AI for Call Centers: Key Features and Applications

Call centers have always embraced innovation and technology. From IVR systems to automatic call routing, virtually all call centers are using tech solutions to improve performance and optimize efficiency. 

AI takes automation to the next level and offers immense opportunities to reduce manual work in call centers. From AI voice agents answering requests without human intervention to automated QA analysis – here are the most useful applications of AI for call centers in 2024. 

Main Challenges of Call Center Management Today

Contact center artificial intelligence addresses key issues call centers are dealing with today, such as: 

Poor Data Management  

Data silos and ineffective data use are critical issues in call centers. The lack of a good flow of relevant data leads to poor strategy-making, slow operations, unreliable service, and poor customer experience. According to Zendesk, 6 in 10 agents say the lack of customer data often causes negative customer experiences. To address this, businesses need a comprehensive strategy and a centralized solution to manage, evaluate, and share relevant information. 

Changing Customer Expectations 

81% of customers now expect highly tailored services, and failing to meet these expectations leads to frustration, lower purchase rates, increased churn, and reduced customer lifetime value. To mitigate these risks, call centers must stay attuned to changing preferences and adopt strategies that deliver personalized and efficient support.

Agent Burnout & Attrition 

High-stress work environments in call centers contribute to agent burnout and high attrition rates. Disengaged employees are often less productive and offer less effective customer service. According to a recent Deloitte survey, 81% of agents feel overwhelmed by information in call centers that do not use AI solutions. Call center AI solutions can help offload repetitive queries to AI while allowing agents to focus on resolving complex and motivating cases. 

Call center AI: Key applications

AI is a versatile technology that has multiple applications in call centers. Here are the most effective applications of AI for contact centers. 

AI call routing

AI call routing is an upgrade over commonly used Automatic Call Distribution systems. Most ACDs use predefined criteria to route calls within call centers. Callers reach specific departments or agents based on simple rules like:

• Time of Day: Calls received during specific hours are routed to designated departments or shift teams. 

• Skills-Based Routing: Calls are directed to agents with expertise in particular areas (e.g., technical support, sales). 

• Caller Inputs: Interactive Voice Response (IVR) systems ask callers to select options, and then direct callers to an appropriate department. 

Call center artificial intelligence augments the capabilities of ACD systems by using a larger amount of data sources to provide more sophisticated and personalized call routing. AI can distribute calls based on data like:

• Historical Interaction Data. AI can use historical interaction data like items left in shopping carts and responses to marketing campaigns to direct a customer to an appropriate agent. For instance, a caller who has shown interest in premium services but hasn't committed yet is routed to a specialist better trained in converting high-value leads.

• Customer Profiles: AI can use detailed profiles including demographics, preferences, and past behavior to route calls to agents best equipped to handle specific customer types. For example, a customer who frequently buys high-value products is directed to senior sales agents who specialize in upselling and cross-selling complementary products.

• Customer Sentiment Analysis. Artificial intelligence for call centers can detect negative sentiment in conversation to direct the customer to an appropriate agent. For example, a customer expressing negative sentiment or who has left negative feedback might be routed directly to a customer retention specialist to address dissatisfaction. 

This allows AI call routing to massively improve the efficiency of call routing, helping businesses to attract and retain high-value customers. 

QA automation

In traditional call center environments, quality assurance (QA) relied heavily on manual reviews. Supervisors would manually listen to a sample of call recordings, often chosen at random, to evaluate agent performance based on predefined criteria. This process was not only time-consuming but also prone to bias, as the selection of calls could skew the assessment results. 

Call center AI can analyze 100% of call recordings and transcripts in real time and instantly flag calls that meet or fail specific QA scorecard criteria. This enables supervisors to focus on resolving call-handling issues rather than listening through numerous recordings. 

The AI features that enable QA automation include: 

Call Transcription Analysis

AI in call centers can generate precise transcripts of all customer interactions and analyze them according to specific parameters. 

Example: AI accurately transcribes the agent’s conversations and identifies instances where he successfully de-escalates customer frustrations using specific phrases. AI highlights that the agent uses empathetic language 90% of the time when handling billing disputes, which can help them earn a promotion.

