How would it be if you could automate the identification of the level of happiness and customer satisfaction in a service.

Knowing that customer satisfaction is the goal of every company that seeks consistent growth, we decided to include among the functionalities of the omnichanne service platform, the Sentiment Analysis for automatic identification of the customer’s behavior and mood.

Automatic identification of customer satisfaction in text messages

You are part of a customer service, technical support or sales operation, you receive messages in large quantities and they all have a very similar level of importance, so how can you prioritize service? By the level of humor of the client is one of the answers.

Not saying that we must necessarily serve the customer who complains the most first. But, of course, when the client’s mood level is low, serving him even more quickly will help in solving the problem or in delivering the information he seeks.

The Sentiment Analysis feature allows you to generate highlights in customer messages automatically identifying whether the message or parts of it are null, positive or negative, green or red.

It is at this moment that cerebral cognition is triggered, as we all know that the green color says “it’s okay, you can go on, it’s cool”, while the red color says “be careful, pay attention, stop and do something”. In this way, the indicators directly in the messages, highlighting the positive and negative passages, immediately assist the attendant’s work in paying attention to the most critical messages.

Your company may also add to this functionality the transcription of audio messages to text, that is, all audio messages from customers can be transcribed into text, which will have average sentiment indicators.

Artificial Intelligence and Machine Learning showing its value

Why is it possible to automatically identify customer satisfaction? The use of Artificial Intelligence and Machine Learning is the answer. Of course, the partner Google and Microsoft once again facilitated the entire implementation of this process.

The libraries available not only for the Portuguese language, but also for countless languages make it possible for Sentiment Analysis to be made available to several countries.

Management and monitoring of conversations maximized

Dashboards (online and realtime), as well as statistical reports offer managers the ability to quickly analyze the entire operation, making important decisions to improve customer service and satisfaction.

Monitoring messages identifying points of attention is also much simpler. This is due to the possibility of quickly identifying which messages are the level of feeling and mood of customers is lower and, therefore, needs to gain attention faster.

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