 2011 Group RFM analysis as a novel framework to discover better customer consumption behavior Chang and Tsai (2011) 2011 Pricing and promotion strategies of an online shop based on customer segmentation and multiple objective decision making Chan etal. (2011) 2011 Incremental clustering of time-series by fuzzy clustering Aghabozorgi etal. (2012) 2011 User action interpretation for online content optimization Bian etal. (2013) 2011 Improved response modeling based on clustering, under-sampling, and ensemble Kang etal. (2012) 2012 559 1 3 A review oncustomer segmentation methods forpersonalized… Table 4 (continued) Title References Years Segmenting customers in online stores based on factors that affect the customer’s intention to purchase Hong and Kim (2012) 2012 Segmenting customers by transaction data with concept hierarchy Hsu etal. (2012) 2012 Apply robust segmentation to the service industry using kernel induced fuzzy clustering techniques Wang (2010) 2012 LRFMP model for customer segmentation in the grocery retail industry: a case study Peker etal. (2017) 2012 Customer segmentation based on buying and returning behaviour Hjort etal. (2013) 2013 Online purchaser segmentation and promotion strategy selection: evidence from Chinese E-commerce market Liu etal. (2015) 2013 Information filtering via collaborative user clustering modeling Zhang etal. (2014) 2013 Discovering valuable frequent patterns based on RFM analysis without customer identification information Hu and Yeh (2014) 2014 Rough set approach for characterizing customer behavior Dhandayudam and Krishnamurthi (2014) 2014 Customer segmentation in a large database of an online customized fashion business Brito etal. (2015) 2014 A new recommendation method for the user clustering-based recommendation system Rapecka and Dzemyda (2015) 2015 Customer segmentation issues and strategies for an automobile dealership with two clustering techniques Tsai etal. (2015) 2015 Performance management using a value-based customer-centered model Abdolvand etal. (2015) 2015 A fuzzy ANP based weighted RFM model for customer segmentation in auto insurance sector Ravasan and Mansouri (2015) 2015 Customer lifetime value determination based on RFM model Safari etal. (2016) 2015 Mining the dominant patterns of customer shifts between segments by using top-k and distinguishing sequential rules Akhondzadeh-Noughabi and Albadvi (2015) 2015 A new latent class model for analysis of purchasing and browsing histories on EC sites Goto etal. (2015) 2015 Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis Sarvari etal. (2016) 2016 Recommender system based on customer segmentation (RSCS) Rezaeinia and Rahmani (2016) 2016 An exploration of improving prediction accuracy by constructing a multi-type clustering based recommendation framework Ma etal. (2016) 2016 Marketing segmentation using the particle swarm optimization algorithm: a case study Chan etal. (2016) 2016 PurTreeClust: a clustering algorithm for customer segmentation from massive customer transaction data Chen etal. (2018) 2017 Data clustering using eDE, an enhanced differential evolution algorithm with fuzzy c-means technique Ramadas and Abraham (2018) 2017 560 M.Alves Gomes, T.Meisen 1 3 Table 4 (continued) Title References Years Customer segmentation with purchase channels and media touchpoints using single source panel data Nakano and Kondo (2018) 2017 User-centered recommendation using US-ELM based on dynamic graph model in E-commerce Ding etal. (2019) 2017 An empirical assessment of customer lifetime value models within data mining Hiziroglu etal. (2018) 2018 Customer segmentation by using RFM model and clustering methods: a case study in retail industry Dogan etal. (2018) 2018 RFM ranking—an effective