Sentiment Insights

AI in a call center can identify specific performance issues by using sentiment analysis. These insights can later be used to improve the agent’s performance. 

Example: Sentiment analysis shows that 70% of the agent’s calls regarding the new feature have negative sentiment scores, indicating a need for clarification. This insight prompts additional training for the agent.

Script Following Evaluation

AI in the contact center can analyze whether the agent is consistently following specific scripts. These insights can be used to quickly fix the issue and improve the agent’s compliance. 

Example: AI compliance reports indicate that the agent follows closing scripts only 60% of the time, prompting targeted coaching sessions.

AI Voice Agents

AI voice agents offer a major upgrade over traditional Interactive Voice Response (IVR) systems. AI voice agents are more effective in relieving the load on the customer support team and increasing customer satisfaction. Here are the main distinctive characteristics of AI voice agents: 

Natural Language Understanding

Unlike IVR systems, which rely on rigid menu options and touch-tone inputs, AI voice agents understand natural language and allow customers to express their needs in their own words. This capability reduces customer frustration associated with navigating complex IVR menus.

Dynamic Interaction 

AI voice agents engage in dynamic, two-way conversations, responding intelligently to customer queries and adapting the conversation flow based on context. This removes the need for customers to listen to long lists of options or repeat information multiple times. 

Personalization 

When integrated with CRM systems and knowledge databases, AI voice agents rely on customer history and preferences to provide personalized responses. They offer tailored solutions based on past interactions and known preferences, which improves the overall customer experience. 

Load Reduction

AI voice agents can handle a wide range of inquiries without human intervention. For example, they can quickly resolve common issues such as account inquiries or password resets. This can be achieved by connecting the voice agent directly with backend systems to execute tasks like updating account information or processing payments.

Seamless Handoff

When complex issues require human intervention, AI voice agents facilitate a smooth handoff to live agents by providing them with detailed interaction histories. This ensures that customers do not have to repeat themselves and improves efficiency and satisfaction. 

Overall, the best AI voice agents: 

• Learn from Human Agents

AI voice agents can learn from the best human agents and use their conversational history to generate appropriate responses.

• Are Grounded in the Knowledge Base

Responses are grounded in the company's extensive knowledge base, which ensures accuracy and relevance. An AI voice agent can also combine knowledge sources with customer history to personalize responses.

• Can Handle a Wide Range of Inquiries

Well-trained AI voice agents can accurately understand and answer over 1,000 categories of tickets. They can easily answer FAQs, which increases first-contact resolution and decreases the costs on customer service. 

• Are Adaptable and Customizable

AI voice agents can easily accommodate and adjust to specific business logic. In some cases, they can even be customized to handle more sophisticated questions, rerouting to human agents only in unique and complex cases. 

Scheduling and staffing

Workforce management systems are commonly used in call centers. Call center AI introduces new scheduling and staffing functionality that improves the performance of WMS systems:

Predictive Staffing

Artificial intelligence in a call center can analyze historical call volume data, trends, and seasonal patterns to accurately forecast staffing requirements. For instance, during holiday seasons when call volumes typically spike, AI can predict the need for additional agents to handle increased demand. In this way, businesses can reduce overtime costs and minimize wait times for customers.

Precise Scheduling

AI in the contact center can create detailed schedules that account for various factors such as training sessions, breaks, and ticket types. For example, if an agent named Emily is scheduled for a training session from 10 AM to 11 AM, the system automatically adjusts her availability. This reduces administrative load and guarantees that every minute is effectively used. 

Real-Time Visibility and Reporting

Supervisors can view what each agent is working on in real time and receive custom reports after shifts end. For instance, a manager could see that an agent spent more time resolving technical issues today compared to previous days. These insights help identify both problem areas and success stories across teams and individuals.

Schedule Adherence and Performance Metrics

AI solutions provide agents with full visibility into their schedules through intuitive dashboards. Agents can know exactly when they're expected to work or take breaks, which improves job satisfaction. 

