A closed loop attribution system may include a user location history storage system containing information indicative of user location history for a plurality of users based on location of a mobile device associated with a user; a user database storage system containing placement tracking information indicative of advertising content presented to the user; and a closed loop attribution processor responsive to said user location history storage system and said user database storage assessing correlations between a user's exposure and a user's location. The user database may include records indicative of user behavior and characteristics. The closed loop attribution processor may be connected to the campaign database and the campaign database may contain an indication of one or more locations of interest to an advertiser. The correlation between user exposure and a user's location may be a correlation between user exposure and the location or locations of interest.

A process, operated by a smart phone connected to a cloud networking, facilitates web-based e-commerce transactions between users. The process is able to initiate a transaction by clicking one of multiple transactional selections managed by a mobile application (app) displayed on a screen of a smart phone. In one aspect, the cloud networking provides an option or recommendation to the user based on user's browsing pattern and categorization codes stored in the storage. After sending a packet representing the transaction together with a phone number associated to the smart phone to an app server, a user account associated with the smart phone is identified based on the phone number. Upon generating an order in response to the packet and retrieved information from the user account, the order is placed from the app server to an ordering system associated to a registered vendor for facilitating the transaction.

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.

Approaches for displaying a user interface including a map based on interaction data are disclosed. A set of interaction data and can be acquired and stored in a data structure. This data can be associated with a plurality of consuming entities that may have purchased something during these interactions. A set of provisioning entities can be determined based on spending or purchasing habits of the consuming entities. Based on this set of provisioning entities, a user interface can be generated which may include various shapes similar to a heat map. These shapes can indicate an average amount spent in a particular neighborhood, among other attributes.

A computer-implemented system and method for transparent automated data gathering flow for calculation of Gross Rating Points (GRP) ratings in compliance with European Union General Data Protection Regulation (GDPR) and to provide corresponding transparent EU GDPR compliance GRP rating reports, wherein the GRP ratings are calculated for different types of media on the same panel based on auto generated surveys without human works. The GDPR non-compliance problem in GRP calculation is solved by computer-implemented smart contract procedure using the distributed ledger as decentralized database provided by blockchain platforms supporting smart contract functionality.

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.

Methods and systems for controlling production resources in a supply chain are described. The system automatically generates predicted supply chain operational metrics across a nodes of a supply chain. The predicted supply chain operational metrics include a value at risk associated with a scheduling of a production run including scheduling a production of a product with a production resource. The system automatically infers causal factors that impact the predicted supply chain operational metrics. The causal factors include a change to a utilization of the production resource. The system communicates a user interface including production runs being scheduled on the production resource including a user interface element representing the scheduling of the production run associated with the value at risk. The system receives input causing a change to the utilization of the production resource. The change to the utilization of the production resource impacts the predicted supply chain operational metrics including the value at risk associated with the scheduling of the production run.

Methods and systems to predict a supply chain performance are described. A system receives supply chain data for delivery of a product. The supply chain data includes input signals comprising operational plans and observed supply chain operational metrics. The input signals include a delivery date of the product. The system automatically generating predicted supply chain operational metrics across including a value at risk that is predicted for the product. The system automatically infers causal factors that impact the predicted supply chain operational metrics including impacting the value at risk that is predicted for the product. The system automatically generates action recommendations for the supply chain. An action recommendation includes a first predicted value impact and a sequence of actions impacting the product the delivery date of the product and the value at risk that is predicted for the product.

Methods and systems for controlling inventory in a supply chain are described. The system receives supply chain data including input signals comprising operational plans and observed supply chain operational metrics. The system automatically generates predicted supply chain operational metrics including a value at risk that is predicted for a product. The system automatically infers causal factors including a shipment of the product. The causal factors impact the predicted supply chain operational metrics. The system communicates a user interface for shipments of the product and the system receives input causing a change to a shipment of the product impacting the predicted supply chain operational metrics including the value at risk for the first product.

Methods and systems for scheduling tasks for field professionals include: receiving a set of first requests for on-site assistance from a first set of users; receiving a set of second requests for remote assistance from a second set of users; assigning a plurality of location-based tasks associated with the set of first requests to one or more field professional; receiving real-time information associated with the one or more field professional including current location; determining based on the real-time information whether the one or more field professional can complete a location-agnostic task associated with a second request after completing a first location-based task and before starting a second location-based task; and assigning the location-agnostic task to the one or more field professional.