The Challenges with Call Center QA Analytics

Surrounded by ever more powerful technologies, we are clearly living in the era of big data collection and analysis.

Defining Call Center Analytics

What, exactly, are call center analytics, and how do they impact call center QA? Analytics are the information that results from analysis of data or statistics.

From ‘Call center analytics allows for an unparalleled opportunity to monitor and improve a variety of service metrics from call times, efficiency, employee performance and customer satisfaction.’ They go on to define call center analytics as ‘a variety of tools that companies can employ…to keep their operation at peak performance.’

Analytics tools can be inward-facing – those we use to measure front line performance – or outward- or customer-facing. The outward-facing tools are used to tailor the customer experience (a bit more on those later).

The call center industry is currently experiencing a massive upsurge in both types of analytics software. But analytics can make the job of evaluating call center ops easier…or more difficult.

Companies leverage data collection and analysis to gain insight into their call center operations. defines the goal of analytics: ‘To improve the business by gaining knowledge which can be used to make improvements or changes.’

Confusion is QA Call Center Analytics

There is a great deal of confusion about the various analytics used in call center quality assurance, and whether they accurately reflect actual operations. It sounds disingenuous to say that analytics – actual data – may not be accurate enough to capture the successes (or failures) of front line agents, but it is true.

The reason?

Sometimes, analytics can be interpreted in wholly different ways.

Results versus Meanings

The results of the data can be clear-cut, while the meaning of the data seems contradictory.  Remember, analysis can depend on vague factors. Shorter call times are great – but they do not necessarily equate to better customer outcomes. First call resolution – a lofty goal – can mean different things to different companies…or callers (see my previous post on FCR here). INSERT LINK TO PREVIOUS POST Another example: the ‘ask’ may have been made by the agent, but was it made correctly, or was there a value-add offered to encourage the customer?

Thus, while data is binary – yes or no, 1 or 2, on or off – it can be difficult for quality assurance analytics to successfully capture customer emotion or experience. Emerging technologies in the field of customer analytics are beginning to address this emotion gap (discussed below).

All of this can boil down to data interpretation in terms of both the call center operational objectives (minimize manpower, costs and call time), and the company’s objectives (maximize customer satisfaction and outcomes).

Aligning the objectives of the overall organization with its various constituent parts can – at times – be a challenging proposition.

Different interpretations of analytics can and do happen – and confirmation bias may come into play. CSR is sometimes brought in to confirm analytics that a customer just didn’t believe, or wasn’t comfortable with and didn’t want to believe.

Bottom line: analytics can tell us that something is done, but not how it has been done.

A good deal of what we here at CSR do is helping companies understand what data points matter, specific to their operations. For example, if call duration is meaningless and positive outcomes are most important (e.g., Zappos), then ‘call time’ becomes a less critical data point.

Customer-Facing Analytics: The Danger of Getting Too Personal

Analytics have huge roles to play beyond measuring the internal success of call center operations. They are also being used to understand customers and what motivates them.

The field of customer analytics is exploding, allowing brands to custom-tailor and personalize the brand experience to individuals and their tastes. Think about all those loyalty cards on your keyring – or in your phone – and imagine how much shopping habit data those retailers have.

Customer Analytics – Insightful or Invasive?

I’ve seen such analytics described as alternately insightful and invasive. Others have pointed to the very real problem in which erroneous data chases away a customer who feels the brand can’t get their act together, or the data is just a bit too personal.

The Target maternity story from a few years ago comes to mind as a cautionary tale when I think about the growing field of customer analytics. (If you aren’t familiar with the story, Target identified a slew of products mothers-to-be typically purchase, and then sent out baby item coupons – much to the consternation of one high-school-age teen’s father…who wasn’t yet in the know. Whoops. You can read the story here: How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did.)

It’s a delicate balancing act that requires companies to be aware of the potential intrusiveness of big data, while attempting to tailor the customer journey as much as possible to the individual.

Data Overload: The Call Center Analytics Explosion

The growth in the amount of analyzable data available to call center operators in recent years has been overwhelming. The profusion of new technologies – from feature-rich phone-based systems, to chat and social media customer service – has led to the simultaneous growth of analytics systems. (The Salesforce page referenced earlier has a great rundown on some of the various types of analytics: speech, text, predictive, desktop and more.)

What does it all mean?

No longer do we just have data surrounding just the technical aspects of the call – duration, time of day, type of call, resolution, etc.), but we also have data relating to the content – written, spoken or otherwise – of the call. Was the caller panicked? Angry? Irritated? Did they respond positively or negatively to certain types of agent comments or statements?

Analytics are emerging that can analyze not just every word, but also how they are being spoken. Think about it: beyond the simple, ‘structured data’ – 30 calls versus 15 calls per hour – there is an entire layer of unstructured data (e.g., emotions) waiting to be captured and put into a format to be measured & quantified.

These are incredibly powerful tools that can transform a company’s understanding of how their agents engage with customers, and what works to improve the customer experience.

But one of the key challenges such technologies (and call center managers & supervisors) face is ‘data overload’ or ‘data fatigue.’ Debate has sprung up over the value of particular data points – beyond the various differences of approach common to call center quality assurance.

The Proper Analytics Deliver Critical Insights

Despite their various limitations, analytics can provide important insights – both internally and externally – that were once beyond reach.  From a call center quality assurance perspective, the challenge is to identify the appropriate analytics that measure the call center’s operations & goals in the context of overall company mission & objectives.



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