In many ways, analytics have altered the role of the contact center quite significantly, taking it from a service offering to a strategic tool capable of enhancing customer satisfaction and driving stronger financial performance.
With so many channels and sources, contact centers take in huge amounts of data every day. Everything from a phone call to a social media post can generate a data point, but the value of this information only becomes clear when you pull these disparate data points together and analyze them in context.
Done correctly, call center analytics can boost customer experience, improve agent performance, and drive revenue generation, so let’s explore the steps you can take to implement an effective analytics program and unlock the hidden value of your data.
Call center analytics involves measuring and analyzing data with the aim of identifying trends, determining root causes, and deriving insights from your performance metrics. It’s a way to contextualize your data, connecting the dots across all channels and sources in order to improve overall performance.
With a wide range of data sources available, from purchase history to direct customer feedback, it can be difficult to manage the volume of information. Analytics doesn’t only make data manageable, it makes it actionable, providing insights that can improve the call center experience for both customers and agents.
In fact, according to a 2023 report, 84 percent of customer service and support leaders believe that customer data and analytics are “very or extremely important” for achieving their organizational goals.
While the terms ‘analytics’ and ‘reporting’ are often used interchangeably in relation to contact centers, there are notable differences between the two processes.
Reporting is about gathering raw, unstructured data and turning it into call center KPIs, metrics, and accessible reports. Analytics, in a way, is the next step in the process—using this newly compiled information to develop an understanding of the ‘why’ behind the numbers and generate actionable insights.
Reporting and analytics do, ultimately, serve a similar purpose and often work in tandem, but to get the best results from each part of the overall process, you need to be aware of the differences between the individual components and how they feed into one another.
Both analytics and business intelligence support effective decision-making by offering insights into your call center operations. They’re complementary processes, working in conjunction to enhance performance across the entire organization. However, as with reporting and analytics, there are some differences.
Business intelligence combines data from agent-customer interactions, payments, CRM systems, and more to discern insights into how customers behave. Using these insights, you can improve your understanding of the customer, facilitating a proactive, predictive approach to customer strategy.
Analytics is about taking a magnifying glass to the data and examining why certain patterns and trends emerged. It enables you to determine causality and contributing factors, and use these answers to forecast the future of your contact center. With this foresight, you can streamline performance, ensuring strong agent engagement and better CX.
Either way, the insights you pull from these processes can play an important role in your contact center. The industry agrees—81 percent of CX professionals believe that using BI and analytics, and sharing the results, is important to future success.
A number of different types of analytics fall under the wider umbrella of call center analytics. Depending on the nature of your organization and your specific goals, you may use all of these approaches, or you may only use one or two. Either way, it’s important to understand how each type of call center analytics works and what it brings to the table.
Using speech analytics, you can assess keywords, speech patterns, and tone in voice-based interactions like phone and video calls to gain a deeper understanding of customer sentiment. Though other channels are growing increasingly popular, 59 percent of customers still rank phone calls as their top preference, so speech analytics remain a key tool.
Live chat, SMS, emails, surveys, social media posts—contact centers never stop generating text. Using neural language processing and machine learning, text analytics puts all these conversations to good use, analyzing the data to identify sentiment, emotion, problems, and more, with the ultimate aim of improving customer experience and interaction outcomes.
This is your contact center crystal ball, enabling you to use information you already have to predict future needs. Using AI and machine learning, predictive analytics can forecast customer behavior and preferences, so agents can be proactive in resolving issues, and also estimate call volumes and peaks, so you can schedule accordingly.
81 percent of consumers want more self-service options, but only 15 percent are highly satisfied with the existing options. With self-service analytics, you can bridge this gap, leveraging data about how customers use tools like FAQs, tutorials, and eBooks, to continuously refine and update your self-service offerings.
Contact centers are using more channels than ever to connect with customers and it’s important to take each channel on its own terms. Using omnichannel analytics, you can assess real-time and historical data from different channels to give you a comprehensive overview of the custom journey and streamline CX at every touchpoint.
Effective call center analytics can lead to significant improvements in customer experience, agent engagement, and organizational processes, resulting in reduced costs, increased revenue, and a better customer experience.
Furthermore, with only 24 percent of executives describing their companies as data-driven, leveraging analytics can give you a competitive edge. Let’s look at some of the key benefits of setting up comprehensive analytics for your call center.
