Luminoso for contact centers
An efficient, effective contact center is a cornerstone for high customer satisfaction and loyalty - but contact centers today aren’t doing a great job of quickly or easily resolving issues.
Get to quicker issue resolutions on first contact and mitigate the number of reps contacted before resolution by automatically classifying all of your incoming support tickets.
Improve the customer experience
Accurately identify your customers’ needs, automate contact center processes, and optimize issue resolution for an improved customer experience.
Auto-labeling and classification
Apply artificial intelligence to understand, classify, and label contact center interactions, such as support tickets, live agent and chatbot transcripts, and email in real-time.
No programming or ontologies required
Our software focuses on concepts, not specific terms, and learns new words automatically based on context. This enables us to handle domain-specific jargon, acronyms, synonyms, and misspellings without training or advanced configuration.
Create new or optimize existing labels
Luminoso’s natural language understanding technology excels at discovering relationships and similarities across interactions without advanced training. This enables it to both create new labels from scratch or optimize an organization’s existing labels within a matter of minutes.
Mobile game developer
By analyzing the first several thousand support complaints after each app update, the support team at Supercell can identify which problems cause the most customer dissatisfaction, and prioritize them for developers. Problems are fixed in hours, preventing support ticket backlog and retaining paying customers who may have otherwise stopped using the app.Read the case study
Office supplies retailer
With Luminoso, this company’s Contact Center Team analyzed its aggregated data, surfacing trends, unknown issues, and root causes. Learn how the retailer drastically reduced contact center calls, negative feedback – and addressed and repaired customer-reported issues, fast.Read the case study