Call Progress Analysis (CPA), also called Call Progress Detection (CPD), is a generic term for signal processing algorithms that operate on audio during call setup. The goal of CPA is to determine the nature of the callee or the outcome of call setup to an external network (traditional or IP). Specifically, when a call or session is being established, the caller or initiator is interested in knowing if someone answered, if the line is busy, etc. When the caller is an automated application, such as an automated dialer or message broadcasting system, CPA algorithms are used to perform the classification automatically.
Modern CPA methods in combination with automated outbound dialing applications, ensure fast and accurate automated call classification, which automatically translate in better efficiency of agents and higher quality customer interactions.
Traditional Call Progress Analysis implementations rely on simple rule-based algorithms which provide sub-optimal accuracy of speed of processing, leading to significant inefficiencies in operations and issues in meeting compliance from new government regulations.
Using statistical models based on Neural Networks to represent the potential outcomes of an outbound call attempt, companies can meet and exceed these regulations. This approach identifies much better the call patterns that represent call progress events and provides CPA results with far superior accuracy, speed and flexibility compared to traditional approaches.
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