Abstract: A computer-implemented method and system for enabling the automated selection of keywords for rapid keyword portfolio expansion are disclosed. Various embodiments are operable to receive a plurality of keywords to be trafficked on a search engine, and automatically select at least one of the plurality of keywords to be omitted from trafficking on the search engine based on pruning criteria. In some example embodiments, the automatically selecting comprises for each one of the selected at least one of the plurality of keywords, determining a corresponding confidence level with which a corresponding predicted value of the keyword(s) is expected to satisfy the pruning criteria, with the automatic selection of each one of the selected at least one of the plurality of keywords being based on the corresponding confidence level and a configurable confidence level threshold.

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 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: A method and system applies analytics models to a tax return preparation system to determine a likelihood of qualification for an earned income tax credit by a user, according to one embodiment. The method and system receive user data and applying the user data to a predictive model to cause the predictive model to determine, at least partially based on the user data, a likelihood of qualification for an earned income tax credit for the user, according to one embodiment. The method and system display, for the user, an estimated tax return benefit to the user, at least partially based on the likelihood of qualification for the earned income tax credit exceeding a predetermined threshold, to reduce delays in presenting estimated earned income tax credit benefits to the user during a tax return preparation session in a tax return preparation system, according to one embodiment.

Abstract: A computer-implemented method and system for combining keywords into logical clusters that share a similar behavior with respect to a considered dimension are disclosed. Various embodiments are operable to order a list of keywords from high activity to low activity, partition the list into at least two sets, a head partition including keywords with an activity level above a predefined threshold, a tail partition including the remainder of the keywords in the list, model the keywords in the head partition based on a set of variables, score the keywords in the head partition based on the modeling, and cluster head partition keywords with tail partition keywords having at least one common variable into at least one keyword cluster.

Abstract: A computer-implemented method and system for enabling the automated selection of keywords for rapid keyword portfolio expansion are disclosed. Various embodiments are operable to receive a plurality of keywords to be trafficked on a search engine, and automatically select at least one of the plurality of keywords to be omitted from trafficking on the search engine based on pruning criteria. In some example embodiments, the automatically selecting comprises for each one of the selected at least one of the plurality of keywords, determining a corresponding confidence level with which a corresponding predicted value of the keyword(s) is expected to satisfy the pruning criteria, with the automatic selection of each one of the selected at least one of the plurality of keywords being based on the corresponding confidence level and a configurable confidence level threshold.

Abstract: A computer-implemented method and system for enabling the automated selection of keywords for rapid keyword portfolio expansion are disclosed. In some embodiments, a report is generated comprising selected keywords and a corresponding value for each selected keywords, with the corresponding value being based on the corresponding keyword being omitted from trafficking on a search engine, and at least a portion of the selected keywords are removed from being trafficked on the search engine based on the selection of the keywords to be omitted, with the removing of the at least a portion of the selected keywords being performed in response to a user input corresponding to the report, the user input being used to determine the at least a portion of the selected keywords to be omitted.

Abstract: A computer-implemented method and system for enabling the automated selection of keywords for rapid keyword portfolio expansion are disclosed. Various embodiments are operable to normalize a plurality of keywords received from a source, filter the normalized plurality of keywords against one or more keyword filtration lists, produce site-specific variants of the filtered plurality of keywords, associate at least one level of dimension data with each of the plurality of keywords, and store the processed plurality of keywords and dimension data in a keyword database.

Abstract: A computer-implemented method and system for enabling the automated selection of keywords for rapid keyword portfolio expansion are disclosed. Various embodiments are operable to normalize a plurality of keywords received from a source, filter the normalized plurality of keywords against one or more keyword filtration lists, produce site-specific variants of the filtered plurality of keywords, associate at least one level of dimension data with each of the plurality of keywords, and store the processed plurality of keywords and dimension data in a keyword database.

Abstract: A computer-implemented method and system for combining keywords into logical clusters that share a similar behavior with respect to a considered dimension are disclosed. Various embodiments are operable to order a list of keywords from high activity to low activity, partition the list into at least two sets, a head partition including keywords with an activity level above a predefined threshold, a tail partition including the remainder of the keywords in the list, model the keywords in the head partition based on a set of variables, score the keywords in the head partition based on the modeling, and cluster head partition keywords with tail partition keywords having at least one common variable into at least one keyword cluster.

Abstract: A computer-implemented method and system for combining keywords into logical clusters that share a similar behavior with respect to a considered dimension are disclosed. Various embodiments are operable to order a list of keywords from high activity to low activity, partition the list into at least two sets, a head partition including keywords with an activity level above a predefined threshold, a tail partition including the remainder of the keywords in the list, model the keywords in the head partition based on a set of variables, score the keywords in the head partition based on the modeling, and cluster head partition keywords with tail partition keywords having at least one common variable into at least one keyword cluster.

Abstract: A computer-implemented method and system for enabling the automated selection of keywords for rapid keyword portfolio expansion are disclosed. Various embodiments are operable to normalize a plurality of keywords received from a source, filter the normalized plurality of keywords against one or more keyword filtration lists, produce site-specific variants of the filtered plurality of keywords, associate at least one level of dimension data with each of the plurality of keywords, and store the processed plurality of keywords and dimension data in a keyword database.

Abstract: A computer-implemented method and system for enabling the automated selection of keywords for rapid keyword portfolio expansion are disclosed. Various embodiments are operable to normalize a plurality of keywords received from a source, filter the normalized plurality of keywords against one or more keyword filtration lists, produce site-specific variants of the filtered plurality of keywords, associate at least one level of dimension data with each of the plurality of keywords, and store the processed plurality of keywords and dimension data in a keyword database.

Abstract: A computer-implemented method and system for combining keywords into logical clusters that share a similar behavior with respect to a considered dimension are disclosed. Various embodiments are operable to order a list of keywords from high activity to low activity, partition the list into at least two sets, a head partition including keywords with an activity level above a predefined threshold, a tail partition including the remainder of the keywords in the list, model the keywords in the head partition based on a set of variables, score the keywords in the head partition based on the modeling, and cluster head partition keywords with tail partition keywords having at least one common variable into at least one keyword cluster.

Abstract: A computer-implemented method and system for combining keywords into logical clusters that share a similar behavior with respect to a considered dimension are disclosed. Various embodiments are operable to order a list of keywords from high activity to low activity, partition the list into at least two sets, a head partition including keywords with an activity level above a predefined threshold, a tail partition including the remainder of the keywords in the list, model the keywords in the head partition based on a set of variables, score the keywords in the head partition based on the modeling, and cluster head partition keywords with tail partition keywords having at least one common variable into at least one keyword cluster.

Abstract: A computer-implemented method and system for enabling the automated selection of keywords for rapid keyword portfolio expansion are disclosed. Various embodiments are operable to normalize a plurality of keywords received from a source, filter the normalized plurality of keywords against one or more keyword filtration lists, produce site-specific variants of the filtered plurality of keywords, associate at least one level of dimension data with each of the plurality of keywords, and store the processed plurality of keywords and dimension data in a keyword database.

Abstract: A computer-implemented method and system for combining keywords into logical clusters that share a similar behavior with respect to a considered dimension are disclosed. Various embodiments are operable to order a list of keywords from high activity to low activity, partition the list into at least two sets, a head partition including keywords with an activity level above a predefined threshold, a tail partition including the remainder of the keywords in the list, model the keywords in the head partition based on a set of variables, score the keywords in the head partition based on the modeling, and cluster head partition keywords with tail partition keywords having at least one common variable into at least one keyword cluster.