How to improve call center customer service

Despite the proliferation of chatbots, self-service channels and social media platforms, call centers remain a critical frontline for customer interaction. The largest can handle millions of conversations daily, acting as a primary direct line of contact that can make or break customer relationships.

Today, consumer expectations are at an all-time high and organizations across the globe are transforming their business processes with automation and artificial intelligence. In this environment, thoughtful attention toward the call center customer experience is essential for continued survival and growth.

According to a recent research from ZenDesk, 83% of consumers believe that the customer experience should be far better than it is today. This is a staggering figure given the rapid adoption of key technologies in the industry over the last decade.

But customers who have positive experiences are more likely to make repeat purchases and recommend a company to others. Conversely, poor call center experiences can quickly erode years of progress, leading dissatisfied customers to abandon a company or share negative experiences widely.

“Good customer service can turn one-time clients into long-term brand champions,” says Manish Goyal, a Senior Partner at IBM Consulting. “And the lifetime value of an NPS promoter can be 10 times more than an NPS detractor. At the same time, around 80% of consumers say that they would rather do business with a competitor after more than one bad experience with a brand.”

Broadly, excellent call center customer service reduces operational costs by improving first-call resolution and decreasing customer churn. At its most valuable, it identifies emerging trends that can inform a business’ broader strategy.

As McKinsey recently found, as much as 80% of value creation by the world’s leading companies comes from unlocking revenue from existing customers. With the intelligent deployment of key AI-powered technologies and effective human-machine collaboration, contact centers can move from expensive issue-resolution hubs to innovative profit engines. 

Customers might use various omnichannel platforms, but phone calls remain the most preferred mode of interaction across generations, according to Gartner. A positive call center customer experience is based on accessibility and lack of friction: Customer calls reach support through their preferred channels without excessive wait times or burdensome menus. And when they do connect with a call center agent, they encounter someone who is knowledgeable and empowered to resolve issues quickly.

From the human agent side, this process gives employees immediate access to a client’s complete information, eliminating the need for customers to repeat themselves or explain the history of their request. Equipped with the most relevant data, an agent can focus on active listening, problem-solving and clear communication, adapting their approach to each customer’s unique situation.

Beyond individual agent performance, excellent call center service is consistent across touchpoints and time periods. Throughout the customer journey, a caller should receive the same quality of service, no matter when they make contact or how busy the center happens to be.

Excellent call center customer service is characterized by various variables that collectively create positive customer experiences. Successful call centers must strike a difficult balance between speed and personalization, requiring organizations to rely on a diverse set of internal metrics and customer feedback rubrics. Some of the most common include first call resolution times, average handle times, customer satisfaction scores (CSAT) and net promoter scores (NPS). 

Modern call centers operate in an increasingly complex environment that presents significant operational challenges, such as high call center agent turnover rates and outdated customer service models. And building customer trust in an increasingly automated landscape requires a transparency overhaul. Some of the primary challenges facing call centers today include:

Call centers face significant pressure to balance cost control with service quality. Budget limitations restrict hiring and technology investments, or require organizations to make difficult choices. For example, should a business invest in more service channels, or focus on the quality of just a few?

Call centers also experience complex staffing challenges as schedules must match call volume while accommodating agents’ skills. The need for specialized knowledge across different product lines or customer segments adds a layer of complexity to workforce management. And, critically, technology infrastructure can further restrict operational flexibility or the ability to scale capacity quickly. 

Customer information is often scattered across multiple systems that don’t communicate effectively with each other. This fragmentation forces agents to move between applications and increases handling time. It also creates opportunities for errors or inconsistent information. 

Call centers regularly experience dramatic increases in call volume due to seasonal patterns, product launches, billing cycles or marketing campaigns. These spikes overwhelm available resources, creating cascading effects. Predicting these surges accurately in order to staff appropriately remains difficult. When call volume exceeds capacity, backlogs can take hours to clear. 

As anyone who has ever made a service call knows, extended hold times represent one of the most significant drivers of customer dissatisfaction. But those long wait times create operational challenges, as well. Customers who abandon calls might experience heightened expectations on the next call, and the pressure to reduce wait times can push agents to rush through calls, potentially sacrificing quality.

