Call center analytics are critical to understanding and improving your contact center operations. It might seem like an obvious statement, but the truth is that more than 50% of businesses are not using call center software or a knowledge base—that’s a ton of useful data left sitting there.
In this article we’ll show you why call center analytics are essential and break down the different types like speech, text, and predictive analytics. Plus, we’ll walk through examples of how generative AI has opened up new avenues for call center reporting and analysis.
Call center analytics involves gathering data from customer conversations, and then analyzing and interpreting that data for insights into operations, CX, agent performance, and more. Using these findings, managers can outline practical plans to improve service quality and more.
Call centers are at the front lines of customer interaction in all businesses, so they’re a goldmine for customer data. Through call center interactions, business leaders can gather a ton of demographic and historical data about customers.
Outside the service context, businesses can also use analytics and reporting to identify problems with goods or services and take measures to address them.
While call center reporting shows you what’s happening in your organization, call center analytics goes a step further, showing you why it happened and even suggesting what you should do next.
Reporting presents raw data like metrics and KPIs from the past and present
Analytics uses that data to pull insights, flag trends, and make predictions for the future
The goal of establishing a contact center is to have a committed team that can prioritize customer requirements and desires. However, this cannot be done efficiently if agents are overworked due to excessive volumes and inadequate staffing. An overworked team leads to:
Contact center analytics helps leadership avoid overwhelmed agents by anticipating staffing requirements—especially around holidays and product launches—so they can react to changes in demand and comfortably handle all inbound calls.
Call centers are separate from other departments, so managers, typically, do not combine historical data from the call center with that of the sales, marketing, and product teams.
Analytics helps align data from all of these sources, breaking down data silos between teams for easier sharing of information. This enables greater access to customer data, so you can better understand how these departments influence one another.
Call center leaders may coordinate their tactics with the wider business objectives to support a better customer experience. Say the marketing team is running a promotion, they may inform the call center agents about it so they can promote it on both inbound and outbound calls.
A promotion could also spark higher call volumes, so the ability to share information between departments would allow the call center manager to forecast more accurately and ensure sufficient staffing numbers.
You can’t make business judgments solely on intuition—and call centers cannot achieve target KPIs or optimal operations based on instinct alone. Call center analytics helps promote a data-driven culture, where information is accessible to everyone within the organization.
Call center managers can assess agent productivity, and identify strengths and weaknesses, so they can determine the potential effects of their decisions on average handle time, conversion rates, and more.
Additionally, managers can use QA reporting and analysis to identify coaching opportunities, and even implement performance-based bonuses thanks to the ability to more accurately track performance with analytics.
An intelligent call center analytics platform proactively reveals opportunities to increase income and productivity, while also increasing efficiency. It predicts potential future customer interests using:
Behavioral profiles
Demographic data
Purchase history
This better equips sales agents to recommend a product or flag a special promotion that appeals to the customer, and supports agents in finding the best time and method for outbound calls.
For example, reporting may indicate that phone calls later in the day lead to higher conversion rates, allowing sales reps to adjust their outbound strategy accordingly. Past interaction data also helps train agents to structure inquiries or modify wording to maximize conversion rates.
Analytics technologies do more than simply gather consumer information. They help evaluate agent performance so leaders can easily pinpoint strengths, weak points, and opportunities for improvement.
By pre-determining KPIs—such as hold duration or first call resolution rate for support agents or closing rates and deal value for salespeople—managers can systematically identify top performers.
And by linking KPIs to business goals, you can determine the best ways to organize your operations and teams to deliver desired outcomes. For example, QA may highlight process inefficiencies and bottlenecks that you can then eradicate to boost overall productivity.
Call centers generate massive amounts of data, so why not use it? This includes:
Customer information
Agent metrics
Sales data
And a plethora of other types
You can track customer complaints, spot problems, and take proactive steps to fix them. Additionally, you can monitor the performance of call center employees to help enhance onboarding and training procedures.
For instance, you could use call whispering to train reps on the job during actual customer conversations and compare the before-and-after performance metrics to ascertain the impact of your real-time coaching.
Not to mention using customer data to create personalized experiences, making them feel valued and welcome—ultimately leading to strong customer loyalty and retention.
The most important types of call center analytics include:
Speech analytics
Text analytics
Predictive analytics
Self-service analytics
Omnichannel analytics
Cross-channel analytics
Interaction analytics
Customer survey analytics
Let’s explore each type in a little more depth.
Speech analytics for call centers are built on voice-based technologies, like phone and video calls. They employ artificial intelligence (AI) to recognize keywords, speech patterns, and tone, and offer information about the agent and product performance.
