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The science vs. the art of predictive analytics techniques Posted on Jun 17 - 2017

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Organizations can benefit greatly from applying predictive analytics to contact center data. Here's a look at four areas ripe for upgrades in those workflows.

Predictive analysis utilizes many different statistical techniques, examining historical data in an effort to project future behavior. While past behavior may not be 100% indicative of future behavior -- think of financial market analysis and how hard it is to pick the next great stock performer -- it can help contact centers work more smoothly and efficiently.

In this case, predictive analytics techniques can be wonderfully useful. However, it's essential to know there's a balance between "science" and "art" that each organization must discover to make its predictive analytics deployments successful.

Historically, contact centers have been using a form of predictive analytics in the area of workforce management for many years. Workforce management analyzes relationships between key pieces of historic data to project future staffing requirements.

With the increase in computing power, the retention of more data and enhanced statistical techniques, predictive analysis can now be used during specific "moments of truth" in the contact center.

Workforce management: The old standby

Predictive analysis is the fundamental concept behind workforce management systems that project future demand of resources by analyzing historical data. If a contact center does not have the staff in place to handle customer inquiries in a timely manner, overall customer satisfaction decreases -- and that, in turn, drives down loyalty and customer retention.

 The science. Workforce management looks at historical data regarding volume of calls, average handle time and other factors to project future call volume and gauge staffing requirements. View More

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