Telecom: Measure and optimize your queue about previous requests
We can implement an AI in realtime to route support cases, this enables you to create a performance enhancing machine for support and sales workers. Incoming support ticket requests can also be seen as an analytics goldmine for data driven decision making.
People that are having to reach out to you for the same problem multiple times are at risk for leaving - they are also costing you the extra time spent in additional support cases.
If you could find these people and prioritize them, you could increase customer retention and satisfaction. You can also use this data to measure your other efforts towards solving cases quickly and in one go.
Let's teach Labelf to find these people. For the sake of this tutorial I generated some support tickets with AI and uploaded them.
We’ll start with creating a model, give it a name, select the dataset and the column with the text that we would like Labelf to read.
It's time to set up the labels
We are ready to start teaching. All we have to do is read through the text and show Labelf where it belongs. This makes custom AI training a breeze for anyone.
Labelf curates your training examples so your time spent training Labelf is highly optimized. We get batch recommendations that really improves the speed.
If we spend an afternoon doing this we will have something ready for production!
Let's try it out!
This is my third time calling you about this, why can't you just fix it!?
I already contacted you about this but nothing is happening!
Now we can see if a person is contacting you about a new or a previous problem!