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An In-depth Study of Commercial MVNO
10 references·1 citing·42 second hop
An In-depth Study of Commercial MVNO (2019)An In-depth Study of Commercial MVNOHow age and gender affect smartphone usage (2016)How age and gender affect sma…A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn (2016)A Big Data Clustering Algorit…Spearman Rank Correlation Coefficient (2011)Spearman Rank Correlation Coe…Churn Prediction Model in Retail Banking Using Fuzzy C-Means Algorithm (2009)Churn Prediction Model in Ret…Churn models for prepaid customers in the cellular telecommunication industry using large data marts (2009)Churn models for prepaid cust…Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques (2008)Churn Prediction in Subscript…A practical guide to analytical method validation, including measurement uncertainty and accuracy profiles (2007)A practical guide to analytic…Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques (2006)Churn prediction in subscript…Spearman Rank Correlation Coefficient (2005)Spearman Rank Correlation Coe…Applying data mining to telecom churn management (2005)Applying data mining to telec…Understanding the Ecosystem and Addressing the Fundamental Concerns of Commercial MVNO (2020)Understanding the Ecosystem a…Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (2004)Handling class imbalance in customer churn prediction (2008)Profit Maximization Analysis Based on Data Mining and the Exponential Retention Model Assumption with Respect to Customer Churn Problems (2015)What's In A Name? Malay Seals As Onomastic Sources (2018)Churn Prediction for Savings Bank Customers: A Machine Learning Approach (2020)The Proof and Measurement of Association between Two Things (1904)Rank Correlation Methods. (1949)Significance Testing of the Spearman Rank Correlation Coefficient (1972)Mobility Detection Using Everyday GSM Traces (2006)Large sample size, significance level, and the effect size: Solutions to perils of using big data for academic research (2017)Investigation of surrogate measures of robustness for project scheduling problems (2019)A Logit Model of Brand Choice Calibrated on Scanner Data (1983)Customer Evaluations of Service Complaint Experiences: Implications for Relationship Marketing (1998)Switching barriers and repurchase intentions in services (2000)Analytics for Customer Engagement (2010)A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (2018)Turning telecommunications call details to churn prediction: a data mining approach (2002)Application of data mining techniques in customer relationship management: A literature review and classification (2008)Modeling partial customer churn: On the value of first product-category purchase sequences (2012)Intelligent churn prediction in telecom: employing mRMR feature selection and RotBoost based ensemble classification (2013)Dynamic churn prediction framework with more effective use of rare event data: The case of private banking (2014)THE DESIGN OF OPTIMUM MULTIFACTORIAL EXPERIMENTS (1946)Design and Analysis of Experiments (1992)Statistics for Analytical Chemistry (1993)Total error and uncertainty: Friends or foes? (2011)Uncertainty profiles for the validation of analytical methods (2011)An overall uncertainty approach for the validation of analytical separation methods (2013)Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Forecasting with artificial neural networks: (1998)Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis (1999)A survey of results on mobile phone datasets analysis (2015)Data mining with big data (2013)Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014)Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics (2017)Optimizing China’s energy consumption structure under energy and carbon constraints (2018)Technology heterogeneity and efficiency of China’s circular economic systems: A game meta-frontier DEA approach (2019)Predicting Fed Forecasts (2016)Beyond Interruptibility (2017)Smartphones and attention, curse or blessing? - A review on the effects of smartphone usage on attention, inhibition, and working memory (2020)Digital Engagement of Older Adults: Scoping Review (2022)Customer churn prediction using improved balanced random forests (2008)Building comprehensible customer churn prediction models with advanced rule induction techniques (2010)
this work references cited by second hop

References

  • 2007A practical guide to analytical method validation, including measurement uncertainty and accuracy profiles731 cited
  • 2005Applying data mining to telecom churn management526 cited
  • 2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques453 cited
  • 2005Spearman Rank Correlation Coefficient254 cited
  • 2016How age and gender affect smartphone usage220 cited
  • 2008Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques123 cited
  • 2016A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn118 cited
  • 2009Churn models for prepaid customers in the cellular telecommunication industry using large data marts102 cited
  • 2011Spearman Rank Correlation Coefficient36 cited
  • 2009Churn Prediction Model in Retail Banking Using Fuzzy C-Means Algorithm24 cited

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  • 2020Understanding the Ecosystem and Addressing the Fundamental Concerns of Commercial MVNO22 cited

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