Call Centers Suffer From Big Data Overload

Call Centers Suffer From Big Data Overload

Nearly half of all contact centers consistently collect and report on metrics that they never use to improve the customer experience, according to a new survey.

Corporate call centers have always been metrics-centered enterprises. And today, they are collecting more data about customer interactions than ever before. The problem is they don't seem to know what to do with it.

Nearly half of contact centers (48 percent) consistently collect and report on metrics they don't use, according to a survey conducted by the International Customer Management Institute (ICMI) and sponsored by White Pages.

The five most collected metrics were average handle time (79 percent), abandonment rate (75 percent), average speed of answer (72 percent), after call work (63 percent) and quality (61 percent).

"Most of this is legacy reports or KPIs determined when agent efficiency was more important than customer satisfaction. It's always easier to present numbers with more confidence than to gauge an experience," says John Neely, director of accounts for White Pages.

"Because a lot of data literally falls off the system, gathering the data isn't that costly. Where the cost occurs is at the reporting and analytical level. Large call centers can have teams of analysts crunching this data into dashboards to present to senior management," Neely says. "The time spent of tracking metrics that do not reflect the focus of your business can be very costly and ultimately gain you nothing."

"Contact centers really need to ask the hard question, 'Who is the automation really meant to help?'" -- John Neely, director of accounts, White Pages

As a result, more than a quarter of the 542 call center professionals surveyed said they were experiencing data overload. Meanwhile, more than 60 percent said that their contact centers can't provide customer information proactively to an agent, according to the survey.

[Related: Cloud Services Pave the Way for Call Center Growth]

The majority of agents (69 percent) have to navigate around multiple screens and interfaces in order to locate information and 40 percent are still manually inputting customer contact information rather than automatically feeding it into their systems via API or Web-based technology.

That additional time spent to collect basic information leads to inefficiency among agents and dissatisfaction among customers on the end of the line. Nearly half (49 percent) of agents report overall productivity and efficiency challenges when they have to ask customers for basic contact information.

Big Data Not Helping the Customer Experience

"Given the advancements in the online customer experience with sales and support, customers expect to be able to get right to what they need when they call," Neely says. "How frustrating is it to have to keep giving the same information again and again only to try to get to where you need to start? Today's customer expects technology to work with them and save them from repetitive, redundant experiences."

[Related: Big Data Analytics Gold for the Call]

While 48 percent of contact centers collect and use the data on the average satisfaction of a contact, 15 percent only collect the information but then don't actually implement or use any of the collected information, according to the survey.

More than a third (36 percent) of agents don't collect data around the satisfaction of a customer at all. And in today's multi-channel world, more than one-half (51 percent) of call centers do not ask for customers' channel preference.

Throwing new systems at the problem may not always help. A quarter of agents surveyed reported that they have to consistently learn new technology or processes to handle their contacts.

[Related: Call Center Outsourcing Buyers Want More Than Cost Savings]

"Contact centers really need to ask the hard question, 'Who is the automation really meant to help?'" says Neely. "Ultimately, it should enrich the customer experience and differentiate the product or service by providing even better service."

Customer support professionals could themselves use some support. "Reps are not hired because they can ask for an address, but rather because they have extraordinary skills to support a sale, product or service. With exceptional people skills, they can then offer a complete and enjoyable customer experience," says Neely.

"Any support you can provide reps to keep the focus of a call on its intended purpose is paramount. Representatives want to do the jobs they are trained for, and allowing them to do that makes them better reps and more likely to find the satisfaction they need to stay working at your center," says Neely.

What Problem Are You Trying to Solve?

Companies that want to streamline the call center experience and make good use out of the customer data available should first ask themselves what problems they're really trying to solve.

"I remember being in meeting after meeting trying to resolve issues that only impacted a very small percentage of calls. Don't make this mistake," says Neely.

Consider everything from the customer's point of view, Neely advises. "Start with a basic list of things that impact all the incoming calls (for example, scripts or areas of focus) and determine which of these can be improved and better supported."

Involving the call center professionals on the front lines and the IT professionals who know the back-end systems in addressing these issues is imperative.

"Ask your reps if reducing the time they spend on superfluous tasks would empower them to engage more with the customer. Would that improve customer satisfaction and first call resolution?" says Neely. "Ask your technology teams how confident they are in the accuracy of your CRM to find out if you are losing customers in redundant records or if you are effectively making them go to the back of the line."

Stephanie Overby is regular contributor to's IT Outsourcing section. Follow everything from on Twitter @CIOonline, Facebook, Google + and LinkedIn.

Read more about big data in CIO's Big Data Drilldown.

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