Jump to ↓

Artificial Intelligence (AI), Natural Language Processing (NLP), machine learning, and Robotic Process Automation (RPA) have influenced contact center trends that are changing the way call centers operate.

In the coming years, AI-powered automations will play an even bigger part in contact center operations, with an expected 80% of customer service teams using some form of generative AI by 2025.[*] AI-driven contact center automations can be used to streamline time-consuming tasks like coaching agents, addressing billing discrepancies, and making product recommendations–all in real time.

This not only increases agent performance and productivity, but it allows companies to create hyper personalized customer experiences, which 65% of consumers say they expect from the brands they do business with.[*] Read on to find out what the biggest contact center automation trends are, how they are changing the way customers and companies interact, specific use cases, and more.

 

What is Contact Center Automation?

Contact center automation is the use of software to complete and optimize repetitive tasks that would otherwise be done manually by call center agents.

Call center automations are created by leveraging technology such as call center software, artificial intelligence, machine learning, etc., and then building workflows by defining rules and conditions that trigger specific actions. For example, whenever a call recording ends, a summary is created and emailed to the supervisor automatically.

Workflows can automate everything from call routing and after call work, to workforce management and marketing.

 

Contact Center Automation Trends for 2024 and Beyond

Recent advancements in contact center technology, specifically in artificial intelligence, machine learning, and natural language processing, have led to some exciting trends in contact center automation. Not only has the range of possible automations expanded, but the tools used to create automations have become easier to use, with more granular customizations.

The automations themselves have also become more sophisticated and accurate, particularly automations that leverage NLP and conversational AI. Here’s a look at the key automation trends expected to impact contact centers in the years ahead.

 

Generative AI

Generative AI is a technology that contact centers use to create content and converse with customers and agents using natural language.

Generative AI can be used to automate a number of common call center tasks, such as drafting follow up texts, creating promotional content, and responding to customer emails.

Leveraging generative AI not only frees up agents to handle more complex matters, but it reduces human error and ensures that customer interactions are consistent, on brand, and in compliance with all company standards.

Example: An online retail company could use generative AI to automatically create a personalized follow up email for every customer after a purchase is made. The AI can analyze the customer’s purchase history, identify tracking numbers and shipping information for the current order, and send a friendly, customized, and helpful message for the customer, just seconds after the purchase was made. This not only relieves stress on agents, but makes every customer feel valued.

 

Omnichannel Integration

omnichannel

Omnichannel customer support has been growing in popularity as more customers seek to interact with brands on the communication channels where they spend their time (email, SMS/MMS text, social media, etc.) Omnichannel integration goes beyond providing a company presence on these channels, and organizes all interaction data under one pane of glass.

Omnichannel integration encourages customer engagement by allowing people to connect on any channel they like or even switch between channels without having to repeat themselves. An omnichannel support system also incorporates analytics to identify key moments and sentiment across channels, allowing for a big picture view of the customer journey.

Example: A customer sets up a free tour at a gym using an online chatbot, and the company’s automation software sends out a confirmation email. The customer then sends a text asking to reschedule, and later calls to ask about accessibility. The omnichannel system will automatically ensure that the employee assigned to the new customer has accurate information on the rescheduled tour date as well as the accommodation request.

 

Personalized Customer Experience

Providing a personalized customer experience has been trending for a while, and is now a requirement for businesses that want to keep up with customer expectations. Contact center automation hyper-personalizes each customer interaction by creating detailed and dynamic customer profiles.

CCaaS software is used to collect customer information across channels, including purchase history, previous interactions, preferences, customer behavior, location, and more. After each interaction, the profile is automatically updated with data such as a call summary, transcript, and/or sentiment analysis.

Customer profiles are displayed on live agents’ screens. This way, agents have the context they need to provide a personalized service experience without the customer having to reintroduce themselves.

Example: A hotel chain uses contact center automation software to create detailed profiles for every customer, including reservation history, personal preferences, and keywords. When a customer calls to make a hotel reservation, the software displays this information for the agent automatically, along with information about the specific hotel the customer is requesting a room at. The agent sees right away that this customer prefers to stay on the top floor, and frequently asks about the pool. The agent then informs the customer that there is a room available on the top floor, and lets them know the pool hours of the hotel.

 

IVR and Self-Service

IVR

Interactive voice response, IVR, or virtual assistant is a call center technology that provides a menu of options to callers and routes calls based on caller selection and agent availability and skills. IVR systems reduce the need for callers to be transferred multiple times, decreasing call times and increasing FCR rates.

Advanced IVR systems can also leverage NLP and AI to analyze a caller’s request in their spoken language, and then respond appropriately using natural language, further reducing the need for human intervention.

Other self service tools such as chatbots and online forms allow a percentage of customers to get the assistance they need without having to make a call. Many customers prefer to use self-service, especially with simple processes like scheduling an appointment or checking an account balance.

Example: A healthcare organization sets up a self service patient portal where clients can securely make appointments, view medical records, and pay bills. This reduces call volume, but does not eliminate it. For the incoming calls that remain, the organization builds IVR call paths to ensure that calls are transferred to the correct department.

 

Automated Interaction Scoring

automated interaction scoring

Automated interaction scoring is a contact center software feature that captures and analyzes data from across voice and digital channels, evaluating communication and scoring agent performance. These scores are based on pre-defined goals and metrics set by the company.

Scores are sent automatically to managers who can then make an additional assessment after reviewing recordings and transcripts, with the final evaluation being sent to the agent for review. Automated interaction scoring helps managers to stay organized and prioritize the interactions that need their attention the most.

