Call center automation is a silver bullet solution to a lean cost structure and elevating your customer experience. In case you didn’t know, the average call center misses 200 calls per day , has a first contact resolution rate of 70–75%, and spends an average of 5 minutes and 2 seconds on one call.

These numbers may look different across industries and for a business that doesn’t have an automated system, but the narrative is likely the same: without automation, call centers bleed money. This is because automation makes your call center’s cost structure more attractive — even more so than virtual call centers .

While that’s a great reason to automate your call center, there’s a lot more you should know to build a competitive advantage with an automated call center. In this guide, we dive deep into call center automation technologies that can help you deliver an immersive and cutting-edge customer experience. Let’s get to it.

What is call center automation?

Call center automation is the use of software solutions like contact center as a service (CCaaS) to automate repetitive tasks. To automate a call center, you need technologies like voice AI, ACD (automatic call distribution) systems, and robotic process automation (RPA).

Most companies automate their call centers to lower costs, scale their support operations, and improve the customer experience. However, many companies end up deploying basic, built-in call center software features.

To truly differentiate your customer experience with automation, you need a cutting-edge tech stack that easily integrates with third-party services.

Technologies used for call center automation

Call center software solutions use a combination of technologies to automate tasks. Here’s a brief overview.

Machine learning

You can think of machine learning as your call center software’s brain. It’s what helps software predict and make decisions, and that’s why almost every other technology on this list relies on AI and machine learning.

Machine learning analyzes customer interactions and other data to find patterns and predict customer behavior and preferences. It learns from every interaction and improves over time. Here are the types of machine learning used in call centers:

• Supervised learning: Models make predictions or classifications based on labeled data. This is how sentiment analysis works as we explain in greater depth in a later section.
• Unsupervised learning: Algorithms scan unlabeled data to find patterns. Unsupervised learning is used for tasks like anomaly detection. If you need help detecting anomalies like unusual call traffic in your call center, you’ll need a machine learning algorithm for unsupervised learning.
• Reinforcement learning: The model learns through trial and error. It performs an action, monitors the feedback, and uses positive feedback as reinforcement. Automated call routing systems use reinforcement learning to optimize agent selection.

Natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG)

NLP, NLU, and NLG are like siblings. All of them use the same underlying technology — AI — but for different aspects of language processing. Here’s a quick overview:

• NLP: NLP is the backbone of an automated call center. It enables your call center system to comprehend and interpret human language using techniques like language detection, tokenization, and syntactic analysis.
• NLU: NLU goes a step further. It analyzes speech to understand intent and context. Call center software uses NLU to extract meaning during customer conversations and identify intent using entity recognition, sentiment analysis, and intent classification.
• NLG: Once NLP and NLU help your call center software understand the customer’s query, NLG jumps in to formulate a human-like response. It synthesizes information, data, and predetermined responses into coherent and contextual responses that use natural language.

Robotic process automation (RPA)

Almost every automation tool you know uses robotic process automation (RPA). This technology automates repetitive and rule-based tasks like data entry. They’re essentially tireless employees, who perform basic tasks that don’t require intelligence. This frees up your humans to focus on more complex issues that involve strategy and creativity.

Voice biometrics

Voice biometrics is your digital detective — a Sherlock Holmes built into your call center software. It analyzes speech patterns, nuances, and traits to identify a person without passwords or security questions. Industry leaders like TD Bank has used voice verification technology since as early as 2017. Here’s how it works:

• Voiceprint: The system asks the caller to speak one or multiple passphrases. The system captures and analyzes the elements of the caller’s speech like tone, pitch, frequency, and cadence to create a unique voiceprint.
• Encryption: Voice biometrics encrypts the voiceprint and stores it in a database. Safeguarding the voiceprint using encryption protocols like AES makes it almost impossible to access without authorization.
• Verification: When a customer calls, voice biometrics uses machine learning models, often based on Gaussian mixture models (GMM) or neural networks, to analyze the caller’s voice and compare it with the voiceprint.

Call center automation use cases

We could go on and on here, but let’s cut to the chase: you can automate almost every part of the support process, and here’s how:

• Answer common questions: Various tools play a role here. Scripted chatbots answer the most common customer questions, while an AI agent addresses a customer’s more complex questions. If a customer requests priority support, IVR jumps in with a predefined menu, where customers press a specific sequence of buttons to reach the right person on the support team. For example, when you call your bank to check your account balance, you might be asked to “press 9 for questions about an unauthenticated transaction on your card.”
• Appointment reminders: Automated call center systems remind customers about upcoming appointments and deliveries by scheduling calls or texts.
• Surveys and feedback: An automated system can run surveys for you and encourage customers to share insights at the end of an interaction. The system also collects data during interactions — things like customer preferences and behavior — to help you make data-backed decisions.
• Call transcription: Transcripts convert voice recordings into searchable information. Automated systems can transcribe calls in real time so you can go back to a conversation, search for specific keywords, and quickly find what you’re looking for.

Your competitors are cutting costs and reinvesting that money towards transforming customer experience, so automating these tasks is crucial to staying competitive — but it doesn’t give you an edge over competitors.

