Systems and methods for distributing online ads with electronic content according to a campaign strategy that is adjusted based on intraday modeling. One embodiment of the invention determines a campaign strategy for a current day allocating a daily budget to automatically bid on online ad opportunities using allocated budget amounts and distributes online ads during a first portion of the current day according to the campaign strategy. Current day data regarding use of the distributed online ads during the first portion of the current day is received and compared with historical data to determine a correction factor that accounts for a magnitude of difference between the current day data and the historical data. The campaign strategy for the current day is adjusted using the correction factor and additional online ads are distributed during a second, later portion of the current day according to the adjusted campaign strategy.

A method of presenting information to a party through a messaging application is described. Responsive to receipt of a communication from a party (e.g., the first user), a reply is sent. The communication and the reply is presented in an interface to the sender. The messaging system determines matching content that is relevant to one or both of the communication and the reply and determines a quality of the match. Determining the quality of the match may include determining a score for an advertisement based on the advertisement's responsiveness to content identified in the reply message that was sent. Based on a determination that the quality is above a threshold, the matching content is presented along with the communication and the reply.

Methods and apparatus for improving automatic selection and timing of messages by a machine or system of machines include an inductive computational process driven by log-level network data from mobile devices and other network-connected devices, optionally in addition to traditional application-level data from cookies or the like. The methods and apparatus may be used, for example, to improve or optimize effectiveness of automatically-generated electronic communications with consumers and potential consumers for achieving a specified target.

In some examples, a computing device may receive data from a plurality of groups of data sources. The computing device may create a training data set from a first portion of the received data and may create a plurality of validation data sets from a second portion of the received data. For example, each validation data set may correspond to a respective one of the groups of data sources. The computing device may train, using the training data set, a plurality of machine learning models configured for synthesizing data. For instance, respective ones of the machine learning models may correspond to respective ones of the groups of data sources. Further, the computing device may validate the respective machine learning models using the respective validation data set corresponding to the respective group to which the respective machine learning model being validated corresponds.

Systems, methods, and computer-readable storage media for determining user journeys during presentations of content. The system first determines an average time spent for sessions associated with a presentation of content. Next, the system identifies a representative group of sessions from the sessions by identifying each of the sessions having a respective time spent within a statistical range from the average time spent for the sessions. The system then determines a most common path of events from the representative group of sessions to yield a most common user journey associated with the presentation of content.

Systems, devices, media, instructions, and methods are provided for presentation of media collections with automated interactive advertising. In one embodiment, a client device receives content elements for display as part of a content collection. Advertising data is also received for display between selected content elements. Interaction elements are merged with the create an advertising element. During display of the advertising data, the interaction elements are presented on the client device output, and are controllable via user inputs. In various embodiments, interaction data recorded at the device is used to manage the presentation of future advertising data.

Embodiments are associated with user behavior simulation. A user behavior simulation apparatus may retrieve, from a unit data store, relevant unit data. The simulation apparatus may also retrieve, from a user behavior data store, user behavior data (and train a user interest decay model based on the retrieved user behavior data) along with a unit bundle generation strategy model from a unit bundle generation strategy data store (and initialize control parameters of the unit bundle generation strategy model). The system may then initialize control parameters of an A/B treatment generation strategy model and repeatedly simulate user interest in unit bundles using the relevant unit data, the user interest decay model, the unit bundle generation strategy model, and the A/B treatment generation strategy model. Based on the simulated user interest in unit bundles, statistics associated with bandit strategy results are collected and transmitted when at least one evaluation condition is satisfied.

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 engaging a user in a primary activity; the primary activity may include a plurality of stages. The operations may include compiling at least one risk-reward score for an advertisement placement of an advertisement for at least one of the plurality of stages. The operations may include updating, dynamically, the at least one risk-reward score and identifying a recommended stage for the at least one advertisement; the recommended stage may be based on the risk-reward score. The operations may include displaying the at least one advertisement to the user at the recommended stage.

A system and method for multi-channel dynamic advertisement testing. The system comprises a multi-platform adaptive ad campaign manager, a dynamic advertisement engine, a campaign database, and an omnichannel text-based communicator. The system receives customer interactions with two advertisement test variants, establishes a real-time media stream between a customer device and a second user device, and monitors the media stream to collect data related to effectiveness of the advertisement variants. The system may analyze media stream data together with a plurality of other data types to statistically determine which of the two advertisement variants resulted in better performance based on a variety of advertisement metrics. The system may use the plurality of data and the statistical analysis to suggest an advertisement element to be altered in the next round of advertisement variant testing. This system can combine data collection and analytics for an ad campaign together into one system.

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 engaging a user in a primary activity; the primary activity may include a plurality of stages. The operations may include compiling at least one risk-reward score for an advertisement placement of an advertisement for at least one of the plurality of stages. The operations may include identifying a recommended stage for the advertisement; the recommended stage may be based on the risk-reward score. The operations may include displaying the advertisement to the user at the recommended stage.