Customer needs are at the heart of what makes a contact center successful. Without an understanding of preferences, behaviors, and sentiment, it becomes difficult to deliver the kind of CX that maintains high CSAT scores and customer loyalty.
With analytics, you can use both historical and real-time data to understand where interactions could turn negative, and even forecast future customer needs based on previous patterns. This way, you can take proactive steps to guide agents and meet these needs.
Contact centers, particularly as they begin to scale, can be complex, with lots of moving parts working together. Inefficiencies in one area can have a knock-on effect elsewhere, contributing to a downturn in overall performance.
Using analytics tools, you can generate a holistic overview of all these different components, identifying gaps in systems and processes that may limit performance. This way, you can understand how different departments interact and identify areas for improvement.
Operational efficiency is a cornerstone of any successful contact center. Failure to sufficiently streamline your operations can overwhelm your agents, leading to poor engagement, weak CX, and the possibility of churn—for both agents and customers.
Using analytics, you can determine how to best allocate resources and tools, empowering your team to manage call volume effectively. You’ll also be able to forecast demand and schedule accordingly, and even fine-tune self-service options to reduce call volume.
The value of CX can’t be overstated. It’s key for brand reputation, revenue generation, and more. 70 percent of customers will spend more for a personalized customer experience and, when they feel appreciated, 87 percent will recommend a brand to friends and family.
Contact center analytics allow you to identify not only what customers are calling about, but why they’re calling. Using this information, you can refine your CX and optimize the customer journey, improving KPIs like first call resolution, average handling time, and more.
Call centers are all about helping people, and the quality of this help depends on your agents. Well-trained, emotionally intelligent agents are capable of not only meeting customer needs, but acting as competitive differentiators for your business.
With analytics, you can establish a credible foundation for agent training using accurate, detailed reports about their performance. This way, you can keep agents invested in their development with call center training tailored specifically to analytics-defined needs.
Analytic tools allow you to make decisions based on concrete data rather than just instincts. In addition to informing hiring, training, and employee recognition, you can use analytics to forecast future customer behaviors and tailor your strategy accordingly.
This analytics-driven approach ensures that you remain agile enough to meet changing customer needs, while also holding sufficient data to back up your decisions and seek approval from necessary stakeholders.
Legislation such as the California Consumer Privacy Act and Europe’s General Data Protection Regulation, alongside the likes of PCI-DSS and HIPAA, have enshrined the importance of regulatory compliance for contact centers.
Beyond legal and financial repercussions, a data breach can also be hugely damaging in terms of brand reputation. With analytics, you can quickly and accurately pinpoint breaches, as well as potential areas for concern, and nip them in the bud before they can escalate.
Contact centers are in a unique position to generate extra revenue via cross-selling and upselling. In fact, Salesforce found that nearly half of companies have already made commerce part of their customer service offering.
By analyzing customer behaviors, trends, and purchase patterns, you can deliver personalized product recommendations at times when customers are most receptive and, using AI, even prompt agents to make an offer to a customer based on real-time data.
While the many benefits of effective call center analytics are clear, the opposite is also true—a faulty approach, or even a total lack of analytics, can be detrimental to the success of a modern contact center.
Failure to integrate data from all sources, lack of expertise from those in charge of the process, and a disconnect between insights and actions, can all contribute to an unsuccessful analytics program, and the resulting negative outcomes.
These negative outcomes include poor decision-making, inefficient resource allocation, missed growth opportunities, and more across every aspect of the organization, from agent performance management to customer experience.
In addition to dragging down important metrics like CSAT and Net Promoter Score, these flaws can negatively impact brand reputation and team morale, leading to both customer and agent churn.
We’ve discussed the benefits of analytics—and the potential downsides of getting it wrong—but now it’s time to talk about best practices. Follow our guidelines to call center analytics below to unlock its full potential for your organization.
To get the most from your call center analytics, you need to link the process to your real-world business goals. This could involve targeting key areas for improvement (customer satisfaction, agent performance, etc.) or identifying specific use cases relevant to your aims.
For example, if a report shows that CSAT is down, analytics may pinpoint high AHT as the culprit. In this case, you could dig even further and ascertain that additional self-service options would lessen call volume, enabling agents to improve AHT and, as a result, CSAT.
Whatever your aims, it’s important that you align all departments and teams around a coherent vision for your analytics approach. Sharing results via accessible dashboards and visuals will maintain transparency and keep all stakeholders invested in the process.
