The present disclosure provides methods and systems for quantifying item performance in a digital shelf. A method for quantifying item performance in a digital shelf may comprise: calculating a value associated with a shelf share of the given item; determining a set of factors for calculating a score indicative of the item performance on the digital shelf, wherein the set of factors includes the shelf share ;generating, using a trained machine learning algorithm, the score based on the set of factors; and displaying the score within a graphical user interface (GUI) on an electronic device.

Member outreach services to be offered to members of a health plan based on predictions generated by a prediction engine using data associated with the members. The prediction engine can include an ensemble of different base predictive models, and the prediction engine can use a weighted combination of base predictions produced by the different base predictive models to generate a final prediction associated with a member. A representative can attempt to contact the member based on an outreach ticket associated with the final prediction. The representative can also provide ticket feedback associated with the outreach, and the prediction engine can use the ticket feedback to adjust the weights associated with the different base predictive models.

Embodiments of vehicle data systems for use in distributed computer network are disclosed. Particular embodiments may determine and enhance vehicle data from various data sources distributed across the computer network, and utilize the enhanced vehicle data in the determination of normalization metrics that account for geography and population density or spatial behavioral patterns. Embodiments may utilize these normalization metrics to assign zone labels to geographic areas and present representations of the geographic areas based on the normalization metrics across the distributed computer network.

Transaction-enabling systems including a controller are disclosed. The controller can access a distributed ledger including an instruction set, tokenize the instruction set, interpret an instruction set access request, and, in response to the instruction set access request, provide a provable access to the instruction set.

Methods, apparatus, systems, and articles of manufacture are disclosed to assign demographic distribution data to digital television ratings. An example apparatus includes factor generation circuitry to calculate a demographic adjustment factor based on digital probability distribution function (PDF) (digital-PDF) impressions and linear PDF (linear-PDF) impressions. The example factor generation circuitry is also configured to calculate a device adjustment factor based on historical calibrated demographic digital impressions and historical computed demographic digital impressions. The example apparatus further includes adjustment calculation circuitry to determine digital demographic impressions by applying the demographic adjustment factor to the current raw digital census impressions. The example adjustment calculation circuitry is also configured to determine digital device-demographic impressions by applying the device adjustment factor to the digital demographic impressions.

A computer-implemented method for directing transaction rewards may comprise receiving, via a user device associated with a user, user preference data regarding a preferred merchant list including one or more merchants and reward criteria for each of the merchants, wherein the preferred merchant list defines a priority of the one or more merchants and the reward criteria defines a dynamic reward for each of the one or more merchants based on the priority on the preferred merchant list; receiving transaction information from a first merchant; determining, via one or more processors, if the first merchant is one of the one or more merchants on the preferred merchant list; and in response to determining the first merchant is on the preferred merchant list, determining, via one or more processors, a reward based on the transaction information; and transmitting, via the one or more processors, the reward to the first merchant.

A computer-implemented method for providing a personalized interface to a user based on whether the user is serious about making a purchase may include: obtaining customer identification data and customer input data a customer, wherein the customer input data comprises a request from the customer; determining a request status of the customer based on the customer identification data and the customer input data; obtaining customer interface activity data of the customer based on the request status; obtaining customer purchasing data of the customer based on the request status; generating a prediction model based on the customer interface activity data and the customer purchasing data; training the generated prediction model by classifying the customer based on the customer interface activity data and the customer purchasing data; obtaining user identification data and user interface activity data of a user via a user device, the user interface activity data indicating interactive activities between the user and a user interface displayed on the user device; determining a rating of the user to purchase a product based on the user identification data, the user interface activity data, and the prediction model; and providing, to the user, an updated user interface on the user device based on the determined rating.

A method, system, and process for predicting growth for group benefits usage where data is received at a processor of a computing device and from at least one external data source connected over a communications network with the computing device, data for populating one or more of the set of variables for each of the plurality of companies. The method includes receiving at the processor of the computing device and from at least one internal data source in operative communication with the computing device, data for populating one more additional variable within the set of variables for each of the plurality of companies and executing the instructions for implementing the scoring model to apply the scoring model to evaluate the plurality of companies using the processor of the computing device in order to generate the single score indicative of growth for each of the plurality of companies.

Systems and methods are provided for use in predicting purchases in regions based on payment account transaction data from in and around the regions. One exemplary method includes generating a purchase propensity model based on historic transaction data and determining a recent sales value of the region based on a set of transactions that occurred in the region during a recent time interval. A set of consumer accounts that include transactions in the region during the recent time interval is determined. The computing device calculates consumer propensity scores for the consumer accounts based on the purchase propensity model. The propensity scores are combined into an overall purchase propensity for the region. The overall purchase propensity and recent sales value of the region are used to determine a predicted sales value of the region, whereby businesses in the region make business decisions confident that the predicted sales value is accurate.

Provided is a process that determines a device-dwell graph based on noisy reported geolocations for mobile computing devices.