approach to customer segmentation Christy etal. (2018) 2018 Improving sparsity and new user problems in collaborative filtering by clustering the personality factors Hafshejani etal. (2018) 2018 Customer online shopping experience data analytics—integrated customer segmentation and customised services prediction model Wong and Wei (2018) 2018 A fuzzy linguistic RFM model applied to campaign management Alberto Carrasco etal. (2019) 2018 A CLV-based framework to prioritize promotion marketing strategies: a case study of telecom industry Nemati etal. (2018) 2018 Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data An etal. (2018) 2018 A study on e-commerce customer segmentation management based on improved K-means algorithm Deng and Gao (2020) 2018 RFM customer analysis for product-oriented services and service business development: an interventionist case study of two machinery manufacturers Stormi etal. (2020) 2019 Hybrid bio-inspired user clustering for the generation of diversified recommendations Logesh etal. (2020) 2019 A novel approach for the customer segmentation using clustering through self-organizing map Barman and Chowdhury (2019) 2019 Effective user preference clustering in web service applications Wang etal. (2020) 2019 A hybrid two-phase recommendation for group-buying E-commerce applications Bai etal. (2019) 2019 Spectral clustering of customer transaction data with a two-level subspace weighting method Chen etal. (2019) 2019 An effective user clustering-based collaborative filtering recommender system with grey wolf optimisation Sivaramakrishnan etal. (2020) 2020 Product recommendation in offline retail industry by using collaborative filtering Pratama etal. (2020) 2020 IECT: a methodology for identifying critical products using purchase transactions Hsu and Huang (2020) 2020 Modified dynamic fuzzy c-means clustering algorithm—application in dynamic customer segmentation Munusamy and Murugesan (2020) 2020 Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks Zhang etal. (2020) 2020 561 1 3 A review oncustomer segmentation methods forpersonalized… Table 4 (continued) Title References Years Identifying omnichannel deal prone segments, their antecedents, and their consequences Valentini etal. (2020) 2020 A combined approach for customer profiling in video on demand services using clustering and association rule mining Guney etal. (2020) 2020 A new framework for predicting customer behavior in terms of RFM by considering the temporal aspect based on time series techniques Abbasimehr and Shabani (2021) 2020 Data analytics and the P2P cloud: an integrated model for strategy formulation based on customer behaviour Lam etal. (2021) 2020 A methodology for classification and validation of customer datasets Nie etal. (2021) 2020 Performance-enhanced rough k-means clustering algorithm Sivaguru and Punniyamoorthy (2021) 2020 Using unsupervised machine learning techniques for behavioral-based credit card users segmentation in Africa Umuhoza etal. (2020) 2020 Customer categorization using a three-dimensional loyalty matrix analogous to FMEA Madzik and Shahin (2021) 2020 An empirical study on customer segmentation by purchase behaviors using a RFM Model and K-means algorithm Wu etal. (2020) 2020 Customer segmentation by web content mining Zhou etal. (2021) 2021 The role of shopping mission in retail customer segmentation Sokol and Holy (2021) 2021 Research and implementation of the customer-oriented modern hotel management system using fuzzy analytic hiererchical process (FAHP) Wang and Zhang (2021) 2021 High utility itemset mining using binary differential evolution: An application to customer segmentation Krishna and Ravi (2021) 2021 Factors affecting customer analytics: evidence from three