The present disclosure relates to systems and methods for operating an online on-demand service platform. The systems may perform the methods to: determine one or more recommendation rules for one or more activities for recommending to one or more candidate users; determine, for each of the one or more activities, one or more preset objects for the activity to achieve on the one or more candidate users; determine, for each of the one or more activities, a completion probability that each of the one or more candidate users will complete the one or more preset objects of the activity; and determine, for each of the one or more activities, at least one target user from the one or more candidate users to send a notice of the activity based on the one or more completion probabilities and the one or more recommendation rules.

A digital content communication system for account management and predictive analytics may be provided. The system may include an analytics system that communicates with one or more servers and one or more data stores to provide digital content management in a network. The analytics system may include a data access interface to receive data associated with a customer, as well as a processor to: standardize the received data using a standardization technique; process the standardized data using a dark data processing technique; generate a customer fit score and a digital density score based on the dark data processing of the standardized data; match received data associated with a customer against at least one variable using at least one matching technique; create a lead analytical record (LAR); prioritize leads in the LAR using a predictive modeling technique; and establish optimized channel assignment based on at least one of the customer fit score, the digital intensity score, the LAR, or the matching and prioritization actions.

Techniques for automatically generating targeting templates are provided. In one technique, multiple sets of targeting criteria provided by different content providers are identified. Based on the multiple sets of targeting criteria, multiple clusters of sets of targeting criteria are created. For each cluster, a targeting template is generated based on a set of targeting criteria that belong to that cluster. In another technique, multiple users are identified and for each user, a set of attribute-value pairs of that user is identified and added to a plurality of attribute-value pairs. Based on the plurality of attribute-value pairs, a targeting template that specifies targeting criteria is generated and presented for selection.

Text analysis includes determining one or more global analysis parameters based on backtranslation of a first corpus between supported languages. A new text analysis model is determined for an unsupported language based on the one or more global analysis parameters and a text analysis model for a first supported language. An input text is analyzed in the unsupported language with the new text analysis model.

This document describes the 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. Specifically, the disclosed devices and systems may be employed to produce one or more marketing and/or advertising campaigns, as well as for monitoring, managing, defining the efficiency, effectiveness, and workability of the campaign with respect to generating user engagement, thereby accurately determining the cost benefits of the campaign. The analytic results provided may then be used to guide the generation of original web content, such as for the purposes of enhancing customer or follower experience, driving business, and for driving advertising campaigns. Alternatively, web content that is in the public domain, and determined to perform well, can be reproduced, referenced, or otherwise referred to, in the context of promoting or presenting the user's web content.

Techniques for integrating third-party analytics with virtual-assistant enabled applications are disclosed. A third-party analytics service trains a machine learning model, using labeled training data including (a) phrases corresponding to sales offers made to consumers and (b) sales conversion outcomes associated with the phrases. The service receives, from a consumer-facing application, a user query submitted via a virtual assistant interface. The service applies the user query to the machine learning model, to obtain a recommended phrase for the consumer-facing application to use in response to the user query. The recommended phrase is: based on one or more of the phrases used to train the machine learning model; responsive to the user query; and based on a likelihood of achieving a sales objective associated with the consumer-facing application. The service transmits the recommended phrase to the consumer-facing application, to use when supplying a response to user query via the virtual assistant interface.

An apparatus for providing customized advertisements includes a database that stores a plurality of electronic advertisements, receives biometric information of a client from at least one biometric device of the client, and receives receptivity information of the client responding to the plurality of electronic advertisements, as well as a processor that accesses the database, and maps the biometric information and the receptivity information and analyzes the mapped information to generate customized marketing data. The processor also calculates a receptivity probability for each of the plurality of electronic advertisements based on the customized marketing data by using current biometric state of the client, selects an electronic advertisement from the plurality of electronic advertisements based on the calculated receptivity probabilities, and outputs to the client the selected electronic advertisement.

A method for determining a ranking of digital video files to be sent to a respective buyer of a product previously associated with such digital video files. Using a ranking based on sales of such products after communication of a particular video file relating thereto, along with the location of the buyer and if known, the personal traits of an identified buyer, the system determines one or a plurality of video files relating to respective products to communicate to the buyer over a network which will more likely result in sale.

A promotion offering system and method is disclosed. The promotion offering system and method selects consumers for a test promotion that has one or more attributes, and is configured to generate test data for multiple consumer groupings. The promotion offering system and method may use the test data in analyzing whether to send a promotion to a consumer. For example, the promotion offering system and method may use one algorithm to generate a list of ranked promotions, and may use the test data in order to adjust the list of ranked promotions (such as replacing a highest ranked promotion with another promotion).

A method for determining a ranking of digital video files to be sent to a respective buyer of a product previously associated with such digital video files. Using a ranking based on sales of such products after communication of a particular video file relating thereto, along with the location of the buyer and if known, the personal traits of an identified buyer, the system determines one or a plurality of video files relating to respective products to communicate to the buyer over a network which will more likely result in sale.