If you want to aim higher and lead the industry with top-notch customer experience, here are more advanced use cases to focus on.

Faster responses and 24x7 availability with an AI call agent

Imagine having a conversation with an AI call agent that can comprehend your words and understand context, intent, and emotions — that’s the kind of experience your customers want.

You need an AI call agent to truly automate a call center. It’s available 24/7 to field customer queries, matching your customer’s need for speed. Access to help at all times acts as a key differentiator. Here’s why: 60% of consumers feel waiting on hold for just one minute is too long, but customers wait an average of 90 seconds when they call support.

Voice AI’s true brilliance lies in its ability to learn, adapt, and respond in natural language. It uses machine learning to learn accents, dialects, and even individual preferences. But a voice AI agent doesn’t just respond. It anticipates your customers’ needs and responds with the finesse of human understanding. Since voice AI is capable of infusing conversations with a human touch, it helps deliver personalized experiences at scale — no frustration or delays.

Most built-in scripted chatbots perform poorly — they might misinterpret speech and generate out-of-context responses. To provide a better experience, use a powerful third-party AI call agent that integrates into your CCaaS for best results.

Personalized interactions using sentiment analysis

33% of customers have screamed or sworn at agents and 3% admit to threatening agents with physical assault. But what if agents could tell when a customer is in a bad mood and drive the conversation more strategically to achieve a more desirable outcome?

That’s where sentiment analysis helps. It helps a support agent personalize responses based on the caller’s mood, using NLP and machine learning to interpret sentiment, emotions, and tone. The process starts with the system collecting data when you’re on the phone with a customer. Here’s what happens next:

• Transcription: Real-time sentiment analysis requires real-time transcription. NLP breaks down text in a transcript into smaller words and phrases (called tokens), removes stop words, and converts it into a format that it can analyze more efficiently.
• Analysis: NLP analyzes the text to identify words and phrases with a positive, negative, or neutral sentiment. The algorithm considers factors like context, idiomatic expressions, and sarcasm to evaluate sentiment.
• Classification algorithms: The final step involves processing features like word frequency and context using classification algorithms like support vector machines (SVM) and Naive Bayes . These algorithms assign labels and a sentiment score. The assigned score may be between -1 and +1, where -1 represents extremely negative sentiment, +1 denotes positive sentiment, and 0 indicates neutrality.

Suppose a customer is enraged over a service outage. They’re interacting with a AI call agent but request to speak to a support agent. Sentiment analysis springs into action and gives the agent a heads-up. The agent, armed with intel from sentiment analysis, is better prepared to deal with this enraged customer and turn their frown upside down, so to speak.

Smarter call routing with predictive behavioral routing

Let’s face it — no one likes playing button bingo with an IVR system to connect to an agent.

Predictive behavioral routing fast-tracks agent access — it uses machine learning algorithms to route customers to the most suited agent based on various factors, including customer preferences, personality, and emotional state.

Your customers will rarely need to speak to support if your system has the right voice AI agent. But some customers may request to speak to a support agent out of choice or because your agreement entitles them to priority support. When they do, you want to put them through straight to an agent without navigating an IVR menu. That’s when predictive behavior routing helps.

Here’s how it works:

• Customer profiles: The process starts with creating customer segments. The software compiles detailed customer profiles, including their preferences, quirks, and previous interactions.
• Predictive models and algorithms: Predictive models and algorithms use information in customer profiles to predict a customer’s behavior when they make the next call.
• Personalized call routing: The next time a customer requests to speak to an agent, the software uses this predictive capability to match the customer’s profile with predicted needs and route them to the best-suited agent or department.

Suppose you’re an FP&A SaaS catering to industries like private equity and investment banking. A customer, let’s call them “savvy Susan,” usually contacts the support team whenever you add a new feature. Time is a precious commodity for her, so she likes to directly use priority support. When Susan calls after you introduce a new feature, predictive behavioral routing predicts Susan’s interest. It wastes no time and connects her to a specialist on your team.

Dynamic knowledge base and agent assistance

A dynamic knowledge base is an intelligent repository of information that empowers agents with up-to-date and contextually relevant insights.

Think of the last time you heard the words “Can I put your call on hold for a moment?" That happens because agents often don’t have the information ready to deliver to callers.

In fact, 61% of people can’t find the information needed to do their job effectively and spend the equivalent of a month each year looking for that information. At the same time, 67% of customers say speed is as important as price — your customers don’t want to wait while the agent looks for information. The result? Slow resolution rates and unhappy customers.

Dynamic knowledge bases address that concern with real-time agent assistance. These intelligent information repositories use AI algorithms and NLP to absorb information from multiple sources — troubleshooting guides, FAQs, and industry publications — and learn from them.

Picture this. Russell calls your support team because when he tries to make a payment on your ecommerce store, he sees an error. When the support agent picks up the call, the dynamic knowledge base transforms into a real-time assistant. It gathers details about the customer’s current session (so it knows the item Russell wants to purchase). It listens to the customer’s problem, scans its knowledge resources, and delivers troubleshooting steps in real-time on the agent’s screen.

