Abstract: Systems, computer program products, and methods are described herein for machine learning-based metadata collection and parameterized graph modeling from communication channels. The present disclosure comprises a communication interaction subsystem (CIS) configured to receive requests from a user input device to query network traffic data associated with a plurality of devices. The request comprises a factor set and a correlation criteria. The CIS analyzes the network traffic data based on at least the request and determines a subset of the plurality of devices based on at least the request. The system also comprises a parameterized graph modeling subsystem (PGMS) operatively coupled to the CIS, which is configured to generate a data traffic topography map associated with the subset of the plurality of devices and transmit control signals configured to cause the user input device to display the data traffic topography map.

Abstract: A flexible listings search system can receive and return results for flexible listing searches. For example, the system can perform micro-flexible searches (e.g., plus or minus a few days) or super flexible searches (e.g., a time span in one or more months), using listing arrays that can be rapidly accessed to efficiently identify and return results. The search system can perform flexible destination searches for different categories of accommodations for display in a viewport.

Abstract: System and methods for handling on-chain cryptographic resources based on conditions generated responsive to an off-chain trigger. The system may receive a transfer request that includes a resource allocation request, generate a first blockchain function request for a list of contributor identifiers and condition sets, receive the list of contributor identifiers and condition sets, determine a subset of the list of contributor identifiers and condition sets, generate a second blockchain function request, and transmit the second operation request to a blockchain node.

Abstract: One embodiment provides a method, including: obtaining, using the set of one or more processors, a plurality of user selections provided via a graphical user interface (GUI); establishing, using the set of one or more processors, an association between the plurality of user selections and display data for a time series of a plurality of inventory items; obtaining, using the set of one or more processors, a time series of forecasting data via an application programming interface (API) call to a remote system; associating, using the set of one or more processors, the time series of forecasting data with the time series of the plurality of inventory items; and thereafter generating or updating, using the set of one or more processors, the display data for the time series of the plurality of inventory item on one or more web pages of an entity associated with the GUI based on the forecasting data and the plurality of user selections. Other embodiments are described and claimed.

Abstract: A method and system for performing a movement of a service within a distributed system. In one embodiment, the method comprises determining one or more locations where the service is to be moved. The method further comprises for each of the one or more locations, computing a metric including a cross-cell cost (C-cost) or a data locality-cost (DL-cost) of the software service if a movement of the software service to a location was to occur, prior to performing the movement of the software service; and modelling a latency and availability of the system due to the movement of the software service to the location based on the metric. The method further comprises performing the movement of the service based on the modelled latency and availability of the system.

Abstract: Disclosed are a profiling method and apparatus based on a personal region of interest. The profiling method includes determining a region of interest (ROI) indicative of an interest region of a user based on the profile of a person, and generating relationship information indicative of a relationship between a specific person and the user based on a degree of intimacy between persons and the ROI. The AI device and the AI system of the present disclosure may be associated with an artificial intelligence module, a drone (or unmanned aerial vehicle (UAV)), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 5G service, etc.

Abstract: A data classification system calculates, for each known class, which is a class indicated in training data used for performing learning of class classification, a known class likelihood indicating a likelihood of target data belonging to the known class among all known classes. The data classification system selects, as candidates for a class to which the target data belongs, classes in which at least one of the known classes is excluded based on the known class likelihood among all classes in the class classification. The data classification system calculates, at least for each of the classes included in the candidates, an all-class likelihood indicating a likelihood of the target data belonging to the class among all the classes. The data classification system estimates the class to which the target data belongs as any one of the classes among the candidates, based on the all-class likelihood.

Abstract: Methods, systems, and computer-readable media for generating a virtual based on location. The method acquires a request for a service based on a type of service and is associated with a user, the user's location, and user preferences. The method then acquires a set of service providers based on the type of service and the user's location who are filtered from a larger set of service providers using user preferences. The method in the next step acquires a machine learning model that is based on stored information associated with other users travel patterns and with service providers providing the service and the geographical information associated with the user's location. The method executed the identified machine learning model to aggregate a subset of service providers based on output from the machine learning model. The machine learning model is inputted the set of service providers, the user's location, the user's preferences, and the geographical information.

