The way that we get data from voice analytics is changing, and it has been for some time. Everyone knows that we are in an era where AI is changing the world. However, the question remains; are humans still required for voice analytics? What about predictive models? Has the use of AI taken over in that realm, too? We all know that companies are using automated tools for data analysis and model building, but is it better? Throughout this article, we will look into these questions and give you our thoughts on the points that appear more and more in call center monitoring. Of course, the two aspects are entirely separate entities that only merge in call centers. Therefore, we will look at them separately and then make a combined judgment at the end of the article.

Human Voice Analytics

For human voice analytics, we do already have an article about it here. However, we will run over its basics in this article so that you do not have to swap and change. Yet, if you want more of a comprehensive look into AI’s benefits and downfalls, you should read it. As I have written in that article, there are some expectations, advantages, and pitfalls of AI voice analytics. First of all, we will recap the expectations:

• Total compliance
• Faultless accuracy
• Inbound call conversion rate increase

While that doesn’t seem like many expectations, they are massive points with colossal backgrounds that are itching to fail us. Again, I have gone into a little more detail about this in the other article. A little bit more back on topic, we will look at the realities of AI voice analytics’s benefits and downfalls. First, we will look at the benefits:

• Saveable data
• Higher productivity
• Instant results
• Less space

While these pros are certainly an excellent thing to have, there are still downfalls to it. We have gone into those in the article mentioned above. While they are still valid points, there are some that we would like to focus on a little more in this article. The reason for that is, we are looking at the human requirement within voice analytics, not the pros and cons. So, the most substantial aspect that we will be looking at in this section is uncertainty.

Uncertainty of Voice Analytics

As you will have seen in a few of our articles, voice analytics has two main modes of operation; phonetics and LVCSR. Phonetics are less accurate than LVCSR; however, they are quicker. We will not go into all of the ins and outs of the uncertainties again here. But, voice analytics are simply not as good as human hearing. The question remains, will AI ever be as good as humans are at hearing all of the nuances in voices and accents? The truth of the matter is probably not. Although technology is advancing at a tremendous rate, the way people use vocabulary, and change it, is also developing. That can lead to some pretty inaccurate results. However, people have a much better ability to hear those nuances and understand them, too. That ability is critical to ensure regulatory compliance and to protect both the customer and the company itself. Furthermore, voice analytics may not pick up words such as “yeah” and “Nah,” which can lead to contractual discrepancies. However, humans can tell the difference, and if not, they can ask other team members to listen and give their opinions. Whereas voice technology will only ever run the call once, and if it picks up the wrong word, that can have devastating effects. So, back to the question, are humans required for voice analytics? In a word, yes. We have only covered the uncertainties in the abilities of voice analytics and found that it needs humans. We could look further into VA’s running, but there is not a lot of need, as we have already determined people’s requirements.

Are Humans Required For Building Predictive Models?

In a word, yes, we need humans to assist, at least, in building predictive models. We will look at the individual sections that we require to develop an accurate predictive model and why we need humans for each of them.

Data Acquisition

Of course, there is no way to make a predictive model without having data. AI is very good at data input from multiple sources, and there is always the risk of human error. Not only do people run the risk of entering the wrong information by mistake, but they are also inclined to only input information that they want to. Therefore, the data that may be entered could be invaluable, insightful, or even unactionable. That means that you need AI to be productive in the acquisition of data, and humans are almost redundant. Of course, the difference is that humans are required to teach the AI which data to collect, from where, and when.

Preparation of Data

Now that you have piles of data, you need to prepare it for modeling. This step is undoubtedly where human involvement becomes a necessity. Of course, within call center monitoring, many different problems require a solution. Those problems can change, too. Therefore, you must have human involvement in deciding which of those problems you need to solve, and at what time. No matter how good AI becomes in the future, it is unlikely to figure out those problems with a high degree of accuracy. Yes, it may predict which issues need a resolution, but it will not always get it right. Only humans know the full business strategy and how each component fits together to produce the result. Furthermore, people need to “clean” the data. You may get regular data collection errors that can be changed within the AI system itself to prevent future occurrences, but you will still get rogue data from time to time.

Modeling

After you have all of the clean data you wish to make a model of, you need to decide which methodology you want to model. There are two main categories of methods, each with subsets of its own. There is regression analysis and classification analysis. We will not go into each of the subgroups and how they work within this article, but again, as with the preparation, people need to decide the factors for the modeling, not AI. AI will complete the modeling for you, but only after you decide which models you want to run. As I have already said, those models may need to change periodically or sporadically with call center monitoring. That leaves AI, no matter how smart it is, vulnerable to errors and reliant on human input.

Output

AI is capable of producing various models based on the same data. Therefore, what you need to do is determine which of those fits your business models and abilities the closest. Not all of the models that AI will provide you are accurate representations of your business model, nor are they always suitable for the business’s abilities or goals. The only way to decide which of those outputs is the right fit for your company is to have human interaction and decisiveness.

Are Humans Required For Voice Analytics And Building Predictive Models? – Conclusion

The most straightforward answer to provide here is, yes, we will always need humans in the voice analytic and predictive modeling process. While there are some steps that AI makes more efficient and quicker, there are also some steps that need human intervention to work correctly. No matter how good AI gets in the future, people who own companies are the deciding factor for their direction. It is only through human interaction and decision that any Artificial Intelligence will give you the results you want and need.