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Using Speech Analytics to Drive Collections Performance

Back in February, my colleague Morgan Nagle blogged about how speech analytics can potentially transform debt collection call center tactics by monitoring and analyzing every collector/consumer interaction to drive compliance, more payments and even customer satisfaction. In short, organizations using speech analytics can deploy rules to guide agents, set appropriate performance baselines and identify behavior or regulatory issues before they turn into problems.

The benefits of speech analytics don’t need to stop there. Consider all the rich consumer descriptive data that’s collected during these calls, not to mention historical data about those consumers. Organizations should be asking themselves: “Are we really doing anything meaningful with all this data?”

Simply analyzing “what’s happening” is good. Thinking about “what do we do next” is better – that’s when the vast potential of speech analytics can be realized.

For example, predictive analytics mine phonetic data for complex, subtle conversational characteristics that can be used to predict the probability of specific outcomes, such as how consumers will respond to a specific payment plan or settlement options. With more productive conversations that make consumers feel more in charge of their situation, organizations can reduce the number and time of calls due to excessive negotiation, while increasing the amount paid.

Predictive models score agent conversations on the prevalence or absence of these characteristics, enabling collectors to continually adjust behaviors. Imagine, managing a collection team, being able to have analytics virtually “script” every collector discussion – and know that over the long haul, your team’s performance will benefit substantially.

Every company will have additional success criteria specific to its organization or to a current initiative. An advantage of these analytic techniques is that they can be used to detect and measure virtually any mix of criteria that make up a contact center’s current definition of success.

Predictive analytics can be applied even when minimal consumer data exists, such as for medical bills or student loans. For example, models based on many companies’ data (called pooled models) can be acquired to identify patterns not yet present in an organization’s customer population. These models can be used to derive strategies that lead to faster handling times and better performance during consumer calls.

If you’re interested in learning more about how predictive analytics can help your organization drive collection success, this year’s FICO World 2014 is the place to be – November 11-14, at the Sheraton San Diego Hotel & Marina. We’ll have a session dedicated to speech analytics, as well as other sessions covering best practices for debt management. For more information on this topic, you can also check out our new speech analytics ebook, The Science of Call Success.

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