For us, our customer support and experience are equally as important as the product we provide. So we always analyze our support level, optimize our service and processes.
Previously we had a dedicated team member whose task was to check and evaluate all our closed tickets. It gave us a better understanding of our company performance, helped us identify and investigate tickets with lower marks, etc.
And since automation is a key aspect of any business process — and ours is not an exception — we decided that this approach must be reevaluated and improved.
To be able to consistently and efficiently track and analyze our support tickets, we developed our first-ever AI that was recently launched. This technology is an own-built neural network that is able to self-learn.
So here’s the pertinent question: how have we actually taught these neural networks to process data and perform the daily analysis of closed tickets?
In short, we’ve uploaded 16 000 closed tickets to our neural network, including all statistical data, and started the assessment. The neural system retrieved data and analyzed it based on various parameters. For instance, for Timing grade the network processed the data based on these parameters:
For Content and Feedback grades the system analyzed the text data from all these closed tickets. Once complete, it compared all data with the grades put by our controller. As of now, the accuracy of data assessment is around 90%.
We are excited to inform you that, with the release of Splynx v4.1, our AI technology is now available to all Splynx customers. You can now take full advantage of this powerful tool to evaluate the quality of your own customer support. By leveraging our AI technology, you will be able to identify areas of improvement, streamline repetitive tasks, and ultimately enhance your overall support performance.
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