Open AccessFOCUS ON ARTIFICIAL INTELLIGENCE FOR PREDICTING THE OUTFLOW OF CLIENTS FROM ON-LINE EDUCATION SITES
Author(s) -
V. Bredikhin,
Т. С. Сенчук,
K. Stuzhuk
Publication year - 2021
Publication title -
komunalʹne gospodarstvo mìst
Language(s) - English
Resource type - Journals
eISSN - 2522-1817
pISSN - 2522-1809
DOI - 10.33042/2522-1809-2021-6-166-2-7
Subject(s) - outflow , computer science , the internet , artificial neural network , artificial intelligence , machine learning , focus (optics) , world wide web , physics , meteorology , optics
The article examines the process of forecasting customer outflows, which is especially important for companies that use a business model based on subscription. It was found that the outflow rate is extremely important for companies with a subscription and transactional business model, which implies regular payments to the company (banks, telecom operators, SaaS-services, etc.). For this purpose, the types, the main reasons for the outflow of customers and the parameters defined to build a predictive model using machine learning algorithms were considered. The result was the hypothesis of the reasons for the outflow of customers from sites that provide training services based on courses that are presented on-line in the Internet space. To build a model of outflow forecasting, the behavioral characteristics of students, their motivation and the structure of the courses themselves were studied. Based on the collected large array of data, their change was analyzed by a large number of parameters and the relationships between the behavioral characteristics of students, course structures and their passage were identified. A variant of the forecasting model was built, for which the accuracy of its operation was increased and the results were integrated into the customer outflow prediction module. The final list of features included more than 100 parameters, which were divided into 6 blocks. As a result, a predictive model was created using the Weibull distribution, as client behavior can be considered as a kind of survival model. To estimate the probability of customer outflow, based on the considered hypotheses, a recurrent neural network with an LSTM layer was developed, where a negative logarithmic likelihood function was used as a loss function for the Weibull distribution. As a conclusion, it was proposed to introduce a stable proactive educational business, when decisions are made not only on the basis of feelings, but also on the basis of data, comes a clearer and more sound understanding of how to improve the educational product.