Your customers talk across channels social, support, forums, app reviews. With AI, you can listen at scale, detect issues early, and react precisely with geo-targeted guidance and proactive service.
Context
Signals are fragmented and time-sensitive. We unify text, location, and context (e.g., weather, outages) to reveal clusters of sentiment and emerging incidents in near real-time.
Approach
- Ingestion. Social feeds, tickets, reviews; entity and location extraction for precise geo-clustering.
- NLP + graphs. Topic/sentiment models enriched with graph relationships (issue region product).
- Playbooks. Pre/post-incident templates that drive targeted communications and ops actions by region.
- Feedback loop. Measurement of call volume, CSAT, and resolution latency to refine models and playbooks.
Impact
- Calls. Reduced inbound calls by ~50% in affected regions during incidents.
- CSAT. Uplifts exceeding 150% on targeted communications vs. generic broadcasts.
- Response. Faster detection -> faster action; clearer attribution from signal to outcome.
Calls
100% -> 50%
~50% reduction
CSAT
100% -> 250%
>150% uplift
Detection Action
Hours -> Minutes
Faster response