Abstract: Embodiments provide methods, systems, apparatuses, and computer program products for predicting behavior. An example method includes determining a classification for a first consumer, where the classification is based on a measure of frequency of purchases by the first consumer; identifying one or more first attributes for the first consumer based on the determined classification, the one or more attributes being attributes selected for predicting the respective one or more metric associated with the first consumer; and determining, based on values for the one or more first attributes, a first prediction value that indicates a programmatically expected number of purchases by the first consumer.

Abstract: A method includes causing a computer of a communication terminal to perform a process including acquiring, in response to receipt of a beacon signal transmitted from a vending machine, identification information and type information of a plurality of drinks from the vending machine by using short-range wireless communication; acquiring preference information on drinks of a user of the communication terminal; generating, based on the identification information, the type information, and the preference information, a push notification screen that recommends at least one drink that matches a preference of the user indicated by the preference information from among the plurality of drinks stored in the vending machine indicated by the identification information; and displaying the push notification screen on a display of the communication terminal.

Abstract: A machine learning model supported trend detection engine can be available via an application programming interface to business or community planners to evaluate the potential for new plans or ventures. The model can be based upon transaction trends and available merchant data from transactions such as location, time, merchant type, and transactions history longevity from particular retailers. The model can generate a dashboard that shows past performance, predicted future performance, trends, similarity between businesses, and profitability. The new plan or venture can be correlated with other retailers that have been established and successful under a similar model and located near another retailer similar to the new plan or venture.

Abstract: Disclosed are methods, systems, and computer-readable medium for providing report results. Viscous attributes and non-viscous may be identified. A smart cube may be received and may include viscous values for the viscous attributes. The smart cube may be stored at a local cache. A report associated with an organization may be initiated. A runtime generation of the report may be generated based on initiating the report. The report may call a viscous attribute from the viscous attributes and call a non-viscous attribute from the non-viscous attributes. The runtime generation may be modified to remove the viscous attribute from the runtime generation. A viscous value for the viscous attribute may be retrieved from the smart cube at the local cache. The modified runtime generation may be executed to retrieve a non-viscous value for the non-viscous attribute from a remote database and a report result may be provided.

Abstract: A candidate attribute combination of a first data set is identified, such that the candidate attribute combination meets a data type similarity criterion with respect to a collection of data types of sensitive information for which the first data set is to be analyzed. A collection of input features is generated for a machine learning model from the candidate attribute combination, including at least one feature indicative of a statistical relationship between the values of the candidate attribute combination and a second data set. An indication of a predicted probability of a presence of sensitive information in the first data set is obtained using the machine learning model.

Abstract: A web based interactive geographic information systems mapping analysis and methods for improving business performance including future scenario modeling are provided. The system uses a radical efficiency model to increase profitability since competitiveness can be improved by the radical efficiency model that allows businesses to reduce money wasted on bad initiatives. The system also gives businesses competitive intelligence on how to find new markets (expanding the pie) and to capitalize on competitors' inefficiencies and weaknesses to capture the competitors' market share (eating the competitor's slice of the pie). The system also shows businesses how to make more money by maximizing their strategic advantages against competitors and by identifying the best places and recommended strategies for business growth. The system also expands the list of potential vendors and customers a business can buy from and sell to. The system also provides advertisement recommendations for the business.

Abstract: Devices and techniques are generally described for regionalizing content based on the availability of similar content. In some examples, a first search query related to a first item may be received. A determination may be made that the first search query is associated with a first region. In some examples, the first item may be determined to be associated with the first region based at least in part on at least one of the first item or items classified as substitutes for the first item being available in a plurality of regions. A first plurality of search results associated with the first region may be determined. In further examples, code may be generated to cause the first computing device to display the first plurality of search results.

Abstract: A computer-implemented image-based property condition determination system and method are provided. The method includes determining one or more element condition ratings from individual images of the property; and determining an overall property condition based on available element condition ratings of the property. An overall condition rating of a portfolio comprising a plurality of properties is determined using the condition ratings of properties in the portfolio.

Abstract: Approaches are provided that determine and fine-tune product assortments carried by retail stores. In aspects, the number of stores that carry the item, the sales performance of the item, the loyalty of customers to the item, whether substitutes for the item can be found, and other factors are used to determine an actions such as increasing or decreasing the shelf space for an item.