Example: A call center uses automated interaction scoring to determine its highest and lowest performing agents, as well as the most common pain points for customers. This information is then used to provide additional training for the lowest performers, as well as a playlist of do’s and don’ts for new hires, and an updated call routing system that sends the most complex calls to the highest performing agents.

 

Predictive Analytics and Forecasting

Predictive analytics leverage AI and machine learning algorithms to predict key metrics such as call volume and CSAT scores. Predictive analytics tools analyze historical call data, customer sentiment, and keywords. The larger the data set, the more accurate these predictions will be.

Contact centers use predictive analytics to properly staff their centers and to know when a call needs to be escalated.

Example: An insurance company uses predictive analytics to find out how many contact center agents they need on call at any given time. The software analyzes years of call data to determine when peak call times are on a daily, weekly, monthly, and annual basis. Contact center automations are used to create employee schedules that take this data into account along with information on each employee’s availability.

 

Agent Assist

agent assist

Agent assist is a contact center software tool that uses AI to provide call center agents with context and assistance in real time so that they can resolve more customer issues at a faster rate, leading to higher customer satisfaction.

Agent assist integrates with a company’s CRM database as well as its knowledge base, call scripts and other brand material to provide employees with the precise information that they need exactly when they need it.

Example: A subscription-based toy company implements agent assist software trained on both customer data and the company’s knowledge base. When a subscriber calls with a technical question about assembling their toy, the agent they speak with is automatically supplied with information about which product the customer has received and assembly instructions.

 

Predictive Call Routing

Predictive call routing goes beyond forwarding calls based on IVR selections, and analyzes each caller's behavioral history, preferences, and persona type. This data is compared to agent skill sets and performance scores to match each customer with the best possible agent.

Making better agent-customer matches results in less customer frustration, higher CSAT scores, and higher first call resolution rates.

Example: A software application company uses predictive call routing to match incoming callers with live agents using skills-based routing. When a developer calls in to customer service, the AI-powered software notes that this customer has called before with complex technical issues and immediately forwards the call to a senior agent with technical experience.

 

AI-Powered Voice Bots

Voice bots are similar to chat bots in that they use AI and NLP to converse with customers, but voice bots are able to interact in spoken language. The earliest versions of voice bots sounded very robotic and were often frustrating for customers, but they have evolved to be extremely realistic.

As the technology has improved, voice bots usage has increased in popularity. The voice bot market for customer service communications specifically is expected to grow by 21.30% from 2022 to 2027.[*]

Example: A car insurance company designs voice bots to assist with common processes such as a change of address, a price quote, or a claim filing. Customers can speak with the voice bot 24/7 using natural language, and live agents are free for more complex matters.

 

AI Speech and Sentiment Analysis

sentiment analysis

AI-driven software is now capable of analyzing speech patterns and caller sentiment with a high level of accuracy. These tools monitor calls and identify key words and phrases that indicate discontent, increases in pitch, and agent-customer overtalk.

Monitoring speech patterns and moods allows supervisors and managers to provide assistance to agents in real time, or take over a difficult call. This improves customer satisfaction and helps agents feel supported.

Example: A supervisor at a high volume call center uses automation software to set up real time alerts whenever the monitoring software detects high levels of frustration from customers. The AI-powered software identifies a customer who uses the phrases: “I can’t get this to work” and “I’ve tried that already” and also detects an elevation in pitch and volume. The supervisor is alerted immediately and is able to activate Call Whisper and give advice to the agent.

 

AI Powered Security Monitoring

Security breaches are not only costly, but can also quickly destroy a company’s reputation. More and more companies are combining robust security measures like MFA, SSO, and E2EE with AI-driven threat detection tools.

AI-powered tools can be used to constantly monitor digital barriers and voice conversations to proactively identify suspicious activity and threats.

Example: A banking institution implements advanced security measures such as end-to-end encryption and around-the-clock systems monitoring to ensure client information is protected.

 

The Benefits of Contact Center Automation

Contact center automations are all about increasing efficiency and productivity. They benefit employees, managers, and customers all at once. Contact center automations do not replace team members, but rather free them from the routine tasks that often prevent them from mastering skills and cause burn out.

Here are some more benefits that contact center automation offers:

  • Workforce Compliance: Predictive analytics and automated scheduling ensure that all team members have appropriate breaks and time off in compliance with local regulations
  • Quality Management: Automation tools constantly monitor interactions to ensure that company standards are being met and customer expectations are exceeded
  • Reduce Operational Costs: Automating repetitive, time-intensive work such as data input, call transfers, agent scheduling, etc., results in less billable hours
  • Increased Agent Productivity: Automated agent assist tools give employees access to comprehensive customer data so that they can quickly resolve issues, resulting in shorter handle times and higher FCR rates
  • Increased Customer Satisfaction: Automation tools enable brands to better address customer needs by providing self-service options, reducing wait times, and freeing up live agents to focus on customer inquiries

 

How AI Technologies Are Transforming Contact Centers

Contact centers are evolving in order to meet rising customer demands while keeping costs down. CCaaS automations not only allow contact centers to help a higher number of customers, but with each new interaction, more data is collected and CX improves.

AI optimizes nearly every aspect of daily contact center operations. From creating agent schedules and employee training to making sales and providing customer support, automation solutions increase operational efficiency and reduce human error.

When using automations, ensure there are built-in mechanisms for customers to easily reach a human agent when needed. This will maintain the balance between efficiency and personalization.

 

FAQs