Customer expectations have risen dramatically, shaped by seamless digital experiences in other areas of their lives. Today, most service professionals—82%—say that customer expectations are higher than they used to be. The majority expects immediate responses and personalized services based on their preferences. Meeting these expectations requires sophisticated integration across systems that many call centers struggle to achieve. 

According to research from Salesforce, 77% of human call center agents report increased or more complex workloads, while more than half report experiencing burnout. These agents face relentless pressure from high call volumes that leave little time to recover between interactions, and they might regularly interact with frustrated or distressed customers. When volumes unexpectedly increase, agents experience intense pressure, which reduces their ability to provide thoughtful, patient service. 

As call centers have adopted various technologies to increase the speed of customer interactions, critical information is frequently stored in multiple systems that are difficult to navigate. Knowledge can be scattered across disparate repositories or lag behind product changes. When call center agent teams rely on information that is slow to access, incorrect or obsolete it undermines customer trust, forcing agents to waste time backtracking or correcting information. 

Many of the preceding issues affecting operational efficiency and customer retention can be mitigated with strategic investment and tools that empower agents to provide exceptional customer service. 

Implementing select AI tools supporting human agents during live interactions can dramatically improve performance. If these tools are carefully designed with real-world outcomes in mind, the benefits will be evident. Real-time AI assistants might suggest responses or surface relevant knowledge-based articles.

In one successful instance, a prominent bank deployed natural language processing (NLP) to immediately suggest a “best possible question” to an agent. This process reduced customer interaction times by 6% and significantly reduced training requirements.

Elsewhere, Avid Solutions, a research and development first, successfully reduced the time it takes to onboard new customers by 25% with agentic AI. Intelligent automation can also generate call summaries and trigger follow-up actions, reducing the administrative burden on human agents and allowing them to focus on more relationship-based tasks.

Most importantly, these technologies generate critical insights that transform cell centers into strategic assets. “With generative AI, you can go through the transcripts of every call made and continuously gather insights on how and why agents are taking a long time to handle certain types of calls,” says Manish Goyal of IBM Consulting. “Or understand granular classifications of complaints on products or services.”

“This insight that an application of generative AI provides allows you operations leaders to find the root cause of a problem faster, and resolve it if it’s in the servicing function. Or alert the product or marketing teams, if they need to take remedial action.”

Organizations must carefully balance the number of human agents against expected demand and ensure that customer service agents have the right mix of specialized skills. As AI capabilities expand, organizations face strategic decisions about the optimal ratio of automated and human support. The goal shouldn’t be to eliminate human agents but rather to reimagine the roles in which they create the most value—for instance, complex problem-solving, high-value customers or emotionally sensitive situations.

“When we’re talking about the customer journey, we’re talking about choice,” Jeanie Walters, a customer experience speaker and training, recently told IBM’s Albert Lawrence. “If I’m in the car, I might want to call and talk to a person. So providing these options for people, even though it might be more efficient on our end to use a virtual agent, we can give these choices to customers as well.” 

While comprehensive training programs are necessary for new hires, that training shouldn’t end after onboarding. Regular trainings and upskilling initiatives should address new products or policy changes and emphasize “soft skills” such as active listening and effective communication.

Increasingly, as call center employees interface with automation and AI technologies, training should include effective human-machine collaboration. This process might include when to trust AI suggestions or how to seamlessly take over from AI agents when escalation is necessary. 

In the fast-paced world of customer service, centralized knowledge management is key. Organizations should consolidate information into a single, authoritative knowledge base serving as a single source of truth, ensuring it’s actively maintained.

Information should be intuitively organized and easy to search—for example, robust search functions with NLP helps agents quickly find relevant information. Advanced call center software can surface relevant data instantly based on a customer’s account information or keywords detected during a conversation.

Human agents are “obviously inundated with emails, and there’s plenty that virtual assistants can automatically respond to,” Morgan Carroll, IBM senior AI engineer, recently told the Smart Talks podcast.

“But if they have something very difficult, and they’re getting a lot of these emails every day, generative AI can actually assist. It’s not going to respond for them, though it might. In this case we’re thinking about how we can make the customer service agents’ jobs easier. And generative AI can help them draft an email or look up information.” 

Modern call routing systems ideally consider multiple factors including issue complexity, historical interaction data or agent skill. Placing the right customer with the right member of a customer service team improves first-call resolution and reduces transfers.

