Predicting customer value per product: From RFM to RFM/P

Heldt, Rodrigo; Silveira, Cleo Schmitt; Luce, Fernando Bins

doi:10.1016/j.jbusres.2019.05.001

Cited by 53 publications

(33 citation statements)

References 17 publications

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“…In this model, customer values of all products are estimated separately first and then added together to obtain an overall customer value. Empirical analysis of financial companies and supermarkets can be performed on this basis [6]. Adnan Amin et al studied the prediction of customer churn in the telecom industry under different conditions by using rough set, classification, and data transformation techniques [9][10][11][12].…”

Section: Rfm Modelmentioning

confidence: 99%

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

Shi

Lin

et al. 2020

Mathematical Problems in Engineering

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In this paper, we base our research by dealing with a real-world problem in an enterprise. A RFM (recency, frequency, and monetary) model and K-means clustering algorithm are utilized to conduct customer segmentation and value analysis by using online sales data. Customers are classified into four groups based on their purchase behaviors. On this basis, different CRM (customer relationship management) strategies are brought forward to gain a high level of customer satisfaction. The effectiveness of our method proposed in this paper is supported by improvement results of some key performance indices such as the growth of active customers, total purchase volume, and the total consumption amount.

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Section: Rfm Modelmentioning

confidence: 99%

“…ey provide comprehensive reviews of data mining techniques and their industrial applications. As to the applications, it includes banking and finance [5,6], retail [7], telecommunication, and insurance [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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

Shi

Lin

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…This model first estimated the customer value of all products individually and then aggregated them to obtain the overall customer value. Based on this, Heldt et al (2019) conducted an empirical analysis on financial companies and supermarkets and the authors were able to verify the significance of results.…”

Section: Rfm Modelmentioning

confidence: 99%

Identifying customer priority for new products in target marketing: Using RFM model and TextRank

Hwang

Lee²

2021

Innovative Marketing

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Target marketing is a key strategy used to increase the revenue. Among many methods that identify prospective customers, the recency, frequency, monetary value (RFM) model is considered the most accurate. However, no RFM study has focused on prospects for new product launches. This study addresses this gap by using website access data to identify prospects for new products, thereby extending RFM models to include website-specific weights. An RF model, built using frequency and recency information from website access data of customers, and an RwF model, built by adding website weights to frequency of access, were developed. A TextRank algorithm was used to analyze weights for each website based on the access frequency, thus defining the weights in the RwF model. South Korean mobile users’ website access data between May 1 and July 31, 2020 were used to validate the models. Through a significant lift curve, the results indicate that the models are highly effective in prioritizing customers for target marketing of new products. In particular, the RwF model, reflecting website-specific weights, showed a customer response rate of more than 30% among the top 10% customers. The findings extend the RFM literature beyond purchase history and enable practitioners to find target customers without a purchase history.

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“…various approaches for making recommendations have been presented, few consider customer lifetime value (CLV) and the effect on product recommendations. CLV is typically used to identify profitable customers and to develop strategies to target customers [49]. In fiercely competitive environments, identifying the CLV or loyalty ranking of users is important for user retention.…”

Section: Althoughmentioning

confidence: 99%

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

Wang

Chen

2020

IEEE Access

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Most traditional recommender systems focus specifically on increasing consumer satisfaction by providing a list of relevant content to consumers. However, the perspectives of other multisided marketplace stakeholders are also equally important, i.e., the exposure for suppliers or providers and profit for the platform. The suppliers want their products to be presented to users, and the objective of the platform is to maximize their profit. Nevertheless, because consumers' preferences and the objectives of providers as well as the platform may conflict with each other, it degrades the utility of the recommendation methods by only considering users' views. Therefore, in this work, we use a many-objective optimization method to maintain a tradeoff among five objectives for three stakeholders and obtain multiple Pareto front solutions in a single run. We first combine customer lifetime value and user purchase preference to create a new similarity model (Sim_RFMP) to increase the recommendation accuracy of the recommendation list. Furthermore, we propose a many-objective model (NBHXMAOEA) for multistakeholder recommendation. In NBHXMAOEA, we present a novel N-block heuristic crossover operator (NBHX) that recombines blocks of chromosomes based on heuristics. Through extensive experiments, the results demonstrate that our proposed NBHXMAOEA achieves superior performance in terms of average accuracy, diversity, novelty, provider coverage, and platform profit to its competing methods. INDEX TERMS Many-objective, recommender systems, similarity model, stakeholders.

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Predicting customer value per product: From RFM to RFM/P

Cited by 53 publications

References 17 publications

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

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

Identifying customer priority for new products in target marketing: Using RFM model and TextRank

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

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