retail cases Griva etal. (2021) 2021 Customer behaviour analysis based on buyingdata sparsity for multi-category products in pork industry: A hybrid approach Apichottanakul etal. (2021) 2021 An extended regularized K-means clustering approach for high-dimensional customer segmentation with correlated variables Zhao etal. (2021) 2021 Online Reviews Analysis for Customer Segmentation through Dimensionality Reduction and Deep Learning Techniques Nilashi etal. (2021) 2021 RFM-based repurchase behavior for customer classification and segmentation Rahim etal. (2021) 2021 562 M.Alves Gomes, T.Meisen 1 3 Table 4 (continued) Title References Years User value identification based on improved RFM model and K-means++ algorithm for complex data analysis Wu etal. (2021) 2021 Deep customer segmentation with applications to a Vietnamese supermarkets’ data Nguyen (2021) 2021 Learning about the customer for improving customer retention proposal of an analytical framework Simoes and Nogueira (2021) 2021 A hybrid method for big data analysis using fuzzy clustering, feature selection and adaptive neuro-fuzzy inferences system techniques: case of Mecca and Medina hotels in Saudi Arabia Alghamdi (2022a) 2022 A hybrid method for customer segmentation in Saudi Arabia restaurants using clustering, neural networks and optimization learning techniques Alghamdi (2022b) 2022 A novel approach for send time prediction on email marketing Araujo etal. (2022) 2022 Multi clustering recommendation system for fashion retail Bellini etal. (2022) 2022 Understanding customer’s online booking intentions using hotel big data analysis Chalupa and Petricek (2022) 2022 A precision marketing strategy ofe-commerce platform based on consumer behavior analysis in the era of big data Zhang and Huang (2022) 2022 Customer behavior analysis by intuitionistic fuzzy segmentation: comparison for two major cities in Turkey Dogan etal. (2022) 2022 Customer segmentation using k-means clustering for developing sustainable marketing strategies Gautam and Kumar (2022) 2022 “I can get no e-satisfaction”. What analytics say? Evidence using satisfaction data from e-commerce Griva (2022) 2022 A two-stage business analytics approach to perform behavioural and geographic customer segmentation using e-commerce delivery data Griva etal. (2022) 2022 Analysis of clustering algorithms for credit risk evaluation using multiple correspondence analysis Jadwal etal. (2022) 2022 Integrated customer lifetime value (CLV) and customer migration model to improve customer segmentation Kanchanapoom and Chongwatpol (2022) 2022 Multi-behavior RFM model based on improved SOM neural network algorithm for customer segmentation . Liao etal. (2022) 2022 K-means customers clustering by their RFMT and score satisfaction analysis Mensouri etal. (2022) 2022 A novel hybrid segmentation approach for decision support: a case study in banking Mosa etal. (2022) 2022 K-means clustering approach for intelligent customer segmentation using customer purchase behavior data Tabianan etal. (2022) 2022 Research on segmenting E-commerce customer through an improved K-medoids clustering algorithm Wu etal. (2022) 2022 563 1 3 A review oncustomer segmentation methods forpersonalized… Author contributions The author MAG had the idea for the article, performed the literature search and data analysis, and drafted the article. The author TM mentored the process with his expertise and critically revised the article. Funding Open Access funding enabled and organized by Projekt DEAL. Declarations Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Abbasimehr H, Shabani M (2021) A new framework for predicting customer behavior in terms of RFM by considering the temporal aspect based on time series techniques. J Ambient Intell Hum Comput 12(1):515–531. https:// doi. org/ 10. 