Why AI is transforming the contact center landscape

Let’s take a look at just a few of the various AI use cases in contact centers.

Improving customer experience (CX) with AI

One handy AI call center solution that has received a lot of attention is the introduction of smarter AI chatbots. AI chatbots, virtual assistants, and similar technologies provide an easy way to manage high call volumes. By allowing chatbots to handle more basic inquiries, customers are empowered to solve some of their own issues, while simultaneously freeing up time for agents to focus their attention on more complex questions, improving the CX for all customers.

Increasing operational efficiency and reducing costs

Contact centers must tackle many repetitive tasks every day. Scheduling appointments, answering frequently asked questions, note taking, and quality assurance can all be tedious and time-intensive work. While necessary, these tasks usually aren’t the high-value work that “moves the needle”. By automating these tasks with AI, call centers can significantly increase their efficiency, getting the simple but repetitive work out of the way and making room for the valuable tasks that require a more human touch. This workflow also provides major cost savings, since more time and money can be channeled toward your valuable human agents.

Enhancing agent support and empowerment

Artificial intelligence for call centers can provide added support to agents. Automatic note-taking, assistance from virtual agents, and speech analytics are all excellent examples of AI-enabled tools that not only provide needed support to agents by minimizing their tedious busy work but also empower those agents to create customer experiences that go above and beyond.

Facilitating data-driven decision-making in customer support

Important business decisions need to be backed up by data, and that is especially true when it comes to improving your customer experience. A variety of AI call center solutions — such as speech and sentiment analytics — can be used to quickly sift through large amounts of data and give call centers a fuller picture of what’s working and how customers feel, both at an individual and organizational level.

Key AI technologies used in contact centers

Call centers use AI tech in a variety of different ways to improve the customer experience and improve their efficiencies.

Natural Language Processing (NLP) for understanding customer intent

Natural Language Processing (NLP) is a type of AI that is focused on mimicking the human ability to recognize and analyze speech and text. AI call center solutions that use NLP can analyze both the content and the tone of speech and writing. This allows call centers to gather informative data about what customers are talking about, how they feel about it, and identify trends in the sentiment of their customer base as a whole. NLP is also instrumental for tech like automatic speech-to-text (STT) transcription and AI chatbots.

Machine learning for predictive analytics and pattern recognition

Machine Learning (ML) is a method for training AI programs. This process entails giving the AI a specified task and prioritizing a particular kind of result. The results that the AI produces are graded based on how close they are to the desired result, and the AI adjusts its algorithm based on the feedback.

Using this methodology, call centers can use AI to look through large amounts of customer service data and train it to look for patterns and make predictions. Over time, as the AI learns more, and can provide more and more accurate predictions, giving the call center a leg up on planning their staffing during peak seasons among other things.

Chatbots and virtual agents for real-time support

When a customer has a simpler inquiry — or if they simply can’t reach a human agent on the first try — chatbots and virtual agents are excellent AI use cases in contact centers. In most cases, customers find that chatbots can effectively solve many of their simpler concerns.

Sentiment analysis for understanding customer emotions

With NLP and speech analytics scrutinizing the words and inflections that customers use, contact centers can identify trends and understand customer sentiment in greater detail, gaining insight into how customers (both individually and collectively) feel about their company’s offering. This better positions contact centers to understand where their customers are coming from, allowing agents to course correct in real-time and allowing team leads to adjust strategies for long-term success.

AI use cases in contact centers

Some of the most beneficial applications of AI tech in call centers include:

Gaining valuable customer insights

When the tools are used with care and understanding, contact centers are able to gain incredibly useful insights into the needs and wants of their customers through a variety of AI-powered customer service tools.

• Turn call audio into accessible transcripts: By automatically parsing call audio and transcribing it into readable, searchable text, contact centers can create a strong backlog of information. This makes it much simpler and easier to review past interactions and analyze the data that comes with them.
• Gain a deeper understanding of customer sentiment: Sentiment analysis is a powerful tool, paying close attention to not only word choice and phrasing but also the speech patterns and tone that customers speak with. This makes it possible to gauge a customer’s apparent satisfaction in real-time, allowing agents to address pain points that might not have surfaced otherwise and address smaller concerns before they escalate.
• Detect trends and prevent problems from escalating: Many AI call center solutions can analyze calls and categorize them based on keywords and sentiments. AI tools can then identify certain trends and common issues, as well as the conditions that lead to them. Together, these insights enable a more proactive approach to customer service, identifying and addressing problems before customers are even fully aware of them in some cases.
• Support agents during customer calls: In real-time, AI agent assistance can provide relevant and actionable coaching to help agents choose or adjust to an effective and helpful customer service strategy while the call is still live.