Abstract: In some examples, a system can execute an iterative optimization routine on a set of candidate physical locations for an object. Each iteration can involve a series of operations. The operations can include selecting one of the candidate physical locations; determining a predicted demand value for the candidate physical location based on a first traffic score and a second traffic score associated with the candidate physical location; and determining a predicted overhead value for the candidate physical location. The operations can also include determining a predicted cannibalization factor for the candidate physical location; and generating an overall score for the candidate physical location based on the predicted demand value, the plurality of predicted overhead values, and the predicted cannibalization factor. The system may then identify one or more of the candidate physical locations as optimal locations based on their overall scores and display the optimal locations on a geographical map.

Abstract: A specialized keypad and associated mobile application in communication therewith that allows for pre-installed markers that are color-coded to be used to drop GPS pins on a map to identify the presence of those pre-installed markers on a geographical map. The specialized keypad can be attached to a pair of handle bars or on a steering of a vehicle, said vehicle then going into specific geographic locations for the markers identified by the pre-installed markers, such that specific items that correspond to the color coded markers, such that when the colored pin is dropped on the map created by the app, there is no need to take the extra time to identify what the GPS pins are identifying, since the pre-installed application has already color-coded the items to the pins on both the interface with the application on the mobile device as well as the keypad associated therewith.

Abstract: Systems and methods include determination of a plurality of call map clusters, each call map cluster associated with a respective type of external call, two or more of a plurality of services, and a service call model modelling, for each of the two or more of the plurality of services, the issuance of outgoing service calls in response to received service calls. A bot is deployed proximate to each of the plurality of services, and each deployed bot is associated with a respective configuration to issue outgoing service calls in response to received service calls as modelled by the service call models for each of one or more services proximate to the deployed bot.

Abstract: A platform may receive a first set of address-occupant pairs from a plurality of source devices, wherein the first set of address-occupant pairs comprises identifications of physical addresses and identifications of occupants. The platform may detect an identification of a physical address that is associated with identifications of a plurality of the occupants. The platform may select, for the physical address, one of the identifications of the plurality of occupants to maintain association with the identification of the physical address. The platform may aggregate infrastructure information with the second set of address-occupant pairs, wherein the infrastructure information indicates one or more services available to be provided at one or more of the physical addresses. The platform may perform one or more actions based on identifying the one or more services available to be provided at one or more of the physical addresses.

Abstract: In an aspect, an apparatus for wage index classification is disclosed. The apparatus may include a processor and a memory. A processor may be configured to receive a wage index from a user. A processor may also extract a candidate datum from a plurality of candidates. Candidate datum is then classified to a wage index classification data. This may be achieved through training a wage index classifier using candidate training data correlating candidate data elements to wage index classification data elements and classifying the candidate data to the wage index classification datum using the wage index classifier. A processor may then generate a wage compatibility score as a function of wage index classification data and a posting datum. Then a processor may be configured to transmit to a display device configured to display a wage compatibility score.

Abstract: A computer-implemented method for providing an estimated utility expenditure to a user may include: obtaining, via one or more processors, historical transactional data of one or more customers other than the user from one or more transactional entities, wherein the historical transactional data includes: at least one address of a given customer of the one or more customers; and a historical utility expenditure associated with the at least one address; generating, via the one or more processors, a heatmap based on the historical transactional data of the one or more customers via one or more algorithms, wherein the heatmap is indicative of at least the estimated utility expenditure associated with the at least one address during a predetermined period; and causing a display of a user device associated with the user to demonstrate the heatmap.

Abstract: Systems and methods for optimally formatting item identifiers (ID) are disclosed. An example method is performed by one or more processors of a system and includes obtaining descriptions of items, identifying, for each item, one or more attributes of the item described in the item's description, extracting a value for each of the identified attributes, identifying a set of common attributes among the identified attributes for which values were extracted for more than a threshold ratio of the items, assigning a priority weight to each of the common attributes using an optimization algorithm, identifying a set of optimum attributes among the set of common attributes based on the priority weights, mapping an optimum code to each unique value extracted for the optimum attributes, and generating an optimum ID format that provides, for each item, a unique ID including the optimum codes mapped to the values of the item's optimum attributes.

Abstract: This disclosure relates to knowledge generation and implementation. A knowledge graph system comprises at least one processor, at least one database communicatively connected to the at least one processor, and a memory storing executable instructions. When executed, the instructions cause the at least one processor to aggregate data associated with a plurality of entities, the aggregated data reflecting one or more relationships between two or more of the plurality of entities. Attribute data identifying loan amounts, property values, and appraisal sources may be extracted from the aggregated data. A knowledge graph data structure may be generated having a plurality of subject notes corresponding to the extracted attribute information. Statistical distributions of attributes associated with one or more appraisal sources may be generated and an anomaly in a first statistical distribution may be detected based on a comparison of the first statistical distribution with a second statistical distribution.

