Let next4biz’s Learning Machine Decide!
As always, call center agents listen to customers and take down notes which they then look over. However, this time next4biz's AI Learning Machine appears on the category suggestion screen.
Since 2012, next4biz along with contributions from Universities has continued working on artificial intelligence through research and development (R&D). The increase in our experiences 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
Let next4biz’s Learning Machine Decide
next4biz Learning Machine – What can it do?
Essentially, our learning machine can be described as artificial intelligence learning from users experiences and then emulating the users. next4biz analyses your existing issues as well as the categories you have specified for them. The Learning Machine enacts a detailed similarity analysis through issues’ wordings, 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 also 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?
With a certain level of accuracy, next4biz’s Learning Machine model can predict any category with an 80% or 95% chance. If this ratio is over a certain number you are at liberty to make an automatic decision, or your users can make the final decision from the list of recommendations. next4biz’s Learning Machine model can provide you with multiple categories with different probability ratios. The users can choose an option from the list of recommendations. As such, this enables accurate categorization without getting lost amidst hundreds of different categories. Another option for the next4biz Learning Machine in terms of its suggested category accuracy probability rate is to make 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 history of use and will also continue to increase the probability of accuracy.
What does next4biz’s Learning Machine provide?
In actual fact, artificial intelligent and our world are not complete strangers. Previously what was referred to under the umbrella term of ‘artificial neural networks’, is now more commonly known as artificial intelligence, learning machine and deep learning methods. So why is it so popular now? The most crucial factor is that we now have almost all data available to us. We also now have the ability to access behavior and decision-making data in order to allow any software to learn. This data is not simply what would be described as ‘traditional data’ but rather data on how people behave and make decisions, in other words, digital actions and choices. So now the software can learn from this data and when it reaches a certain level of maturity it too can make similar decisions in alike situations.
One of our aims as a company who develops this type of technology is to increase the efficiency of customer service where a vast number of experts work and to enable management of issues more effective from digital channels (self-service, e-mail etc.). Below is a list of advantages which briefly summarize the functions of next4biz’s Learning Machine model:
- Shorten the amount of time taken by customer service professionals to understand and categorize notifications.
- next4biz’s Learning Machine model automatically identifies and guides all relevant workflow when it finds the category correct.
- It can prevent loss of time and resources due to incorrect categorization.
- It can prevent dissatisfaction on behalf of customers as a result of incorrect categorization.
- It can be used in channels including Call Centres, Self-service and E-mails.
- next4biz’s Learning Machine model continues to learn and improve its accuracy.
How does next4biz’s Learning Machine work?
next4biz’s Learning Machine in Call Centres and Customer Services
As always, call center agents listen to customers and take down notes which they then look over. However, this time Mi4biz’s AI Learning Machine appears 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 less cost. In our new versions, we envisage to apply our Learning Machine to other channels. For example:
next4biz Learning Machine on an E-mail channel
Another application area for next4biz’s Learning Machine model is its e-mail channel. It enables all issues from e-mails to be automatically categorized and directed towards the relevant workflow. As such, it is possible to analyze issues from your organization’s e-mail addresses dedicated to customer service without the need for human intervention and automatically direct them to the relevant work workflow.
next4biz Learning Machine in Self-Service application
Within the self-service channel, it is also possible to provide similar efficiency. Furthermore, you will have spared your customer from the burden of choosing a category. The next4biz/Self Service application which will be placed on your website will be able to identify the category of the issue while the customer issue is being written and therefore will be able to direct the customer accordingly. Your brand’s loyalty will be increased along with being able to define each category during the introduction of the notification. You will then be able to immediately start the relevant flow. Just imagine, a customer uses self-service and you ask them only one single question, “How may we help you?”. And as the customer types, you are already producing suggestions for them!