- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
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.
Customer retention is extremely important to MNOs because of fierce competition in the mobile sector. Hence, companies in the sector are increasingly strengthening their CRM strategies in order to retain customers. A number of factors can influence the decision for customers to purchase or adopt a product or service. However, customers are also likely to trust the WOM from someone within their social network. Tools and techniques are required to build models in near real-time and also allow companies to explore external factors. Machine learning and ABMS have both been widely adopted to better understand customer behavior. However, their combination as a means to generate models using only market determinants is novel. Consequently, models can be generated in a timely manner and in response to market change. It is this level of automation that allows companies to explore possible future scenarios or active interventions. This paper presents the CADET method, a novel data-driven approach to ABMS, that addresses the effective utilization of large and rapidly changing datasets. Agents are not predefined before data analysis activities, determinants are used to uncover both agents and behaviors automatically. March and Smith describe artifacts as constructs, models, methods, and instantiations to delineate core research activities. Artifacts naturally contribute to the process of modeling and simulation primarily as a method for model building, the model itself, and as an instantiated simulation. This study presents the CADET method as a datadriven framework for initial building (or initialization) activities – identifying agents, attributes of interest and customer behaviors.