Embodiments include apparatuses, methods, and systems for segmenting a target region for a target user based on payment transactions within a payment processing system. The embodiments include identifying a target user from users based on payment transaction data, and determining target regions for the target user based on the payment transaction data of the target user and users. The target user may have a propensity for initiating transaction activity in the target regions within a predetermined time period. The embodiments include generating propensity scores for the target regions based on the payment transaction data, identifying a target region for the target user based on the propensity scores, and transmitting merchant offers to the target user based on the identified target region. The propensity scores may be ranked into a list of target region rankings, where the target region is a maximum score in the list of target region rankings.

A device may receive first customer data, and may receive first contact data. The device may generate second customer data that includes the first customer data, and may generate second contact data that includes the first contact data and additional contact data. The device may generate a quantity of simulated future communications based on differences between the first customer data and the second customer data and between the first contact data and the second contact data, and may process the quantity of simulated future communications, with a machine learning model, to determine a probability distribution for an opt out event. The device may determine a relationship between the quantity of simulated future communications and probabilities of the opt out event, and may identify a particular probability of the opt out event based on the relationship. The device may perform actions based on the particular probability.

Embodiments are disclosed for automated evaluation of compatibility of a data structure with a user device. An example method includes receiving, by communications circuitry, a set of user device characteristics regarding the user device, and retrieving, by the personalization circuitry, a set of data structure characteristics regarding the data structure. The example method further includes calculating, by the personalization circuitry, a set of characteristic-level compatibility scores, and generating, by the personalization circuitry and based on the set of characteristic-level compatibility scores, a compatibility score for the data structure and the user device. Subsequently, the example method includes generating, by an aggregator and using the generated compatibility score, an indication of relative compatibility of the data structure for the user device, and causing transmission, by communications circuitry, of a control signal to the user device based on the indication of relative compatibility. Corresponding apparatuses and computer program products are also provided.

A computer-implemented method for characterizing a crowd that includes recording a video stream of individuals at a location having at least one reference point for viewing; and extracting the individuals from frames of the video streams. The method may further include assigning tracking identification values to the individuals that have been extracted from the video streams; and measuring at least one type classification from the individuals having the tracking identification values. The method may further include generating a crowd designation further characterizing the individuals having the tracking identification values in the location, the crowd designation comprising at least one measurement of probability that the individuals having the tracking identification values in the location view the at least one reference point for viewing.

A system includes a hardware processor and a memory storing software code for controlling distribution of ad surveys to target audiences. For each target audience, the hardware processor executes the software code to receive ad data describing an ad volume distributed to the target audience during a predetermined time interval of an ad campaign, and to receive survey data describing a survey volume distributed to the target audience and a survey response volume collected from the target audience during the predetermined time interval. The software code also determines a next survey sampling rate for the target audience based on the ad data and the survey data, such that the volume of survey responses collected from the target audience during matches an ad distribution volume for the target audience within a predetermined threshold during each predetermined time interval of the ad campaign.

A system for building a messaging campaign queue with contextualization includes a processor, an interactive display, and a memory module. The memory module includes stored computer-executable program code that, along with the memory module and the processor is configured to carry out a number of operations to create and customize a set of campaign interactions. One such operation involves creating a campaign queue based on a campaign type and a set of campaign parameters. The campaign queue includes a set of campaign interactions, each of which is associated with an intended recipient. Another such operation involves providing, via the interactive display, interaction context associated with the campaign interactions. An additional operation involves customizing the campaign interactions based on customization input received via the interactive display.

This paper presents a practical method for measuring the impact of multiple marketing events on sales, including marketing events that are not traditionally trackable. The technique infers which of several competing media events are likely to have caused a given conversion. The method is tested using hold-out sets, and also a live media experiment for determining whether the method can accurately predict television-generated web conversions.

Technology for predicting online user shopping behavior, such as whether a user will purchase a product, is described. An example method includes receiving current session data describing a current session for a current user, extracting a current clickstream from the current session data classifying the current clickstream as a purchase clickstream or a non-purchase clickstream by processing the current clickstream using one or more sets of Hidden Markov Model parameters produced by one or more Hidden Markov Models, and computing, using the one or more computing devices, a purchase probability that the current user will purchase a product during the current session based on the classifying.

Systems and methods for providing a report of an analytic result value based on IP data are disclosed. An example system may include a controller configured to: access a distributed ledger including a plurality of intellectual property (IP) data about a plurality of IP assets, where the IP assets include an aggregate stack of IP; tokenize the plurality of IP data; and interpret a distributed ledger operation corresponding to at least one of the IP assets. The controller may also be configured to determine an analytic result value in response to the distributed ledger operation and the tokenized plurality of IP data, where the analytic result value is one of: a number of access events, an access time; or an access processing time and to provide a report of the analytic result value; and record a transaction on the distributed ledger in response to providing the report.

The disclosure relates to securing and enabling user control of profiling data, blockchain-driven matching of users and advertiser-identified anonymous profiling data records of interest, and smart contracts encoded by blockchain for executing transactions. The system may include an anonymized database of profiling data, which is unlinked to any user. The system may implement a private blockchain to store user-defined settings that provide user control over whether and how the profiling data may be used. If a grant to use the data is received, a link is stored that allows the system to identify a user associated with the anonymous profiling data records. If the grant is revoked, the link may be removed. The system may also implement public blockchain technology to record a public information relating to grants, online marketing transactions, making them verifiable, immutable, and transparent for various stakeholders including advertisers, publishers, and users.