The present disclosure is directed toward systems and methods for identifying contributing audience segments associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify audience segments associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors and segments that caused the particular anomaly and how those factors correlate with each other.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. In a digital medium environment for performing a statistical analysis of network application data, a method for determining statistically significant audiences that contributed to a metric anomaly comprising: identifying an anomaly of a network application metric by analyzing analytics data associated with a network application; identifying a plurality of dimension elements that contributed to the anomaly of the network application metric; determining, by at least one processor, a statistical significance of each of the plurality of dimension elements that contributed to the anomaly of the network application metric; and generating one or more segments, wherein each segment comprises an intersection of the statistically significant dimension elements that contributed to the anomaly of the network application metric. 2. The method as recited in claim 1, wherein identifying an anomaly of a network application metric by analyzing analytics data associated with a network application comprises: identifying a range from the analytics data associated with the network application that indicates how the network application metric is expected to perform during a period of time; identifying a value of network application metric that exists outside the identified range. 3. The method as recited in claim 1, wherein determining a statistical significance of each of the plurality of dimension elements that contributed to the anomaly of the network application metric comprises: calculating a statistical significance score for each of the plurality of the dimension elements that indicates the statistical significance of each of the plurality of the dimension elements; normalizing the statistical significance score for each of the plurality of the dimension elements such that all the calculated statistical significance scores all reside within a comparable range. 4. The method as recited in claim 1, wherein generating one or more segments comprises: identifying dimension elements in the plurality of dimension elements with the top normalized statistical significance scores; and generating unique, non-singular combinations of the identified dimension elements in the plurality of dimension elements with the top normalized statistical significance scores. 5. The method as recited in claim 4, further comprising ranking the generated one or more segments to identify an audience that contributed most significantly to the anomaly of the network application metric. 6. The method as recited in claim 5, wherein ranking the generated one or more segments comprises: determining an audience size associated with each unique, non-singular combination of the identified dimension elements in the plurality of dimension elements with the top normalized statistical significance scores; identifying a percentage contribution associated with each unique, non-singular combination of the identified dimension elements in the plurality of dimension elements with the top normalized statistical significance scores based on the normalized statistical significance scores of the dimension elements in the unique, non-singular combination of the identified dimension elements in the plurality of dimension elements with the top normalized statistical significance scores that represents how each unique, non-singular combination contributed to the anomaly of the network application metric; for each unique, non-singular combination of the identified dimension elements in the plurality of dimension elements with the top normalized statistical significance scores, calculating a weighted confidence score based on the audience size and the percentage contribution; and ordering the one or more segments based on the weighted confidence scores. 7. The method as recited in claim 6, further comprising presenting a top number of the ordered one or more segments. 8. The method as recited in claim 7, further comprising: receiving a selection of one of the top number of ordered one or more segments; and configuring a segment report based on the selected segment that identifies the audience associated with the selected segment. 9. In a digital medium environment for performing a statistical analysis of network application data, a method for determining statistically significant audiences that contributed to a metric anomaly comprising: receiving a selection of a network application metric from a display of network application metrics associated with actions or characteristics of users of a network application; identifying anomalies of the network application metric by analyzing analytics data associated with a network application, wherein the anomalies of the network application metric represent unexpected increases or decreases in the network application metric; receiving a selection of an anomaly of the network application metric, identifying a plurality of dimension elements that contributed to the selected anomaly of the network application metric; determining a statistical significance score for dimension elements that contributed to the selected anomaly of the network application metric; generating a plurality of segments comprising unique, non-singular combinations of dimension elements having high statistical significance scores; determining an audience size associated with each segment; identifying a percentage contribution associated with each segment based on the statistical significance scores of the dimension elements in the unique, non-singular combination of dimension elements forming the segment; calculating a weighted confidence score for each segment based on the audience size and the percentage contribution; and ordering the plurality of segments based on the weighted confidence scores. 10. The method as recited in claim 9, wherein identifying anomalies of the network application metric by analyzing analytics data associated with a network application comprises: identifying a range from the analytics data associated with the network application that indicates how the network application metric is expected to perform during a period of time; and identifying values of network application metric that exists outside the identified range. 11. The method as recited in claim 10, wherein determining a statistical significance score for dimension elements that contributed to the selected anomaly of the network application metric comprises normalizing calculated Pearson Residuals. 12. The method as recited in claim 9, wherein calculating a weighted confidence score for each segment based on the audience size and the percentage contribution comprises calculating weighted confidence scores according to the following equation: (a contribution score weight*average contribution score of the dimension elements in the unique, non-singular combination forming the segment))+(intersection weight*percentage contribution)+(audience size weight*audience size). 13. The method of claim 12, wherein the audience size weight is lower than both the contribution score weight and the intersection weight. 14. The method of claim 9, wherein the network application is a website. 15. The method as recited in claim 9, further comprising presenting a top number of the ordered segments. 16. The method as recited in claim 15, further comprising: receiving a selection of one of the top number of ordered segments; configuring a segment report based on the selected segment that identifies the audience associated with the selected segment. 17. A system comprising: at least one processor; and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the system to: identify an anomaly of a network application by analyzing analytics data associated with a network application; identify a plurality of dimension elements that contributed to the anomaly of the network application metric; determine a statistical significance of each of the plurality of dimension elements that contributed to the anomaly of the network application metric; and generate one or more segments, wherein each segment comprises an intersection of the statistically significant dimension elements that contributed to the anomaly of the network application metric. 18. The system as recited in claim 17, further comprising instructions, that when executed by the at least one processor, cause the system to determine a statistical significance of each of the plurality of dimension elements that contributed to the anomaly of the network application metric by: calculating a statistical significance score for each of the plurality of the dimension elements that indicates the statistical significance of each of the plurality of the dimension elements; normalizing the statistical significance score for each of the plurality of the dimension elements such that all the calculated statistical significance scores all reside within a comparable range. 19. The system as recited in claim 18, further comprising instructions, that when executed by the at least one processor, cause the system to generate one or more segments by: identifying dimension elements in the plurality of dimension elements with the top normalized statistical significance scores; and generating unique, non-singular combinations of the identified dimension elements in the plurality of dimension elements with the top normalized statistical significance scores. 20. The system as recited in claim 19, further comprising instructions, that when executed by the at least one processor, cause the system to: rank the generated one or more segments to identify an audience that contributed most significantly to the anomaly of the network application metric by: determining an audience size associated with each unique, non-singular combination of the identified dimension elements in the plurality of dimension elements with the top normalized statistical significance scores; identifying a percentage contribution associated with each unique, non-singular combination of the identified dimension elements in the plurality of dimension elements with the top normalized statistical significance scores based on the normalized statistical significance scores of the dimension elements in the unique, non-singular combination of the identified dimension elements in the plurality of dimension elements with the top normalized statistical significance scores that represents how each unique, non-singular combination contributed to the anomaly of the network application metric; for each unique, non-singular combination of the identified dimension elements in the plurality of dimension elements with the top normalized statistical significance scores, calculating a weighted confidence score based on the audience size and the percentage contribution; and ordering the one or more segments based on the weighted confidence scores.