How to Improve Call Centers with AI and Machine Learning

call center and ai

Apparently, we can’t help Barbara anymore. But how can we avoid the premature aging of Megan, John, Anthony, and all of their hard-working colleagues in traditional call centers?

And how to improve the user experience of their customers?

Both these questions have a common answer: implementing AI. Specifically, machines can help enhance call centers by:

  • Reducing call volumes
  • Offering intelligent call routing
  • Personalizing the user experience
  • Assisting human agents in their duties

Call centers are evolving!

What about the last time you contacted a call center? Did you have to type the entire first canto of the Divine Comedy on your phone screen to move through the prompts?

Have you talked to fifteen different operators and ended up yelling at the last poor guy on the other side of the phone?

Fortunately, organizations are beginning to understand that their call centers are critical interaction channels for improving customer satisfaction and engagement, providing quick problem solving and a personalized experience.


Implementing AI is the right way

According to a study by American Express, 78% of consumers did not make an intended purchase due to poor service experience.

To avoid such situations, many call centers are focusing on modern artificial intelligence and machine learning technologies. This allows them to gain valuable insights into customer preferences, potential churn and retention rates, and willingness to buy.

Once this information is gathered, call centers that have implemented AI can use all available data to guide each customer interaction.

Somebody said “data”? Let’s stop one moment on this concept, which can enlighten us on AI’s potential.

Leveraging data with Machine learning

Artificial intelligence can play an essential role in call center activities thanks to its ability to manage data better and faster than humans. The reason for this amazing capability lies first of all in AI’s most recent evolution, known as machine learning (ML).

Machine learning is a branch of AI focused on creating algorithms that can learn from data, recognize specific patterns, and make predictions about it. And the best part is that these algorithms are not even specifically programmed to do so!

Think about an algorithm to distinguish between cats and dogs in a pic. Our machine, which is a notoriously rebellious student and doesn’t trust its professors, will teach itself how a cat or a dog looks by analyzing thousands of animal pics.

The algorithm’s target, which is to identify cats and dogs, has been defined by programmers, but the path to reach this aim will be understood by the machine itself after training on data.


The same logic can be applied in call centers!

If we feed an ML-based system with data such as employees’ performances and career or customers’ behavior, issues, and account history, the machine will start to recognize patterns and categorize people in different archetypes with common traits.

Some practical examples?

When a call center operator proposes an offer to his customer, it can be a good idea to record any crucial information about this interaction (starting with positive or negative responses) and pair it with information about the client itself.


How do we use pattern recognition?

AI can analyze a wide series of repeated interactions with many customers and discover patterns. Maybe a certain offer or marketing strategy turns out to be attractive to a specific type of client.

By combining data about the agents, their actions, and the responses received, AI will offer companies efficient negotiating models to follow.

AI could even understand that specific operators tend to excel in some tasks and, on the other hand, struggle when facing different issues.

An operator may be a sales wizard, but he could have some trouble dealing with the stress of repeated complaints from dissatisfied customers.


The great step forward: deep learning

A further step has been taken with the development of deep learning, a branch of ML that trains machines to learn from huge amounts of data thanks to artificial neural networks.

DL-powered systems can recognize the most hidden and unpredictable patterns by digging deeply into the data and processing the information through the sprawling structure of their networks.

Deep learning unlocks even more surprising innovations, first of all in the field of speech recognition and interactive voice response.

Basically, any virtual assistant and chatbot make extensive use of these algorithms to process the human voice and respond accordingly. This allows companies to automate a wide range of processes in their customer care, including call centers.

Another interesting application of speech recognition is linked to behavioral analysis and aims to identify signs of discomfort during a customer’s phone call, for example by analyzing the tone of voice or the choice of words.


1. AI to reduce call volumes

All right, now that you have a clearer idea of ​​how these new technologies work, let’s take a closer look at the benefits AI can deliver to call center companies.

The first is to prevent customers who wait for an operator from turning into skeletons after seventy years in the queue. That’s what happened to Yuri. You can see a recent pic of him below.

ai and call center

Reducing waiting times is one of the many positive consequences of good call volume management, together with greater customer satisfaction and a reduction in operator stress.

But how to do so? Well, we can count on some tricks strictly related to AI and ML.


Clients won’t call… if they don’t need to call

What if we eliminated the need for customers to call when there is, for example, some network fault or any other technical issue?

Yep, prevention is a great way to reduce call volumes.

Thanks to machine learning and deep learning algorithms, we can use speech recognition  to identify the presence of a problem (from the irritated tone of voice of several customers), the very nature of the problem (“bad connection!!!!” ), and the location of the malfunction (from the place where they call).

All this data will be analyzed by an ML-based system, which will notify technical support or directly to all customers living in the same affected location of the callers.

That way, they’ll know the company is aware of the problem and won’t overload the phone lines. Or they will call anyway, cause complaining is people’s favorite hobby.


ML-based interactive voice response

As we have already mentioned, AI-based speech recognition unlocks exciting possibilities in the field of interactive voice response (IVR).

