Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a specially trained machine-learning model to generate an emerging user segment based on a target outcome for digital survey responses and respondent attributes of respondents to such digital surveys. In some cases, for instance, the emerging user segment includes a group of users that share the same or similar characteristics as the subset of respondents. By analyzing respondent attributes of digital survey respondents that match a target outcome, the disclosed systems can use the specially trained machine-learning model to dynamically predict users that likely have (or are at risk of having) the same or a similar target outcome—even if such users did not respond to the relevant digital survey.

Abstract: A mechanism to visualize data to a user in a sufficient manner. The user selects a visualization type to visualize a selected subset of a data model. To fit the data well into a visualization of that visualization type, the system then evaluates the user selections of the visualization type of the subset of data against the rule set. Based on the evaluation, the system determines that the subset of data overpopulates the visualization type. In some embodiments, the system further identifies one or more filters to apply to the subset of data which would decrease the population of data within the virtualization type. Then, a visualization of the selected visualization type is to be displayed using at least one of the one or more identified filter.

Abstract: A mechanism to visualize data to a user in a sufficient manner. The user selects a visualization type to visualize a selected subset of a data model. To fit the data well into a visualization of that visualization type, the system then evaluates the user selections of the visualization type of the subset of data against the rule set. Based on the evaluation, the system determines that the subset of data does not populate or insufficiently populates the visualization type. In some embodiments, the system further recommends additional data to supplement the selected subset of data to more sufficiently utilize the visualization to display the subset of data in conjunction with the supplemented data. The system may further display the visualization based on the selected subset of the data model perhaps before and/or after supplemented with the supplemented data.

Abstract: Providing visualizations based on natural language searches. A method includes receiving a natural language query from a client. The method further includes based on the natural language query, obtaining a semantic model of the natural language query. The method further includes based on the semantic model, obtaining a list of a plurality of visualizations, the visualizations being based on a bias ranking of the visualizations in the list. The method further includes providing the list of the plurality of visualizations to the client, where at the client a set of visualization construction rules are applied to select a visualization from the list to apply results from the natural language query to the visualization.