Call Center Analysis: How To Use It (& Act On Key Data)

Call center tech has advanced rapidly in the last decade with the rate of automation in agent communication increasing significantly, yet customer care leaders are still at a crossroads when it comes to developing cost-effective customer support strategies that drive revenue and save time. 

Why? With AI shifting the way customers interact with brands, call center teams are trying to accommodate increasing customer demand for more personalized yet expedient interactions, all while dealing with increasing turnover and budget constraints. 

That’s why customer service and sales leaders are investing more in call center analytics, which allows them to improve operations and get a richer perspective of the journey customers take across all phone – and even non-phone – interactions.

But how do you actually analyze call center data in a strategic way? We explain what data matters most, how to gather it, and how to put it into action in this article.

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Why Call Center Analytics Are so Important

Call center monitoring has always been essential for sales and customer support teams looking to improve CSAT scores and customer churn KPIs. 

But they’re even more important now because, with widespread AI adoption and self-service tools, call centers that use their sales and customer support data will be significantly more efficient. 

This is because they’re better at identifying which queries are more suitable for automation and which make more sense to handle at a higher, more personalized level.

As NICE’s CX Division President Barry Cooper explains for Forbes, “The gap will widen between good and bad AI. AI is the answer to managing the overwhelming complexity of customer service in the digital omnichannel age. But that does not mean that all AI is created equal.”

In other words, to gain a meaningful advantage from AI technology, you need to use it in conjunction with other analytics so you know exactly when and how to apply it to serve both the customer and your operations.

His suggestion? “Intelligently communicating with customers, supporting contact center agents, and operating in the cloud at scale.” Data is essential to improve customer calls, fill agent skill gaps, and fix tech issues. That means call centers need even more powerful technology and granular analytics to improve their level of care and personalization.

What Types of Call Center Analytics Are There?

Comprehensive call center analytics rely on programming languages, machine learning models, and AI to deliver a full range of valuable insights. Here are a few types of analytics these tools might gather: 

• Speech analytics: By analyzing audio streams from calls, voicemail messages, and IVR call menu responses, speech analytics tools provide actionable insights into how your customers interact with your call center agents.
• Interaction analytics: These metrics draw from all customer interaction channels to determine where call center agents may need to improve in terms of customer care.
• Sentiment analysis: Platforms like CloudTalk use customer sentiment tools to score sales and customer support interactions based on tone, talk/listen ratio, and other conversational and language cues. 
• Text analytics: These help call center teams study customer interactions over SMS or chatbot with Natural Language Processing (NLP).
• Predictive analytics: This analytics process uses historical data to predict future customer behavior and create better sales and customer experience strategies.

How to Analyze Call Center Data (& Act on It)

Your call center analytics will only be useful to you if they’re part of a larger strategy to improve your processes and boost revenue. Here’s how you can take a hodgepodge of data and KPIs and turn them into a cohesive plan of action.

#1 Work Cross-functionally to Map the Customer Journey

Let’s say your company’s revenue has been steady for the last couple of years, but growth has stagnated and customer retention is becoming a looming issue. Your C-suite agrees they’d like to see it trending upward again at 20% to 30% a year. 

So, they ask your call center sales team to create their own departmental goals that improve call center performance and contribute to revenue growth.

What should your call center’s sales leader do? Work together with your customer support teams – and any client-facing teams – to map the customer journey. By inventorying and studying customer touchpoints from acquisition to churn, you can get more clarity about where you might be losing customers.

Customers aren’t just data points in your business. They’re unique individuals with distinct needs and expectations. Every interaction they have with your brand, whether it’s online, in-store, or over the phone, holds immense power to shape their perception of you.

— Jeannie Walters, CX expert and speaker

The best way to start when mapping: Use call center analytics software that offers a real-time dashboard for tracking call and agent productivity metrics and integrates with your CRM. That way, sales and customer support teams can use speech, interaction, text, and sentiment analysis to uncover why sales are stagnating.

