Abstract: A method and system provide estimated tax refund data to a user of a tax return preparation system throughout personalized tax return preparation interview. The method and system receive current user tax related data associated with the user, retrieve tax rules data, and gather historical tax related data associated with historical users of the tax return preparation system. The method and system further generate probabilistic inference data including inferences about tax related characteristics of the user based on the historical tax related data and the tax rules data. The method and system provide estimated tax refund data to the user based on the probabilistic inference data.

Abstract: An ordered combination of machine-learning models may be used to identify users who are likely to abandon use of an application, predict the reasons why those users are likely to abandon, and identify intervening actions that the application can perform to reduce the probability that the users will abandon the application. For example, a first machine-learning model determines a retention-prediction value indicating a probability that the user will complete a target action in the application before a session terminates. If the retention-prediction value satisfies a threshold condition, a second machine-learning model determines a reason why the session is likely to terminate before the user completes the target action. A third machine-learning model determines an intervention action for the application to perform to increase the probability that the user will complete the target action before the session terminates.

Abstract: Systems described herein apply an ordered combination machine-learning models to identify users who are likely to abandon use of an application, predict the reasons why those users are likely to abandon, and identify intervening actions that the application can perform to reduce the probability that the users will abandon the application. A first machine-learning model determines a retention-prediction value indicating a probability that the user will complete a target action in the application before a session terminates. If the retention-prediction value satisfies a threshold condition, a second machine-learning model determines a reason why the session is likely to terminate before the user completes the target action. A third machine-learning model determines an intervention action for the application to perform to increase the probability that the user will complete the target action before the session terminates.

Abstract: Methods, systems and articles of manufacture for using one or more predictive models to predict which tax matters are relevant to a particular taxpayer during preparation of an electronic tax return. A tax return preparation system accesses taxpayer data such as personal data and/or tax data regarding the particular taxpayer. The system executes a predictive model which receives the taxpayer data as inputs to the predictive model. The predictive model generates as output(s) one or more predicted tax matters which are determined to be likely to be relevant to the taxpayer. The system may then determine tax questions to present to the user based at least in part upon the predicted tax matters determined by the predictive model.

Abstract: Systems described herein apply an ordered combination machine-learning models to identify users who are likely to abandon use of an application, predict the reasons why those users are likely to abandon, and identify intervening actions that the application can perform to reduce the probability that the users will abandon the application. A first machine-learning model determines a retention-prediction value indicating a probability that the user will complete a target action in the application before a session terminates. If the retention-prediction value satisfies a threshold condition, a second machine-learning model determines a reason why the session is likely to terminate before the user completes the target action. A third machine-learning model determines an intervention action for the application to perform to increase the probability that the user will complete the target action before the session terminates.

Abstract: An ordered combination of machine-learning models may be used to identify users who are likely to abandon use of an application, predict the reasons why those users are likely to abandon, and identify intervening actions that the application can perform to reduce the probability that the users will abandon the application. For example, a first machine-learning model determines a retention-prediction value indicating a probability that the user will complete a target action in the application before a session terminates. If the retention-prediction value satisfies a threshold condition, a second machine-learning model determines a reason why the session is likely to terminate before the user completes the target action. A third machine-learning model determines an intervention action for the application to perform to increase the probability that the user will complete the target action before the session terminates.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, according to one embodiment. The method and system filters out user experience options from delivery to users, if the user experience options are non-compliant with one or more business rules, to maintain business relations for the service provider and to maintain user confidence in the services provided by the service provider (e.g., a tax return preparation system), according to one embodiment.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options to some users while concurrently testing user responses to other user experience options, among a variety of user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, at least partially based on feedback from users, according to one embodiment. The method and system determines bias weights from characteristics of the analytics model and uses the bias weights to compensate for data biases when updating or generating analytics models.

Abstract: A method and system provide estimated tax refund data to a user of a tax return preparation system throughout personalized tax return preparation interview. The method and system receive current user tax related data associated with the user, retrieve tax rules data, and gather historical tax related data associated with historical users of the tax return preparation system. The method and system further generate probabilistic inference data including inferences about tax related characteristics of the user based on the historical tax related data and the tax rules data. The method and system provide estimated tax refund data to the user based on the probabilistic inference data.

