Abstract: Subject innovations are directed towards a churn model using dynamic state-space modeling to determine churn risks for each active subscriber of a service provider having exhibited a precise sequence of behaviors. The churn model identifies complex behavioral patterns that are consistent with those of subscribers who have churned in a defined past, allowing for a personalized determination of churn risk. The churn model may also use static contextual data to assist in refinement of the churn model through identification of subscriber segments. A churn index is produced that may be used by an automated contextual marketing model to refine decision making for selectively marketing to a subscriber based, in part, on that individual subscriber's churn risk.

Abstract: Subject innovations are directed towards a platform architecture, system, and special purpose software and hardware that employs a machine learning approach using transactional behavioral events and data about customers to allow a telecommunications marketer to express a set of configurable business rules and constraints, and within a dynamic optimization approach, derive solutions or marketing approaches to optimize delayed key performance indicators. In one embodiment, this is obtained using marketing experiments that seek to learn and influence long term behaviors of customers.

Abstract: Techniques train a tree to identify offers to send to a particular customer. Messages that include offers and having attributes are sent to a target user group. Feature measure results from the messages on the target user group, is used with feature measure results for a control user group, to train the tree with branch splits being identified based on maximizing an information gain from the feature measure results for a message/user attribute, where each node within the tree includes target and control distributions for the feature measure. The tree is traversed for a given marketing message/user, drawing randomly from feature measure distributions in the tree to determine whether to send the given marketing message to the user. By drawing randomly from the feature measure distributions, exploration and exploitation of various messages may be performed to minimize ignoring of messages that may have an information gain for particular customers.

Abstract: Techniques disclosed herein employ entity-activity data expressed in a discrete distribution (histogram) form having one or many dimensions to dynamically classify the entity's usage and/or behavior patterns, where groupings or segmentations of different entities that exhibit similar usage patterns are identified using various approaches, including dimensionality reduction, and/or clustering procedures. A consensus or ensemble clustering may be generated that represents a clustering of clusters, based on subclusterings themselves, and/or any combination of subclusters with entity-activity data to selectively execute a market offering campaign. In one embodiment, the resulting ensemble clusterings enable selective directing of targeted offerings to a telecommunication provider's customers.

Abstract: A computing system facilitating electronic bill presentment and payment features for authorized users via a user interface is introduced including an analysis engine. The analysis engine, in response to user interaction with the user interface, selectively performs user-defined analysis on at least a subset of retrieved billing information associated with one or more billers. The analysis engine further generates one or more user-defined report(s) based, at least in part, on the user-defined analysis.

Abstract: Techniques are disclosed that automatically identify and order the most differentiated clusters from a given collection of clusters within a dataset. A measure of dissimilarity is computed for each cluster from a defined reference cluster, and the clusters are ordered according to the chosen dissimilarity. At least N clusters are selected as the most differentiated clusters relative to the defined reference. Within each cluster, the top-M most distinguishing cluster attributes can be automatically identified by an analogous process that computes the dissimilarity of each cluster attribute to its corresponding attribute in the reference cluster, and orders the attributes by dissimilarity. This then allows for automatic surfacing of what it is about a cluster that differentiates its members relative to the population as a whole, and to provide insight on what action or treatment might be made to address that specific segment of the underlying population.

Abstract: A computing system facilitating electronic bill presentment and payment features for authorized users via a user interface is introduced including an analysis engine. The analysis engine, in response to user interaction with the user interface, selectively performs user-defined analysis on at least a subset of retrieved billing information associated with one or more billers. The analysis engine further generates one or more user-defined report(s) based, at least in part, on the user-defined analysis.

Abstract: Techniques are disclosed that leverage time series techniques to express entity-activity data in a longitudinal temporal form, which may then be employed to dynamically classify the entity's behavior. In some embodiments, groupings or segmentations of different entities that exhibit similar profiles of longitudinal temporal form are identified using various techniques, including frequency-domain analysis, and/or unsupervised model-based clustering. The clustering of entities enables directing of offerings to, for example, a telecommunication's customer based on characteristics of the cluster.

Abstract: A computing system facilitating electronic bill presentment and payment features for authorized users via a user interface is introduced including an analysis engine. The analysis engine, in response to user interaction with the user interface, selectively performs user-defined analysis on at least a subset of retrieved billing information associated with one or more billers. The analysis engine further generates one or more user-defined report(s) based, at least in part, on the user-defined analysis.

Abstract: A computing system facilitating electronic bill presentment and payment features for authorized users via a user interface is introduced including an analysis engine. The analysis engine, in response to user interaction with the user interface, selectively performs user-defined analysis on at least a subset of retrieved billing information associated with one or more billers. The analysis engine further generates one or more user-defined report(s) based, at least in part, on the user-defined analysis.

Abstract: Embodiments are directed towards enabling telecommunications networked services providers to maximize sales of products, services, content, and applications to their customers by detecting contextual occasions in which to present a customer an offering of a product, service, content, or application. The occasion may be defined for the customer within their cultural environment by ethnographic research and anthropological modeling. The occurrence of a contextual occasion may be realized for a customer, in part, based on predictive and behavioral analytics of demographic, behavioral, and/or psychographic customer attributes. The customer's activities, location, time, social network activity, and events occurring in the world are also monitored to identify or predict an occurrence of the targeted occasion in which to present a contextually relevant product, service, content, and/or application offering to the customer.

Abstract: A computing system facilitating electronic bill presentment and payment features for authorized users via a user interface is introduced including an analysis engine. The analysis engine, in response to user interaction with the user interface, selectively performs user-defined analysis on at least a subset of retrieved billing information associated with one or more billers. The analysis engine further generates one or more user-defined report(s) based, at least in part, on the user-defined analysis.

Abstract: A system and method for assisting a customer in choosing among commodities based on a business profile of the customer that includes asking the customer for a definition of its business; searching a decision rules database and applying decision rules to the business definition; creating a set of business requirement questions based upon the applied decision rules database; answering the set of business requirement questions by the customer; implementing a business rule filter to the answers of the set of business requirement questions; recommending to the customer commodity types based upon the business rule implementation; and optimizing the recommended commodity types to find the best value commodity, which can be a product or service.

Abstract: A system and method for assisting a customer in choosing a combination of commodities based on preferences of the customer. A combination is a set of related commodities, wherein bundling discounts may be applied to particular bundles of related commodities. Combination options are created by optimizing the categories within the combination in parallel, and then selecting the best value options from each category into a grouping. The effective cost of a grouping is calculated as a total of the effective costs of each option within the grouping. The effective costs consider weighted values of performance features in addition to the actual cost of a commodity. The groupings are ranked and presented to the user, so that the user may select a grouping as combination of commodities for purchase.

Abstract: Embodiments are directed towards enabling product and/or service providers to maximize sales of products, services, and content to their existing customers. In one embodiment, a process, apparatus, and system are directed towards optimizing a selection of offers for any customer touch-point to ensure the provider delivers the best offer to the right customer at the most appropriate time. Offers are optimized not only according to a customer's interests and preferences but also according to revenue and profitability potential using predictive analytics.