Abstract: Technologies for computationally aligning aggregated data include obtaining a set of member records from an online system and obtaining a set of job records from a management system. An aggregate alignment score is calculated by comparing member skills data extracted from the set of member records to job skills data extracted from the set of job records. The aggregate alignment score is a measure of a mismatch between the member skills data for the set of member records and the job skills data for the set of job records. Based on the aggregate alignment score, a skill is identified that is actionable to address the mismatch between the member skills data and the job skills data. Actionable output is generated that replaces, modifies, or supplements previously-generated course recommendation data that does not map to the identified skill with new course recommendation data that maps to the identified skill.

Abstract: Technologies for calculating a score based on a computational alignment of heterogenous datasets are provided.

Abstract: This disclosure relates to systems and methods for expanding a social network. A method includes generating respective social graphs for two or more entities, calculating one or more social metrics for each of the two or more entities using the respective social graphs, retrieving one or more performance metrics for each of the two or more entities, correlating the social metrics and the performance metrics to generate one or more associations between the social metrics and the performance metrics, and transmitting a recommendation for a requesting entity.

Abstract: In some embodiments, the disclosed subject matter involves metrics collection and analysis to quantify customer engagement with an objective score to measure customer engagement in an c-learning system. Embodiments may generate a single engagement score as a one-number summary of e-learning product usage by a customer The one number summary may be generated as a normalized weighted sum of individual metrics scores. An embodiment may use activation, login, view, or other usage rates as part of the weighted sum. The weighted sum for a product customer may be normalized as compared to other customers for the same or similar product, where the other customer may be similar in size and/or industry to the target customer. An embodiment may use metrics relating to skills attained and applied as a skill assessment score. Other embodiments are described and claimed.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of features associated with a customer, wherein the set of features includes profile data from an online professional network and one or more standardized features related to a role of the customer. Next, the system applies a set of whitelists and a set of blacklists to the features to identify a market segment for the customer. The system then uses the market segment to generate output for use in targeting the customer with an educational technology product.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of features associated with a customer, wherein the set of features includes profile data from an online professional network. Next, the system uses a statistical model and the features to predict a likelihood of acquiring the customer for an educational technology product. The system then uses the likelihood to generate output for use in targeting the customer with the educational technology product.

Abstract: The disclosed embodiments provide a system that processes data. During operation, the system obtains a set of features for a customer of an educational technology product. Next, the system uses an account type of the customer to select a statistical model from a set of statistical models for evaluating potential customer spending with the educational technology product. The system then uses the statistical model and the features to predict a potential spending of the customer with the educational technology product. Finally, the system outputs the potential spending for use in managing sales activity with the customer.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of features associated with a sales lead for an educational technology product, wherein the set of features includes profile data from an online professional network. Next, the system uses the set of features to generate a set of quality indicators for the sales lead. The system then aggregates the quality indicators into a lead score representing a quality of the sales lead. Finally, the system outputs the lead score for use in managing sales activity with the sales lead.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of overall scores representing predicted purchase behaviors of a set of customers with an educational technology product. Next, the system displays a graphical user interface (GUI) comprising a customer prioritization chart for the educational technology product. The system then displays representations of the overall scores in the customer prioritization chart. Finally, the system displays, in the GUI, the set of overall scores and a breakdown of the overall scores into a set of sub-scores that characterize different components of the overall scores.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of features for a customer of an educational technology product. Next, the system uses the set of features to calculate an overall score representing a predicted purchase behavior of the customer with the educational technology product. The system then uses multiple subsets of the features to calculate a set of sub-scores that characterize different components of the overall score. Finally, the system outputs the overall score and the sub-scores for use in managing sales activity with the customer.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a first set of rules for assigning a first set of sales professionals to a first set of accounts, wherein the first set of rules comprises a representative load rule, a matching rule, and a balancing rule. Next, the system applies an optimization technique to the first set of rules and a first set of parameters associated with the first set of sales professionals and the first set of accounts to produce a first set of assignments of the first set of sales professionals to the first set of accounts. The system then outputs the first set of assignments for using in managing sales activity of the first set of sales professionals.

Abstract: This disclosure relates to systems and methods for expanding a social network. A method includes generating respective social graphs for two or more entities, calculating one or more social metrics for each of the two or more entities using the respective social graphs, retrieving one or more performance metrics for each of the two or more entities, correlating the social metrics and the performance metrics to generate one or more associations between the social metrics and the performance metrics, and transmitting a recommendation for a requesting entity.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of features for a customer of a product. Next, the system uses the set of features to identify a likelihood of purchasing the product through a first channel by the customer and estimate a first customer lifetime value (CLV) for the customer through the first channel and a second CLV for the customer through a second channel. The system then selects an acquisition channel for the customer from the first and second channels based on the likelihood and the first and second CLVs. Finally, the system outputs a recommendation of the selected acquisition channel for use in marketing the product to the customer.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system inputs a set of features for a customer of a product into a first statistical model, wherein the set of features comprises a company segment of the customer. Next, the system uses the first statistical model to predict a churn risk of the customer. When the churn risk exceeds a first threshold for the company segment, the system outputs a notification of a high churn risk level for the customer.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of data for a set of customers of a product, wherein the set of data comprises a set of churn risk levels for the customer. Next, the system uses the set of data to display a graphical user interface (GUI) comprising a chart of renewal opportunities with the set of customers, for the product, over an upcoming time interval. The system then displays, in the GUI, a representation of a churn risk level for each customer in the set of customers with a renewal opportunity in the chart.