Disclosed is a computer implemented system and method for retail management. The computer implemented system and method for retail management provides data driven and insightful decisions for businesses by implementing an Artificial Intelligence (AI) Platform. The AI platform utilizes the tacit industry knowledge with geospatial information and proprietary AI algorithms to make data driven decisions. In an embodiment, the computer implemented system and method includes DBAS Engine for retail management, prediction, and business optimization. The computer implemented system and method for retail management provides a layered set of solutions to drive the bottom-line revenue and further adds value by identifying new markets for expansion. In addition, the layered solution implemented by using AI algorithms identify the preferred customer profiles to serve targeted customers in a personalized way. The DBAS Engine provides a user interface, which includes all essential tools for business analytics to provide a full spectrum of features for a comprehensive user experience.

Apparatus and associated methods relate to automatically prioritizing predicted events in a dynamic predicted queueing profile (PQP) for a service provider (SP) for a finite future time period. In an illustrative example, a central orchestration engine (COE) may generate, in response to a request for service from a service seeking entity (SSE), a dynamic queueing event profile (DQEP) associating the SSE with the PQP for the SP at the future time period. The COE may, for example, generate a confidence level of execution (CLE) for each DQEP in the PQP based on a historical behavior profile (HBP) of the SP and of each corresponding SSE. The COE may, for example, apply the confidence level of execution to each corresponding DQEP to assign a priority in the PQP. Various embodiments may, for example, advantageously dynamically prioritize a queue based on historical behavior of an SP and SSEs in the queue.

A system to dynamically generate, monitor and optimize an allocation of resources includes a processor-based server to process requests received from the client devices. The processor-based server includes a server processor to generate the allocation of resource sharing one or more display devices selected based on an allocation criterion defined by the information emitter, target rating point goal, a geolocation, a format, and a quality coefficient of each display device in the set of available display devices. The server processor monitors availability of previously unavailable display devices and track real-time changes to the quality coefficients for each display device in the updated set. The server processor dynamically updates and optimizes the allocation of resources based on the updated quality coefficients for the updated set of available display devices.

A unified ecommerce platform system that enables sellers to acquire in depth insight into their business market and consumers utilizing a centralized cloud based platform with enhanced consumer purchasing and information gathering abilities and allows users enhanced granular security tools and information for purchase.

An approach is provided for estimating electric vehicle (EV) charge needs among a population in a particular region. A method includes obtaining region-specific information associated with the particular region; wherein the region-specific information includes a number of EV drivers in the particular region. The method includes generating needs prediction data that indicates the EV charge needs among the population by applying the region-specific information to a mobility simulation of electrical vehicle drivers in the particular region; wherein the mobility simulation comprises a plurality of probability distribution functions. The method includes generating, based on the needs prediction data, on a display device of a computing device, a display that suggests a plurality of locations at which to place EV charging stations within the particular region to satisfy the estimated EV charge needs indicated in the needs prediction data.

A disclosed example includes aggregating first performance results of computer-generated machine learning models to generate aggregated performance results, the first performance results based on a comparison of audience member demographic data to training results, the training results generated by the computer-generated machine learning models based on at least one of: (a) a composition of a household, (b) a type of first media, (c) a daypart during which the first media was accessed, or (d) a time at which the first media was accessed; selecting at least one of the computer-generated machine learning models based on a comparison of ones of the first performance results to the aggregated performance results; and applying the at least one of the computer-generated machine learning models to correct a computer-generated error in computer-collected audience measurement data, the computer-collected audience measurement data corresponding to accesses to second media.

Systems and methods for suggesting drop-box locations include collecting data from one or more sensors or databases, determining equipment location trends and asset usage trends in a geographic area based on the data, determining a customer opportunity score for one or more pieces of equipment in a geographic area, and, using an algorithm with a plurality of factors, combining the equipment location trends and asset usage trends with the customer opportunity score to output a geographic location for a drop-box. In some embodiments, the plurality of factors can include at least one or more customer distances from the geographic location.

Systems, methods, and apparatus are described herein for determining a location from anonymous data. For example, a computing device may receive anonymous data associated with a browser session initialized by a user via a browser on a user computing device. The computing device may determine that the user has not been assigned a unique identifier. The computing device may determine whether the user opted-in to location tracking. If the user opted-out of location tracking, the computing device may determine a latitude coordinate and a longitude coordinate of the user computing device during the browser session. The computing device may identify a physical address for the user based on the latitude coordinate and the longitude coordinate, for example, using a map application programming interface (API). The computing device may assign the unique identifier to the user. The computing device may associate the unique identifier to the physical address.

A method includes: acquiring a plurality of historical routes of movement of a plurality of historical users in a geographic area, wherein each historical route includes at least a portion of a plurality of locations; determining, based on the plurality of historical routes and a plurality of current positions of a plurality of users in the geographic area, a set of predicted locations among the plurality of locations that the plurality of users will visit in the future, respectively, wherein the plurality of users use a tour guiding service associated with the plurality of locations that is provided by a mobile network; selecting a set of popular locations from the set of predicted locations based on the number of users among the plurality of users who will visit each predicted location in the set of predicted locations; and scheduling tour guiding resources associated with the set of popular locations.

Systems and methods, and computer readable media for inventory demand estimation of a region are disclosed. The method receives an item identifier associated with an item for demand estimation. The method may then access overall demand forecast data for the item and identify geographical regions, and evaluate a demand share estimate of the item in the geographical regions. The method may also determine a set of item identifiers associated with a segment of items related to the item and determine the demand estimation for the segment of items. The method may then generate demand estimation of the item at the geographical regions using a Bayesian framework with demand share of the item and demand estimation of the segment of items in a geographical region, and overall demand forecast data for the item as input. The method may use the generated demand estimation at a region to generate demand estimation in other encompassing geographical regions.