Abstract: Embodiments relate to a method comprising: analyzing a historical data of an entity over a past period comprising a past transaction and a past revenue to develop a buyer persona of a customer in order to identify ideal prospects; selling a target revenue goal for the entity; generating revenue economics using conversion metrics from the target revenue goal as an input; generating a marketing budget working backward from the revenue economics; and generating a marketing plan for a future period that is based on the marketing budget; wherein the method comprises conducting a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to take an inventory of current situation of the entity and to identify target market segments by generating the opportunities that are in alignment with the strengths of the entity; and wherein the method is for generating a growth master plan for the future period.

Abstract: Methods and apparatus to determine reach with time dependent weights are disclosed. An example method disclosed herein includes determining a first subset of panelists exposed to media at a first time; determining a second subset of the panelists exposed to the media at a second time; applying a first plurality of weights to the first subset of panelists to generate first weighted exposures; applying a second plurality of weights to the second subset of the panelists to generate second weighted exposures; for a first panelist in the first and second subsets, determining a first cumulative weighted exposure by determining which of the first and second weighted exposures is largest for each of the first panelist; for a second panelist in one of the first and second subsets, determining a second cumulative weighted exposure by based on the weight applied to the second panelist; and determining a reach of the media for the first and second times by combining the first and second cumulative weighted exposures.

Abstract: In some embodiments, a computer implemented method for determining and generating an electronic recommendation and/or other outputs, such as observations and tasks, in which the method may include the steps of: receiving input from the user through a client device in which the input may include data for populating a key member or key player data record; identifying, via a computing device processor, a first rule corresponding to the key player data record; retrieving, via a computing device processor, a first observation in which the first observation is associated with the first rule; and displaying to the user, via a display screen of the client device, the first observation. In further embodiments of the method, an observation may be associated with a recommendation and the recommendation may include a pre-recorded video multimedia file specific for the observation.

Abstract: One or more portions of a text corpus indicative of operational risk from a set of risk assessment documents is identified. Contextual features from the one or more portions of the text corpus are determined by applying a natural language processing (NLP) algorithm on the one or more portions. Risk identifiers are extracted based on the determined contextual features. A risk assessment software is generated based on the extracted risk identifiers and an operational risk category.

Abstract: Systems, methods, devices, and non-transitory media of the various embodiments may provide for managing data quality anomalies in an enterprise system. Various embodiments may enable one or more network computing device to identify control points for monitoring data sets in the enterprise system, track data quality metrics using at least one threshold for each of the identified control points, determine whether a data quality anomaly is detected, and classify a review priority in response to determining that a data quality anomaly is detected.

Abstract: In an approach, a processor receives a request to purchase an item from a retailer, where the request includes a location. A processor receives a request to purchase an item from a retailer, where the request includes location information associated with a buyer. A processor identifies that the item is unavailable from the retailer. A processor determines a plurality of users predicted to have a surplus quantity of the item beyond each respective user's estimated needs at a time of the request. A processor sends an offer to the plurality of users to transfer the item in accordance with the request. A processor, responsive to receiving an acceptance of the offer, selects at least one accepting user, of the plurality of users, based on geographic proximity to the location information. A processor coordinates fulfillment of the request by the at least one user.

Abstract: Techniques are described for collecting customer data through a software application in order to optimize registration form to maximize registration success rates, based on a user engagement score related to user's interest, needs, registration behavior patterns, and source of user. In some examples, a method includes evaluating user data indicating interactions of a user with a software application. The method further includes determining a user engagement score for the user based at least in part on the user data. The method further includes determining, based at least in part on the user engagement score, a number and a type of one or more registration fields. The method further includes outputting the one or more registration fields to a display device based at least in part on a triggering event.

Abstract: This disclosure provides systems, methods and apparatuses for scheduling tasks in an electronic system. In some implementations, a dynamic scheduling system allows performance-based access to a task schedule. In distributing tasks to be completed, the dynamic scheduling system prioritizes resources that are more proficient at completing the tasks over resources that are less proficient. For example, resources that are more proficient may receive higher-priority access to the task schedule than resources that are less proficient. Each resource may be assigned a proficiency score based on quantitative or qualitative performance indicators associated with tasks previously completed by the resource. Each resource is dynamically provided access to the task schedule based on its proficiency score.

Abstract: A device may detect a first entry of a first user device of a first user into a merchant area. The device may monitor a movement of the first user device within the merchant area. The movement may include a transition from a shopping area of the merchant area to a checkout area of the merchant area. The device may detect a transaction between the first user and the merchant. The device may determine a shopping duration for the first user and a checkout duration for the first user. The device may detect a second entry of a second user device of a second user into the merchant area. The device may perform one or more actions based on detecting the second entry. The one or more actions may be performed selectively based on the shopping duration or the checkout duration of the first user.

