Every call center, especially contact centers, is built around the concept of the customer journey.
The customer journey is the path that a customer takes from the first time they hear about your brand all the way through to when they purchase your product or service.
It’s important to remember that the customer journey is not a fixed path.
The more you interact with your customers, the more loyal they’ll be to your brand. However, there is one big question; which do you choose, Artificial Intelligence, human voice analytics, or a mixture of both?
When designing the customer journey, the most important thing to remember is that the customer is at its center.
The customer journey is about what the customer wants and needs and not about what you want.
However, you still have to ensure your business functions fit the customer journey’s goal: to ensure that the customer interactions satisfy everyone and have a good experience with your company.
As a business owner, you’re one of the most important members of your customer’s experience.
If you can build a relationship with your customers and show that you’re there to help them, then they’re more likely to continue to purchase from you and recommend you to their friends and family.
A call center’s primary aim is to deliver a positive customer experience at every touchpoint.
If a customer calls a call center with a query, the call center team member on the other end of the phone should answer the customer’s question as quickly as possible.
When someone takes the time to contact you, it’s essential to respond to them promptly. It’s the courteous thing to do, and it’ll go a long way in making them feel valued and appreciated.
One of the most important things any business can do is to understand who its audience is and tailor its products and services to fit the needs of its audience, and for that, you need excellent business insights.
Artificial Intelligence provides many benefits to call centers, primarily speed, accuracy, and repeatability.
The speed at which AI can pick up on words, phrases, and voice tone are much quicker and more consistent than any human alternative with a higher accuracy rate.
AI is the simulation of human intelligence processes by machines, especially computer systems.
It is related to using computers to understand human intelligence, but AI does not have to confine itself to biologically observable methods.
Deep learning is a branch of machine learning research based on algorithms that attempt to model high-level abstractions in data.
Deep learning algorithms are loosely inspired by information processing and communication patterns in a biological nervous system, such as neural coding that attempts to define a relationship between various stimuli and associated neuronal responses in the brain.
Natural language processing is a field of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, mainly how computers can analyze and understand human speech and text.
The best part about AI is that it can increase your contact center performance in various ways.
For example, all of the interactions you find at the beginning of many calls – “say your name now,” “please tell me your date of birth,” etc., are all features that save time for customer service call center agents.
They allow for record retrieval before anyone talks to the agent, decreasing the requirement for drawn-out customer conversation.
Coupled with natural language processing capabilities, emotional intelligence, and real-time transcription services, AI provides predictive models and advanced analytics of calls based on customer type, allowing for pinpoint actionable insights – all at a fraction of the time it would take human agents to do the same.
Human analytics is still one of the most accurate methods of scoring calls, assessing data, and changing processes to improve a wide range of aspects of a call.
The truth is, while computers are fast and machine learning is getting better by the day, the human conversation can include things that machines cannot pick up on.
For example, rapid speech and different accents are things that can be a challenge for machines.
There are many different personal interactions that go alongside customer issues.
Sentiment analysis allows us to see what people feel when they talk to our representatives.
That helps us identify if there’s an issue or problem within their mind. We also use this method to determine whether someone has been satisfied with the experience or not.
If customers aren’t happy, we know that we need to change some aspects of our process to be more likely to come back next time around.
However, while AI sentiment analysis and speech recognition are outstanding, it still doesn’t quite compare with the ability of any human agent.
People have an inherent want for customer satisfaction, and they can understand the patterns of customers.
For example, a human agent can hear the tone of a voice, even a simple sigh or smile. It is possible that AI will get to this level of intelligence.
However, that is not likely to be very soon.
Many companies within the customer engagement business model want some sort of quality assurance, and rightly so.
Not using technology to understand and improve contact center agent interactions, whether through human or artificial intelligence, puts your business behind others in the same space.
However, the most common solutions that QA businesses provide are:
It is rare to see a combination of contact center AI and human interactions in any Quality assurance business. However, the merger of the two is so beneficial to the industry that it is more surprising that there are not more businesses joining the two. If we think about the main functions of the two QA methods, we will understand why there is such a huge benefit:
There are positive and negative sides to each of the methods, and that is why, here at Call Criteria, we have created the perfect ratio of each.
The outcome of merging these two technologies is impressive! The best part is that both parties work better as one unit. That means that the company gets all the benefits of both systems without having to pay twice.
In addition, the costs associated with running the system are reduced because fewer resources are needed.
We believe that combining the two gives the best result, and our accuracy rate of >95% has shown it works time and time again.
Any contact center that uses AI and humans will see a huge benefit in many aspects. The most prominent area of benefit is increasing the customer experience.
Using machines to provide real-time speech analytics gives you a 360-degree view of how your agents interact with your customer and get better the longer that you use it and create machine-learning models.
The most common type of AI that you find in use in contact centers, especially as a customer, is Interactive Voice Response – the voice that asks you to say your name, date of birth, social security number, etc.
However, that is certainly not the only speech analytics tool you will find.
As mentioned above, there are basic AI programs, and there are more advanced Contact Center AI that use Emotional Intelligence to understand how the customer is really feeling.
For example, a person may say they are “fine” about a situation, but the AI detects that they may not be “fine” about the situation by the tone of their voice.
To make things even better, some AI systems can notice that in real time and provide notifications to the operator, acting as virtual assistants and increasing agent performance.
While scoring calls, AI can pick up on the same things while working in real time.
Therefore, you can get a complete view of everything that has happened in all calls throughout your call center.
As said before, though, natural language understanding is not 100% accurate when using artificial intelligence alone.
There are aspects that computer programs cannot pick up on very well, such as different accents and speech speed. That is when you need human assistance.
The biggest problem with using human scoring for calls is it is not cost-effective for real-time analysis or 100% call monitoring, even after call completion – you need as many analysts as you do agents.
For this question, we need to look at a typical scenario using both AI and human monitoring.
The more you use this combination, the better results from the 100% AI monitoring.
Furthermore, if you ever change your process, scorecard questions, or requirements, you have instant access to the data and a simple adjustment method.
Strangely, there are not many call center Quality Assurance businesses that utilize the two methods together in unison.
Call Criteria is the original supplier of such a dynamic mix, producing the perfect AI and human intervention ratio.
However, as more call centers notice the benefits of the amalgamation of the two, you can be confident that there will be more push toward it.
There are many reasons you should consider a mixture of the two; AI and human checks have proven to provide such high accuracy that there are no reasons why you should not try I – even the price makes sense.