Abstract: An enterprise's data source relevant to their customers is obtained at predefined intervals of time. The data is processed through classification machine learning models (MLMs) and labeled with features. The labeled data is provided as input to an attrition predicting MLM and one or more lists are provided as output identifying customers likely to leave the enterprise and customers with a high likelihood of remaining with the enterprise when provided an incentive to do so. The one or more lists are provided to enterprise interfaces and/or promotion systems for mitigating customer attrition. In an embodiment, results for the attrition predicting MLM are compared against results predicted by a Recency, Frequency, Monetary (RFM) analyzer in view of subsequent actual observed results for the customers with the enterprise. A continuous feedback loop for retaining the attrition prediction MLM is processed based on the comparison to improve the prediction MLM's F1 accuracy metric.

Abstract: Systems and methods for inferring recommendations and experiences for anonymous users of an online website are disclosed. Anonymous users of the online website are assigned anonymous user identifiers, and the browsing activity of the anonymous users is converted into features and aggregated over time. The anonymous users' interactions are monitored and used to generate labels that are combined with the feature dataset to produce a training dataset which is used to train a machine learning model. The browsing activity of an anonymous user may be converted into features and aggregated over time and fed into the trained machine learning model from which personalized experiences and recommendations may be generated and provided to the anonymous user.

Abstract: Systems and methods for tracking anonymous visitors of an online website or mobile app are disclosed. The browsing activity by an anonymous visitor of the online website or mobile app is converted into features and a visitor-identifier associated with the browsing activity generated by the anonymous visitor is determined. The features are stored with the visitor-identifier in a super-visitor-state before the visitor-identifier is associated with a super-visitor-identifier. After the visitor-identifier is associated with the super-visitor-identifier, the features are stored with the super-visitor-identifier in the super-visitor-state. After the visitor-identifier is associated with the super-visitor-identifier, the features associated with the visitor-identifier in the super-visitor-state may be combined, e.g., aggregated, with the features associated with the super-visitor-identifier and the visitor-identifier may be removed from the super-visitor-state.

Abstract: Devices and methods for client application user experience tracking may include generating a user experience score based on tracked operation and user inputs to the client application. The user experience score is transmitted to an experience tracking service (e.g., remote computer server, locally executed application).

Abstract: Cross-channel and cross-source data are aggregated into an aggregated data store. Custom segmentation is generated from the aggregated data. A campaign is monitored for the custom segmentation with successes and failures provided as dynamic feedback to a machine learning process that dynamically adjusts the segmentation and the campaign for optimal performance. In an embodiment, a final recommendation is provided identifying a final optimal segmentation and campaign.

Abstract: Devices and methods for client application user experience tracking may include generating a user experience score based on tracked operation and user inputs to the client application. The user experience score is transmitted to an experience tracking service (e.g., remote computer server, locally executed application).

Abstract: Cross-channel and cross-source data are aggregated into an aggregated data store. Custom segmentation is generated from the aggregated data. A campaign is monitored for the custom segmentation with successes and failures provided as dynamic feedback to a machine learning process that dynamically adjusts the segmentation and the campaign for optimal performance. In an embodiment, a final recommendation is provided identifying a final optimal segmentation and campaign.