Systems and methods evaluating and predicting fuel station usage are provided herein. An example method includes establishing geo-fencing parameters around one or more fueling stations; determining when vehicles encounter geo-fences of the fueling stations based on the geo-fencing parameters, such that encounters relative to the geo-fences are associated with entry timestamps; determining an aggregate number of the vehicles encountering each of the geo-fences over a period of time based on the entry timestamps; determining waiting times for the vehicles at each of the fueling stations; and transmitting a message to a portion of the vehicles that indicate which of the fueling stations are busy or have short wait times based on the aggregate number of the vehicles encountering each of the geo-fences and the waiting times.

Disclosed herein are systems and methods of individual level learning that include receiving purchase event data from a merchant device that indicates that a purchase event occurred by a user on a user device, and transmitting the purchase event data to an analytics server. The methods may also include processing the purchase event data. The processing may include calculating a time gap for each of two sequential purchase events in a list of purchase events, and calculating an average duration of consecutive events by averaging all of the purchase events in the list of purchase events. The method may determine a purchase hazard probability that a purchase event will occur on a given day, when the average duration of consecutive events is larger than a standard deviation of the event occurring. When the purchase hazard probability is above a threshold, the system may push a message to the user device.

A computing system comprises a database of garments in a retailers inventory, a database of users, a database of garments not in inventory, and a database of mappings, wherein a mapping maps one or more users of the database of users to one or more garments of the database of garments and wherein each mapping includes a weight for its corresponding user-garment pair.

A residual value forecasting system may utilize heterogeneous data, such as used market data, industry-specific data, and non-industry-specific data, from disparate data sources to produce residual value forecasts of an item based on a sophisticated residual value forecasting model particularly configured for agility. The system can dynamically and quickly adapt to change in data inputs and produce custom outputs. The system may determine a baseline value for an item using the used market data, a microeconomic factor using the industry-specific data, and a macroeconomic factor using the non-industry-specific data, as well as adjustments such as locality adjustments and modifications. Given the macroeconomic factor and the microeconomic factor relative to the locality-adjusted value of the item and in view of the competitive sets of similar and/or substitute items in the same industry, the system can generate an accurate forecast residual value of the item at a future time point.

A method of user proportion investigation and population estimation in a region for mobile communication operators, including the following steps: S1. completing a WiFi network construction in the region; S2. a mobile phone user in the region sending a verification code request for WiFi login to a background by using a client; S3. the background receiving the request from the user, and recording a mobile phone number and a request time of the user; S4. the background completing an identification and classification processing of the operator of the mobile phone number, and obtaining the user proportion in the region for the communication operators within a set period of time; and S5. calculating a population amount in the region according to a number of mobile phone signaling provided by one mobile communication operator thereof and the user proportion in the region for the corresponding communication operator obtained in S4.

This disclosure describes systems and methods for managing the reputation of a practitioner, such as a medical practitioner or another professional service provider, by requesting reviews after a procedure or other service has been provided. A targeted patient or client acquisition system leverages the strengthened reputation of the practitioner to acquire target patients. The targeted patient acquisition system identifies a target geographic region for targeted advertisement based on a weighted function of targeting factors for each of a plurality of geographic regions within a travel threshold of an office of the practitioner. A marketing management subsystem manages the electronic distribution of advertisements to individuals within the identified target geographic region. Calculated profitability metrics of new patients acquired via the targeted advertisements are used to adjust the target geographic region.

A system for updating offers includes a computer for receiving consumer data. A data entry application removes pertinent data from the consumer data as a function of a type of products being offered. An application program interface maps the pertinent data to products as determined by the appropriateness of the pertinent data for a respective product. A clock stores a time period for which the consumer requires a renewal of the product and determines when such time period elapses and outputs a signal to the application program interface before the time period has elapsed. The application program interface forwards the pertinent data to two or more vendors in response to the clock signal; the two or more vendors chosen as a function of the mapping of the stored pertinent data to products offered by the vendor, and receives product offers corresponding to the pertinent data. The application program interface forwards the received product offers to the computer for display to the consumer as product offerings.

A multi-region risk relationship data store may contain electronic records representing a plurality of multi-region risk relationships and, for each multi-region risk relationship, an electronic record identifier and a set of multi-region attribute values including a plurality of hierarchical local risk relationship attribute values. A back-end application computer server may receive an indication of a selected risk relationship and display at least some of the associated multi-region attribute values. The server may receive adjustments to some of the multi-region or local risk-relationship attribute values, and displayed information may be automatically and dynamically configured based on the received adjustments and a standalone risk calculation unit. At least some local risk relationship attribute values may be replicated for multiple regions. When adjustments have been completed, the server may automatically assemble a multi-region risk relationship document.

Embodiments are disclosed for a method that may include accessing events in a field-searchable data store. The events may include raw machine data associated with a timestamp. The raw machine data may represent interactions between a mobile device and one or more network devices at a locale. The method may further include determining, based on the interactions, one or more geographic positions of the mobile device, and calculating a metric for the locale using the geographic positions.

Recommendation filtering is provided. The method comprises creating a user feature matrix that cross-references users with user attributes and items and creating a user similarity matrix of user similarities according to the user attributes. Nearest neighbors of the users are then determined based the user similarities. The system creates a user-item matrix is that cross-references the users with the items, wherein the user-item matrix identifies users who use the item and users who do not use the items. The user-item matrix is multiplied by a penalizing factor for users who do not use the items. A top N number of the items is recommended to a new user based on the item scores according to user attributes of the new user.