Semantic Extraction
No two conversations are alike. That’s why Conviva transforms raw transcripts into structured, analyzable signals. Think of it as conversation intelligence in the wild:
- Intent & Task: “Modify booking,” “return item,” “upgrade plan.”
- Product Parameters: SKU, order ID, price, quantity.
- User Sentiment: Frustrated, urgent, resistant, cooperative.
- Resolution Claims: “Resolved,” “cancel,” “will buy later.”
These semantics are customizable by business context — whether it’s flight changes for an airline, size exchanges for a retailer, or feature upgrades for a SaaS product.
Once extracted, semantics turn free-form chat into actionable data.
Patterns from Conversations
“Good conversation” isn’t the same as a good outcome.
LLM-based evaluations (“LLM as a judge”) can tell you if your AI agent sounds helpful — but not if it actually is. Extract intent, sentiment, confusion, and resolution signals to connect in-chat and out-of-chat behavior, so you can prioritize the improvements that drive business impact.
Conviva Digital Product Insights goes beyond surface-level conversation quality by automatically:
- Capturing every conversation across your app, website, or agent.
- Extracting semantics — intent, sentiments, confusion, and resolution signals.
- Surfacing user patterns from the conversation.
Conviva Pattern Analytics continuously scans every session and conversation, surfacing experience patterns that connect to negative outcomes such as:
- Repeated queries
- Misrouted topics
- Unclear intents that expose hidden confusion, friction, and inefficiency
It also identifies positive outcomes like purchases, bookings, and resolved support instances.
Connect Conversation Patterns to Out-of-Chat Behaviors
Traditional product analytics and LLM traces stop at the edge of the chat window. They don’t connect conversation behavior to what happens next — whether that’s a purchase or an abandonment. Agent success starts before the conversation and doesn’t end when a user taps out of the conversation.
Most AI programs plateau because they can’t answer three simple questions:
- Is my AI agent making things better or worse?
- Are customers getting confused — and why?
- What should I build, optimize, or fix next?
Conviva bridges that gap with holistic patterns beyond chat — stitching user journeys that connect in-chat and out-of-chat behavior. AI, product, and marketing teams totie specific conversation types to measurable business outcomes. Agent success starts before a conversation and doesn’t end when it does.
Prioritize improvements — so your adjustments drive measurable business impact.
Understand user or segment conversation patterns — every conversation is personal.
You can:
- Understand how AI agent responses influence actions on apps or websites.
- Detect when conversations trigger human handoffs to sales or support.
- Quantify which prompts or model changes move resolution, satisfaction, and retention.
In the real world, patterns are complicated. Here are three examples to illustrate how semantics, patterns, and outcomes are connected.
Example: A customer asks an agent for “women’s trail running shoes” and then asks a question around item color and size. If they click a recommended item, the agent helped conversion. If they fallback to the application multiple times or website and use manual search, the agent was unsuccessful. Conviva Pattern Analytics makes those distinctions automatically — connecting semantics (intent) with behavior (action) to reveal what’s truly working.
Example: Conviva revealed that an AI agent repeatedly shared the same documentation link three times before users clicked “Contact Support.” That pattern: Question → repetition → escalation —showed exactly where to improve the prompt and knowledge base.
Example: A global retailer found users asking, “Can I return this gift?” triggered agent loops that never surfaced return labels. Conviva identified this recurring pattern and its link to cart abandonment — prompting an update that boosted retention by 11%.
Build a Continuous Feedback Loop Between Conversation and Outcome
The best AI experiences learn from production, not theory.
Conviva turns every conversation into outcome-driven intelligence — so teams can continuously sharpen prompts, improve workflows, and ship fixes that move the needle.
Unlike dashboards or sample traces, DPI provides full-census, real-world data connecting:
- What users say and do
- How agents respond
- Agent and customer engagement
Every iteration becomes an opportunity to increase trust, resolution, and satisfaction at scale.
As Conviva CEO Keith Zubchevich says:
“Static journeys are dead. Predictable customer paths are an illusion. With Conviva Digital Product Insights, enterprises can see and respond to the real-time patterns that define every customer interaction. This is analytics built for what’s next.”
The Takeaway
AI and digital leaders don’t need more dashboards — they need actionable intelligence that connects every conversation, click, and conversion.
Conviva Digital Product Insights delivers exactly that:
- Connects chat and product behavior to outcomes.
- Extracts semantics to make conversations measurable.
- Reveals friction patterns automatically.
- Prioritizes improvements by KPI impact.
It’s how the next generation of AI-first products will grow — with continuous, outcome-based learning.
Conviva Digital Product Insights is now in public beta, with GA in 90 days. Request a demo to see how your AI and digital teams can turn every conversation into predictive intelligence that drives results.