Overall, AI in workforce management systems allows to improve both operational efficiency and employee satisfaction through clear communication of expectations and recognition of performance. 

AI Knowledge Base Management

Many businesses use knowledge bases to provide customers with self-service options, and agents – with up-to-date, quickly accessible information. 

A knowledge base is a centralized hub for collecting, storing, and sharing information. For customers, it offers direct access to educational content, FAQs, troubleshooting guides, how-to videos, manuals, and product announcements. For support staff, it provides quick access to the necessary information to assist customers in real time. 

Integrating AI with your knowledge base allows you to massively improve the usability of content and boost the productivity of customer support agents. 

Here are the features AI call center can provide to make a knowledge base more efficient: 

Efficient Information Retrieval and Content Discovery 

AI can provide instant answers sourced from company knowledge, which allows customers and agents to find information in just a few clicks. AI search can unify content from all sources to provide comprehensive results, such as retrieving "annual leave policy" details from various platforms simultaneously. 

Advanced Multimedia and Semantic Search Capabilities

AI can transcribe audio and video files and identify clips that match search terms, which is particularly useful for locating specific segments in how-to videos or webinars. Deep indexing and semantic search technologies can quickly get the most relevant answers within vast document libraries, helping agents access relevant information. 

AI-Powered Assistance

AI can provide information to agents via chatbots directly during case resolution, which speeds up response times and allows agents to provide smoother and more high-quality service. It also simplifies self-service – instead of having to find a relevant article in the Help Center, customers can just use a chatbot to ask a question in a natural language. This significantly reduces the need for live agent intervention. 

Overall, AI for call centers can significantly improve your knowledge management process. By providing your customers live responses via chatbots and giving agents an opportunity to quickly search through database data, AI can make customer service faster and more efficient. 

AI Voice Agent Case Study

In 2024, Apiko developed an AI Voice Agent solution capable of making bookings, taking orders, and answering customer queries of varied levels of complexity. The system is designed to increase the efficiency of customer service in restaurants, hotels, and similar establishments.

When applied in a pizzeria chain, the system successfully answered 100% of calls, taking 2284 orders over 63 days and saving the business $12 000 on customer service. The system also managed to take 256 order calls which would have been otherwise missed by the team. 

Here are the key features of the AI Voice Agent Solution Apiko developed: 

Customizable and Trainable Agents 

The voice agents are fully trainable to mimic brand-specific tones, pronunciations, and key phrases. The system allows the use of editable prompts, templates, and knowledge bases tailored to individual agents or locations. Further, the system provides upselling capabilities tailored to business needs, which increases revenue opportunities.  

Efficient Call Handling  

The system manages multiple inbound calls simultaneously to prevent customer wait times. It effectively automates routine tasks such as taking orders, scheduling appointments, and providing product or service information. When necessary, the system reroutes calls to relevant departments. 

Reminders and follow-ups 

The system can provide seamless customer aftercare with outbound calls for updates, reminders, and feedback requests.  It automates follow-ups to make sure that customers remain informed and engaged, fostering loyalty. It can also send reminders and updates via text to reduce missed bookings or orders.  

Post-Call Task Automation 

The system streamlines processes after calls by automating tasks like receipt printing, work order submission, and text-based communication. It is also integrated directly with existing systems for smooth operation and minimal manual intervention.  

Actionable Metrics and Insights  

The system effectively tracks and reports metrics like order volumes, inquiry types, and average call lengths for operational insights. It can also monitor interaction quality with recorded conversation history and live call access, and identify trends for specific customers to personalize future services.  

Overall, the solution developed by Apiko has proven its value, providing significant savings for businesses and improving customer experience. 

Conclusion

AI for call centers can be used to improve the effectiveness of call routing, automate scheduling, streamline QA processes, and answer queries via AI voice agents and chatbots. AI reduces the need for manual replies and provides information quickly, which allows agents to resolve issues faster and focus on more complex tasks.

As an AI software development company, we have extensive experience developing AI solutions like AI chatbots and AI voice agents for various industries. If you have an AI project in mind, don’t hesitate to reach out to us today!