Conversation Graph

The data layer that understands clicks and conversations. Clicks, chats, mood and intent—stored in one query‑ready timeline.

Clicks, chats, mood and intent—stored in one query‑ready timeline.

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Clicks are out. Conversations are in.

70 % of B2C engagement now happens outside the clickstream. Legacy tools can't hear it—Zigment can.

Structured to Unstructured

Human-like agents that qualify, nurture and sell—on any channel, 24x7

Siloed Marketing Data

Drag-and-drop journeys that react to any CRM update, ad click or uploaded list

LLMs are here

Every click, message, intent and decision in a single timeline—query with prompt

Why It Matters

Digital journeys have outgrown clickstream tables.

Pain today	Business impact	How Conversation Graph fixes it
Chat logs isolated from CRM	Slow response, generic nurture	One timeline that stitches clicks, chats, email, voice
No sentiment or intent fields	Can't prioritise hot or at-risk leads	NLP layer tags every utterance with intent, mood, entities
Manual revenue attribution	Weeks to prove ROI	Graph links ad click → conversation → purchase in seconds

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The Platform

Every message, mood and click in a single timeline—unlocking AI-ready insights no CDP or CRM can match

Unified data view

Qualitative and quantitative data in single graph

Emotion tiggered workflows

Query everything

Key Capabilities

Source Attribution

Resolve lead source attribution for every prospect or customer.

Intent Signals

Capture & store qualitative signals like mood or intent on the event timeline.

Realtime Triggers

Drive dynamic workflows based on click, mood or intent.

Query Unstructured Data

Show users who are 'High Intent' but found it 'too expensive'.

Stack Integration

Connect any software in the funnel. CRM, CDP, chatbot or automation.

Open Lakehouse Export

Push to Snowflake, ClickHouse, BigQuery.

Integrations

Conversation Graph adds an Agentic AI layer across the existing stack

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Unstructured to Structured

Real-time Conversation Processing

Any discounts for students?

Absolutely—use code STUDENT10 at checkout.

{
  "actor_id": 102947,
  "utterance": "Any discounts for students?",
  "intent": "discount_inquiry",
  "sentiment": "curious",
  "confidence": 0.92
}

This node is instantly searchable and can trigger a nurture path for similar requests.

High-Value Use Cases

Use Zigment for every use case - conversion, onboarding, retention or support.

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Micro-segment Nurturing

Query: "Users expressing 'concern about price' in last 7 days." → personalised discount workflow.

Agent Escalation

Trigger human hand-off when intent = high-value lead & sentiment = frustrated.

Revenue Attribution

Join ad_click_id → conversation path → purchase event; include mood shifts as weighting factors.

Feedback Mining

Vector search for utterances similar to "too expensive".

Comparison to Legacy Tables

Feature	CRM	CDP	Conversation Graph
Click & purchase events
Raw chat & voice turns	Notes only
Sentiment & intent fields
Vector similarity search
Real-time orchestration triggers	Limited	Webhook hacks	Native

Turn every conversation into a revenue-driving journey

From a quick DM to a week-long nurture thread, Zigment drives every conversation forward on autopilot.

Meet customers across every channel and anytime

Extract qualitative signals - mood, urgency, intent

Instantly Orchestrate the next best step in the journey

Conversation Graph records every signal, context and event

From Clicks to Conversations, From Structured to Sentient

Clicks, views and CRM fields don't tell you why a prospect buys—or why they ghost you. Legacy CDPs cannot store or query fuzzy constructs like "mildly frustrated. Zigment can.

Message replies and support-chat rants carry the urgency a pageview or add-to-cart never shows.

Ready for a data layer that actually
speaks human?

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