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System and method for managing routing of customer calls to agents

專利號
US11176461B1
公開日期
2021-11-16
申請人
MASSACHUSETTS MUTUAL LIFE INSURANCE COMPANY(US MA Springfield)
發(fā)明人
Sears Merritt
IPC分類
H04M3/51; H04M3/523; G06N5/02; G06N20/00; G06Q30/02; H04M3/436; G06Q30/06; H04M3/42
技術(shù)領(lǐng)域
customer,call,queue,inbound,predictive,in,enterprise,model,data,agents
地域: MA MA Springfield

摘要

A call management system of a call center retrieves from a customer database enterprise customer data associated with an identified customer in a customer call, which may include customer event data, attributions data, and activity event data. The customer database tracks prospects, leads, new business, and purchasers of an enterprise. The system retrieves customer demographic data associated with the identified customer. A predictive model is selected from a plurality of predictive models based on retrieved enterprise customer data. The selected predictive model, including a logistic regression model, and tree-based model, determines a value prediction signal for the identified customer, then classifies the identified customer into a first value group or a second value group. The system routes a customer call classified in the first value group to a first call queue assignment, and routes a customer call classified in the second value group to a second call queue assignment.

說明書

In an embodiment, for each customer a pre-sale predictive model generates a value prediction signal indicative of potential value of a sales transaction for that customer. The predictive model can provide various types of value prediction signal including, for example: (a) buy-only signal, representative of the likelihood that the customer will accept the offer to purchase the product; (b) lapse-only signal representative of the likelihood that the customer will lapse in payments for the purchased product; (c) buy-don't-lapse signal representative of the likelihood that the customer will accept the offer to purchase the financial product and will not lapse in payments for the purchased product; as well as predictive models providing combinations of these signals.

Predictive models 410 effect a degree of feature selection. In various embodiments, predictive models 410 identify high importance features that have the most pronounced impact on predicted value. Different types of model may identify different features as most important. For example, a model based upon a buy-only signal may identify different leading features than a model based upon a lapse-only signal.

TABLE 1 Features from l1 buy-don't-lapse model Importance Feature

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