Dr RAMA DEVI ODUGU, Sai Krishna Pothini, Mulpuru Prasanna Kumari, Sowjanya. V, Uppalapati Naga Sai Charan
{"title":"使用机器学习预测客户流失:OTT平台的订阅续订","authors":"Dr RAMA DEVI ODUGU, Sai Krishna Pothini, Mulpuru Prasanna Kumari, Sowjanya. V, Uppalapati Naga Sai Charan","doi":"10.1109/ICAAIC56838.2023.10140287","DOIUrl":null,"url":null,"abstract":"The goal of predicting subscriptions for OTT (Over-The-Top) platforms using machine learning is to devise a model which can accurately predict whether a customer will continue using this platform or not. This information is important for OTT companies to understand and optimize their marketing and retention efforts. Relevant data, such as customer demographics and viewing habits, is collected and analyzed to train the model. This process involves cleaning the data, selecting important features, and training a machine learningmodel. The model is then tested and validated using performance metrics. In short, this problem requires a comprehensive understanding of customer behavior and the use of machine learning to predict subscription decisions. The results can provide valuable insights for OTT companies to improve their customer understanding and retention efforts.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Customer Churn Prediction using Machine Learning: Subcription Renewal on OTT Platforms\",\"authors\":\"Dr RAMA DEVI ODUGU, Sai Krishna Pothini, Mulpuru Prasanna Kumari, Sowjanya. V, Uppalapati Naga Sai Charan\",\"doi\":\"10.1109/ICAAIC56838.2023.10140287\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The goal of predicting subscriptions for OTT (Over-The-Top) platforms using machine learning is to devise a model which can accurately predict whether a customer will continue using this platform or not. This information is important for OTT companies to understand and optimize their marketing and retention efforts. Relevant data, such as customer demographics and viewing habits, is collected and analyzed to train the model. This process involves cleaning the data, selecting important features, and training a machine learningmodel. The model is then tested and validated using performance metrics. In short, this problem requires a comprehensive understanding of customer behavior and the use of machine learning to predict subscription decisions. The results can provide valuable insights for OTT companies to improve their customer understanding and retention efforts.\",\"PeriodicalId\":267906,\"journal\":{\"name\":\"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAAIC56838.2023.10140287\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAAIC56838.2023.10140287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Customer Churn Prediction using Machine Learning: Subcription Renewal on OTT Platforms
The goal of predicting subscriptions for OTT (Over-The-Top) platforms using machine learning is to devise a model which can accurately predict whether a customer will continue using this platform or not. This information is important for OTT companies to understand and optimize their marketing and retention efforts. Relevant data, such as customer demographics and viewing habits, is collected and analyzed to train the model. This process involves cleaning the data, selecting important features, and training a machine learningmodel. The model is then tested and validated using performance metrics. In short, this problem requires a comprehensive understanding of customer behavior and the use of machine learning to predict subscription decisions. The results can provide valuable insights for OTT companies to improve their customer understanding and retention efforts.