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Data Mining In the Contact Center

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The Team at CallMiner

November 08, 2013

Young, motivated business team with manager at a call center

According to UCLA, data mining “is the process of analyzing data from different perspectives and summarizing it into useful information.”

Grocery store chains might use data mining processes to analyze consumer shopping patterns to better understand if they need to order more or less of certain products at different times of the year, what kinds of products sell better in different markets, or even use the data to print better targeted coupons to shoppers. The information pulled from data mining is typically used to increase profits, decrease costs, or some combination of the two.

For instance, the grocery store chain might learn that a certain neighborhood is more likely to purchase organic produce (which is more costly) than those that shop at another store in a different neighborhood. The first grocery store would want to add more organic items to their shelves to increase sales while the second would want to have fewer organic items to lower costs.

But what can a contact center learn from data mining?

With a speech analytics solution in place, contact centers can record, monitor, and re-visit 100% of their inbound calls. There is wealth of information about your customer base, as well as your own company, to be had! Think about all the information your agents gather during a typical interaction, plus the information your system already has about that particular customer; name, age, address, marital status, buying history, past interactions—all of that data times 1000s of callers! Data mining can turn that data into valuable, actionable information.

A speech analytics solution automatically categorizes calls based on reason. The reason for the contact is often referred to as the call driver.

For example, you may call your bank for a balance inquiry, and as a follow up you may conduct a transfer – each of these would be reasons for the call. Were you to call your bank back because the transfer did not go through that would be a separate call with a separate reason. Data mining would involve taking those various call drivers from every interaction in your contact center and generating insights as to the most common reasons people call in.

For instance, a cable company might learn that a large percentage of their inbound calls are generated when field agents are late to installation appointments. Armed with that knowledge, the company could give agents more time to get from appointment to appointment, or ensure they are only visiting a certain area on certain days to cut down on travel time. More on-time field agents means fewer inbound calls, which could help cut down on costs in the contact center as well as improve the overall customer experience.

Or let’s say a utilities company is looking to better understand their above average call duration. Data mining, with the help of the information collected using speech analytics, might reveal that contact center agents have not been properly trained when dealing with billing questions. Callers might be getting bounced from agent to agent, increasing the average call time, because no one on the floor has the knowledge needed to answer their question. The utility company could then adjust their training programs to give agents a better handle on the billing process, such has how to set up an online bill-pay account, so they can be more effective on the phone.

The whole point of data mining is to turn large databases of data into new information. Data mining “can grant immense inferential power” because it enables you to connect all of the dots and gives a clear understanding of the real picture. In the contact center, data mining can give you and your agents a better look at why your customers are calling, which in turn can be used to adjust internal policies and procedures, saving time and money.

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