Abstract: Systems and methods are disclosed for payment system that uses one-time use tokens for securely facilitating and controlling user purchases using company funds. A user (e.g., driver) may generate a request to make a purchase using a user application. The request may be sent to a server running a payment system that may generate a one-time use token for the purchase of an item (e.g., fuel). The one-time use token may have an expiration time and may only be redeemable at certain locations (e.g., fueling stations) that have been preapproved. The system may send the user application running on the user device the token for purchasing fuel and approved locations. The user may input the token to a controller at an approved location which may request that the server authenticate the token and approve the purchase. Once authenticated and approved, the controller may permit the purchase of the desired item.

Abstract: Search points in a search space may be projected onto images from cameras imaging different parts of the search space. Subimages, corresponding to the projected search points, may be selected and processed to determine if a target object has been detected. Based on subimages in which target objects are detected, as well as orientation data from cameras capturing images from which the subimages were selected, positions of the target objects in the search space may be determined.

Abstract: Systems and methods electronically generate sample dots, produce resources associated with the dots and estimate a resource for a target point from known resources of dots near the target point based on client side version of digital rules, cataloged data and coarse values previously received from the online service platform. A client receives cataloged data of a cataloged domain in which the cataloged data includes data representing a plurality of dots and a respective computed resource value for each of the dots and each dot of the plurality of dots represents a point in the cataloged domain. In response to confirming a target point is in the cataloged domain, the system discovers a closest one or more dots to the target point based on the cataloged data, estimates a statistic for a resource for the target point based on the respective computed resource values of the closest one or more dots, stores the estimated statistic in a memory, and produces the local estimate based on the estimated statistic.

Abstract: The disclosure relates to dynamically selecting a radius threshold for a device. The system identifies, based on sensor data detected by a sensor of the device, a location of the device. The system generates a feature representation for each of a plurality of features based on a query input into the device, the location of the device, and a plurality of entity locations corresponding to the query. The system accesses a data structure storing optimum radii correlated with a presence of the plurality of features and a corresponding performance metric based on network activity. The system determines the radius threshold based on the optimum radii and the plurality of features. The system identifies an eligible entity location having a distance from the device that is within the radius threshold. The system selects, for display on the device, a content item for the eligible entity location.

Abstract: A method, system and computer program product for identifying a geographic market share. Mobility data is acquired from applications running on mobile devices of users located within a geographic area. Mobility data is then used to infer shopping habits within the geographic area. Geo-demographic profiles are then created. The geographic market share is then determined using the created geo-demographic profiles.

Abstract: A user-to-entity communication channel is established for providing increased information regarding entities to the general population. Ambassadors for a entity are identified and selected based on location history of devices for which location reporting is authorized. The ambassadors may provide information regarding the entity to the public through the communication channel. Communications between the users and ambassadors may be reported to the entity owner for analysis by the entity owner.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that estimate the overlap between sets of data samples. In particular, in one or more embodiments, the disclosed systems utilize a sketch-based sampling routine and a flexible, accurate estimator to determine the overlap (e.g., the intersection) between sets of data samples. For example, in some implementations, the disclosed systems generate a sketch vector—such as a one permutation hashing vector—for each set of data samples. The disclosed systems further compare the sketch vectors to determine an equal bin similarity estimator, a lesser bin similarity estimator, and a greater bin similarity estimator. The disclosed systems utilize one or more of the determined similarity estimators in generating an overlap estimation for the sets of data samples.

Abstract: The disclosed embodiments generally relate to in-store customer traffic analysis systems and methods. The system includes at least one sensor positioned within a retail store, at least one display associated with the at least one sensor, one or more memory devices storing instructions and one or more hardware processors configured to execute the instructions to perform operations. The operations include receiving a sensor signal indicating that a user device is within a proximity to the at least one sensor in the retail store, extracting a user device identifier from the sensor signal, correlating the sensor signal to the at least one display, generating a foot traffic record associated with the user device identifier and the at least one display, based on the received sensor signal, and storing the generated foot traffic record.