Also, clear escalation criteria and policies for customer issues should remain a priority for call centers, whether a client’s first contact is with a human agent or a self-service tool. When such escalations are necessary, transfers should retain all interaction history and context to reduce customer frustration. 

Unified customer data platforms provide a critical backbone to call center operations. Integrating customer information from all relevant systems—including customer relationships management (CRM) software, billing, order management, support history and web analytics—into a unified system gives call center agents complete context for every interaction.

Successful systems will include real-time data synchronization and analytics integration. By connecting operational data from a call center with broader business intelligent platforms, organizations gain critical analysis of how customer service impacts retention and revenue. 

Traditional call centers relied primarily on automatic call distributors, using caller information like a dialed number or interactive voice response (IVR) input to route incoming calls. As early automation technologies became common across call centers, they still focused on narrow applications.

Speech recognition systems might transcribe calls for quality monitoring. Basic chatbots handled simple scripted interactions like password resets or FAQ responses. These implementations cut costs for many businesses, but resulted in an uneven customer response.

According to ZenDesk, customer demand for transparency in AI has risen 63%, even as AI increasingly handles customer needs. And as Forrester recently predicted, in 2026 a third of companies will harm their brands with frustrating AI self-service. Rigid chatbots following set decision trees often resulted in frustrating interactions, leading callers to insist on bypassing automation.

Today, agentic AI is poised to upend call center operations, transforming entire business processes rather than automating them. In addition to engaging in natural, nuanced conversations these systems can proactively access multiple data sources and synthesize information. They can also retain memory over time, increasing the accuracy of their suggestions and performance.

Given a set of goals and parameters, this makes agentic AI advantageous to both customers and call center workers: AI agents can quickly synthesize customer data, third-party information and product specifications to suggest solutions to difficult queries.

They can also proactively monitor call center workers’ performance, suggesting potential coaching opportunities, or independently organize research and data-entry tasks. This ability to resolve more complex issues and handle actions across systems ultimately allows for better self-service, fewer escalations and more personalized follow-up. In return, these benefits help call center employees to focus on their unique empathetic abilities. 

Gartner predicts that by 2029, agentic AI will autonomously resolve up to 80% of common customer services issueswithout human intervention. But to reap the benefits of agentic AI, particularly in complex and data-rich environments like call centers, training isolated agents and deploying them isn’t enough.

AI agent orchestration platforms intelligently coordinate multiple specialized AI agents. Each is designed to excel at specific tasks or handle particular kinds of customer inquiries. Rather than relying on a single AI system, orchestration allows organizations to deploy a collection of purpose-built AI agents to work together seamlessly.

This specialization produces deeply optimized AI agents with vast knowledge in a specific domain, whether it’s product recommendations, billing inquiries, troubleshooting or human agent support. When an interaction requires expertise beyond a single agent’s scope, orchestration enables smooth transitions between this network of agents; it also allows multiple AI agents to collaborate simultaneously on complex issues.

Data is at the core of AI automation and orchestration efforts, providing the context for purpose-built AI to train on and the external tools for each agent to call on. This might include comprehensive customer data to enable personalized service and bulk interaction data for wide-scale analysis. It can also include operational data to drive workforce management or sentiment analysis data to optimize call center operations at large.

By unifying reliable, clean and accurate data organizations create a feedback loop in which more data converts to better AI performance—and reduces friction across call center operations. 

Assessing call center customer service requires a balancing act between efficiency metrics and softer rubrics. When an organization automates too aggressively at the expense of empathetic support, it suffers. Conversely, when average handle times increase customers find themselves on hold for too long and organizations flail to capture expected call center return on investment (ROI).

Any customer experience technology transformation should track various diverse call center metrics, preferably using real-time dashboards and analytics. Weighing customer feedback and voice-of-the-customer scores (net promoter scores, customer satisfaction scores and customer effort scores) can have a significant impact on enterprise-wide numbers.

For example, when one telecom CEO directly addressed customer complaints and their CSAT score jumped to the top of the industry, they saw their churn rates cut by 75%. Within three years, the company’s revenue nearly doubled.

Particularly with new technology implementations, business leaders should take a holistic view of call center performance: Are service-level key performance indicators (KPIs) like average handle times or number of calls resolved increasing, but customer loyalty metrics or post-call support suffering? To streamline call centers and ensure that long-term success, workflows and new initiatives should be continuously analyzed across multiple axes.