Most customer feedback platforms on the market organize feedback into categories, by breaking down received feedback into (sub)categories, like personnel - information. This is great, but not enough to know exactly what is going on.
Our AI drills down the categorization even further to the exact root cause of the problem. If certain words are used more often, ISAAC automatically creates a new subsegment for you. This way all the feedback that mentions this topic is automatically linked so you know where to act precisely.
You can only improve customer experience if you have access to tangible insights that allow you to prioritize actions. Our smart filters combine the power of our feedback analysis and your operational data so you can truly win at CX.
Smart filters allow you to configure any data key you add to the platform as a filter, so you can drill down your CX data even further and uncover trends over certain periods of time or for specific customer segments. This allows you to make relevant changes in the customer journey without losing time and resources trying to fix things that aren't the problem.
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We won't let you waste valuable time trying to figure out what to do. In addition to the multi-level breakdown, you have an overview of top-ranking positive and negative feedback topics. Easy peasy, no more wasting time figuring out trends.
Interested to learn how we get our AI to work so accurately? Learn more here.
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Per industry, users can access relevant filters to highlight areas like digital experiences, customer service, and in-store experience, so you can pinpoint the right experience that needs improvement.
We also equipped our AI with churn alerts. We trained it to recognize when customers are threatening to leave you, or when they require fast follow-up. The urgent category contains everything that needs your immediate attention.
What customers are talking about is important, but how they are feeling is even more relevant. Our AI detects sentiment, including emojis. Happy customers don't leave, so finding out what customers are (un)happy about is of crucial importance.
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