finding-the-voice-of-the-customer

A lot has been written about how digital technology has led to an era of big data, where massive amounts of information can be collected, processed and analyzed in a way that would have been impossible to imagine a little over a decade ago.

This data revolution has affected many areas of life, and business is no exception.
I firmly believe that big data and its implications will affect every single business, from Fortune 500 enterprises to mom and pop companies’ writes Bernard Marr in a recent piece for Forbes.

While one could spend hours trying to understand the many complex ways big data can affect business, this piece will focus on one exciting piece of technology that takes advantage of the data revolution to improve customer service center operations: text analytics.

Essentially, text analytics is a technology that records and transcribes all customer calls into text. This allows a company to create a massive record of all customer calls that can then be searched by running a keyword analysis on the data. Companies can identify common customer complaints, call back requests, how frequently certain problems are mentioned and so forth. In addition, text analytics allows companies to set up alerts on these keywords so they can get real-time feedback regarding the specific issue or issues they are targeting.

Ultimately, this allows you to monitor why customers are calling, whether they are having positive or negative experiences, and what product features they like or dislike. The reporting provided through these real-time alerts allows you to respond quickly to these trends, and the collected data allows companies to measure how effective they are in addressing common customer issues.

In an increasingly competitive marketplace, quickly responding to customers’ needs is an important way to build loyal customers. With this in mind, it might be worth asking: How can text analytics improve how your contact center handles your customer interactions?

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September 8th, 2015

Posted In: Big Data, Data Analytics

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