We're proud that these organizations use our feedback analytics to improve their customer experience.
Customer feedback analysis tools use text analytics to transform large amounts of unstructured text data into meaningful data for further usage like analysis, notifications, and reports. For customer experience platforms, the main goal is to find relevant themes and patterns in customer feedback and convert it to analytics to help you take action.
There are different text analytics methods that you can apply, but all of them try to capture the complexity of the human language in mathematic models. The three most-used text analytics approaches are word spotting, manual rules, and text categorization.
In short, feedback analysis tools help organizations to understand the needs and frustrations of customers so that they can improve customer satisfaction and reduce churn. Most customer feedback analysis platforms on the market organize customer feedback into categories, by breaking down received responses into (sub)categories and applying sentiment analysis to customer feedback.
Hello Customer has an industry-leading AI that takes customer feedback analysis to a whole new level and delivers you detailed insights to make revenue-impacting CX decisions rapidly.
Our customer feedback analysis tool analyzes your customer feedback in detail and in real time. Thanks to our industry-leading accuracy, you can act with confidence.
Don't waste time waiting for insights on what to improve. Our AI is community-based and gives you insights from the first feedback responses you collect, so you can act quickly.
Our customer feedback analysis is industry-based and provides you with relevant insights for your business. Merge your feedback with other customer data you already own to receive insights that align with your KPIs.
Customer feedback analysis using NLP organizes 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 text 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.
We won't let you waste valuable time trying to figure out what to do. Our customer feedback analysis tool centralizes, analyzes, and categorizes all incoming feedback and points exactly where you need to take action.
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.
Our detailed text classification and categorization + your operational data = winning at CX. We will tell you exactly which problem to tackle and your operational data (branch, customer segment, collection, status) allows you to come up with an effective solution.
This powerful combination allows you to make relevant changes in the customer journey without losing time and resources trying to fix things that aren't the proble
What customers are talking about is important, but how they are feeling is even more relevant. Our text analysis also performs sentiment analysis on customer feedback and recognizes sentiment from emojis. Happy customers don't leave, so finding out what customers are (un)happy about is of crucial importance.
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The AI text analysis helped us tackle the challenges we were already facing and we discovered other pain points which we were unaware of in the organization, allowing us to improve the customer experience of our webshop."Read this case
Thanks to ISAAC, the AI that drives the Hello Customer platform, we can easily evaluate differences between shops and employees, identify what goes wrong and improve our service accordingly.Read this case
The customer feedback insights give our teams the right arguments to convince customers to trust Luminus as their energy provider. Allowing us to defend our market position in a highly competitive market.Read this case