Customer Churn Prediction in the Software by Subscription models IT business using machine learning methods
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| Format: | Recurso digital |
| Langue: | anglais |
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2021
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| _version_ | 1866902092885524480 |
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| author | Kolesnikova, Kateryna |
| author_facet | Kolesnikova, Kateryna |
| contents | <p>The article presents the results of research related to the problems of development of IT products based on the Software by Subscription model in the IT sphere. Operating in market conditions, such enterprises are forced to develop modern IT products to support small and medium-sized businesses based on the interaction of many potential external customers (users of the IT system), who later, under favorable conditions, become customers of these enterprises. At the same time, the very nature of the Software by Subscription company is largely determined by its average client, and is a topic played out in many empirical rules of Software by Subscription metrics. To improve the efficiency of customer interaction with SaaS companies, the authors proposed a hypothesis about the possibility of using various forecasting methods in machine learning. A comparative characteristic of the use of different models and algorithms for predicting the outflow of customers for an IT company is carried out. At the same time, the development of innovative IT products should be carried out with the fullest satisfaction of the interests and needs of all major stakeholders. The article offers a mathematical description of the model and method of modeling these interactions.. To conduct chain training, the Python functionality is used, with the processing of user activity data sets. The analysis is carried out and conclusions are made about the effectiveness of the proposed approach.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17560350 |
| institution | Zenodo |
| language | eng |
| publishDate | 2021 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Customer Churn Prediction in the Software by Subscription models IT business using machine learning methods Kolesnikova, Kateryna Random Forest Customer Churn Prediction Machine Learning <p>The article presents the results of research related to the problems of development of IT products based on the Software by Subscription model in the IT sphere. Operating in market conditions, such enterprises are forced to develop modern IT products to support small and medium-sized businesses based on the interaction of many potential external customers (users of the IT system), who later, under favorable conditions, become customers of these enterprises. At the same time, the very nature of the Software by Subscription company is largely determined by its average client, and is a topic played out in many empirical rules of Software by Subscription metrics. To improve the efficiency of customer interaction with SaaS companies, the authors proposed a hypothesis about the possibility of using various forecasting methods in machine learning. A comparative characteristic of the use of different models and algorithms for predicting the outflow of customers for an IT company is carried out. At the same time, the development of innovative IT products should be carried out with the fullest satisfaction of the interests and needs of all major stakeholders. The article offers a mathematical description of the model and method of modeling these interactions.. To conduct chain training, the Python functionality is used, with the processing of user activity data sets. The analysis is carried out and conclusions are made about the effectiveness of the proposed approach.</p> |
| title | Customer Churn Prediction in the Software by Subscription models IT business using machine learning methods |
| topic | Random Forest Customer Churn Prediction Machine Learning |
| url | https://doi.org/10.5281/zenodo.17560350 |