Know Exactly Which Agent Follow-Ups Drive Revenue.
Journey Analytics tracks enrollment, engagement, drop-off, and revenue attribution at every step your AI Agent triggers, so you know which follow-ups convert, where contacts exit, and exactly how to reduce CAC.
Based on aggregated data from Geta.ai customers across Real Estate, Education, E-Commerce, and Finance verticals. Individual results may vary.
Enrollment Funnels
Step-by-step completion and drop-off rates per Journey
Revenue Attribution
Deals and meetings traced back to the Journey step
Branch Distribution
See how contacts split across conditional paths
Cross-Journey Comparison
Compare ROI and conversion across all your Journeys
Conversion Goal Tracking
Define the metric that matters, measure it precisely
A/B Variant Analytics
Side-by-side test performance with confidence scores
Channel Performance
Email vs. SMS vs. WhatsApp vs. Voice AI, per step
Real-Time Updates
Live data, no lag between event and dashboard
Enrollment Funnels
Step-by-step completion and drop-off rates per Journey
Revenue Attribution
Deals and meetings traced back to the Journey step
Branch Distribution
See how contacts split across conditional paths
Cross-Journey Comparison
Compare ROI and conversion across all your Journeys
Conversion Goal Tracking
Define the metric that matters, measure it precisely
A/B Variant Analytics
Side-by-side test performance with confidence scores
Channel Performance
Email vs. SMS vs. WhatsApp vs. Voice AI, per step
Real-Time Updates
Live data, no lag between event and dashboard
What's inside
Full-funnel analytics for every Journey you run.
Journey Analytics gives you the precise data needed to optimize every sequence, from step-level drop-off analysis to cross-Journey revenue attribution, so every optimization decision reduces CAC further.
Open Rates Tell You What Happened. Analytics Tell You What It Was Worth.
Journey Analytics connects every send, click, reply, and engagement event to downstream CRM outcomes, deal stage advances, meetings booked, contracts signed. Revenue is attributed back to the Journey, the trigger type, and the specific step that preceded the conversion. For the first time, your marketing and sales ops teams can answer: exactly how much revenue did this automated sequence generate?
- Deal and meeting attribution traced back to Journey and step
- Revenue per enrolled contact, clear ROI calculation per Journey
- Trigger-type attribution: CRM stage vs. behavioral vs. time-based
- Channel-level attribution: which send, Email, WhatsApp, Voice, closed the deal
Aggregate funnel, all journeys
Journey performance
New Lead Welcome
Proposal Follow-up
Re-engagement
Stage: New Lead
Trigger
Wait 15 min
Delay
Send Welcome Email
Action
Email Opened?
Condition
WhatsApp Follow-up
Not opened
Send Proposal
Opened
Stage → Qualified
Update CRM
Find the Step That's Costing You Conversions.
Every contact that drops out of a Journey mid-sequence represents acquisition spend that didn't convert. Drop-off analysis surfaces the exact step where contacts stop progressing, whether it's a message that isn't landing, a delay that's too long, or a channel switch that's losing engagement. Once you know the drop point, you can fix that specific node without rebuilding the entire Journey.
- Step-by-step funnel: see where the biggest drop-off points are
- Compare drop-off rates across branches, find the underperforming path
- Identify whether drop-off correlates with channel, timing, or content
- Fix the underperforming node in the live Journey, no restart required
Test Variants. Measure Rigorously. Scale the Winner.
A/B Testing and Journey Analytics work together inside Geta.ai. When a test is running, the analytics panel shows side-by-side step-level performance for both variants, enrollment, engagement, conversion rate, and revenue attribution. Statistical confidence is surfaced automatically. When the winner is clear, promote it with one click and the analytics baseline updates immediately. Every test iteration compounds the CAC improvement.
- Side-by-side variant funnels: step-level comparison at a glance
- 95% confidence indicator before acting on test results
- Revenue attribution per variant, not just open rate comparison
- Promote winner with one click, analytics update automatically
Configured triggers
Stage Change
Lead Stage → Qualified
Welcome + Proposal sequence
Behavior
Email Opened + Link Clicked
Hot Lead nurture flow
Time-based
No reply in 48 hours
Re-engagement sequence
Event
Form submitted: Demo Request
Demo prep sequence
Live, just fired
Priya Sharma: Lead Stage → Qualified
How Journey Analytics Works
From Raw Event Data to Actionable Revenue Insight
Four steps from tracking every contact interaction to continuously reducing CAC through data-driven Journey optimization.
Track enrollment and engagement at every node
From the moment a contact enrolls, every interaction is recorded: which step they reached, whether they opened the email, replied to the WhatsApp, answered the Voice AI call, or advanced their CRM stage. Data is captured at the individual contact level and rolled up into Journey-level metrics in real time.
Identify drop-off, find the bottleneck
The step-by-step funnel view shows exactly where contacts stop progressing. A sharp drop between Step 3 and Step 4? That's a content or timing problem, and now you know exactly where to focus your optimization effort. Without step-level data, you can only guess. With it, you act precisely.
Connect Journey activity to revenue
Journey Analytics connects enrollment and engagement data to CRM outcomes, deal stage advances, meetings booked, contracts signed. Revenue attribution is assigned back to the Journey, trigger type, and channel step that drove each conversion. You see which Journeys produce the highest revenue per enrolled contact.
Optimize, then measure the improvement
With precise drop-off and attribution data in hand, you update the underperforming step, adjust the timing, swap the channel, or A/B test a variant. After the change, the analytics panel shows whether conversion improved. Each iteration reduces CAC further, compounding returns from a single analytics investment.
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