Abstract: Systems and methods are disclosed for tracking consumer spend behavior to predict attrition. One method includes: receiving past transaction data related to a plurality of past payment transactions of a consumer; receiving environmental and/or behavioral data associated with each of the past payment transactions of the consumer; determining a spend behavior model of the consumer; subsequent to determining the spend behavior model of the consumer, receiving transaction data related to one or more current payment transactions of the consumer; receiving environmental and/or behavioral data associated with the one or more current payment transactions; determining, based on an analysis of the current transaction data and environmental and/or behavioral data associated with each of the current payment transactions, a current spend behavior of the consumer; and determining, based on a comparison of the current spend behavior with the spend behavior model, the likelihood of an attrition of the current spend behavior.

Abstract: Systems, methods, and computer program products for generating job value predictions by a trained model for a tenant include receiving live data associated with a tenant, where the live data includes information associated with a job to be performed by the tenant. A trained model associated with the tenant is retrieved, where the trained model is configured to generate a job value prediction for the job to be performed. The live data is preprocessed to obtain a set of features associated with the job, the set of features consumable by the trained model, and the trained model is applied to the set of features to generate the job value prediction for the job to be performed by the tenant.

Abstract: Oil field resources are allocated using machine learning and optimization. A well of a set of wells is identified. A user interface is presented to obtain a priority of a set of priorities. A schedule is generated for a set of resources for a job using the priority. A job schedule is presented that includes a set of jobs for the set of wells. The job schedule is generated using the set of priorities. An update to the priority is obtained. An updated job schedule is presented based on the update to the priority.

Abstract: Systems and techniques may be used for providing information related to an underperforming product. An example technique may include determining, for products in a particular zone of a website, an underperforming product based on pageviews or interaction metrics of users visiting the website. The technique may include identifying a product type of the underperforming product, and outputting a recommendation for display. The recommendation may include at least one replacement product of the product type, other than the underperforming product, to replace the underperforming product in the particular zone.

Abstract: Systems and methods are disclosed for attributing payment vehicle purchase events to previous activity of a purchaser. One method comprises: receiving purchase information associated with a purchase event by a purchaser; comparing the received purchase information to a profile data store to identify a purchaser profile associated with the purchaser, the profile data store comprising a plurality of purchaser profiles, wherein each purchaser profile comprises payment vehicle data and a tracking element; determining the tracking element of the identified purchaser profile; identifying one or more activities of the purchaser using the tracking element of the identified purchaser profile; for each of the identified one or more activities, assessing the strength of attributing the purchase event to the activity, using environmental and/or behavioral data associated with the tracking element; and determining whether to attribute the purchase event to one or more of the identified activities based on the assessment.

Abstract: A method for price optimization for a product or service at a store using machine learning is disclosed. The method includes determining a price through a reaction model, positioning model, and a forecast model. The reaction model includes determining the probabilities of a competitors' pricing reaction due to the store's price changes. The positioning model includes determining conditional probabilities for attaining an objective and/or sub-objective based on store and competitor's data. The conditional probabilities are used for generating a price proposal for achieving the objective and/or sub-objective. The forecast model provides a forecast for factors such as volume sale using machine learning.

Abstract: An example operation may include one or more of storing sensor data on a blockchain, the sensor data sensed of an object which is cooperatively managed by a plurality of providers, detecting an event with respect to the object, determining, via chaincode deployed on a blockchain peer, one or more providers from among the plurality of providers that are liable for the event based on the sensor data stored on the blockchain, and outputting a notification to a computer system of the blockchain identifying the determined one or more providers.

Abstract: Aspects described herein may relate to methods, systems, and apparatuses that provide new capabilities for recommending purchases to a user based on the user's past purchasing history and the purchase history of others. A new descriptor referred to as “purchase embeddings” is disclosed, which are data records in a new multi-dimensional space for describing and tracking purchases of goods and services.

Abstract: Methods and apparatus to estimate cardinality across multiple datasets represented using Bloom filter arrays are disclosed. Disclosed examples include processor circuitry to execute and/or instantiate instructions to determine an inclusion-exclusion expression that defines an audience size for a user group of interest. Terms in the inclusion-exclusion expression corresponding to either a first cardinality of a first one of at least three Bloom filter arrays or a second cardinality of a union of two or more of the Bloom filter arrays. Different ones of the Bloom filter arrays representative of different sets of users who accessed media. The at least one processor further to estimate, based on the inclusion-exclusion expression, the audience size of the user group of interest.

Abstract: A device, system and method for mapping and filtering project data, such as construction project criteria, which may have the steps of selecting a map view option from of list of application features; launching a map view in response to selecting the map view; selecting at least one database filter; applying the at least one selected database filter to a system database to generate a list of selected filtered records; and generating a map showing the geographic locations of the selected filtered records. The at least one filter may be selected from at least one of the group of personnel, equipment, project sites and/or offices. The at least one filter can be a personnel filter, filtered by at least one of trade, skill level and/or active status.

Abstract: A method and system are provided in which predictions are generated, using one or more machine learning-based prediction models, for one or more process parameters associated with a running process. Explanation-oriented data elements are generated that correspond to the generated predictions and include confidence indicators associated with the generated predictions. The explanation-oriented data elements are presented in one or more dashboards of a visualization platform. The explanation-oriented data elements are representative of an explanation framework for explaining the predicted business process parameters generated by a machine learning-based prediction model and in a manner so that a user can understand and trust the basis for the predictions to facilitate effective and appropriate intervention in a running process.