1007/ s12652- 020- 02015-w Abdolvand N, Albadvi A, Aghdasi M (2015) Performance management using a value-based customercentered model. Int J Prod Res 53(18):5472–5483. https:// doi. org/ 10. 1080/ 00207 543. 2015. 10266 13 Aghabozorgi S, Saybani MR, Teh YW (2012) Incremental clustering of time-series by fuzzy clustering. J Inf Sci Eng 28(4):671–688 Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723 Akhondzadeh-Noughabi E, Albadvi A (2015) Mining the dominant patterns of customer shifts between segments by using top-k and distinguishing sequential rules. Manag Decis 53(9):1976– 2003. https:// doi. org/ 10. 1108/ MD- 09- 2014- 0551 Alberto Carrasco R, Francisca Blasco M, Garcia-Madariaga J, Herrera-Viedma E (2019) A fuzzy linguistic RMF model applied to campaign management. Int J Interact Multimed Artif Intell 5(4):21–27. https:// doi. org/ 10. 9781/ ijimai. 2018. 03. 003 Alghamdi A (2022) A hybrid method for big data analysis using fuzzy clustering, feature selection and adaptive neuro-fuzzy inferences system techniques: case of Mecca and Medina hotels in Saudi Arabia. Arab J Sci Eng. https:// doi. org/ 10. 1007/ s13369- 022- 06978-0 Alghamdi A (2022) A hybrid method for customer segmentation in Saudi Arabia restaurants using clustering, neural networks and optimization learning techniques. Arab J Sci Eng. https:// doi. org/ 10. 1007/ s13369- 022- 07091-y Alves Gomes M, Tercan H, Bodnar T, Meisen T, Meisen P (2021) A filter is better than none: improving deep learning-based product recommendation models by using a user preference filter. In: 2021 IEEE 23rd int conf on high performance computing and communications; 7th int conf on data science and systems; 19th int conf on smart city; 7th int conf on dependability in sensor, cloud and big data systems and application (hpcc/dss/smartcity/dependsys) (pp 1278–1285). https:// doi. org/ 10. 1109/ HPCCDSS- Smart City- Depen dSys5 3884. 2021. 00195 An J, Kwak H, Jung S-g, Salminen J, Jansen BJ (2018) Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data. Soc Netw Anal Mining. https:// doi. org/ 10. 1007/ s13278- 018- 0531-0 564 M.Alves Gomes, T.Meisen 1 3 Apichottanakul A, Goto M, Piewthongngam K, Pathumnakul S (2021) Customer behaviour analysis based on buying-data sparsity for multicategory products in pork industry: a hybrid approach. Cogent Eng. https:// doi. org/ 10. 1080/ 23311 916. 2020. 18655 98 Araujo C, Soares C, Pereira I, Coelho D, Rebelo MA, Madureira A (2022) A novel approach for send time prediction on email marketing. Appl Sci. https:// doi. org/ 10. 3390/ app12 168310 Bai L, Hu M, Ma Y, Liu M (2019) A hybrid two-phase recommendation for group-buying e-com- merce applications. Appl Sci. https:// doi. org/ 10. 3390/ app91 53141 Barman D, Chowdhury N (2019) A novel approach for the customer segmentation using clustering through self-organizing map. Int J Bus Anal 6(2):23–45. https:// doi. org/ 10. 4018/ IJBAN. 20190 40102 Bellini P, Palesi LAI, Nesi P, Pantaleo G (2022) Multi clustering recommendation system for fashion retail. Multimed Tools Appl. https:// doi. org/ 10. 1007/ s11042- 021- 11837-5 Ben Ayed A, Ben Halima M, Alimi AM (2014) Survey on clustering methods: Towards fuzzy clustering for big data. In: 2014 6th international conference of soft computing and pattern recognition (SoC- PaR) (pp 331-336). https:// doi. org/ 10. 1109/ SOCPAR. 2014. 70080 28 Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203. https:// doi. org/ 10. 1016/ 0098- 3004(84) 90020-7 Bian J, Dong A, He X, Reddy S, Chang Y (2013) User action interpretation for online content optimization. IEEE Trans Knowl Data Eng 25(9):2161–2174. https:// doi. org/ 10. 1109/ TKDE. 2012. 