Randomized Agent-Based Model for Mobile Customer Retention Behaviour Prediction

Sandhya, N.; Samuel, Philip; Chacko, Mariamma

doi:10.1007/978-3-030-19562-5_28

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, we implement the proposed Intersection-Randomized Algorithm on MapReduce platform. Telecommunication customer retention is predicted using the agent-based model (ABM) (Sandhya et al, 2018).…”

Section: Proposed Methodologymentioning

confidence: 99%

Feature intersection for agent-based customer churn prediction

Sandhya

Samuel

Chacko

2019

DTA

Self Cite

View full text Add to dashboard Cite

Purpose Telecommunication has a decisive role in the development of technology in the current era. The number of mobile users with multiple SIM cards is increasing every second. Hence, telecommunication is a significant area in which big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. The paper aims to discuss this issue. Design/methodology/approach The authors recommend an Intersection-Randomized Algorithm (IRA) using MapReduce functions to avoid data duplication in the mobile user call data of telecommunication service providers. The authors use the agent-based model (ABM) to predict the complex mobile user behaviour to prevent customer churn with a particular telecommunication service provider. Findings The agent-based model increases the prediction accuracy due to the dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents. Research limitations/implications The authors have not considered the microscopic behaviour of the customer churn based on complex user behaviour. Practical implications This paper shows the effectiveness of the IRA along with the agent-based model to predict the mobile user churn behaviour. The advantage of this proposed model is as follows: the user churn prediction system is straightforward, cost-effective, flexible and distributed with good business profit. Originality/value This paper shows the customer churn prediction of complex human behaviour in an effective and flexible manner in a distributed environment using Intersection-Randomized MapReduce Algorithm using agent-based model.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Feature intersection for agent-based customer churn prediction

Sandhya

Samuel

Chacko

2019

DTA

Self Cite

View full text Add to dashboard Cite

show abstract

Predictive Analytics for Customer Behavior

Rosário,

Cruz,

Moniz

et al. 2024

Advances in Marketing, Customer Relationship Management, and E-Services

View full text Add to dashboard Cite

Predictive analytics have become indispensable tools for implementing data-driven strategies and decisions. These innovations are used to predict future activities, thereby enabling businesses to plan accordingly. The main purpose of this study is, through a systematic bibliometric literature review, to understand how businesses apply predictive models to predicting consumer behavior. The review includes 74 articles published in the Scopus® database, presenting up-to-date knowledge on the topic. The bibliographic search included peer-reviewed articles published up to 2024. The research findings identified three major ways predictive analytics helps forecast customer behavior. These include predicting their purchasing patterns, churn, and engagement. While predictive analytics for customer behavior offers significant advantages, it also poses challenges related to data privacy, ethical considerations, and the need for sophisticated analytics skills.

show abstract

Randomized Agent-Based Model for Mobile Customer Retention Behaviour Prediction

Cited by 2 publications

References 14 publications

Feature intersection for agent-based customer churn prediction

Feature intersection for agent-based customer churn prediction

Predictive Analytics for Customer Behavior

Contact Info

Product

Resources

About