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Let next4biz’s Learning Machine Decide!

Since 2012, next4biz, along with contributions from various universities, has continued working on artificial intelligence through research and development (R&D). The increase in our experience over the past few years, as well as our R&D efforts, has provided us with some very significant results. As in many other areas, we have also taken a lead role in the realm of artificial intelligence.


Let next4biz’s Learning Machine Decide

next4biz Learning Machine – What can it do?

Essentially, our learning machine can be described as artificial intelligence which learns from users’ experiences and then emulates the users. next4biz analyses your existing issues as well as the categories you have specified for them.

The Learning Machine then enacts a detailed similarity analysis of issues through wording, spelling mistakes, categorization, and the user experience data within the account. next4biz keeps learning while you use it to resolve customer issues. In addition, in the initial phase, we can teach our Learning Machine by using your legacy issue management data. When the Learning Machine attains a certain level, next4biz automatically categorizes your issues and can also predict the category of a new issue.

To what extent can we trust Artificial Intelligence?

next4biz’s Learning Machine model can predict any category with 80% to 95% accuracy. If the ratio of a given category is over a certain predetermined number, the AI will make an automatic decision; if it is under that threshold, you may define a rule allowing your users to make the final decision from a list of recommendations. next4biz’s Learning Machine model provides you with multiple categories with different probability ratios. Users can choose an option from the list of recommendations. This enables accurate categorization without getting lost amidst hundreds of different categories. Another option for the next4biz Learning Machine in terms of the accuracy of its suggested categories is to set up automatic routing for your agents so that they can select the right category at an above-average rate. next4biz’s Learning Machine model will continue to learn from your existing use history and will also continue to increase the probability of accuracy.

What does next4biz’s Learning Machine provide?

Artificial intelligence and the world we live in are not complete strangers. What was previously referred to under the umbrella term of ‘artificial neural networks’ is now more commonly known as artificial intelligence, learning machines and deep learning methods. So why is it so popular now? The most crucial factor is that we now have almost all the data available to us. We also now have the ability to access behavioral and decision-making data in order to allow any software to learn. This data is not what would be described as ‘traditional data’, but rather data on how people behave and make decisions – in other words, digital actions and choices. The software can learn from this data and, when it reaches a certain level of maturity, it too can make similar decisions in similar situations.

One of our aims as a company developing this type of technology is to increase the efficiency of customer service, where a vast number of experts work, and to enable more effective management of issues from digital channels (self-service, email, etc.). Below is a list of advantages which briefly summarize the functions of next4biz’s Learning Machine model:
  • Shortens the amount of time needed by customer service professionals to understand and categorize notifications.
  • Automatically identifies and guides all relevant workflows when it finds the correct category.
  • Prevents loss of time and resources due to incorrect categorization.
  • Prevents dissatisfaction on behalf of customers as a result of incorrect categorization.
  • Can be used in multiple channels including call centers, self-service and emails.
  • Continuously learns and improves its accuracy.

How does next4biz’s Learning Machine work?

next4biz’s Learning Machine in call centers and customer service departments

As always, your call center agents listen to your customers and take down notes, which they then look over. However, this time they also have access to next4biz’s AI Learning Machine on the category suggestion screen.

One of the first applications of next4biz’s Learning Machine is the customer service interface. The main aim is to speed up the identification process of categories, reduce the rate of an agent selecting the wrong category and redirect notifications to the relevant workflows in quicker time with lower cost. In our future versions, we envisage applying our Learning Machine to other channels. For example:

next4biz Learning Machine: Email

One prime application area for next4biz’s Learning Machine model is email. It enables all issues from emails to be automatically categorized and directed towards the relevant workflow. As such, it is possible to analyze issues from your organization’s email addresses dedicated to customer service without the need for human intervention and automatically direct them to the relevant workflow.

next4biz Learning Machine: Self-service

Within the self-service channel, it is also possible to provide similar efficiency. Furthermore, you will spare your customers the burden of choosing a category. The next4biz Self-Service application, which will be placed on your website, will be able to identify the issue category while the customer issue is being written and can therefore direct the customer accordingly. Your customers’ brand loyalty will be increased. In addition to defining each category as customer issues are introduced, you will also be able to immediately start the relevant flow. Just imagine a customer starts using self-service and you only have to ask them a single question: “How may we help you?” – and then, as the customer types, you are already producing suggestions for them!

Gürkan Platin
Gürkan Platin, a graduate of Hacettepe University Management and Organization, worked as a manager in various positions at Mensan, Citibank, Garanti Bank and Credit Registration Bureau, respectively. Platin has been blogging since 1996 and his articles are published in various national and international publications.
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