130 Birtolo C, Diessa V, De Chiara D, Ritrovato P (2013) Customer churn detection system: identifying customers who wish to leave a merchant. In: International conference on industrial, engineering and other applications of applied intelligent systems (pp 411–420) Boettcher M, Spott M, Nauck D, Kruse R (2009) Mining changing customer segments in dynamic markets. Expert Syst Appl 36(1):155–164. https:// doi. org/ 10. 1016/j. eswa. 2007. 09. 006 Brito PQ, Soares C, Almeida S, Monte A, Byvoet M (2015) Customer segmentation in a large database of an online customized fashion business. Robot Comput-Integr Manuf 36:93–100. https:// doi. org/ 10. 1016/j. rcim. 2014. 12. 014 Burri M, Schär R (2016) The reform of the EU data protection framework: outlining key changes and assessing their fitness for a data-driven economy. J Inf Policy 6(1):479–511 Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat-Theory Methods 3(1):1–27 Chalupa S, Petricek M (2022) Understanding customer’s online booking intentions using hotel big data analysis. J Vacat Mark. https:// doi. org/ 10. 1177/ 13567 66722 11221 07 Chan CCH (2008) Intelligent value-based customer segmentation method for campaign management: a case study of automobile retailer. Expert Syst Appl 34(4):2754–2762 Chan C-CH, Cheng C-B, Hsien W-C (2011) Pricing and promotion strategies of an online shop based on customer segmentation and multiple objective decision making. Expert Syst Appl 38(12):14585– 14591. https:// doi. org/ 10. 1016/j. eswa. 2011. 05. 024 Chan CCH, Hwang Y-R, Wu H-C (2016) Marketing segmentation using the particle swarm optimization algorithm: a case study. J Ambient Intell Humaniz Comput 7(6):855–863. https:// doi. org/ 10. 1007/ s12652- 016- 0389-9 Chang H-C, Tsai H-P (2011) Group RFM analysis as a novel framework to discover better customer consumption behavior. Expert Syst Appl 38(12):14499–14513. https:// doi. org/ 10. 1016/j. eswa. 2011. 05. 034 Chen X, Fang Y, Yang M, Nie F, Zhao Z, Huang JZ (2018) Purtreeclust: a clustering algorithm for customer segmentation from massive customer transaction data. IEEE Trans Knowl Data Eng 30(3):559–572. https:// doi. org/ 10. 1109/ TKDE. 2017. 27636 20 Chen X, Sun W, Wang B, Li Z, Wang X, Ye Y (2019) Spectral clustering of customer transaction data with a two-level subspace weighting method. IEEE Trans Cybern 49(9):3230–3241. https:// doi. org/ 10. 1109/ TCYB. 2018. 28368 04 Christy AJ, Umamakeswari A, Priyatharsini L, Neyaa A (2018) RFM ranking—an effective approach to customer segmentation. J King Saud Univ-Comput Inf Sci 32(10):1215. https:// doi. org/ 10. 1016/j. jksuci. 2018. 09. 004 Cooper HM (1988) Organizing knowledge syntheses: a taxonomy of literature reviews. Knowl Soc 1(1):104–126. https:// doi. org/ 10. 1007/ BF031 77550 565 1 3 A review oncustomer segmentation methods forpersonalized… Coussement K, van den Bossche FAM, de Bock KW (2014) Data accuracy’s impact on segmentation performance: benchmarking RFM analysis, logistic regression, and decision trees. J Bus Res 67(1):2751–2758. https:// doi. org/ 10. 1016/j. jbusr es. 2012. 09. 024 Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell PAMI 1(2):224–227. https:// doi. org/ 10. 1109/ TPAMI. 1979. 47669 09 De Jong K (2016) Evolutionary computation: a unified approach. In: Proceedings of the 2016 on genetic and evolutionary computation conference companion (pp 185–199) de Marco M, Fantozzi P, Fornaro C, Laura L, Miloso A (2021) Cognitive analytics management of the customer lifetime value: an artificial neural network approach. J Enterp Inf Manag 34(2):679–696. https:// doi. org/ 10. 1108/ JEIM- 01- 2020- 0029 Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodol) 39(1):1–22 Deng Y, Gao Q (2020) A study on e-commerce customer segmentation management based on improved k-means algorithm. Inf Syst E-Bus Manag 18(4):497–510. https:// doi. org/ 10. 