What is Discover?
Discover acts as your customers’ AI advisor, generating proactive insights, recommendations, and content. This includes automated knowledge base management, workflow optimization, and comprehensive support analytics.
Discover automatically extracts insights from your customer interactions, automatically identifies customer trends, recommends automation actions and keeps your knowledge base up to date.
These insights can help you understand your customer pains, streamline your support operations, and cut down significantly on your support costs all while improving customer happiness.
Why is it important?
Understanding customer interactions through customer queries that are unstructured and unpredictable is valuable yet could be time consuming. With Discover, we leverage NLU (Natural Language Understanding) to automatically and dynamically categorize tickets with performance metrics that are updated daily. This unlocks the use of customer interaction data to further bolster data driven decision making process across your organization.
- Optimize cost savings –Instantly surface recommended workflows, knowledge articles to build, and a case tagging taxonomy to help support teams save time, reduce costs and improve customer happiness.
- Predict and prevent escalations –Leverage customer interaction data to identify new pains and get ahead of potential issues before they become critical.
- Collaborate on AI-driven insights –Visualize and share all of your data across departments to help teams proactively plan operations and identify improvement opportunities to streamline the support experience.
How can you leverage Discover?
Maximize Deflection and CSAT with Optimized Workflows
Identify bottlenecks in existing workflows and get AI-powered recommendations to maximize self-serve coverage.
Automate Manual Processes with Knowledge Base Management
Keep knowledge base up to date with proactive gap detection, and effortlessly create new articles using generative AI.
Unlock Game-Changing Insights with Comprehensive Analytics
Extract insights automatically, identify new support trends and topics, and automate actions with generative AI. Share customer support insights across departments to help teams proactively plan operations and identify improvement opportunities
Optimize cost savings
Instantly surface recommended workflows, knowledge articles to build, and a case tagging taxonomy to help support teams save time, reduce costs and improve customer happiness.
Predict and prevent escalations
Leverage customer interaction data to identify new pains and get ahead of potential issues before they become critical.
How can you leverage Discover?
- Recommendations for improving self-service support
Surface a series of topics to automate based off ticket volume and how fast it is to resolve. Discover will also include what the improved ROI is if automated.
- Content gaps and recommendations
Identify and recommend relevant macros support agents can use in Assist to answer questions faster and provide customers with a more consistent experience
- Customer sentiment analysis
Proactively identify customer sentiment and pains to get ahead of potential issues and improve CSAT
- Improved ticket routing
Generate a case tagging taxonomy and update routing rules to ensure agents are working on the correct cases, whether it’s skill or product based
- Discover emerging trends
Understand and identify trending issues CX leaders might not be aware of so that they can take action before potential escalation or churn.
- Granular filtered insights
Filter the automatically clustered tickets with associated ticket and agent data to get specific and granular insights.
What type of data can you classify?
Any type of incoming customer support ticket query in multiple languages can be classified.
What volume of tickets do I need?
At least 100,000 tickets historically to enable proper classification.
What customer support performance metrics are supported?
Performance metrics include:
- First contact resolution rate
- Full resolution time
- Reply time
- First resolution time
- Number of customer support replies
- Customer sentiment during a ticket interaction
What is the difference between structured and unstructured data?
Structured data includes data that can be organized into a spreadsheet, like phone numbers, addresses, product names, and currency. This kind of data is easily analyzed and understood but because it doesn't provide many details, it's quite limited in terms of how helpful and insightful it can be for teams.
Unstructured data on the other hand makes up over 80% of support data. This includes emails, audio and video files, phone calls, and long-form articles. There’s a lot of valuable insights available through this type of data and can provide customers with a gold mine of feedback for their teams.
Which help desk is supported?
For other help desks, contact customer success managers or the sales team.
What languages are supported in Discover?
Discover supports 36 languages currently. Here is the list - arabic, bulgarian, bengali, catalan, central kurdish, czech, danish, german, greek, english, spanish, estonian, basque, persian, finnish, french, irish, galician, hindi, hungarian, armenian, indonesian, italian, japanese, korean, lithuanian, latvian, dutch, norwegian, portuguese, romanian, russian, swedish, thai, turkish and chinese.