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Relevant bibliographies by topics / SBG Model Beta distribution Beta geometric Beta binomial

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Academic literature on the topic 'SBG Model Beta distribution Beta geometric Beta binomial'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "SBG Model Beta distribution Beta geometric Beta binomial"

1

Ghantasala, Naga Lakshmi Subha Pavan Kumar. "Enhancing Player Retention in Mobile Gaming through Predictive Customer Lifetime Value Modeling using BG/NBD." AI Matters 10, no. 2 (2024): 9–11. https://doi.org/10.1145/3694712.3695754.

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In the competitive mobile gaming industry, retaining players and maximizing engagement are critical for sustained success. This study employs the Beta-Geometric/Negative Binomial Distribution (BG/NBD) model to predict customer lifetime value (CLTV) and identify players at risk of churning. By targeting these high-risk players with tailored marketing strategies, gaming companies can significantly improve retention rates and overall player engagement.

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2

Zayed, Mohammad A., Amal S. Hassan, Ehab M. Almetwally, Ahmad M. Aboalkhair, Abdullah H. Al-Nefaie, and Hisham M. Almongy. "A Compound Class of Unit Burr XII Model: Theory, Estimation, Fuzzy, and Application." Scientific Programming 2023 (April 26, 2023): 1–17. http://dx.doi.org/10.1155/2023/4509889.

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The current research offers an enhanced three-parameter lifetime model that combines the unit Burr XII distribution with a power series distribution. The novel class of distribution is named the unit Burr XII power series (UBXIIPS). This compounding technique allows for the production of flexible distributions with strong physical meanings in domains such as biology and engineering. The UBXIIPS class includes the unit Burr XII Poisson (UBXIIP) distribution, the unit Burr XII binomial distribution, the unit Burr XII geometric distribution, and the unit Burr XII negative binomial distribution. T

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3

Manash, Pratim Kashyap. "A SIMPLE MODEL FOR ANALYZING THE CUSTOMER RETENTION COMPARING RURAL AND URBAN STORE." March 1, 2019. https://doi.org/10.5281/zenodo.2644748.

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Many businesses follow the repeat operation of a particular customer with a same store in different discrete time point. Customer retention comprises the probability of customer that alive in particular store in different purchase occasion. Different purchase occasion with a particular store occur at regular time intervals. The main goal of this paper is to predict future purchase patterns for rural and urban customer that can be described by the structural characteristics. The discrete time beta geometric model allows to heterogeneity of each of rural and urban customer behavioral process. Th

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4

Al-Balushi, Zainab M., Amadou Sarr, and M. Mazharul Islam. "Beta-Geometric Regression for Modeling Count Data on First Antenatal Care Visit (ANC) with Application." Journal of Biostatistics and Epidemiology, October 31, 2023. http://dx.doi.org/10.18502/jbe.v9i1.13977.

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Introduction: Little attention has been paid to modeling count data with the geometric distribution. There are many real-life phenomena with a constant probability of first success. However, in practice, the probability of the first success may vary, making simple geometric models unsuitable for modeling such data. One can assume one of many continuous distributions for modeling the probability of first success with the parameter space [0, 1]. In this respect Beta distribution defined on the standard unit interval [0,1] is the most useful distribution due to its ability to accommodate a wide r

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5

H S Yashaswini and Prabhudeva S. "Customer Lifetime Value Prediction." International Journal of Advanced Research in Science, Communication and Technology, June 25, 2022, 805–10. http://dx.doi.org/10.48175/ijarsct-5162.

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In this era of Internet, there are many lots of retailers in the e-commerce industry for whom the customers are assets. E-commerce means buying and selling the products through Internet in online mode. E-commerce established many employment opportunities to the people from anywhere because there is no direct interaction between the seller and the buyer. Many people are purchasing things using this e-commerce application. There are many e-commerce websites available for the customers. So the Retailers want to analyze their relationship with the customers so that they can produce or buy the good

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