Abstract: Notifying a user about relevant data for opportunities includes obtaining, from a database, opportunities, the opportunities representing a complex record structure in the database, in which each of the opportunities captures a number of fields of metadata, analyzing the metadata associated with the opportunities to identify patterns for the opportunities, identifying, based on the patterns, correlations for the opportunities, and notifying, based on the correlations, the user about relevant data for the opportunities.

Abstract: A home cost analysis server for executing a customized home search may include a processor programmed to define a user's budget constraint and a level of flexibility thereof, including a threshold difference from the budget constraint, retrieve a list of homes and public listing information associated with each home, and determine a monthly cost associated with each home. The processor may also isolate a first subset of homes having a monthly cost that satisfies the budget constraint, and a second subset of homes having a monthly cost within the threshold difference of the budget constraint, and display the first and second subsets along with respective first and second graphical indicators, as well as a third graphical indicator of a first adjustable expense data element, the third graphical indicator recommending reducing the first adjustable data element to move at least one first home from the second subset into the first subset.

Abstract: Methods, systems, and computer-readable media for generating a virtual based on location. The method acquires a request for a service based on a type of service and is associated with a user, the user's location, and user preferences. The method then acquires a set of service providers based on the type of service and the user's location who are filtered from a larger set of service providers using user preferences. The method in the next step acquires a machine learning model that is based on stored information associated with other users travel patterns and with service providers providing the service and the geographical information associated with the user's location. The method executed the identified machine learning model to aggregate a subset of service providers based on output from the machine learning model. The machine learning model is inputted the set of service providers, the user's location, the user's preferences, and the geographical information.

Abstract: Provided is a method for developing a geographic agnostic machine learning model. The method may include selecting transaction data associated with payment transactions conducted by a first plurality of users, wherein the transaction data includes first transaction data associated with payment transactions conducted by a first plurality of users in a first geographic area and second transaction data associated with payment transactions conducted by a second plurality of users in a second geographic area, formatting the first transaction data associated with payment transactions conducted by the first plurality of users in the first geographic area and the second transaction data associated with payment transactions conducted by the second plurality of users in the second geographic area to provide training data, and generating the geographic agnostic machine learning model using the training data. A system and computer program product are also disclosed.

Abstract: A system can comprise one or more processors; and one or more non-transitory computer-readable media storing computing instructions configured to run on the one more processors and perform a method for providing uniform tracking information, in a standard format, based on tracking information provided by multiple delivery driver networks, each having different hardware and software platforms. The method in this embodiment can comprise: receiving a user tracking request from a customer; receiving driver tracking information from the platform of the delivery driver network of the driver selected for the grocery delivery; converting the driver tracking information into uniform tracking information in a standard format; and providing the uniform tracking information, with an accurate estimated delivery time, to be rendered on a user interface of a user device of the customer.

Abstract: Disclosed herein are system, method, and computer program product embodiments for scrubbing anomalies from an expanding dataset. In an embodiment, a data sanitization system may determine whether data is anomalous to a set of data stored on a first blockchain. The data sanitization system may perform this determination using a first machine learning algorithm trained using the set of data. Upon determining that data is anomalous, the data sanitization system may publish the data in a second blockchain different from the first blockchain. The data sanitization system may monitor data of the second blockchain and apply a second machine learning algorithm to this data to identify a pattern of anomalous data. In response to identifying the pattern, the data sanitization system may publish the anomalous data of the second blockchain to the first blockchain.

Abstract: As described herein, a system, method, and computer program are provided for a financial-based recommendation engine. In use, financial-related data is gathered for a plurality of users from a plurality of sources. Additionally, the plurality of users are clustered into a plurality of cohorts, based on the financial-related data. Further, a plurality of user profiles are created for the plurality of cohorts, including for each cohort of the plurality of cohorts, creating a corresponding user profile based on the financial-related data for the users in the cohort. Still yet, each user profile of the plurality of user profiles is matched to one another and to one or more financial products. Moreover, the one or more financial products matched to each user profile of the plurality of user profiles are recommended to the users in the cohort that corresponds to the user profile.

