Abstract: In some examples, an environment evaluation system accesses interaction data recording interactions by users with an online platform hosted by a host system and computes, based on the interaction data, interface experience metrics. The interface experience metrics includes an individual experience metric for each user and a transition experience metric for each transition in the interactions by the users with the online platform. The environment evaluation system identifies a user with the individual experience metric below a pre-determined threshold, identifies a transition performed by the user that has a transition experience metric below a second threshold, and analyzes the transition to determine users who have performed the transition. The environment evaluation system updates the host system with the individual experience metrics and the transition metrics, based on which the host system can perform modifications of interface elements of the online platform to improve the experience.

Abstract: In some embodiments, techniques for identifying bot activity are provided. For example, a process may involve receiving a plurality of samples, wherein each sample is a record of click activity; classifying the plurality of samples among a first class and a second class, using a machine learning model trained by a training process, to produce a corresponding plurality of classification predictions; filtering click activity data, based on information from the plurality of classification predictions, to produce filtered click activity data; and causing a user interface of a computing environment to be modified based on information from the filtered click activity data. The training process includes training the machine learning model to classify samples among the first and second classes, using a training set of samples of the first class, a training set of samples of the second class, and values of a topological loss function calculated based on the training sets.

Abstract: Determination of high value customer journey sequences is performed by determining customer interactions that are most frequent as length N=1 sub-sequences, recursively determining most frequent length N+1 sub-sequences that start with the length N sub-sequences, determining a first count indicating how often one of the sub-sequences appears in the sequences, determining a second count indicating how often the one sub-sequence resulted in the goal, and using the counts to determine the most or least effective sub-sequences for achieving the goal.

Abstract: Determination of high value customer journey sequences is performed by determining customer interactions that are most frequent as length N=1 sub-sequences, recursively determining most frequent length N+1 sub-sequences that start with the length N sub-sequences, determining a first count indicating how often one of the sub-sequences appears in the sequences, determining a second count indicating how often the one sub-sequence resulted in the goal, and using the counts to determine the most or least effective sub-sequences for achieving the goal.

Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for generating lookalike segments corresponding to a target segment using decision trees and providing a graphical user interface comprising nodes representing such lookalike segments. Upon receiving an indication of a target segment, for instance, the disclosed systems can generate a lookalike segment from a set of users by partitioning the set of users according to one or more dimensions based on probabilities of subsets of users matching the target segment. By partitioning subsets of users within a node tree, the disclosed systems can identify different subsets of users partitioned according to different dimensions from the set of users. The disclosed systems can further provide a node tree interface comprising a node for the set of users and nodes for subsets of users within one or more lookalike segments.