#2 Set Goals Based on Customer Pain Points

As you learn more from current, previous, and prospective customers about what causes them to choose another provider or churn, take note of how customers speak about your product or service as well as their favorite products and services. 

Ask: What pain points are coming up again and again? And how are your sales and customer support teams speaking to those pain points? 

For example, it could be that your sales teams are overwhelming potential customers during phone interactions by extolling the many features and benefits your product offers.

Customers want solutions that not only meet their needs but do so both quickly and accurately. Products that have too many features or are too complex to use are at odds with their needs.

— Charlie Claxton, Thomson Reuters SVP and Global Head of Design in a Fast Company article

One takeaway pain point, then, might be that potential customers are having trouble finding a solution that’s simple to utilize, quick to implement, and that doesn’t come with a myriad of features they’re unlikely to use. 

As a result, you might set a goal to make call center sales conversations more targeted to individual customers based on each prospect’s unique needs. 

#3 Determine How You’ll Use Analytics and KPIs as Signposts for Success

Call center analytics don’t only point to issues – they help you make data-driven decisions and identify when you’re on the right track to hitting department and company targets. But you need to decide which analytics make the most sense to track based on the objectives you’re prioritizing.

So, to meet your revenue growth goals of 20% to 30% per year, or 1.5% to 2.5% per month, you decide your call center sales team should focus on achieving key metrics like: 

• Longer call times
• Increases in monthly demos and onboarding times
• Higher lead conversion rates
• Shorter time to convert
• Decreased customer acquisition cost (CAC)
• Increased pipeline value
• More completed customer surveys

#4 Use Those Targets to Inform Call Center Agent Messaging and Training

If your call center sales and customer support teams are trying to have more positive interactions with customers that lead to increased revenue, they need context-specific training and sales messaging.

The answer to improving training and messaging: Embracing clear branding and marketing, or how you talk about your product in a way that meets customer needs. 

If that sounds like a distraction from the process of increasing sales revenue, it’s not: McKinsey research found that both B2B and B2C companies that view branding and advertising as a top two growth strategy are twice as likely to see revenue growth of 5% or more than those that don’t.

Still, how do you refine your branding to make it call center-specific? Use a call center intelligence platform to analyze call recordings in depth. This allows you to identify where calls went wrong, or what wording an agent may have used that “turned off” prospective and current customers.

How to Choose Call Center Analytics Software

You may not have time for a glut of features that lack relevance to your call center’s current needs, so you should focus on functionality like:

• Call center automation technology – Look for platforms that integrate conversation intelligence, NLP, and machine learning capabilities into their core offerings. 
  These features can do the heavy lifting of analyzing and grading your customer interactions across multiple touchpoints. Focus specifically on features that allow you to record, transcribe, summarize, and tag key moments in conversations.
• Full, international coverage – To reach customers around the world without connectivity issues and high calling costs, you’ll need a platform that offers international numbers, which can route you to a network of telco providers and more easily connect you to any recipient.
• CRM integration – Share insights that benefit everyone with a call center software that automatically uploads all call analytics and insights to preferred CRMs like Pipedrive, Salesforce, and HubSpot.
• Real-time call monitoring – To be a better coach to call center agents, you’ll need unique call whispering and call listening tools to see where they need to improve and jump in when they need help closing a sale or solving a query.

Lead Better Calls and Close More Deals with CloudTalk

The usefulness of call center analytics isn’t as narrow as you think. Everyone in your company can leverage the data they yield, no matter their role in your organization. Doing so allows them to understand the customer better and, in turn, exceed their service expectations. 

As long as they have the tools to uphold call quality and record essential customer data, any team – not just sales and customer support – can use call center analytics to refine their customer-centric strategies.

That’s where CloudTalk comes in. CloudTalk is a calling and customer intelligence platform that helps sales and customer support teams lead clearer, better calls and make more data-driven decisions from AI-powered insights.