Methods and apparatus to generate consumer data are disclosed. An example method of selecting a sample of transaction data corresponding to a membership program includes defining a first type of member of the membership program; defining a second type of member of the membership program; calculating, via a processor, a target for the sample; selecting, via the processor, a first portion of the transaction data for the first type of member in accordance with the target; generating, via the processor, an updated target by recalculating the target with the first portion of the transaction data removed from consideration; and selecting, via the processor, a second portion of the transaction data for the second type of member in accordance with the updated target.

To determine an impression metric for an organization, a server device generates a statistical model for estimating the impression metric using machine learning techniques. The server device obtains training data for the statistical model by randomly selecting geographic locations within a geographic area and determining the number of users eligible to receive a particular type of advertisement for each randomly selected geographic location. For example, a user may be deemed eligible when displaying the geographic location via a mapping application. When an organization requests an estimate of a number of impressions for an advertising campaign, the server device applies data included in the request (e.g., the time period for the advertising campaign, the number of organization locations, identifiers for the organization locations such as geographic coordinates or an address, etc.) to the statistical model to estimate an impression metric for the organization.

A method and system for identifying recurrent stops of a vehicle fleet having a plurality of vehicles. The method comprises retrieving historical GPS tracks of the vehicle fleet over a period of time; detecting stops made by the vehicle fleet along travelled routes that are associated with the historical GPS tracks; constructing a coverage area that covers the travelled routes; discretizing the coverage area into a plurality of cells; determining whether a cell is a recurrent stop based on a fleet stay period associated with that cell; and classifying a determined recurrent stop into a plurality of categories.

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 determine or predict one or more metrics about participants in a network.

Techniques are disclosed to provide customized loans on a per-user basis. With user permission or affirmative consent, user data may be monitored for several users, which may be used to calculate initial loan specifics such as a loan rate and term based upon a portion of this user input data. The user data may include demographic data, behavioral data, or other data indicative of a user's future potential earnings or other relevant information that may be analyzed to determine, for that specific user, the current likelihood that the user will default on the loan and a future likelihood of default. When this future statistical likelihood is determined, the initial loan specific may be further modified and/or a targeted notification may be sent indicating these customized loan specifics.

Disclosed are systems and methods for identifying location-based information associated with a product on a web page. The method may include: detecting user navigation by the user of the web page; detecting the at least one product on the web page; identifying one or more merchants having the detected at least one product in stock; determining a user location of the user; determining the identified one or more merchants having the detected at least one product in stock and having a location within a predetermined distance of the user location; generating a list of merchants, the list including the determined one or more merchants having the detected at least one product in stock and having the location within the predetermined distance of the user location; and executing a browser extension to display, on the web page associated with the at least one product, the generated list of merchants.

A method including displaying content elements on one or more websites to users. The classification of the users into segments can be based on each impression of content elements being displayed on the one or more websites to a user of the users, tracking impression response data comprising (a) a response of the user to the content element of the content elements displayed on the one or more websites, and (b) one or more segments of the segments in which the user is classified. The method can also include receiving a request from a first user. The method can also include generating a mixture distribution for the first segment for the first content element based on the impression response data for the first segment and generating the webpage to comprise the selected content element. Other embodiments are disclosed.

A server is communicably connected to a plurality of terminal apparatuses and includes a server controller. The server controller is configured to: determine that the plurality of terminal apparatuses are within a same geographical area; transmit, to each terminal apparatus in the plurality of terminal apparatuses, display content that is at least partially different per terminal apparatus. The display content is determined based on attribute information pertaining to respective users of the plurality of terminal apparatuses and on the geographical area.

Implementations of the present specification disclose a method and a system for recommending a target transaction code setting region. The method includes the following: dividing a target region to obtain multiple sub-regions, where the multiple sub-regions include one or more label sub-regions with known target transaction code setting effects and one or more sample sub-regions with unknown target transaction code setting effects; obtaining an association feature between the multiple sub-regions; obtaining predicted effect values of setting a target transaction code in the one or more sample sub-regions by using a prediction algorithm based on at least estimated effect values of setting a target transaction code in the one or more label sub-regions and the association feature; and determining at least one recommended region for setting a target transaction code from the one or more sample sub-regions based on at least the one or more predicted effect values.

A method for obtaining credit card pricing for a merchant includes obtaining a merchant category classification (MCC) code. A sales volume, a number of credit card transactions, an average dollar amount of the credit card transactions and a percentage of credit card transactions that are keyed are obtained. The MCC code, the average dollar amount of the of credit card transactions processed and the percentage of credit card transactions that are keyed are compared with corresponding data from a database of merchant credit card transactions. A matched merchant is identified whose transaction profile closely matches a combination of the MCC code, the average dollar amount of the credit card transactions processed and the percentage of credit card transactions that are keyed. Credit card processing pricing information for the matched merchant is obtained from the database. The credit card processing pricing information is used to calculate credit card processing pricing for the matched merchant.