Needless to say, Russell loves the quick resolution and appreciates the zero minutes he spent on hold.

How to use voice AI to get an edge

Don’t implement voice AI because everyone else is doing it. Use it to redefine the very fabric of customer interactions with your brand and inject a potent dose of agility into your operations. Now, you’re probably wondering, “Great, but how do I do that?”

Here’s the short answer: Pivot from mere automation to true personalization .

Instead of viewing voice AI as a technology that solves problems. Think of it as a technology that anticipates them, empathizes with customers, and helps customers like it's an expert, but caring human. If you’re laying the groundwork to lead your industry with top-notch customer experience using a voice AI agent, here are two strategies:

Use voice AI-enabled predictive analytics

Think of predictive analytics as a psychic that helps voice AI predict a customer’s needs before they explicitly mention them.

Suppose you want to go on a family vacation next month. You call your travel booking partner and a voice AI agent greets you and asks how it can help you today. You don’t have a place in mind so you ask for suggestions. At this point, the voice AI agent taps into predictive analytics. It looks at previous bookings and finds that the customer likes adventure sports, budget hotels, and warm weather.

The AI call agent suggests a few places where you can go river rafting or paragliding and currently has pleasant, slightly warm weather. It also suggests the best budget hotels in that town and family-friendly activities you might like. Within seconds, you get vacation options tailored to your preferences. That type of personalized experience differentiates a brand.

The future of call center automation

The world is racing toward more intelligent, seamless customer interactions. Researchers and developers are working on groundbreaking advancements that promise the next decade will revolutionize how customers get support. Let’s talk about some areas of ongoing research that will shape the future of call center automation.

Emotionally intelligent AI

AI-powered emotion recognition systems have achieved remarkable accuracy so far. But these systems are most accurate when identifying basic emotions like happiness, sadness, and surprise. There’s still work to be done when it comes to identifying complex emotions like sarcasm and mixed feelings, and factoring in the variation in emotional expressions across cultures.

Researchers are trying to process these multimodal inputs to categorize emotions using advanced machine learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

There’s also great interest in generating empathetic responses. Researchers are using reinforcement learning frameworks and exploring methods to train models using massive datasets that capture the nuances of human emotion, implied meanings, and emotional undertones — one of the biggest NLP challenges.

Emotionally intelligent AI will transform customer experiences across industries. Along with delivering fast and accurate responses, voice AI will factor in the caller’s emotional state and generate responses that connect with customers on a deeper level. Mind you, we’re talking about a level of emotional connection that even human agents find difficult to develop with customers.

Multimodal interactions

Multimodal interactions is an exciting new frontier. Researchers are working on multiple technologies to enable immersive and interconnected customer experiences. Let’s talk about two key areas researchers are working on:

• Combination of computer vision and NLP: Developers are trying to integrate AI and AR/VR through a combination of computer vision algorithms and NLP. This fusion will enable the system to comprehend human speech and interact with customers within augmented and virtual realities.
• Spatial understanding and interaction: Researchers are trying to map spoken language to spatial cues through a combination of simultaneous localization and mapping (SLAM) and NLP.

Multimodal interactions will allow your customers to use speech and gestures to manipulate their artificial or virtual reality. For example, your customers will be able to interact with your product first-hand and ask questions to a voice AI agent that will provide accurate information in real time.

Call center automation best practices

Before you jump in and set up your automated call center, consider the following best practices:

• Define clear objectives: Clearly defined goals are like guardrails that keep you focused on achieving specific objectives. Start by asking yourself why you want to automate your call center. Answer the question as specifically as you can. If your goal is to improve customer satisfaction, define customer service KPIs to track this goal — for customer satisfaction, track KPIs like average resolution rate and CSAT score.
• Choose the right platform: The AI call agent you choose drives your customer’s experience during an interaction. Look for a platform that uses advanced conversational and generative AI technologies. The company’s commitment to implementing latest tech and providing frictionless customer service is also critical.
• Know your audience: An AI call agent is capable of learning by itself. But that happens when it interacts with your audience. You need to offer the agent some basic information — like audience demographics, preferred communication style, and industry-specific jargon — right off the bat. If you’ve conducted surveys in the past, created user personas based on research, or have access to previous conversations with customers, use that data to train the voice AI agent.
• Monitor KPIs: Monitor KPIs like resolution and completion rates. If your voice AI agent has predictive analytics, you can go a step further and use the collected data to predict what these metrics might look like in the future. If you don’t like the direction that these metrics are going, test, validate, and implement changes to fix the root cause.

Looking for more insights? Read our extensive guide on voice automation best practices .

Take the first step towards advanced call center automation

You’re doing it wrong if your plan is to buy call center software and call it a day. Call center software is great for automating basic tasks like data entry and appointment reminders, but that doesn’t impress your customers.

Give yourself a competitive advantage by partnering with specialists who can help you leverage advanced technologies — transform customer experiences starting tomorrow with Ada’s AI agent. Give yourself the AI edge with Ada and your customers the experience they deserve.