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 behaviour. 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 behaviour 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 data-set provided by a mobile network operator is utilized to automate decision trees 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 as to 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 behaviour -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.
Customer retention is a critical concern for most mobile network operators because of the increasing competition in the mobile services sector. This concern has driven companies to exploit data as an avenue to better understand customer needs. Data mining techniques such as clustering and classification have been adopted to understand customer retention in the mobile services industry. However, the effectiveness of these techniques is debatable due to the increasing complexity of the mobile market itself. This study proposes an application of Agent-Based Modeling and Simulation (ABMS) as a novel approach to understanding customer retention. A dataset provided by a mobile network operator is utilized to automate decision trees and agent based models. The most popular churn modeling techniques were adopted in order to automate the development of models, from decision trees, and subsequently explore customer churn scenarios. ABMS is used to understand the behavior of customers and detect possible reasons why customers churned or stayed with their respective mobile network operators. Data analysis is able to identify that location and choice of mobile devices were determinants for the decision to churn or stay with their mobile network operator -with word of mouth as an important factor. Importantly, agent based simulation is able to explore further the determinants in the wider marketplace.
Customer retention is a critical concern for most mobile network operators because of the increasing competition in the mobile services sector. This concern has driven companies to exploit data as an avenue to better understand customer needs. Data mining techniques such as clustering and classification have been adopted to understand customer retention in the mobile services industry. However, the effectiveness of these techniques is debatable due to the increasing complexity of the mobile market itself. This study proposes an application of Agent-Based Modeling and Simulation (ABMS) as a novel approach to understanding customer retention. A dataset provided by a mobile network operator is utilised to automate decision trees and agent based models. The most popular churn modeling techniques were adopted in order to automate the development of models, from decision trees, and subsequently explore customer churn scenarios. ABMS is used to understand the behavior of customers and detect possible reasons why customers churned or stayed with their respective mobile network operators. Data analysis is able to identify that location and choice of mobile devices were determinants for the decision to churn or stay with their mobile network operator -with word of mouth as an important factor. Importantly, agent based simulation is able to explore further the determinants in the wider marketplace.
Abstract:The combination of sensor data with analytic techniques is growing in popularity for both practitioners and researchers as an Internet of Things (IoT) offers new opportunities and insights. Organisations are trying to use sensor technologies to derive intelligence and gain a competitive edge in their industries. Obtaining data from sensors might not pose too much of a problem, however subsequent utilisation in meeting an organisation's decision making can be more problematic. Understanding how sensor data analytics can be undertaken is the first step to deriving business intelligence from front line retail environments. This paper explores the use of the Microsoft Kinect sensor to provide intelligence by identifying and sensing gestures to better understand customer behaviour in the retail space.
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