scholarly journals A Data-driven Methodology for Agent Based Exploration of Customer Retention

2016 ◽  
Author(s):  
Chidozie Mgbemena ◽  
David Bell ◽  
Nurul Saleh
Keyword(s):  
Customer Retention ◽  
Data Driven ◽  
Agent Based

scholarly journals Data-driven agent-based exploration of customer behavior

SIMULATION ◽  
2017 ◽  
Vol 94 (3) ◽  
pp. 195-212 ◽  
Author(s):  
David Bell ◽  
Chidozie Mgbemena
Keyword(s):  
Decision Tree ◽  
Customer Retention ◽  
Mobile Network ◽  
Mobile Services ◽  
Customer Behavior ◽  
Data Driven ◽  
Agent Based ◽  
Services Sector ◽  
Customer Churn ◽  
Mobile Network Operators

Customer retention is a critical concern for mobile network operators because of the increasing competition in the mobile services sector. Such unease has driven companies to exploit data as an avenue to better understand changing customer behavior. Data-mining techniques such as clustering and classification have been widely adopted in the mobile services sector to better understand customer retention. However, the effectiveness of these techniques is debatable due to the constant change and increasing complexity of the mobile market itself. This design study proposes an application of agent-based modeling and simulation (ABMS) as a novel approach to understanding customer behavior through the combination of market and social factors that emerge from data. External forces at play and possible company interventions can then be added to data-derived models. A dataset provided by a mobile network operator is utilized to automate decision-tree analysis and subsequent building of agent-based models. Popular churn modeling techniques were adopted in order to automate the development of models, from decision trees, and subsequently explore possible customer churn scenarios. ABMS is used to understand the behavior of customers and detect reasons why customers churned or stayed with their respective mobile network operators. A CART decision-tree method is presented that identifies agents, selects important attributes, and uncovers customer behavior – easily identifying tenure, location, and choice of mobile devices as determinants for the churn-or-stay decision. Word of mouth between customers is also explored as a possible influence factor. Importantly, methods for automating data-driven agent-based simulation model generation will support faster exploration and experimentation – including with those determinants from a wider market or social context.

scholarly journals Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1402 ◽  
Author(s):  
Haibo Zhang ◽  
Xiaoming Liu ◽  
Honghai Ji ◽  
Zhongsheng Hou ◽  
Lingling Fan
Keyword(s):  
Cooperative Control ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Data Driven ◽  
Experimental Comparison ◽  
Signal Control ◽  
Signal Timing ◽  
Agent Based ◽  
Multi Agent ◽  
Traffic Signal Timing

Data-driven intelligent transportation systems (D2ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal control strategies, the proposed MA-DD-DACC method combined with an online parameter learning law can be applied for traffic signal control in a distributed manner by merely utilizing the collected I/O traffic queueing length data and network topology of multi-direction signal controllers at a single intersection. A Lyapunov-based stability analysis shows that the proposed approach guarantees uniform ultimate boundedness of the distributed consensus coordinated errors of queuing strength. The numerical and experimental comparison simulations are performed on a VISSIM-VB-MATLAB joint simulation platform to verify the effectiveness of the proposed approach.

Hybrid operations of human driving vehicles and automated vehicles with data-driven agent-based simulation

2020 ◽  
Vol 86 ◽  
pp. 102469
Author(s):  
Fugen Yao ◽  
Jiangtao Zhu ◽  
Jingru Yu ◽  
Chuqiao Chen ◽  
Xiqun (Michael) Chen
Keyword(s):  
Data Driven ◽  
Automated Vehicles ◽  
Agent Based Simulation ◽  
Agent Based

scholarly journals An open-data-driven agent-based model to simulate infectious disease outbreaks

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0208775 ◽  
Author(s):  
Elizabeth Hunter ◽  
Brian Mac Namee ◽  
John Kelleher
Keyword(s):  
Infectious Disease ◽  
Open Data ◽  
Disease Outbreaks ◽  
Data Driven ◽  
Agent Based Model ◽  
Agent Based ◽  
Infectious Disease Outbreaks

Clicking position and user posting behavior in online review systems: A data-driven agent-based modeling approach

2020 ◽  
Vol 512 ◽  
pp. 161-174 ◽  
Author(s):  
Guoyin Jiang ◽  
Xiaodong Feng ◽  
Wenping Liu ◽  
Xingjun Liu
Keyword(s):  
Agent Based Modeling ◽  
Data Driven ◽  
Online Review ◽  
Agent Based ◽  
Modeling Approach
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