1007/ s10257- 018- 0381-3 Dhandayudam P, Krishnamurthi I (2014) Rough set approach for characterizing customer behavior. Arab J Sci Eng 39(6):4565–4576. https:// doi. org/ 10. 1007/ s13369- 014- 1013-y Di Zhang, Huang M (2022) A precision marketing strategy of e-commerce platform based on consumer behavior analysis in the era of big data. Math Prob Eng. https:// doi. org/ 10. 1155/ 2022/ 85805 61 Ding L, Han B, Wang S, Li X, Song B (2019) User-centered recommendation using US-ELM based on dynamic graph model in ecommerce. Int J Mach Learn Cybern 10(4):693–703. https:// doi. org/ 10. 1007/ s13042- 017- 0751-z Dogan O, Aycin E, Bulut ZA (2018) Customer segmentation by using RFM model and clustering methods: a case study in retail industry. Int J Contemp Econ Admin Sci 8(1):1–19 Dogan O, Seymen OF, Hiziroglu A (2022) Customer behavior analysis by intuitionistic fuzzy segmentation: comparison of two major cities in turkey. Int J Inf Technol Decis Mak 21(02):707–727. https:// doi. org/ 10. 1142/ S0219 62202 15006 07 Donath W, Hoffman A (1973) Lower bounds for the partitioning of graphs. IBM J Res Dev 17(5):420–425 Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-sepa- rated clusters. J Cybern. https:// doi. org/ 10. 1080/ 01969 72730 85460 46 Eiben AE, Smith JE (2003) Introduction to evolutionary computing, vol 53. Springer, Berlin European-Parliament (2016) Regulation (eu) 2016/679 of the european parliament and of the council. https:// eurlex. europa. eu/ legalconte nt/ EN/ TXT/ PDF/? uri= CELEX: 32016 R0679. Accessed 7 June 2023 Fan Y, Huang GQ (2007) Networked manufacturing and mass customization in the ecommerce era: the Chinese perspective. Taylor & Francis, Milton Park Fiedler M (1973) Algebraic connectivity of graphs. Czechoslov Math J 23(2):298–305 Firdaus S, Uddin MA (2015) A survey on clustering algorithms and complexity analysis. Int J Comput Sci Issues (IJCSI) 12(2):62 Gautam N, Kumar N (2022) Customer segmentation using k-means clustering for developing sustainable marketing strategies. Biznes Inf-Bus Inf 16(1): 72–82. https:// doi. org/ 10. 17323/ 2587- 814X. 2022.1. 72. 82 Gennari JH (1989) A survey of clustering methods Gomes MA, Meyes R, Meisen P, Meisen T (2022) Will this online shopping session succeed? predicting customer’s purchase intention using embeddings. In: Proceedings of the 31st ACM international conference on information & knowledge management (p. 2873–2882). Association for Computing Machinery, New York, NY, USA. Retrieved from https:// doi. org/ 10. 1145/ 35118 08. 35571 27 Goto M, Mikawa K, Hirasawa S, Kobayashi M, Suko T, Horii S (2015) A new latent class model for analysis of purchasing and browsing histories on EC sites. Ind Eng Manag Syst 14(4):335–346. https:// doi. org/ 10. 7232/ iems. 2015. 14.4. 335 Griva A (2022) “I can get no e-satisfaction". what analytics say? evidence using satisfaction data from e-commerce. J Retail Consum Serv. https:// doi. org/ 10. 1016/j. jretc onser. 2022. 102954 Griva A, Bardaki C, Pramatari K, Doukidis G (2021) Factors affecting customer analytics: evidence from three retail cases. Inf Syst Front. https:// doi. org/ 10. 1007/ s10796- 020- 10098-1 Griva A, Zampou E, Stavrou V, Papakiriakopoulos D, Doukidis G (2022) A two-stage business analytics approach to perform behavioural and geographic customer segmentation using e-commerce delivery data. J Decis Syst. https:// doi. org/ 10. 1080/ 12460 125. 2022. 21510 71 566 M.Alves Gomes, T.Meisen 1 3 Guney S, Peker S, Turhan C (2020) A combined approach for customer profiling in video on demand services using clustering and association rule mining. IEEE Access 8:84326–84335. https:// doi. org/ 10. 