Automating post-call work

One of the most tedious tasks that contact center agents have historically been responsible for is post-call documentation, note-taking, and planning. In traditional call centers, agents often have to manually summarize conversations, update the customer’s records accordingly, and determine which follow-up actions might be necessary to address the customer’s pain points. AI tools can greatly streamline that process by automatically executing those tedious tasks. This saves time, reduces the agent’s post-call workload, and allows the agent to handle more customer interactions in the same amount of time.

Automating compliance and quality management (QM) processes

Instead of taking a retroactive and randomized approach to quality assurance, call center supervisors can allow AI tools to monitor all calls at once, providing real-time alerts for potential compliance risks. The same tools can also provide an automated score to each call, making it easier and more effective to comb through calls and provide both praise and correction to agents who need it.

Improving customer experiences

Not only can AI call center solutions provide real-time coaching to agents, but they can also improve the overall customer experience by effectively handling more routine inquiries with automated tools and enhancing the self-service options available to customers.

Enhancing agent productivity with AI tools

Augmented their own expertise with AI call center solutions, agents can enhance productivity at both the personal and organizational levels.

AI-powered knowledge bases for quick reference

Similar to other large language models (LLMs) that generate answers to text prompts using a massive database of public information and text samples, call centers can create their own customized knowledge base. Agents can “chat” with these knowledge bases, filled with the company’s combined institutional knowledge, and receive accurate answers to specific questions about the company’s policies, procedures, and expertise. This allows agents to get quick answers to important questions, enhancing the support they can provide to customers.

Real-time coaching and guidance for agents

NLP technology can act as an agent copilot, listening for subtle changes and signals in the tone and sentiment of the conversation and providing real-time coaching to agents while the call is live. This allows agents to adjust their strategy and provide more effective customer support at the moment.

Automating repetitive tasks to free up agent time

AI call center solutions can streamline an agent’s workflow by automating the administrative work that bogs things down. With repetitive and tedious tasks like post-call work, answering FAQs, and scheduling follow-up tasks out of the way, agents have much more time and bandwidth to help a greater amount of customers in the same amount of time. It also prevents burnout by allowing agents to focus on the valuable customer support they provide instead of dividing their attention among different types of tasks between calls.

Providing personalized recommendations for upselling and cross-selling

AI tools can pay attention to the expressed needs and wants of customers, as well as make inferences on what those needs might be based on a variety of circumstances. This allows agents to have a better idea of which upsells and cross-sells particular customers might be more open to, and provides the opportunity to base their customer service strategy on more customized recommendations.

AI-driven customer insights for improved service

Learning more about your customers can improve their experience in a variety of different ways.

Analyzing interaction data for customer preferences

By poring over a customer’s interactions across various channels, AI call center tools can glean individual customers’ preferences, whether that encompasses the ways in which they prefer to be served or the types of products that they are partial to.

Predicting customer needs and anticipating future issues

Extrapolating from current data and circumstances, AI tools can make predictions about how the customer’s needs might evolve over time, and notify agents if current data shows that an issue is developing to prevent it from becoming more serious.

Segmenting customer data to tailor service strategies

Customer interaction data is useful for categorizing and segmenting your customers into a handful of differences based on demographics, location, buying behavior, and more. Artificial intelligence call center tools can make your customer segmentation more streamlined and effective, allowing you to tailor your service strategies to get the most bang for your buck.

Enabling targeted customer retention initiatives

When you know more about your customers, it not only gives you the tools to court new customers, but it also allows you to keep the customers you already have. With a better understanding of your customers from AI-powered tools, you’ll be better equipped to retain their business by catering specifically to their needs.

AI in contact center security and compliance

Compliance with regulations, company policy, and security measures are all imperative to maintain customer trust.

Enhancing data privacy through automated monitoring

AI tools can be trained to recognize risky language that tends to signal sensitive information. In real-time, these automated monitoring systems can redact irrelevant sensitive customer information, making it far easier to prevent security or privacy breaches during conversations.

Identifying and preventing fraudulent activity

Taking stock of a customer’s interactions, AI algorithms can identify unusual behaviors, especially those that hint at something fraudulent happening. This flags potential fraud, and helps to ensure that customer data isn’t being used in illegitimate ways, all while allowing innocent interactions to carry on, unencumbered.

Ensuring compliance with real-time analysis

It’s unreasonable to expect one human operator to catch every non-compliant interaction that their agents have with customers in real-time. It’s an impossible task. However, AI call center tools can automatically evaluate every conversation at once and alert team leads of non-compliance as it’s happening. This way, supervisors can make better use of their time while more efficiently correcting compliance issues at the moment.

