They used an unsupervised topic
identification approach and learned: They were outbound calls to payers for benefits information. They included long hold times.
They most commonly
occurred on Fridays. Less experienced Forex Database agents took longer to navigate the call and call length. Scenario two (the middle of the funnel) The second scenario is about focus, determining where to focus and how to leverage machine learning (ML) to help predict consumer responses.
For example, one pharmaceutical
company brought a new drug to market. Despite Calling Nationwide, Not Local: The Myth of the 888 Area … the company’s forecasts, it was blown away by the public’s overwhelming response
: they received six times more
calls than initially expected. During the post-launch period, the organization used automated and manual methods to help keep its finger on the pulse of these conversations and gather conversation data.
They pulled calls from the middle
of the funnel and used ML to understand the nature of those conversations. Analysts listened to an additional calls and teams met weekly to discuss, evaluate, and address raised concerns. This approach supported immediate change, such as offering agent coaching and developing proactive ways to address common pain points, resulting in reduced call volume.
