Conversation Analytics

You can’t improve a bot you can’t measure.

EnableX Conversation Analytics tells you exactly where customers drop off, which flow paths underperform, where the KB has gaps, what intents the bot misclassifies, and which prompts cause confusion. Per-conversation, per-flow node, per-channel. The instrumentation your team needs to improve containment quarter on quarter.

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Drop-off heatmap · Containment rate · CSAT signal · KB gap detection
Heatmap drop-off per flow node
Auto CSAT extraction
KB gaps surfaced + prioritised
Trends per flow version over time

What sets it apart

Drop-off heatmap

Per-flow-node drop-off rate. See exactly where customers leave the conversation. Diagnose the flow logic, the prompt wording, the KB gap, and fix it.

Containment + CSAT

Bot containment % per intent. CSAT signal extracted from conversation tone (no survey needed). Compare across flows, channels, time periods.

KB gap detection

Conversations where the bot retrieved low-confidence KB chunks (or none). Surfaces the questions your KB doesn’t answer. Roadmap for what to write next.

How it works

Heatmap of drop-off rate at every node in your flow. Higher = more customers abandoned at this step. Diagnose: prompt unclear? KB returned bad answer? Flow logic wrong? Compare drops over time as you ship fixes.

  • Per-flow-node drop-off rate
  • Heatmap visualisation
  • Compare flow versions over time
  • Drill into specific drop-off conversations
  • Root-cause classifier (prompt / KB / logic)

% of conversations the bot resolves end-to-end without escalation. Per intent, per channel, per flow. The KPI that drives bot ROI. Coupled with CSAT to prevent containment at the cost of frustrated customers.

  • Containment % per intent
  • Per-channel containment
  • Containment vs CSAT correlation
  • Trend over time
  • Customer-segment splits

LLM extracts CSAT signal from the conversation tone: closing words, sentiment trajectory, escalation pattern. No need for end-of-call surveys, which most customers ignore.

  • Auto CSAT extraction
  • Sentiment + closing-words analysis
  • Compare with survey CSAT where you run one
  • Per-conversation, per-agent, per-flow
  • Trend tracking + alerting

Surface the customer questions where the KB had no good answer. Prioritise by frequency and customer-segment value. Actionable roadmap for your content team: write this doc next, ranked by the escalations it should recover.

  • Low-confidence retrieval surfacing
  • Frequency-weighted prioritisation
  • Customer-segment value scoring
  • Suggested doc topics
  • Containment lift estimate

Use cases

Product

Bot improvement roadmap

Where to invest next: prompt tuning, KB writing, flow logic, intent training.

Conversation intelligence
CX manager

Coaching priorities

Which agents handle escalations well; which conversations are coaching candidates.

Conversation intelligence
Operations

SLA + queue health

Where bots time out, where queues back up, where SLAs slip.

Conversation intelligence
All verticals

Containment tuning

Lift bot containment with data-driven KB and flow improvements.

AI for customer support
Sales

Lead-flow tuning

Where in the qualification flow leads drop off. Fix the wording, lift conversion.

Lead qualification AI
Compliance

Regulated-conversation audit

Where regulatory disclosures fired vs missed. Audit-grade reporting.

Conversation intelligence

Explore related

Conversation Intelligence

The full intelligence platform.

Conversation
Capability

Unified Dashboards

Aggregate analytics across channels.

Unified
Capability

Sentiment + Intent

Real-time scoring that feeds analytics.

Sentiment
Capability

BI Exports

Send analytics to your BI stack.

BI

FAQ: Conversation Analytics

The percentage of conversations the bot resolves end-to-end without escalating to a human. Conversation analytics shows containment per intent and per flow so your team can see what to tune next and track the lift over time.

Every conversation flow has nodes (greeting, intent, KB lookup, ask question, etc.). EnableX tracks how many conversations enter each node and how many exit successfully vs abandon. The heatmap shows per-node drop-off rate; high drop-off means something wrong with that node's prompt, KB answer, or flow logic.

Auto-CSAT is a signal inferred from the conversation itself, useful for daily operational decisions; it is not a replacement for survey CSAT in regulated industries that need explicit customer feedback. Most customers run both: auto-CSAT for daily ops, survey CSAT for compliance.

A customer question where the bot's knowledge-base retrieval returned low-confidence chunks (or none). The bot couldn't answer well. EnableX surfaces these questions, ranks by frequency times customer-segment value, and suggests what doc topics to write next. Closing the most frequent gaps is usually the fastest way to lift containment.

Yes. EnableX BI Exports pushes per-conversation and aggregate data to BigQuery, Snowflake, Databricks, Looker, Power BI, Tableau. Real-time stream plus scheduled batch. Your data team can build its own reports on top.

Cloud: instant. Analytics are part of the EnableX platform from day one. Custom dashboards (per-vertical, per-team) can be built in hours. The work is in tuning containment and CSAT thresholds for your business, which is ongoing iteration after launch.

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