Abstract: In an illustrative embodiment, systems and methods for predicting subscriber churn include machine learning algorithm(s) for classifying the subscriber's decision to stay with the present subscription provider or to switch (churn) to a new provider. The machine learning algorithms may include a logistic regression/neural network for modeling churn propensity in subscribers. The churn risk analysis systems and methods may identify a group of subscribers most likely to churn. Further, the churn risk analysis systems and methods may identify a group of subscribers least likely to churn. The identified subscribers may be presented to a representative of the subscription provider, for example through a user interface.

Abstract: In an illustrative embodiment, systems and methods for performing cascading matching of data records from disparate data sources comprise identifying matches using at least one uniquely identifying data field and at least one additional data field shared by a first data set and a second data set. Potential matches may be resolved through calculating differences between one or more shared data fields of a matched data record of the first data set and both a first matched record and a second matched record of the second data set, and determining a best match through analyzing the calculated differences. Unmatched records may be iteratively matched using a different uniquely identifying data field and/or different at least one additional data field(s).

Abstract: In an illustrative embodiment, systems and methods for predicting subscriber churn include machine learning algorithm(s) for classifying the subscriber's decision to stay with the present subscription provider or to switch (churn) to a new provider. The machine learning algorithms may include a logistic regression/neural network for modeling churn propensity in subscribers. The churn risk analysis systems and methods may identify a group of subscribers most likely to churn. Further, the churn risk analysis systems and methods may identify a group of subscribers least likely to churn. The identified subscribers may be presented to a representative of the subscription provider, for example through a user interface.

Abstract: In an illustrative embodiment, systems and methods for performing cascading matching of data records from disparate data sources comprise identifying matches using at least one uniquely identifying data field and at least one additional data field shared by a first data set and a second data set. Potential matches may be resolved through calculating differences between one or more shared data fields of a matched data record of the first data set and both a first matched record and a second matched record of the second data set, and determining a best match through analyzing the calculated differences. Unmatched records may be iteratively matched using a different uniquely identifying data field and/or different at least one additional data field(s).

Abstract: In an illustrative embodiment, systems and methods for performing cascading matching of data records from disparate data sources comprise identifying matches using at least one uniquely identifying data field and at least one additional data field shared by a first data set and a second data set. Potential matches may be resolved through calculating differences between one or more shared data fields of a matched data record of the first data set and both a first matched record and a second matched record of the second data set, and determining a best match through analyzing the calculated differences. Unmatched records may be iteratively matched using a different uniquely identifying data field and/or different at least one additional data field(s).