A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include receiving an opt-in from a user and detecting, automatically, an experience of the user. The operations may further include determining an impact potential of the experience and deciding a response strategy based on the impact potential. The operations may also include implementing the response strategy.

A hosting server streams a media item to a participant device, and receives from the participant device time-stamp information indicating an interaction time associated with a participant interaction with the media item. In response to receiving the time-stamp information, the hosting server determines, from the time-stamp information, a consumed portion of the media item. The consumed portion of the media item corresponds to a portion of the media item streamed to the participant device prior to the interaction time. A determination is made, based on the consumed portion of the media item, whether to transmit survey content to the participant device. In response to determining that survey content is to be presented, the survey content is transmitted to the participant device.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of collecting historical data of a user; converting the historical data of the user into at least one feature vector; calculating a first user propensity score for the user using the at least one feature vector; calculating a second user propensity score for the user using the at least one feature vector, the second user propensity score representing a different user propensity than the first user propensity score; normalizing the first user propensity score; normalizing the second user propensity score; using the first user propensity score, as normalized, to place the user into a first segment; using the second user propensity score, as normalized, to place the user into a second segment different than the first segment; and facilitating delivery of a message to the user based on the first segment and the second segment.

A computerized method of automatically generating a recommendation output includes training a machine learning model with historical feature vector inputs to generate a recommendation output, generating a set of inputs specific to an entity, and transforming the set of inputs into a profile data structure. The profile data structure includes multiple attributes. The transforming includes, for each attribute, assigning a preference according to the set of inputs. The method includes obtaining structured supplemental data associated with the entity. The method includes obtaining a set of option identifiers by filtering the option identifiers according to option criteria specific to the entity. The method includes creating a feature vector input according to the set of option identifiers, the structured supplemental data, and the assigned preferences of the profile data structure. The method includes processing, by the machine learning model, the feature vector input to generate the recommendation output.

This document describes the autonomous collection, generation, distribution, and management of online web content. The devices, systems, and methods described herein can be used to collect and generate online web content and communications in an automatic and autonomous manner. Specifically, the disclosed methods, devices, and systems may be employed to produce one or more communications and/or advertising campaigns, as well as for monitoring, managing, defining the efficiency, effectiveness, and workability of the campaign with respect to generating predicted user engagements, thereby accurately determining the cost benefits of the communication campaign. The system may track, evaluate, and provide analytic results that may then be used to better guide the system parameters for customizing autonomous communications directed one or more characteristics of a defined target audience.

Users may engage in a target user activity via digital systems, such as a website, and/or non-digital systems. Users may user various digital channels to arrive at the digital systems or non-digital systems. Users may also arrive at digital systems, such as the website, via different entry pages. A propensity analyzer can, based on activity data associated with users, determine propensities of one or more of the digital channels, digital systems, non-digital channels, and/or entry pages to drive users to perform a target user activity. The propensity analyzer can generate recommendations for revising digital channels, digital systems, non-digital channels, and/or entry pages to increase their propensities to drive users to perform the target user activity.

A system for managing customer feedback regarding a product or service is disclosed, particularly, at a point-of-sale location. The system includes a backend system and a frontend system wherein feedback from a customer regarding the product or service is collected using the frontend system. The feedback is transmitted to the backend system where one or more sales or business hypothesis are generated to present to the customer to acquire further feedback from the customer. One or more action items, such as product offering optimization, marketing campaign customization, and inventory management can be suggested based on the customer response to the generated hypothesis.

Links are generated between local merchants and community programs for merchant incentives to customers for the programs which are tracked for online-offline customer transactions using incentives. Participants' identifiers are linked to payment sources. Offline-online transaction data is collected in a data storage area. All data in the data storage area may be utilized by logic tool, which may provide information, such as details of consumer behavior and analytic reporting. Rich data provided by pre-transaction chat sessions between merchants and potential customers are matched with rich data provided by subsequent transactions between the merchants and their customers, and a level of certainty may be determined as to the accuracy of the match of a prior chat session to a subsequent transaction.

Various embodiments relate generally to data science and data analysis, computer software and systems, and computing architectures and data models configured to facilitate management and performance of enterprise functions, and, more specifically, to an enterprise computing and data processing platform configured to identify and aggregate engagement data for managing enterprise data and work flows, and, in response to data values of aggregated engagement data, the enterprise computing and data processing platform is further configured to generate a command, for example, to modify automatically an enterprise data flow or work flow. In some examples, a method may include analyzing a pool of data including project, billing, and supply chain data to generate an engagement dataset including attributes based on aggregated subsets of project, billing, and supply chain data, and calculating updated values for the engagement dataset automatically.

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating conversational survey questions. The systems and methods analyze a received survey question response to identify characteristics of a survey response, including topics and other response features. For example, the systems can determine a sentiment associated with a given product or service that a respondent expresses within a response. Based on the determined sentiment, and further based on a set of logic rules received from a survey administrator, the systems and methods generate provide conversational follow-up questions associated with the identified product or service.