Virtual assistant and chatbot based solutions offer customers a fast and automatized response to their needs and have become increasingly popular. Just think about AI developments such as voice search on consumer devices.

In the same way, automatization tech can be leveraged to speed up call center customer care by quickly delivering relevant suggestions or answers to clients in need without the direct involvement of a physical agent.

Of course, understanding the context is critical for chatbots to respond better to prompts, and recent advances in machine learning have secured a big boost in this matter by improving natural language processing.

Previously, interactive voice response systems were more of a source of frustration than help, and many customers tried to skip this step by repeatedly pressing “talk to agent”.


A practical example: Humana’s IVR system

A similar approach has been developed, for example, by the health insurance giant Humana. As of 2016, its centers received over one million calls each month, 60% of which were simple inquiries about basic insurance policy information.

To face this challenge, Humana partnered with IBM and implemented an AI solution based on natural language understanding (NLU) software that could identify and offer the specific information callers required.

This system applies seven language models and two acoustic models to recognize over 90% of the customers’ spoken sentences.

Since its implementation in 2019, the percentage of customers using this virtual assistant has doubled and the cost of execution has dropped by two thirds. Not bad!

“Since its implementation in 2019, the percentage of customers using Humana’s virtual assistant has doubled and the cost of execution has dropped by two thirds.

2. AI-based intelligent call routing

Sometimes bots aren’t enough. Sometimes, the only solution is a reassuring human voice to support you psychologically guide you through the tortuous process of starting up your new PC.

“Look, the laptop I just got from your courier won’t start.”

“Did you try to plug it in?”

“……….. hey, now it works!”

In these cases, what we need is a good call routing system to optimize HR costs and help bring the right customer to the right operator.


From “traditional” to AI-based routing

Call routing strategies were already applied in the 90s through the use of skills-based routing software, which connected a certain segment of customers with the most suitable operators considering their skills and the type of assistance required.

This involved rigorous profiling of both the client and the agent. In particular, the latter was selected on the basis of the experience level, sales numbers, customer-care capabilities, and so on.

And what about psychological traits? Some operators may be good at handling negative calls, others at offering promotions.

In recent years, the growing implementation of AI in such processes has helped to power predictive behavioral routing, which is precisely focused on these psychological factors.

This approach builds on the data analytics and behavioral analysis mentioned earlier to match callers with specific personality patterns to agents who can effectively handle those types.

call center and using of ai

3. AI to personalize the user experience

AI-based call routing is just the first step in personalizing the user experience, offering customers the service that best suits their needs.

The second way is customer relationship management (CRM), which is basically the process of analyzing and managing a company’s interactions with its past, current, and potential customers.

This approach leverages client history data analytics to strengthen business relationships with them, with a particular focus on customer retention and sales growth.

Data comes from a wide array of communication channels, such as the company’s social media, website, telephone, or email, and can be related to promotions, activity history, payments, and so on.


What to do with all this data?

A personalized service, which most customers expect today, is only possible with robust CRM combined with AI-based call routing.

Through the CRM approach, companies learn from data more about their target audience and how to best meet their requests.

In particular, call center agents can use customized procedures and promote products that clients are more likely to purchase.


4. AI to assist call center operators

One last aspect I want to focus on in today’s article concerns the general support provided by the AI to our heroic operators.

Free virtual hugs to encourage them after an entire week of screaming customers’ calls? Maybe someday, my friends.

For now, we have to make do with a different kind of help. Which one?

Well, for example… forecasting!


AI is the best advisor to call center operators

Machine learning and predictive analysis are incredible tools when it comes to detecting behavioral patterns.

By analyzing the previous customer behavior of a client, AI-powered systems can offer useful insights to call center operators to improve up-sales or choose the best problem resolutions.

Think about an unsatisfied customer. Let’s call her Karen, which is probably the internet’s least favorite name nowadays.

Karen’s inbound calls provide precious information on her psychological traumas how to approach her according to her level of dissatisfaction and risk of churning.

Thanks to predictive behavioral analysis, AI will provide us with suggestions about possible promotions to calm her down.


AI support is already a reality

Sounds like a joke? No sir! In fact, a growing number of companies are implementing machine learning algorithms to scan data and process it into customer risk scores.

Data such as how many times a customer has uttered a phrase like “I will change Internet operator” during their calls.

Once a certain limit is reached, the AI system will notify the sales teams and suggest recommendations for personalized offers and benefits.

This kind of tech is especially useful for inexperienced operators who still didn’t develop the ability to precisely detect the mood of a client.


AI Implementation is slow but ongoing

Maybe it will surprise you, but despite all the interest regarding AI, just a few companies have actually implemented it in their call center processes.

On the other hand, this evolution is expected to accelerate significantly in the next few years, especially after the disastrous 2020 pandemic that forced many call centers to switch to a remote working model.

Some estimates even speak of growth in the AI ​​market for call centers from $ 800 million in 2019 to $ 2,800 million by 2024, with a compound annual growth rate (CAGR) of 28.5%.

While we wait for this process to complete, remember to treat call center operators nicely, even if your day sucked.

Cause karma exists.