Support tickets are a rich and valuable source of customer feedback. However, they're unstructured, unpredictable and often high volume.
At its core, our system is built around the latest developments in machine learning-based NLP. When it processes historical ticket logs, our system accurately understands the specific meaning and emotion of qualitative text—even if it's unstructured and unpredictable.
Trusted by the customer service leaders of the world's most loved companies
• All our technology is built in-house. Years of experience and learning have gone into it.
• We can analyse any channel of conversation or feedback. Our latest release includes voice calls, too.
• Our AI is customised to your data—which means its granular and accurate, whatever the industry.
• It surfaces insights in real-time (well, a few seconds).
• It's continuously supervised by our team of NLP scientists to ensure it's accurate.
Machine learning-based systems learn rules and patterns from the data based on statistical inference.
"ML based systems are more robust when it comes to analysing misspelled words or ones that haven't been seen before. A rule based system would need a lot of rules to capture all three of these tickets, and output would need explicit error handling, which is time-consuming and difficult." Read more below.
Our data science team has developed a robust understanding of the topics and sentiment that typically lead to churn and dissatisfaction.
This means we have prioritise and route tickets based on urgency and risk. For example, our technology knows that 'cancel order' is an unhappy customer, but also that 'out of stock' creates a lot of anger, too.
By integrating with you help desk, we can prioritise urgent tickets efficiently to help reduce churn and further dissatisfaction.
Most tagging systems take a lot of manual work or the insights can't be trusted to back up business decisions. Usually tags are inaccurate, inconsistent or generic, so customer support is like a black box.
Integrations with all the usual providers. Anything that's voice or text—we got it.