Abstract: A method for generating business targets includes accessing data (300) corresponding to a number of customers. The data includes variables (310-330) associated with each of the customers and an observed value for each of the customers. The observed value for a customer may represent revenue associated with that particular customer. The method also includes identifying a neighborhood that includes a first customer and a number of the other customers. The method further includes calculating a target for each of the customers in the neighborhood, where the target may represent the potential revenue from each of the customers.

Abstract: Sources of operational problems in business transactions often show themselves in relatively small pockets of data, which are called trouble hot spots. Identifying these hot spots from internal company transaction data is generally a fundamental step in the problem's resolution, but this analysis process is greatly complicated by huge numbers of transactions and large numbers of transaction variables to analyze. A suite of practical modifications are provided to data mining techniques and logistic regressions to tailor them for finding trouble hot spots. This approach thus allows the use of efficient automated data mining tools to quickly screen large numbers of candidate variables for their ability to characterize hot spots. One application is the screening of variables which distinguish a suspected hot spot from a reference set.

Abstract: Disclosed are methods and systems that can develop productivity scores for quantitatively comparing employees with somewhat different job task assignments. Evaluations of modeled task performance scores can be obtained and analysis of the evaluations, e.g., a regression analysis, can be performed to obtain performance parameters. The performance parameters can be applied to employee task performance scores over a time frame to obtain productivity scores for the employees. Statistical control charts based on the productivity scores can be used to identify outstanding and/or poorly performing employees.

Abstract: Sources of operational problems in business transactions often show themselves in relatively small pockets of data, which are called trouble hot spots. Identifying these hot spots from internal company transaction data is generally a fundamental step in the problem's resolution, but this analysis process is greatly complicated by huge numbers of transactions and large numbers of transaction variables to analyze. A suite of practical modifications are provided to data mining techniques and logistic regressions to tailor them for finding trouble hot spots. This approach thus allows the use of efficient automated data mining tools to quickly screen large numbers of candidate variables for their ability to characterize hot spots. One application is the screening of variables which distinguish a suspected hot spot from a reference set.

Abstract: Sources of operational problems in business transactions often show themselves in relatively small pockets of data, which are called trouble hot spots. Identifying these hot spots from internal company transaction data is generally a fundamental step in the problem's resolution, but this analysis process is greatly complicated by huge numbers of transactions and large numbers of transaction variables to analyze. A suite of practical modifications are provided to data mining techniques and logistic regressions to tailor them for finding trouble hot spots. This approach thus allows the use of efficient automated data mining tools to quickly screen large numbers of candidate variables for their ability to characterize hot spots. One application is the screening of variables which distinguish a suspected hot spot from a reference set.

Abstract: A method for generating business targets includes accessing data (300) corresponding to a number of customers. The data includes variables (310-330) associated with each of the customers and an observed value for each of the customers. The observed value for a customer may represent revenue associated with that particular customer. The method also includes identifying a neighborhood that includes a first customer and a number of the other customers. The method further includes calculating a target for each of the customers in the neighborhood, where the target may represent the potential revenue from each of the customers.

Abstract: A method for generating business targets includes accessing data (300) corresponding to a number of customers. The data includes variables (310-330) associated with each of the customers and an observed value for each of the customers. The observed value for a customer may represent revenue associated with that particular customer. The method also includes identifying a neighborhood that includes a first customer and a number of the other customers. The method further includes calculating a target for each of the customers in the neighborhood, where the target may represent the potential revenue from each of the customers.

Abstract: Sources of operational problems in business transactions often show themselves in relatively small pockets of data, which are called trouble hot spots. Identifying these hot spots from internal company transaction data is generally a fundamental step in the problem's resolution, but this analysis process is greatly complicated by huge numbers of transactions and large numbers of transaction variables to analyze. A suite of practical modifications are provided to data mining techniques and logistic regressions to tailor them for finding trouble hot spots. This approach thus allows the use of efficient automated data mining tools to quickly screen large numbers of candidate variables for their ability to characterize hot spots. One application is the screening of variables which distinguish a suspected hot spot from a reference set.

Abstract: Sources of operational problems in business transactions often show themselves in relatively small pockets of data, which are called trouble hot spots. Identifying these hot spots from internal company transaction data is generally a fundamental step in the problem's resolution, but this analysis process is greatly complicated by huge numbers of transactions and large numbers of transaction variables to analyze. A suite of practical modifications are provided to data mining techniques and logistic regressions to tailor them for finding trouble hot spots. This approach thus allows the use of efficient automated data mining tools to quickly screen large numbers of candidate variables for their ability to characterize hot spots. One application is the screening of variables which distinguish a suspected hot spot from a reference set.

Abstract: The disclosed methods and systems prioritize network devices and components that more likely result in a network outage by evaluating their current and historical outage information relative to a dynamically-generated, statistical threshold based on such outage information. The prioritized list of network devices can be organized by, for example, quantity of outages, number of outage minutes, and/or number of customer-outage minutes so as to provide diagnosis sequences by type of network device, network location, type of component within the network device, and/or by operation during particular time periods/events.

Abstract: A method and apparatus for training a neural network to compute hazard functions for customers and analyzing hazard functions, both for an individual customer, and for set of customers to focus marketing techniques. The hazard function represents the likelihood of churn for a particular customer. The gain in lifetime value is also calculated for each customer which incorporates the present value of the customer with the future value of the customer if a new contract is entered. The overall shape of the hazard function, combined with the gain in lifetime value, specifies what marketing techniques are to be applied together with what additional incentives are to be offered to the customer in order prevent churn.

Abstract: A computer-executable method for diagnosing trouble causes in telephone cable segments when trouble data are available only for entire cables is presented. This method diagnoses problems in the components of processes when the component location of process problems is not known. It gives a statistical relationship between component attributes and process problem rates, to facilitate assessment and improvement of policies about the process. This method may be used for diagnosing systematic problems in segments of telephone cable connections of given central offices.

Abstract: A computer-executable method for diagnosing trouble causes in telephone cable segments when trouble data are available only for entire cables is presented. This method diagnoses problems in the components of processes when the component location of process problems is not known. It gives a statistical relationship between component attributes and process problem rates, to facilitate assessment and improvement of policies about the process. This method may be used for diagnosing systematic problems in segments of telephone cable connections of given central offices.

Abstract: A computer-executable method for diagnosing trouble causes in telephone cable segments when trouble data are available only for entire cables is presented. This method diagnoses problems in the components of processes when the component location of process problems is not known. It gives a statistical relationship between component attributes and process problem rates, to facilitate assessment and improvement of policies about the process. This method may be used for diagnosing systematic problems in segments of telephone cable connections of given central offices.