Abstract: Method starts with a processor receiving from client devices user activity data associated with interactions by a plurality of users with a website displayed on the client devices and identifying, based on the user activity data, errors associated with the website. Processor determines first error that is statistically significant by determining that a number of sessions from the plurality of sessions including first error and not including first error are greater than a minimum number of sessions threshold. Processor generates a test statistics value associated with first error based on the number of sessions not including first error, the number of sessions including the first error, an error free conversion rate, and an error conversion rate. Processor identifies first error as being statistically significant when test statistic value is greater than test statistic threshold; and causes a user interface including first error to be displayed. Other embodiments are described herein.

Abstract: The subject technology receives a metric based on an analysis of user activity on a retail website, the user activity corresponding to a set of sessions occurring over a period of time at the retail website, the set of sessions corresponding to a set of users that visited the retail website over the period of time. The subject technology determines a first value of a conversion adjustment threshold estimation based at least in part on the metric. The subject technology determines a second value of a conversions opportunity estimation based at least in part on the first value. The subject technology determines a third value of a revenue opportunity estimation based at least in part on the second value. The subject technology provides for display an interface including a representation of the third value of the revenue opportunity estimation.

Abstract: Systems and techniques may be used website error detection. An example technique may include identifying an error corresponding to a website, retrieving a first set of user sessions where the error occurred and a second set of user sessions where the error did not occur, and determining, for an access category, whether members of a set of values of the access category are correlated to the error, using the first set of user sessions and the second set of user sessions. The example technique may include comparing a characteristic of at least one member of the set of values that was determined to be correlated to the error to a threshold. The example technique may include displaying an indication of the error and an indication of the at least one member of the set of values.

Abstract: A system and method for benchmarking user experience quality on websites across an industry. The method includes computing first site metrics for a first website, wherein a site metric is a quantitative description of user activity on a web site; computing a first industry satisfaction score for the first website, wherein the industry satisfaction score quantifies an overall level of satisfaction of users interacting with the first website, wherein the industry satisfaction score is computed based on satisfaction score pillar values and the computed site metrics, identifying usage metrics within the collected usage; comparing the first industry satisfaction score computed for the first website to a second industry satisfaction score previously computed for a second website, wherein the first website and the second website are from the same industry; and reporting the comparison results.