Funnel intelligence and conversion forecasting
Tryozi sees where conversion is likely to stall.
A unified funnel model separates broken journeys from normal delay and forecasts conversion with the uncertainty in plain view.
The impact
81%
less time compiling funnel reports
Funnel intelligence and conversion forecasting
Case study facts
- Industry
- Telehealth
- Team
- Marketing
- Systems connected
- 4
- Time to live
- 2 weeks
- Engagement
- AI implementation
The impact
Results for the team
- less time compiling funnel reports
- 81%
- less time compiling funnel reports
- Connected event data and shared funnel definitions reduce the time spent assembling reports manually.
- forecast accuracy at the agreed horizon
- 92%
- forecast accuracy at the agreed horizon
- Forecasts are checked against actual conversion outcomes at the agreed prediction horizon.
- earlier visibility into funnel changes
- 2 days
- earlier visibility into funnel changes
- Funnel changes are surfaced sooner, giving the team more time to investigate affected stages and adjust plans.
Situation
One journey. Separate reports.
Separate campaign, page and intake reports make it difficult to explain a conversion change. Recent cohorts may look weak simply because users have not had enough time to finish.
Challenge
A drop in conversion needed context.
Recent cohorts had different amounts of time to finish, and a tracking problem could look like a behavior change. Forecasting needed consistent event definitions, comparable cohorts and visible uncertainty.
What we built
Funnel evidence connected to a forecast.
The workflow maps events to a common funnel and aligns cohorts by time since entry. It checks tracking quality before interpreting movement, estimates expected completion and highlights stages that depart from their usual pattern.
Forecasts show their assumptions and uncertainty, so the team can plan with them without treating them as booked outcomes.
- 01
Unify events
Map campaign, landing-page and intake events into one shared journey.
- 02
Audit tracking
Catch missing events, duplicates and shifting definitions automatically.
- 03
Align cohorts
- Cohort analysis
Compare users on equal footing by time since entering the journey.
- 04
Pinpoint drop-offs
- Anomaly detection
Separate real stage movement from changes in traffic mix.
- 05
Forecast completion
- Predictive model
Project likely outcomes with uncertainty and lag built in.
- 06
Direct the investigation
Point the team to the exact segment and stage to inspect.
Evals and guardrails
Test forecasts. Expose tracking gaps.
Forecast evals
Test forecasts against later results the model has not seen. Measure errors and whether outcomes fall within predicted ranges at each stage.
Data-quality evals
Test missing, duplicate and delayed events. Check that customer groups are comparable and tracking gaps do not distort forecasts.
Guardrails
Check event fields and dates. Withhold forecasts when data checks fail and require analyst review before acting on unusual patterns.
Results
Teams can investigate the right stage.
Connected event data and shared funnel definitions reduce the time spent assembling reports manually. Forecasts are checked against actual conversion outcomes at the agreed prediction horizon.
Funnel changes are surfaced sooner, giving the team more time to investigate affected stages and adjust plans.
- less time compiling funnel reports
- 81%less time compiling funnel reports
- forecast accuracy at the agreed horizon
- 92%forecast accuracy at the agreed horizon
- earlier visibility into funnel changes
- 2 daysearlier visibility into funnel changes
From Tryozi
“We had tried other partners and nobody could unblock it. Revensi came in, understood the problem quickly, and turned a bottleneck into something that just runs. Total game changer.”
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