Abstract: A method and system provide estimated tax refund data to a user of a tax return preparation system throughout personalized tax return preparation interview. The method and system receive current user tax related data associated with the user, retrieve tax rules data, and gather historical tax related data associated with historical users of the tax return preparation system. The method and system further generate probabilistic inference data including inferences about tax related characteristics of the user based on the historical tax related data and the tax rules data. The method and system provide estimated tax refund data to the user based on the probabilistic inference data.

Abstract: Computer-implemented methods, articles of manufacture and computerized systems for identifying or alerting a user of certain data in electronic tax returns. A computerized tax return preparation system including a tax return preparation software application executed by a computing device receives first and second tax data and populates respective fields of the electronic tax return. The system executes a predictive model such as logistic regression, naive bayes, K-means clustering, clustering, k-nearest neighbor, and neural networks. First tax data is an input into the predictive model, which generates an output, which is compared with second tax data. An alert is generated when the second tax data does not satisfy pre-determined criteria relative to the first output generated by the predictive model. The same or other predictive model may be used as additional tax data is received for subsequent tax data analysis.

Abstract: A computing platform identifies one or more characteristics of a user accessing application software or a software service via a user interface and a field to be filled or completed in the user interface of the application software or a software service. The computing platform further determines and presents, at one or more automatic fill or completion modules that are stored at least partially in memory and function in tandem with one or more computer processors in the computing platform, a list of one or more completion candidates in the user interface of the application software or a software service at least by performing one or more incremental searches based in part or in whole upon the one or more characteristics. The field is then populated with a completion candidate from the list of one or more completion candidates.

Abstract: A computer analytic system for allocating resources of an electronic tax return preparation system, the system includes an information module configured to collect taxpayer data of a user, the taxpayer data including an indicator of an increased likelihood that the user will abandon the electronic tax return preparation program. The system also includes a memory module configured to store the collected taxpayer data. The system further includes a retention module configured to analyze the collected taxpayer data and generate a resource allocation recommendation based on the collected taxpayer data.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model to identify a predictive model that selects or determines the user experience options, according to one embodiment. The method and system analyzes user responses to the predictive model and/or user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, according to one embodiment. The method and system dynamically and automatically defines, evaluates, and updates analytics models to provide progressively improving personalization of user experiences in a software system.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, according to one embodiment. The method and system dynamically and automatically defines, evaluates, and updates analytics models to provide progressively improving personalization of user experiences in a software system.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options, from a variety of different user experience options, to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, at least partially based on feedback from users, according to one embodiment.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options, from a variety of different user experience options, to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The characteristics of the analytics model are adjusted and/or tuned to control/reduce uncertainty in identifying effective user experience options, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, at least partially based on feedback from users, according to one embodiment.

Abstract: A method and system provides personalized user experiences to users of a tax return preparation system, at least partially based on likelihoods of occurrence of life events for the users in a tax year, according to one embodiment. The method and system applies the user data to one or more predictive models to determine the likelihood that one or more available life events occurred in a user's life in a tax year, according to one embodiment. The method and system display life event icons that represent the one or more available life events, and the life event icons are ranked, sorted, and/or emphasized, based on the likelihood that the one or more available life events occurred in a user's life, to increase a user's confidence in the tax return preparations system's capability to address the user's life changes while preparing the user's tax return, according to one embodiment.

Abstract: Computer-implemented methods, articles of manufacture and computerized systems for identifying or alerting a user of certain data in electronic tax returns. A computerized tax return preparation system including a tax return preparation software application executed by a computing device receives first and second tax data and populates respective fields of the electronic tax return. The system executes a predictive model such as logistic regression, naive bayes, K-means clustering, clustering, k-nearest neighbor, and neural networks. First tax data is an input into the predictive model, which generates an output, which is compared with second tax data. An alert is generated when the second tax data does not satisfy pre-determined criteria relative to the first output generated by the predictive model. The same or other predictive model may be used as additional tax data is received for subsequent tax data analysis.

Abstract: A method and system identifies users who benefit from filing itemized deductions over standardized deductions to reduce an average time consumed for users preparing tax returns with a tax return preparation system, according to one embodiment. The method and system receives user data that is associated with a user, and applies the user data to a predictive model to cause the predictive model to determine a likelihood that the user will decrease his/her taxable income by filing an itemized deduction, according to one embodiment. The method and system deemphasizes and/or postpones the presentation of tax return questions that are related to the itemized deduction, if the likelihood that the user will decrease his/her taxable income by filing the itemized deduction is below a threshold, to reduce a quantity of time consumed by the user to prepare his/her tax return with a tax return preparation system, according to one embodiment.