![]() Specifically, we propose a model based on the Multivariate Hawkes Process (MHP), which is an exogenous stimulis-driven self-exciting point process, to model the exogenous stimulis and endogenous excitations simultaneously. To better understand the popularity evolution, in this paper, we aim to forecast the popularity evolution of mobile apps by incorporating complex factors, i.e., exogenous stimulis and endogenous excitations. However, existing works lack the capabilities to model such complex factors. The popularity evolution of mobile apps is usually a long-term process, affected by various complex factors. Therefore, it is significant and necessary to model and forecast the future popularity evolution of mobile apps. To thrive in this competitive app market, it is vital for app developers to understand the popularity evolution of their mobile apps, and inform strategic decision-making for better mobile app development. However, the global prevalence of mobile apps also leads to fierce competition. In recent years, with the rapid development of mobile app ecosystem, the number and categories of mobile apps have grown tremendously. The proposed method is expected to be used as an effective tool for customer complaints monitoring over time, which enables responsive and preventive quality management. We applied and validated the proposed method using a mobile game service, offering a guideline for its implementation and customisation. The integration of two analyses makes it possible to monitor customer complaints at acceptable time and cost. The sentiment analysis enables systematic identification of a customer satisfaction score from customer review data while the statistical process control chart analysis allows early detection of significant customer complaints and prevents service failures. The use of customer review data for statistical process control analysis extends the scope of research and application from the supplier perspective to customer-centric service quality management. Recognising the value of customer reviews as a pool of 'voice of the customer', we propose an integrated method of sentiment and statistical process control analyses. This study presents a data-driven method to monitor customer complaints for efficient service quality management.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |