An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm

Wu, Jun; Shi, Li; Lin, Wei‐Cheng; Tsai, Sang‐Bing; Li, Yuanyuan; Yang, Liping; Xu, Guangshu

doi:10.1155/2020/8884227

Cited by 60 publications

(43 citation statements)

References 30 publications

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“…K-Means is a fast method in clustering analysis, but the accuracy and running time of clustering results largely depend on the location of the initial clustering center [22][23][24]. In order to solve the problem that K-means is sensitive to initial points, Arthur et al proposed the K-means++ algorithm to improve the method of K-means randomly selecting initial clustering centers, that is, to make the 2 Wireless Communications and Mobile Computing distance between the clustering centers as far as possible when selecting initial clustering centers.…”

Section: K-means++mentioning

confidence: 99%

User Value Identification Based on Improved RFM Model and K‐Means++ Algorithm for Complex Data Analysis

Shi

Yang

et al. 2021

Wireless Communications and Mobile Computing

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In recent years, with the development of machine learning and big data technology, user data has become an important element in the production process of enterprises. For today’s e-commerce platforms, the deep mining of user’s purchase behavior is helpful to understand user’s purchase preferences and accurately recommend products that meet user expectations, which can not only improve user satisfaction but also reduce platform marketing cost. To accurately identify the user value of online purchasing on an e-commerce platform, this paper uses an improved RFM model to extract user features and uses the K -means++ clustering algorithm to realize user classification. The indicators of the traditional RFM model characterize user features from three angles: recent purchase time ( R ), purchase frequency ( F ), and total consumption amount ( M ). The user group and scenarios studied in this paper are different from the previous literature: (1) the user group is relatively fixed, (2) the consumer goods are relatively single, and (3) the characteristics of repeated purchase are obvious. Therefore, based on the existing literature, this paper extracts the user characteristics studied and improves and models the traditional indicators. Based on the real purchasing data from September to December 2018, it calculates the indicators that improved RFM, empowers the weight to indicators, and finally classifies the value of users by using the K -means++ algorithm. The experimental results show that the user classification based on the improved RFM model is more accurate than the user classification based on the traditional RFM model, and the improved RFM model can identify the user value more accurately, which provides a strong support for the e-commerce platform to realize the accurate marketing strategy based on big data.

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Section: K-means++mentioning

confidence: 99%

User Value Identification Based on Improved RFM Model and K‐Means++ Algorithm for Complex Data Analysis

Shi

Yang

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

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“…From these transactions, and with the use of the quantifiable variables already mentioned, it is possible to easily explain the groupings in profiles by similar behaviour, to identify the most valuable customers, and to establish individual or group actions [10]. On the other hand, the collection of information is performed through corporate tools such as CRM [56] so that we do not have to carry out large information aggregation processes to obtain results that are understandable to decision-makers [57].…”

Section: Advantages and Limitations Of The Rfm Modelmentioning

confidence: 99%

RFID: A Fuzzy Linguistic Model to Manage Customers from the Perspective of Their Interactions with the Contact Center

2021

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In an increasingly globalized market, the relationship between the customer and the brand goes beyond the purchasing process. It is very important to understand the customer, to know their needs and to propose actions to increase the value of the brand for them. In the literature, there are several models capable of determining and segmenting customers according to variables dependent on the purchasing process. However, we have not found any study that applies to the business case of classifying customers according to their relationship with the contact centre. In this paper, we establish a working model that allows us to define the value of the customer in the process of interaction with the contact centre, so that we can propose actions both in the sales phase and during the post-sales service, so that the value and perception of the brand is increased. In this model, we propose using the value of recency, frequency, importance and duration of customer interactions with the post-sales service, thus obtaining a ranking, and grouping of customers to help establish personalized communication strategies. We have verified this model by presenting a business case applied to the telecom sector.

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“…In this research, various CRM strategies are brought forward to get a high level of customer satisfaction. The method's effectiveness is supported by improvements in several key performance indicators such as active customer growth, total purchase volume, and total consumption (Wu, et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…K-Means Clustering is an effective method to group customers based on the similarity of predetermined characteristics. The effectiveness of the process is supported by the results of improvements in several key performance indicators (Wu, et al, 2020). From these clusters, a suitable marketing strategy can be determined for each cluster.…”

Section: Research Flowmentioning

confidence: 99%

Marketing strategy planning at alfamart lodadi stores using the clustering, ahp, and ar-mba method

Azhra

Fadhlurrohman

Putra

et al. 2021

Int. J. Ind. Optim.

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Nowadays, people are very facilitated by the existence of various shopping centers, including retail. Because many retailers are close to each other, Alfamart Lodadi must have a good marketing strategy. So far, the strategy used is sometimes inaccurate because it is not based on customer segmentation. Therefore, the purpose of this research is to help retail owners to make decisions regarding the right marketing strategy with three methods so that Alfamart Lodadi can compete and increase sales. The Analytical Hierarchy Process (AHP) is employed to find the priority variables of customer segmentation; meanwhile, the K-Means Clustering is used to group customers based on the similarity of predetermined characteristics. AR-MBA is used to find out the best rules, and products are rarely, sufficient, and frequently purchased. The results of this research, based on AHP, obtained five segmentation priority variables based on the largest eigenvector values. There are income, age, expenditure, distance, and shopping intensity with each eigenvector value of 0.13; 0.16; 0.12; 0.12; 0.17. From clustering, there are three customer clusters with different strategies, including free shipping when shopping, product discounts for certain periods, and providing catalogs and discounts on each transaction and offer notifications. Then, this research also obtained three strategies based on AR-MBA. These include making a catalog by bringing frequently purchased products closer together, choosing a layout for shopping places by bringing frequently purchased products closer together, and making shopping coupons for rarely purchased products. With several strategic choices, companies can make decisions appropriately according to the desired criteria.

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An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm

Cited by 60 publications

References 30 publications

User Value Identification Based on Improved RFM Model and K‐Means++ Algorithm for Complex Data Analysis

User Value Identification Based on Improved RFM Model and K‐Means++ Algorithm for Complex Data Analysis

RFID: A Fuzzy Linguistic Model to Manage Customers from the Perspective of Their Interactions with the Contact Center

Marketing strategy planning at alfamart lodadi stores using the clustering, ahp, and ar-mba method

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