Abstract: A method and system for cloud based service provider performance evaluation are disclosed. The method may include extracting service record data and ticket status change record data from the service ticket data and aggregating the service record data and the ticket status change record data. The method may include calculating ticket level performance metric data based on the aggregated record data and generating ticket level performance scores based on the ticket level performance metric data. The method may further include generating service level performance scores based on the service level performance metric data and generating service feedback performance scores based on the service feedback metric data. The method may further include merging the ticket level performance scores, the service level performance scores, and the service feedback performance scores to generate performance vectors and evaluating overall performances of the service providers based on the set of performance vectors.

Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for presenting cross-sell products. The program and method provide for receiving indication of a user selection to display cross sell data for at least one product made available for purchase by a website; determining a set of cross-sell products for the at least one product, each cross-sell product in the set of cross-sell products having been previously sold together with the at least one product in association with website; determining, for each cross-sell product in the set of cross-sell products, a set of metrics that relate the cross-sell product to the at least one product; and causing, for each cross-sell product in the set of cross-sell products, display of the respective set of metrics that relate the cross-sell product to the at least one product.

Abstract: The present invention relates to a system and method for assessing performance of a software release in a production environment. The attempt is to measure impact of delivered features and correlate with cost incurred in building those features to calculate a return on investment made. The disclosure, thus, provides for fetching relevant details from plurality of tools or data sources, correlating the fetched information and linking it with milestone data. This milestone data is labeled with a milestone id to enable querying of data sources and determine impact delivered. The determined impact is linked with calculated cost identifier to finally assess the release performance.

Abstract: An example apparatus includes at least one memory, instructions, and at least one processor to execute the instructions to generate electronic mobile measurement data based on network communications received from first client devices, select attributes associated with the electronic mobile measurement data to include in a model, generate the model based on the attributes and a first portion of the electronic mobile measurement data, determine a percentage of a second portion of the electronic mobile measurement data that the model correctly associates with corresponding first users of the first client devices, and when the percentage satisfies a threshold determine: (a) when a second user operating a second client device is a primary user, and (b) when the user operating the second client device is a third user, and associate demographic information of the second user with the electronic mobile measurement data to reduce a misattribution error.

Abstract: The disclosure generally pertains to systems and methods to predict a vehicle preference of a customer of a rental vehicle agency. An example method to do so involves a computer executing a prediction procedure to obtain and evaluate information associated with a customer. The information can include vehicle ownership history and/or monitoring data obtained from vehicles used by the customer (a personal vehicle, a taxi, and/or a ride share service vehicle, for example). The information may be evaluated by the computer to determine a personal profile of the customer. The personal profile can include items such as physical attributes of the customer, family size, driving characteristics, and/or past vehicle ownership. The personal profile of the customer may then be used by the computer to predict a type of vehicle preferred by the customer and to select, from a vehicle fleet, a vehicle that matches the preferred type of vehicle.

Abstract: In some embodiments, systems and methods are provided herein useful to assess or determine available cargo capacity for multiple commercial product delivery vehicles (“delivery vehicles”). In some embodiments, systems are provided to assess delivery vehicles and may include multiple electronic user devices each having a delivery agent interface operating thereon and an association with one or more delivery agents. At least one control circuit is communicatively coupled to the electronic user devices. By one approach, the control circuit may estimate delivery requirements for purchased orders, which includes volume requirements and geometric configurations of purchased orders.

Abstract: A plan model system provides interactive graphical user interfaces allowing users to view and navigate to multiple alternative views of measures modeled in one or more planning models. A modeled event is provided by a user relating to a measure of a particular plan model modeling outcomes of a particular business domain of a business organization. An effect of the event on values of one or more measures of the plan model is determined. A graphical representation is presented in the graphical user interface illustrating the effect.

Abstract: The present disclosure includes an object to acquire information indicating a tendency on a visit spot for each vehicle sort. The present disclosure provides an information processing apparatus including a controller configured to execute: acquiring first information about a plurality of vehicles, the first information including vehicle information about a vehicle, the first information not being capable of identifying an individual user or an individual vehicle but reflecting at least part of attributes or preferences of a user associated with the vehicle, and spot information about a visit spot of the vehicle; and acquiring tendency information indicating a tendency on the visit spot of the vehicle for each vehicle classification based on the vehicle information, from the first information about the plurality of vehicles.

Abstract: Methods, systems and computer program products for shared content management systems that provide performance analytics pertaining to a project. Embodiments include establishing one or more network communication links between a content management system that manages a plurality of shared content objects and a plurality of applications that cause modifications to the shared content objects in accordance with workflows of the project. Iteraction events that correspond to modifications over the shared content objects are recorded such that interaction events associated with the plurality of applications are selected based at least in part on attributes associated with the interaction events. Relationships between the recorded interaction events such as time durations between certain of the interaction events are calculated. Project performance measurements are generated based on the calculations and/or based on other relationships between the interaction events.

