Do you have accumulated a lot of customer feedback but don't know exactly what to do with it?
Analyzing everything manually is practically impossible. Understand your customer with smart text analysis.
We will instmane new and practical information from an unordered pile of customer feedback.
In any language. Content analytics is almost human-like and has a lot of superhuman abilities that you will appreciate.
We automatically monitor customer feedback and report results to you.
Get an accurate overview of customer feedback. Including tracking trends over time.
Finally,you measure your resources exactly where it's needed most.
You will get to know your strong supporters and critics.
Your key staff will receive tailor-made outputs.
We'll find feedback information from customers you didn't even know about.
We can also find hidden topics and new trends. Customer feedback is under your control.
Your customers' satisfaction will increase significantly thanks to the rapid recognition and resolution of new situations.
Identifying the reasons for customers leaving will help you increase retention.
Analytics will provide you with an efficient and operational overview of incoming feedback.
Analysts in Alberta were able to categorize a smaller portion of customer feedback – only about 30%. This lengthy process consumed 120 MD per year. And the results of the analysis were not reliable. The chain was therefore looking for a solution that would free employees from routine toil and sort feedback automatically, quickly and reliably.
SentiSquare's artificial intelligence swallowed historical data and learned to recognize topics and sentiment in feedback forms without human help – almost with human precision. Without breathing, it handles 100% of incoming feedback. And this has made it possible to introduce a new and more detailed categorization of the topics that customers are addressing.
In Alberta, thanks to automation and artificial intelligence, efforts and resources in customer care are now directed where they are most needed. And much more efficiently than before.
Despite the large volume of feedback, T-Mobile was unable to measure the causes of changes in customer satisfaction — there was no overall overview, information was distorted and fragmented by teams. T - Mobile tried the most advanced solution with classification using rules and keywords. Without satisfactory results.
In order to make sorting SMS into topics successful, we used T-Mobile's historical data in machine learning processes. Artificial intelligence has learned to identify the topics and sentiments of high-accuracy news. Users of analytics applications in data simply search and filter and receive clear outputs and visualizations.
In T-mobile, tens of thousands of SMS from customers are no longer afraid. Analytics helps them get to know their customers' real needs every day.