Dynamics of customer segments: A predictor of customer lifetime value

Mosaddegh, Abdolreza; Albadvi, Amir; Sepehri, Mohammad Mehdi; Teimourpour, Babak

doi:10.1016/j.eswa.2021.114606

Cited by 26 publications

(22 citation statements)

References 21 publications

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“…To obtain dynamics of customer segments in the banking sector, Mosaddegh et al (2021) had a first approach step toward CLV calculation, which the model of RFM is employed. CLV is not only a significant contribution toward market segments but also support marketing campaigns, due to the real value of each customer is measured.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Unlikely, once CLV in this article is measured, then the market segmentation is concerned and overcomes the traditional approaches using dynamic the matrix of two components, for example, the tenure as LOS of customer and CLV, as dynamic variables (Mosaddegh et al, 2021), in which the indicator of LOS is employed as a new explored suggestion and CLV (Cuadros & Domínguez, 2014;Hwang et al, 2004;Kim et al, 2006) In sum, based on inheritance of previous scholars, such as Bansal et al (2020) and Y. Chen et al (2009), the historical data of prepaid subscribers involved in voice calls, SMS, data, and top-up are recruited in the quantitative model.…”

Section: Application Of CLV Toward Market Segmentmentioning

confidence: 99%

“…According to arguments of H. Chen et al (2019), market segmentations based on demographic factors, consumer behavior, and psychology are popular for the customer heterogeneity measurement, while the market segmentations based on inheritance results of genetic algorithm are not popular, particularly for the telecommunication sector. To reach customer segment dynamics, Mosaddegh et al (2021) used Big Data analytics on bank customers during 42 consecutive months of 34 million clients and measured the real value of each customer as CLV based on the variables of RFM. After that, market segment is conducted by the popular clustering algorithms of classification method, for example, K-means.…”

Section: Application Of CLV Toward Market Segmentmentioning

confidence: 99%

“…Then the customers are divided into different groups based on using the algorithm of K-means. But this market segment only focuses on three indicators of RFM, without demographic characteristics of customers and dynamic variables (Mosaddegh et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Measure of CLV Toward Market Segmentation Approach in the Telecommunication Sector (Vietnam)

2021

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This study is based on data used by a telecommunications industry over a period of 6 months, beginning July 1, 2018, until December 31, 2018. The approached model of customer lifetime value (CLV) was concerned on the sample size of 245,355 records made by cell phones of active prepaid subscribers. The aim of this article is to estimate CLV through survival function with adjusted variables enclosed, for example, marketing cost. To do this, a statistical model was used to predict the individual CLV and confirmed a different finding to some previous study, in which this new finding brings an important message that the telecom providers should take care of customer earlier before too late because after the 10th of tenure, the subscribers can churn the telecom operator’s service. The result of CLV is well supported to segment market. It will also benefit the telecom providers to maintain their market competitiveness.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Application Of CLV Toward Market Segmentmentioning

confidence: 99%

Section: Application Of CLV Toward Market Segmentmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Measure of CLV Toward Market Segmentation Approach in the Telecommunication Sector (Vietnam)

2021

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“…There are many other ways to highlight the essence of what customers represent for any organization. Modern marketing starts from premises like the importance of customers as the most valuable assets the companies own (Mosaddegh et al, 2021), customer centricity, the fact that different customers deserve different treatment, and not all customers are the same value (Gupta and Zeithaml, 2006;Estrella-Ramón et al, 2013). Customer metrics and greater measurability are also prerequisites without which modern marketing could not exist.…”

Section: Introduction Context Importance Visionmentioning

confidence: 99%

Investigating the Importance of Customer Lifetime Value in Modern Marketing – A Literature Review

Popa¹,

SASU²,

Tarcza³

2021

AUOES

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In this paper, we investigate the importance of Customer Lifetime Value (CLV) as part of the modern marketing strategy. There is no doubt that CLV is one of the most important metrics to be considered in Customer Relationship Management and Relationship Marketing. It represents an instrument meant to enhance the client’s loyalty and to bring some clearance for the marketing strategies regarding the customer segmentation. At the same time, it is one of the most difficult to calculate. As part of this study, we explore how the concept is approached in the academic literature, the notoriety of the concept, the areas, and the topics with which it is associated. In this investigation, we are using WordStat and QDA Miner software applications for content analysis and text mining aiming to extract themes and trends regarding the approached topic from a significant number of academic articles.

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BuB: a builder-booster model for link prediction on knowledge graphs

2023

Self Cite

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Link prediction (LP) has many applications in various fields. Much research has been carried out on the LP field, and one of the most critical problems in LP models is handling one-to-many and many-to-many relationships. To the best of our knowledge, there is no research on discriminative fine-tuning (DFT). DFT means having different learning rates for every parts of the model. We introduce the BuB model, which has two parts: relationship Builder and Relationship Booster. Relationship Builder is responsible for building the relationship, and Relationship Booster is responsible for strengthening the relationship. By writing the ranking function in polar coordinates and using the nth root, our proposed method provides solutions for handling one-to-many and many-to-many relationships and increases the optimal solutions space. We try to increase the importance of the Builder part by controlling the learning rate using the DFT concept. The experimental results show that the proposed method outperforms state-of-the-art methods on benchmark datasets.

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Dynamics of customer segments: A predictor of customer lifetime value

Cited by 26 publications

References 21 publications

Measure of CLV Toward Market Segmentation Approach in the Telecommunication Sector (Vietnam)

Measure of CLV Toward Market Segmentation Approach in the Telecommunication Sector (Vietnam)

Investigating the Importance of Customer Lifetime Value in Modern Marketing – A Literature Review

BuB: a builder-booster model for link prediction on knowledge graphs

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