When it comes to prioritizing KPIs, the old mantra “less is more” applies. Ask yourself what kind of insights would feed into your business goals, and which data sources would likely provide these insights. Then settle on relevant, trackable KPIs for your aims.
First call resolution, for example, is an extremely important KPI for call centers, with 84 percent of customers expecting quick, accurate solutions. In this case, you would prioritize analysis of FCR-relevant data like agent notes, voice-of-the-customer data, and routing info.
To accurately measure contact center performance, it’s important that you identify benchmarks for comparison. While you can use global or industry standards as the basis for your assessments, business-specific benchmarks are often more beneficial.
To determine benchmarks, you can analyze historical data, and also leverage input from experienced managers who understand what constitutes strong performance. Remember too that you may need different benchmarks for different teams or departments.
Establishing an effective analytics approach can be an overwhelming task, particularly if your call center has not previously used data in this manner. By starting with a few basic metrics like inbound call volume or schedule adherence, you can derive relevant insights quickly.
This avoids data overload, where you’re sifting through reams of information from every channel and source, struggling to identify the actionable items. Modern analytics tools can also help, providing instant insights via built-in filters and visual dashboards.
As noted, call centers are dealing with a flow of incoming data from multiple sources—from call recordings and customer feedback to agent notes and CRM metadata. Wherever you’re drawing your data from, it’s important that you funnel it all into one central hub.
By keeping all your data in one location, you can connect disparate data points and generate a comprehensive overview of your entire operation. This eliminates data silos and the disconnected information that they provide, while also keeping things manageable.
Every agent contributes to your overall success, so it’s critical that you provide your staff with the support they need to perform at their best. Analysis of metrics like AHT, FCR, and calls handled can help identify agent needs, while also establishing performance benchmarks.
If, for example, you see that an agent is struggling with average handling time, you could schedule a tailored coaching session, helping them to strike a balance between getting to the point and actively listening to the customer’s needs.
We can’t talk about agents without mentioning one of the industry’s most persistent challenges—agent turnover. With more than half of CX leaders identifying recruitment and retention of talent as their top challenge, it’s clear that agent churn is a common concern.
Analytics can improve evaluation accuracy and transparency, enhance training quality, and assist with scheduling, all of which contribute to greater agent engagement and reduce the chances of burnout and stress. AI analytics can even offer in-conversation tips and support.
We know that a contact center can be a stressful, high-pressure workplace, so it’s important that you do everything you can to show agents that they are valued and keep them connected to the wider business aims of the organization.
One approach is to incorporate agent feedback in your analytics process. This doesn’t only encourage collaboration, improve morale, and reduce attrition, it also gives you qualitative data that contextualizes the quantitative performance data you get from KPIs and metrics.
Customers are another useful source of feedback. In fact, 72 percent of CX professionals believe that customer feedback has become more important since 2020. It may seem obvious, but nobody knows what customers want better than the customers themselves.
Incorporating customer survey feedback can complement the rest of your analytics, offering greater context for the numbers and direct insights from your target audience. Even survey completion, or lack thereof, is a measure of customer engagement.
Using technology, you can enhance and expand your analytics process, establishing a data ecosystem that is capable of collecting, sorting, and continuously reviewing the information you feed into it.
Modern call center technology uses automation and artificial intelligence to support agent performance, and some tools are even capable of delivering real-time insights to agents in the middle of an interaction, empowering agents to deliver the best possible CX.
At the end of the day, it’s all about turning insights into action and, if you’ve tied your analytics process to specific business goals as suggested, it will be easier to identify potential actions.
The key is to deliver insights to decision-makers in a way that will naturally prompt action. You can do this with analytics software that delivers information in an accessible, visual manner, encouraging a culture of data-driven decision-making in your call center.
You can also track the impact of any decisions using analytics. In doing so, you will be able to highlight the impact of these decisions and demonstrate the importance of analytics to executive management.
Better customer experience, improved agent performance and engagement, streamlined operations—the value of effective call center analytics is clear. The real trick is leveraging different types of analytics to deliver accessible information to key decision-makers.
With our upcoming integrated business intelligence solution, Scorebuddy BI, you can turn complex data into actionable insights and share your findings with stakeholders via clear, visual dashboards, enhancing the value of your existing QA process.
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