Abstract: A method and system for generating Customer Decision Tree (CDT) for an entity in accordance with an attribute value (AV) based demand transfer estimation for a product category using machine learning, is disclosed. The method includes aggregating very high volume of data associated with a plurality of AVs of a product category at a plurality of aggregation levels. Further, generating a data matrix, which represents data is a structured format for machine learning, at a predefined aggregation level for the product category and generating a prediction model with the data matrix to determine predicted AV sales for each AV at the predefined aggregation level. Further, optimizing the trained prediction model. Thereafter, generate the CDT utilizing the optimized prediction model, a Demand Transfer (DT) estimator, a scenario generator and a hierarchy generator. Machine learning based DT is more accurate, effectively generating more accurate CDT tree.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting consumer behavior. In one embodiment a method is provided comprising determining a classification for a first consumer, wherein the classification is based on a measure of frequency of purchases by the first consumer; identifying one or more first attributes for the first consumer based on the determined classification, the one or more attributes being attributes selected for predicting the respective one or more metric associated with the first consumer; and determining, based on values for the one or more first attributes, a first prediction value that indicates a programmatically expected number of purchases by the first consumer.

Abstract: A candidate attribute combination of a first data set is identified, such that the candidate attribute combination meets a data type similarity criterion with respect to a collection of data types of sensitive information for which the first data set is to be analyzed. A collection of input features is generated for a machine learning model from the candidate attribute combination, including at least one feature indicative of a statistical relationship between the values of the candidate attribute combination and a second data set. An indication of a predicted probability of a presence of sensitive information in the first data set is obtained using the machine learning model.

Abstract: Techniques for improving a customer experience include presenting a selectable component to a user through a graphical user interface of a computing device that is communicably coupled to a server system through a network, the selectable component particularly associated with the user, the server system associated with a vendor and including a plurality of vendor product descriptions; receiving an activation of the selectable component from the user; based on the activation, identifying a plurality of profile data associated with the user; culling the plurality of vendor product descriptions to generate, based on the plurality of profile data, a subset of vendor product descriptions from the plurality of vendor product descriptions; and presenting the subset of vendor product descriptions to the user through the graphical user interface.

Abstract: A method of determining and optimizing the location of a new insurance agency is disclosed to increase market penetration of underrepresented markets. The method comprises the use of a scoring algorithm to rank various geographic regions or related zip codes. The scoring algorithm may be implemented by a location modeling system based on variables selected by a user. In addition, the various ranked geographic regions or related zip codes may be analyzed for proximity to natural or man made perils.

Abstract: A user-to-entity communication channel is established for providing increased information regarding entities to the general population. Ambassadors for a entity are identified and selected based on location history of devices for which location reporting is authorized. The ambassadors may provide information regarding the entity to the public through the communication channel. Communications between the users and ambassadors may be reported to the entity owner for analysis by the entity owner.

Abstract: The disclosure relates to an electronic apparatus and a method of controlling the same. The electronic apparatus includes: a communication interface; and a processor configured to receive log data of a plurality of devices connected to a network through the communication interface, acquire operation time information of each of the devices from the received log data, calculate similarity of the operation time between the plurality of devices based on the acquired operation time information, and determine a device group including two or more devices with relatively high calculated similarity among the plurality of devices.

Abstract: A system and method are disclosed for receiving a request to track data of an area of interest (AOI). The system receives a plurality of geolocation data records corresponding to a plurality of mobile devices. The system determines which mobile devices of the plurality of mobile devices have visited the AOI. For each mobile device determined to have visited the AOI, the system determines a path of the mobile device. The path including a prior location of the mobile device and a next location of the mobile device. The system transmits for display by a client device, a report including the path for each mobile device determined to have visited the AOI. The path indicating an identification of additional AOIs based on the prior location and the next location.

Abstract: A campaign server disclosed herein includes a graphic user interface (GUI) and underlying campaign server functionality that together enable a user to build, execute and analyze the results of a campaign with increased ease and accuracy. The campaign server advantageously includes a segmentation engine which operates to dynamically update target segment population data. The campaign server GUI enables the user to construct the campaign content and to launch the campaign towards members of the dynamically updated target segment. The campaign server further operates to collect and present campaign results to the user.

Abstract: Merchant transaction data for a group of merchants, category transaction data for the group of merchants, and merchant location data for the group of merchants may be obtained for a first time period and a second time period. The merchant transaction data may include a first set of parameters. The category transaction data may include a second set of parameters, and the location data may include a third set of parameters. Additional data during the first time period and the second time period may also be obtained. The additional data may include a fourth set of parameters. The merchant transaction data, the category transaction data, the merchant location data, and the additional data may be analyzed using a machine learning program to generate a set of key parameters to predict merchant performance. A first list of merchants that are predicted to grow may be generated using the key parameters.