Abstract: Event management system and method. Events comprise physical and logical attributes. Tuples are created to identify a set of logical attributes. The tuples are arranged in hierarchized relations by creating binarized co-occurrence matrices, each co-occurrence matrix reflecting different time intervals and indicate occurrence of tuples in time windows of the time intervals. Tuple pairs are analyzed to determine probabilistic score related to co-occurrence, and tuple families are created from tuple pairs based on the probabilistic score. From tuple families, events are used to extract tuple instances including physical attributes, which are arranged as tuple-instance families using the corresponding tuple families as reference.

Abstract: Event management system and method. Events comprise physical and logical attributes. Tuples are created to identify a set of logical attributes. The tuples are arranged in hierarchized relations by creating binarized co-occurrence matrices, each co-occurrence matrix reflecting different time intervals and indicate occurrence of tuples in time windows of the time intervals. Tuple pairs are analyzed to determine probabilistic score related to co-occurrence, and tuple families are created from tuple pairs based on the probabilistic score. From tuple families, events are used to extract tuple instances including physical attributes, which are arranged as tuple-instance families using the corresponding tuple families as reference.

Abstract: The invention provides a method for managing a plurality of events, wherein each event comprises physical attributes and logical attributes by creating tuples with the events with the same logical attributes, providing a set of hierarchized relations between tuples, by means of a pipeline algorithm, wherein parent-child relations are provided between tuples, classifying the tuples in families, each family contains all the tuples related according to the parent-child relation provided by the pipeline algorithm, identify the parent tuple of each family, defined as the tuple which has at least one children and has no parent and present the parent tuples, together with the physical attributes of the events associated to each parent tuple.

Abstract: The invention provides a method for managing a plurality of events, wherein each event comprises physical attributes and logical attributes by creating tuples with the events with the same logical attributes, providing a set of hierarchized relations between tuples, by means of a pipeline algorithm, wherein parent-child relations are provided between tuples, classifying the tuples in families, each family contains all the tuples related according to the parent-child relation provided by the pipeline algorithm, identify the parent tuple of each family, defined as the tuple which has at least one children and has no parent and present the parent tuples, together with the physical attributes of the events associated to each parent tuple.

Abstract: One embodiment is a method that predicts a probability of users responding to a second email campaign based on data gathered from a first email campaign. Data from the first email campaign is applied to different models to identify attributes that are predictive of users who responded to the first email campaign. These attributes are used to predict a probability of users responding to the second email campaign.

Abstract: Analyzing plurality of participants of a computer-implemented social network. Data related to the plurality of participants is accessed. A query that includes a plurality of attributes associated with the plurality of participants is created. The plurality of attributes includes at least one social networking attribute and at least one business value attribute. The data related to the plurality of participants is evaluated based on the query and the plurality of attributes based on the data related to the plurality of participants is computed.

Abstract: A non-transitory, computer-readable storage medium contains software that, when executed by a processor, causes the processor to perform various operations such as to receive transactional data, industry-specific data, output requirements, and instructions to run specific data analysis models and tests. The software may also cause the processor to identify, based on the industry-specific data and the output requirements, a set of candidate models and to assess the performance of each candidate model based on the transactional data to select a final model and to perform the selected final model on the transactional data to generate processed data.

Abstract: Systems, methods, and computer-readable and executable instructions are provided for awarding a group-targeted promotion. Awarding a group-targeted promotion can include defining a first condition for a customer to join a list of promotion-eligible customers and defining a second condition for a number of groups of customers from the list to qualify for a group-targeted promotion. Awarding a group-targeted promotion can include awarding the group-targeted promotion to a group among the number of groups that complies with the second condition, wherein the method is performed by a computing device.

Abstract: A method to elicit a customer's product preference propensities among sub-groups, with each of the sub-groups having multiple members based on at least one common attribute, begins when customer action data is collected through the actions of the customer in the sub-group. The actions include the customer's propensities to purchase a product while within the sub-group and the customers' responses to displayed marketing messages or surveys while within the sub-group. The customer action data collected in the sub-group is analyzed to determine a customer's product preference propensities in the sub-group. The customer is targeted, when within the sub-group, with an electronic display that includes at least one product that corresponds to the customer's product preference propensities within the sub-group.

Abstract: A computer system for multi-channel campaign planning includes a digital processor, and computer readable instructions to plan and manage a multi-channel campaign. The instructions are embedded on a non-transitory, tangible memory device and executable by the processor. The instructions include a scenario outcome predicting module to predict an outcome for a scenario having a set of parameters defined for each channel of a phase of a plurality of iterative phases of the multi-channel campaign. The instructions include an adaptive learning module to generate an optimized learning component of the multi-channel campaign. The instructions include a decision optimization module to optimize the multi-channel campaign over the plurality of iterative phases. The instructions include a campaign execution module to execute the multi-channel campaign and collect outcome data. An initial phase of the plurality of phases is executed without prior outcome data for the scenario of the initial phase.

Abstract: Embodiments of the present invention are directed to methods and systems for developing customer retention and loyalty strategies. In one aspect, a method comprises calculating (202) likelihoods of next action taken by customers, based on customer attributes and associated attribute weights stored in a customer data base, and calculating (203) customer churn-risk scores, based on customer attributes that vary over time using the computing device. The methods also determines (207) what-if-scenarios for each customer based on churn-risk scores in order to identify the next-best-action to reduce probability of customer churn, and determines (208) when-to-act time thresholds for each customer based on churn-risk scores in order to identify when a non-high risk customer of churning will likely become a high-risk customer of churning at some later time.

Abstract: System, including method, apparatus, and computer-readable storage media, for evaluating probabilities of next actions by customers to permit selective customer targeting. Customer data (20) may be received (32). The customer data (20) may represent a plurality of actions (14) taken by customers (12). At least a portion of the customer data (20) may be transformed (34) according to action number into stratified data (80) including strata, with each of the strata representing actions for one or more action numbers (74). A conditional proportional hazard function (84) may be estimated (36) from a stratum of the stratified data (80). Likelihoods of a next action may be calculated (38) using the hazard functions. The likelihoods may be the likelihoods of a next action at one or more times by individual customers (12) whose latest action has an action number for which the stratum represents actions.

Abstract: One embodiment is a method that predicts a probability of users responding to a second email campaign based on data gathered from a first email campaign. Data from the first email campaign is applied to different models to identify attributes that are predictive of users who responded to the first email campaign. These attributes are used to predict a probability of users responding to the second email campaign.