Call center leaders can use them to warn agents when the conversation takes a negative turn and they’re running the risk of losing the customer—or even when they need to intervene themselves to diffuse a crisis.
AI is also used in call center text analytics to identify patterns, tones, and keywords in customer conversations. However, in contrast to speech analytics, it analyzes written material rather than voice and spoken language, to find trends and connections. This includes:
Call center text analytics are crucial for contemporary firms because they serve as a supplementary tool for social listening. Think of things like:
Predictive analytics uses machine learning to forecast customer behavior, preferences, and demands.
Suppose a customer mentions that they prefer writing in blue rather than red. In that case, call center analytics would catch this preference in the data and predict that customers are more interested in the blue pen product, informing the sales approach.
These predictive capabilities also offer insight into peak hours and seasons, so business leaders can ensure adequate staff are scheduled to meet customer demand—no matter the scenario.
Giving customers the tools to solve their own problem quickly is not only more convenient (and easier for your agents), it’s also a self-improving function thanks to call center analytics. By identifying the most popular keywords and phrases on self-service channels, you can refine tools like AI agents, chatbots, and IVRs to provide a more satisfying customer experience.
For instance, if you find that the most frequently asked question is "How long does the shipment take?" you might provide shipping times on the FAQ page. Consequently, you enhance customer satisfaction while reducing the volume of incoming calls you receive for minor, everyday queries.
Omnichannel analytics offer a comprehensive view of customer interactions across all communication channels—phone, email, chat, and social media combined. Tracking and analyzing these touchpoints paints a picture of your customer's journey, behavior, preferences, and pain points. This allows you to:
Streamline workflows
Personalize experiences
Improve service consistency
For example, you may spot that customers face frequent issues on live chat, but not on the phone, or that response times vary too greatly between different channels. These findings then facilitate quick corrective action so you can reduce friction and boost CSAT.
Cross-channel analytics enables the omnichannel experience that customers crave. It examines data from every channel and provides a comprehensive view of customer journeys.
This allows you to identify channels preferences and understand how customers use each channel differently. Using this information you can then segment and personalize for stronger CX.
A true cross-channel approach requires a tool that integrates with all of your contact center platforms. Anything less and you won’t have a complete 360-degree overview of customer touchpoints.
Interaction analytics focuses on all types of customer conversations (calls, chats, emails, etc.) using a mix of speech and text analysis. By tracking conversation patterns, emotional cues, and keyword trends, you can find insights into both customer concerns and agent performance, such as:
Recurring process issues
Customer sentiment
Training opportunities
And by tracking these data points, you can improve your contact center’s performance by spotting and filling communication gaps, reducing issue resolution times, and ensuring agent compliance with your quality standards.
Customer survey analytics involves analyzing feedback from post-interaction surveys such as:
Customer satisfaction (CSAT)
Net Promoter Score (NPS)
Customer effort score (CES)
By tracking these survey responses, you gain valuable insights into customer happiness, their loyalty, and how easy it was to solve their issues. With customer survey analytics, you can identify friction points, or find trends in your positive feedback to recreate successful strategies.
Monitoring these survey results gives you an understanding of agent performance from the perspective of the customer. And with this extra perspective, you can more accurately pinpoint process improvements and focus on the most pressing customer concerns first.
GenAI can quickly process and analyze huge volumes of data, including customer conversations, so you can spot patterns and issues that may not be obvious to the human eye. This helps you focus on strategic improvements rather than time-consuming manual analysis.
With AI, you can get a real-time view of customer sentiment and agent performance, as well as many other KPIs and contact center performance metrics. This helps your team address potential issues as they arise, improving customer satisfaction and cutting down on operational bottlenecks. It also allows you to step in and intervene before bad situations can escalate.
GenAI allows more precise segmentation and personalization by analyzing behavioral patterns across different channels. Personalization has become critical to today’s consumers—56% say they will return to a business that offers a personalized experience, up 7% from previous years.
AI tools can completely overhaul how your call center handles call quality and agent performance. With it, you can track how agents perform—and speed up the review and evaluation process—and create custom training sessions.
It can also help detect deviations from compliance procedures and reinforce best practices by identifying coaching opportunities and assisting agents in need.
Your call center creates a ton of data—so why not use it? By investing time and resources into call center analytics (speech and text, self-service, omnichannel, or anything in between), you can paint a clear picture of how your center is performing.
This data isn’t just for observing, either. It allows you to:
Improve response times
Elevate the quality of your service
Boost sales numbers
Keep your agents focused
And more
Scorebuddy gives you access to a powerful AI platform so you can take a more data-centric approach to quality assurance in call centers. Try an interactive AI-powered QA demo to see how Scorebuddy can make your day-to-day QA work more impactful on the wider business.
