SentiSum's AI engine automates support ticket tagging with unparalleled accuracy and granularity.
Drive product roadmaps, improve retention and identify areas for automation easily with real-time trends & insights across every customer contact channel.
"No more tagging, just root cause insights on-demand. Every team has started using support insights!"
Director of Customer Service, +$50m revenue fast-growth company
SentiSum is built using machine-learning based NLP. It's designed to understand and tag customer contacts in a way that's:
1/ Accurate: It tags based on meaning, not keywords.
2/ Granular: Tags get to the root cause of the topic.
3/ Real-time: Everything happens in milliseconds.
You can trust SentiSum tags to underpin strategic change across your business.
P.s. We cover 100s of languages and channels (even voice.)
Our daily digest email ensures you stay up-to-date with performance, top ticket drivers and the largest rises and falls in topics.
Subscribe anyone to bring company-wide access to support data. You'll see a refocus on customer experience that helps you to reduce ticket volumes.
Like Google for your support data, you'll be able to answer queries from other departments by simply searching any keyword or topic.
With the addition of our voice analytics technology, you'll even be able to search for terms within phone calls.
With SentiSum's simple-to-use UI, you won't need technical know-how to understand the topic and sentiment of every support ticket.
You, or anyone across your business, can simply switch between contact channels, filter by topic or subtopic, and have instant access to the issues facing your customers across every contact channel.
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.
SentiSum is a single source of truth. In one simple-to-use dashboard, you'll understand the topic and sentiment of every customer conversation, survey and review.