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Here's Why You Should Consider Business Intelligence Over Contact Center Metrics

Get the full picture of what's going in your business beyond a few vanity metrics

Kindra Cooper

Business intelligence

Contact centers live and die on metrics – from measuring average speed to answer to handle time to making qualitative inferences about customer sentiment and intent. 

Typically, the contact center supervisor pinpoints 10-20 core metrics used to rate customer satisfaction and agent performance, often forgetting that the numbers are but a proxy for the actual customer experience.

While contact centers might conduct focus groups and Voice of Customer research, most of their day-to-day operations involve tracking just a few key analytics or vanity metrics on a dashboard while largely ignoring the rest.  

Of course, there’s the Facebook-era scourge of businesses aggregating more data than they know what to do with, but what if you have a highly technical product subject to bugs and glitches, complicated B2B relationships with other businesses or rely on third-party solution providers to deliver a crucial service?

Uber, for instance, relies on Google Maps for driver- and customer-facing navigation, and uses telecommunication provider Twilio to send text messages.

Read more: A Glimpse Into the AI-Powered Classroom of the Future 

If one of those software solutions were to suffer a breakdown, causing a blip in Uber’s service, who will Uber’s customers blame? Uber, of course. 

When the customer experience can break in innumerable ways, it’s important to see all the data rather than monitoring a dashboard for a few specific indicators, says Ira Cohen, founder and CEO of Anodot. This means relying on business intelligence - not just data analytics. 

Ira Cohen Anodot“If your app stops working for everybody, that’s an obvious one, but that’s easy to catch,” Cohen told CCW Digital during an AI conference in New York City. “The problem is, there are a lot of subtle things that happen, or problems that happen to only a segment of your user base and you don’t notice it if you don’t track it.”

For instance, when one well-known video streaming service provider upgraded its video player, it wasn’t compatible with older versions of Internet Explorer.

Given that the issue affected such a small segment of its unpaid user base, no customers complained and the company discovered the glitch by chance eight months later. During that time, the company lost over $300,000 in ad revenue, to say nothing of customer churn.

“A poor experience leads to churn and direct revenue loss if you’re selling something or leveraging advertising, but mainly you get very unhappy customers,” said Cohen. “People today expect companies to pick up on things – they don’t want to call, they don’t want to complain.”

Read more: When is it OK to Say 'No' to a Customer?

Normally when customers complain, the problem has already reached a critical mass in terms of number of people affected or duration of the disruption. What’s more, research shows that for every customer who complains, 26 simply switch brands. 

Business intelligence becomes even more crucial when you offer a product or service on the backs of third-party solution providers, as in Uber’s case. One Anodot client, a telco providing prepaid mobile services, discovered that a swath of its customers was having trouble topping up their calling card.

After attempting payment, customers received a generic error message that didn’t specify why their card was declined. Naturally, they assumed it was the telco’s fault. 

In reality, the payment issues came from cardholders of a specific bank that was experiencing a connection outage, and had nothing to do with the telco. Without being able to see granular data not just on the number of declined credit card payments but from a specific payment provider, the telco wouldn’t have been able to determine the root cause of the problem. 

This is where business intelligence comes in. Founded in 2014, Anodot uses machine learning algorithms to learn the normal patterns from all data being collected across the business and flag anomalies. Another set of algorithms helps with root cause analysis by finding anomalies and correlating them. 

Read more: How to Use Customer Data Insights to Build a Better CX 

In the CX world there’s an obsession with metrics, but contact center metrics keeps CX siloed in the contact center, while business intelligence aggregates data from all departments and gives a business a readout on everything from the health of its IT infrastructure to pages per session on its website to the number of online purchases in a given hour – three things which could be easily correlated if something were to go wrong. 

“When you monitor the customer experience things can go wrong in any way, shape or form – and they will,” says Cohen. “But if you try and think about the [metrics] beforehand you’re zooming in on a very small portion of the data.” 

Shifting from tracking department-specific metrics on a dashboard to collecting business intelligence constitutes a paradigm shift in how organizations collect data, integrate and organize it. It also makes the customer experience an organization-wide concern because you can correlate data from different sources and departments. 

“If I’m an IT guy, usually all I care about is errors in the software,” says Cohen. “Now if I see errors in the software I can see if it impacts the customer experience. If it doesn’t, maybe I can ignore it for now and work on something else that does impact the customer experience.”