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1. A system comprising:
a processor; a user interface, controlled by the processor, configured to:
display data on a display based on first and second scoring criteria for scoring a customer; andreceive the first and the second scoring criteria for scoring the customer, where one of the first and second scoring criteria comprises criterion selected from a group comprising of:
billed usage increase propensity criteria that identify characteristics of customers subject to a threshold increase in billed usage over a period of time, andcustomer loyalty criteria that identify a customer's propensity to stop using a product; a data mart, controlled by the processor, with customer data about individual customers in a customer population; an application program comprising processing instructions that when executed by the processor load the data mart with the customer data; a data mining tool, controlled by the processor, configured to:
analyze customer data;prepare first and second statistical models for scoring customers based on customer attributes quantified in said customer data;generate, using said first and second statistical models, first and second scores for the customers in the customer population by quantifying the customer attributes in said customer data using the first and second scoring criteria; andidentify a subset of customers from the customer population based on the first score satisfying the first scoring criterion and the second score satisfying the second scoring criterion, by
calculating for each of the customers:
a revenue trend line;revenue slope classes based on the revenue trend line; anda size of customer population within the revenue slope class;classifying each of the customers into the revenue slope classes based on the revenue trend line;defining a first plurality of distribution classes for grouping customers according to the customer scores generated by the first statistical model and define a second plurality of distribution classes for grouping customers according to the customer scores generated by the second statistical model,
wherein the first statistical model is configured to calculate a customer's propensity to generate increased revenue and score the customer based on said propensity to generate increased revenue, andwherein the second statistical model is configured to calculate a customer's propensity to respond to a marketing campaign and score the customer based on said propensity to respond;assigning customers to distribution classes within said first plurality of distribution classes based on their first score and assign customers to distribution classes within said second plurality of distribution classes based on their second score;filtering the customers assigned to the distribution classes of said first plurality of distribution classes by the distribution classes to which the customers are assigned in said second plurality of distribution classes;sizing, on a bubble chart, a bubble for each of the revenue slope classes to correspond to the size of the customer population within the revenue slope class as the percentage of overall customer population; andidentifying the subset of customers from the customer population by the size of the bubble for each revenue slope class on the bubble chart; a data manipulation module comprising processing instructions that when executed by the processor:
prepare the customer data stored in the data mart for data mining;transport said prepared data to the data mining tool; andstore the first and second scores for the customers in the data mart; and a reporting tool, comprising processing instructions that when executed by the processor: access the customer data stored in the data mart, including the first and second scores for the customers; and
report, by the user interface displaying, customer distributions based on said first and second customer scores,where the user interface is further configured to:receive commands from the user to manipulate; andcontrast the customer data based on the received first and second scoring criteria by displaying the customer distributions in the bubble chart for a customer segment distributed between the revenue slope classes and an average revenue of each of the revenue slope classes.
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