Abstract: Embodiments are directed to executing one or more campaigns. Initialization identifiers may be provided to campaign agents associated with campaign owners. The campaign agents may be associated with campaigns based on the initialization identifiers. Resource collection requests may be provided to sources based on source information provided by the campaign agents. Metrics may be provided based on monitoring actions of the sources. Outlier characteristics associated with the resource collection requests may be identified based on a comparison of the metrics to other campaigns. The resource collection requests may be modified based on predicted metrics associated with the outlier characteristics. The modified resource collection requests may be provided to the sources associated with the outlier characteristics. Resources provided from the sources may be distributed to the campaign owners.

Abstract: Embodiments herein are directed to a vehicle floating fleet vehicle system for determining a location of a user and one or more car-sharing vehicles. The system includes a server computing device operated by a floating fleet service provider. The server computing device includes at least one processor; a memory communicatively coupled to the at least one processor; and machine readable instructions stored in the memory. The machine readable instructions cause the vehicle floating fleet system to perform at least the following when executed by the at least one processor receive a request for the one or more car-sharing vehicles, determine the one or more car-sharing vehicles in one or more nearby gated parking facilities, send information about the one or more car-sharing vehicles to the user, and receive a selected car-sharing vehicle in a nearby gated parking facility of the one or more nearby gated parking facilities from the user.

Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises predicting details associated with collected data of an activity by generating a simulated activity based on a result associated with a plurality of attributes within the collected data associated with the activity; simulating the activity within a virtual reality environment by generating virtual items for assistance with a performance of the simulated activity within the virtual reality environment; in response to receiving user feedback for each generated virtual item based on the simulated activity, generating a need-based inventory from user input based on an analysis of the received user feedback for each respective item associated with the simulated activity within the virtual environment; and automatically obtaining each respective item within the generated need-based inventory.

Abstract: An information processing apparatus comprising a traffic line information generation unit configured to generate, based on information provided from an image capturing apparatus arranged in a store, traffic line information in the store of a customer who visits the store, an acquisition unit configured to acquire purchased product information associated with a product purchased by the customer in the store, a purchase information generation unit configured to generate purchase information based on the purchased product information by associating the traffic line information and the purchased product with the customer, and a display information generation unit configured to generate, based on the purchase information, display information for displaying information about one of a customer and a product in association with a time taken to select the purchased product and a selection order of the purchased product.

Abstract: An unstructured data input is accessed that includes an electronic communication. Content of the unstructured data is parsed to determine one or more terms in the unstructured data input. It is determined that one or more particular elements defined in a structured business data model correspond to the terms. Tags are assigned to the unstructured data based on the terms corresponding to the one or more particular elements, where the tags define an association between the unstructured data and the structured data model.

Abstract: A communication by a particular user within a digital collaboration is determined as related to a particular business entity. A relationship between one or more additional business entities and the particular business entity is identified from one or more business models. The communication is associated with the additional business entities based on the relationship.

Abstract: Described herein are various technologies pertaining to generating and/or updating a schedule such that the schedule includes a recurring appointment. The schedule is generated and/or updated based upon inputs received at a GUI that includes a matrix of checkboxes, such that the schedule is able to be constructed to include a recurring appointment for an Nth occurrence of a selected day in each month.

Abstract: Methods, apparatus, and processor-readable storage media for determining user retention values using machine learning and heuristic techniques are provided herein. An example computer-implemented method includes processing multiple forms of input data pertaining to interactions between a user and an enterprise; generating one or more user sentiment values from the processed input data by applying machine learning techniques to the processed input data; determining a user-specific estimate for the enterprise retaining the user, wherein determining the user-specific estimate comprises combining the one or more sentiment values with one or more storage system heuristics-based values derived from enterprise-related data; and outputting the user-specific estimate to at least one entity within the enterprise for use in connection with user-support actions.

Abstract: A method may include receiving a request to save the exception to the first rule; in response to the request, updating the user interface, wherein: the first portion is updated to include a parameter of a second rule to exclude a subset of meetings from the ECDS; the second portion is updated to include an impact metric that indicates a percentage of meetings excluded from the ECDS based on an application of the second rule to the ECDS; and the third portion is updated to include selectable elements for creating an exception to the second rule; and a fourth portion of the user interface includes an indication of a number of meetings excluded from the ECDS based on application of the first rule with any exceptions to the first rule.

Abstract: Embodiments for implementing intelligent customer journey prediction and customer segmentation of a processor in a computing environment. A response outcome of a customer journey for a user may be predicted according to an assigned score based on one or more discriminatory sequence patterns identified between one or more groups of customers using one or more machine learning operations.

Abstract: Embodiments of this application provide a seed group spreading method performed at a server. The method includes the following steps: obtaining a positive sample set; obtaining a negative sample set; concatenating each positive sample in the positive sample set with a corresponding positive sample feature to form a positive sample feature vector, and concatenating each negative sample in the negative sample set with a corresponding negative sample feature to form a negative sample feature vector; obtaining a target sample feature set from the positive sample feature vector and the negative sample feature vector according to at least two of a target group index, an information gain, and a logistic regression model; and sending the target sample feature set to a decision end, receiving feedback information of the decision end, and determining, according to the feedback information, whether to spread the seed group.