1109/ ACCESS. 2020. 29920 64 Hafshejani ZY, Kaedi M, Fatemi A (2018) Improving sparsity and new user problems in collaborative filtering by clustering the personality factors. Electron Commer Res 18(4):813–836. https:// doi. org/ 10. 1007/ s10660- 018- 9287-x Hiziroglu A (2013) Soft computing applications in customer segmentation: state-of-art review and critique. Expert Syst Appl 40(16):6491–6507. https:// doi. org/ 10. 1016/j. eswa. 2013. 05. 052 Hiziroglu A, Sisci M, Cebeci HI, Seymen OF (2018) An empirical assessment of customer lifetime value models within data mining. Baltic J Modern Comput 6(4): 434–448. https:// doi. org/ 10. 22364/ bjmc. 2018.6. 4. 08 Hjort K, Lantz B, Ericsson D, Gattorna J (2013) Customer segmentation based on buying and returning behaviour. Int J Phys Distrib Logist Manag 43(10):852–865. https:// doi. org/ 10. 1108/ IJPDLM- 02- 2013- 0020 Hong T, Kim E (2012) Segmenting customers in online stores based on factors that affect the customer’s intention to purchase. Expert Syst Appl 39(2):2127–2131. https:// doi. org/ 10. 1016/j. eswa. 2011. 07. 114 Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417 Hsieh NC (2004) An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Syst Appl 27(4):623–633. https:// doi. org/ 10. 1016/j. eswa. 2004. 06. 007 Hsu P-Y, Huang C-W (2020) IECT: a methodology for identifying critical products using purchase transactions. Appl Soft Comput. https:// doi. org/ 10. 1016/j. asoc. 2020. 106420 Hsu C-C, Y-gC Chen (2007) Mining of mixed data with application to catalog marketing. Expert Syst Appl 32(1):12–23. https:// doi. org/ 10. 1016/j. eswa. 2005. 11. 017 Hsu F-M, Lu L-P, Lin C-M (2012) Segmenting customers by transaction data with concept hierarchy. Expert Syst Appl 39(6):6221–6228. https:// doi. org/ 10. 1016/j. eswa. 2011. 12. 005 Hu Y-H, Yeh T-W (2014) Discovering valuable frequent patterns based on RFM analysis without customer identification information. Knowl-Based Syst 61:76–88. https:// doi. org/ 10. 1016/j. knosys. 2014. 02. 009 Hughes AM (1994) Strategic database marketing: the masterplan for starting and managing a profitable, customer-based marketing program. Irwin Professional, USA Jadwal PK, Pathak S, Jain S (2022) Analysis of clustering algorithms for credit risk evaluation using multiple correspondence analysis. Microsyst Technol-Micro- Nanosystemsinf Storage Process Syst 28(12):2715–2721. https:// doi. org/ 10. 1007/ s00542- 022- 05310-y Jiang T, Tuzhilin A (2009) Improving personalization solutions through optimal segmentation of customer bases. IEEE Trans Knowl Data Eng 21(3):305–320. https:// doi. org/ 10. 1109/ TKDE. 2008. 163 Jonker JJ, Piersma N, van den Poel D (2004) Joint optimization of customer segmentation and marketing policy to maximize long-term profitability. Expert Syst Appl 27(2):159–168. https:// doi. org/ 10. 1016/j. eswa. 2004. 01. 010 Kanchanapoom K, Chongwatpol J (2022) Integrated customer lifetime value (CLV) and customer migration model to improve customer segmentation. J Mark Anal. https:// doi. org/ 10. 1057/ s41270- 022- 00158-7 Kang P, Cho S, MacLachlan DL (2012) Improved response modeling based on clustering, under-sam- pling, and ensemble. Expert Syst Appl 39(8):6738–6753. https:// doi. org/ 10. 1016/j. eswa. 2011. 12. 028 Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. J R Stat Soc Ser C (Appl Stat) 29(2):119–127 Kendall MG (1938) A new measure of rank correlation. Biometrika 30(1/2):81–93 Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks (vol 4, pp 1942–1948) Kim KJ, Ahn H (2004) Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems. In: Kim TG (eds) Artificial intelligence and simulation (vol 3397, pp 409–415) Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69