Automating sensitive data redaction in call transcripts

Not only can sensitive information be redacted during calls, but it can also be redacted from the recordings and transcripts. This leaves far less room for human error and reduces the likelihood that a data breach would reveal a customer’s sensitive or identifying information to malicious parties or the general public.

Common challenges in implementing AI in contact centers

These are some of the most common challenges contact centers experience when implementing AI tools into their workflow.

Initial setup costs and integration with legacy systems

If your call center is outfitted with outdated infrastructure, then integrating AI call center solutions can be a more significant investment to accomplish. Legacy systems tend to have problems with compatibility with modern software, so making use of these new tools may require a greater upfront cost for call centers still running on legacy systems.

Balancing automation with human interaction

The human touch cannot be lost when it comes to customer service. AI tools can augment and enhance the work that contact centers do, but they should never replace them outright. When a customer is frustrated about something, the last thing that they want is to feel like customer service is ignoring them or trying to get out of talking to them. AI tools can be great resources for smaller issues, but if a customer has a bigger problem that requires decisive action, they will more than likely want to speak to a human.

Addressing data privacy and security concerns

When customers interact with customer service, they are demonstrating an inherent level of trust that the data they share will be handled appropriately. Mishandling customer data not only breaks the trust of your customers and causes them real harm, but it can also incur severe fines and penalties. For those reasons, it’s extremely important that call center AI is outfitted with features like anonymization, data encryption, access control, and other powerful security tools.

Continuous training and improvement of AI models

The advantage of AI and ML is the ability to train the model on new data to further contextualize and refine the desired result. Contact centers that use AI-powered tools need to be sure that they monitor their AI models, ensuring that the adaptations of the algorithm are appropriately guided for maximum effectiveness.

Measuring the impact of AI in contact centers

It’s not merely implementing an AI tool into your contact center and hoping for the best. It’s also important to measure the results of using these tools to determine their success.

KPIs for evaluating AI effectiveness in customer support

Key Performance Indicators (KPIs) are important for tracking success in all areas of your business and AI tools are no different. To accurately determine how these tools affect your business, it’s important to pay close attention to the KPIs that they most influence, such as response times, customer retention, and issue resolution rates. Knowing how these tools influence your results will give you a fuller idea of the tools’ impact.

Tracking customer satisfaction (CSAT) improvements

One of the most important metrics in customer service is your customer satisfaction (CSAT). Be sure to take notice of your CSAT scores before, during, and after (if applicable) your use of any AI contact center solutions to track how these tools affect your customer experience.

Measuring operational efficiency gains

The time saved on repetitive tasks is a major advantage that AI tools can offer, so tracking related metrics like average handling time (AHT) or cost-per-interaction can show plainly the time you save and the efficiency you gain from a given tool.

Monitoring agent productivity and engagement metrics

It also pays to quantify the productivity and employee engagement boost that certain tools provide your call center at the individual level. Knowing how satisfied agents are with the work they do as well as their individual task completion rates can be a direct indicator of a tool’s impact.

The future of AI in contact centers

Based on contact centers’ brief and recent history with AI, let’s explore some of the many ways that AI is predicted to influence the future of contact centers.

AI trends shaping the future of customer support

Various new AI use cases in contact centers are providing a glimpse into the future of the industry. Technologies such as conversational AI, sentiment analysis, and predictive analytics all point toward a future of more intuitive customer service, allowing agents to provide an excellent customer experience augmented by AI tools.

Evolving role of human agents in an AI-driven environment

While AI tools have a place in reducing the stress of repetitive, tedious tasks, it’s important to also recognize the evolving role of human agents. Any good contact center will tell you that human agents will always be necessary, however, their role is predicted to shift in the future, putting more focus on empathy and creativity, as well as prioritizing dynamic skills like collaboration and strategic thinking.

Expanding multilingual and multi-channel AI capabilities

AI tools are becoming more accessible in more parts of the world, which is allowing multilingual call centers to benefit from the advantage of AI call center solutions. Multi-channel support is also becoming more common which is allowing call centers to benefit from these technologies with more seamless workflows.

The rise of hyper-personalized customer interactions

With more contextualized data and customer insights, contact centers are becoming much more able to provide a quality customer experience by personalizing each communication to the specific user. Customers tend to report higher satisfaction and show greater loyalty when they feel like agents are taking their needs and specific communication styles into account.

To explore your own AI call center solutions, talk to a representative of ROI CX Solutions today! We can provide you with an expert team of agents, equipped with powerful AI tools that strengthen your customer experience and boost your efficiency!