Funnel friction and intent analysis
User Yield makes funnel drop-offs easier to investigate.
An analysis agent reads behavioral signals and buyer feedback together, pinpointing where a journey frustrates intent and attaching evidence to every hypothesis.
The impact
76%
less time locating funnel issues
Funnel friction and intent analysis
Case study facts
- Industry
- SaaS
- Team
- Marketing
- Systems connected
- 3
- Time to live
- 2 weeks
- Engagement
- AI implementation
The impact
Results for the team
- less time locating funnel issues
- 76%
- less time locating funnel issues
- The analysis brings event patterns and buyer feedback together, reducing the time analysts spend locating issues in the funnel.
- more friction hypotheses investigated
- 3×
- more friction hypotheses investigated
- Analysts investigate more friction hypotheses, with affected segments and supporting evidence attached to each.
- from signal to a test proposal
- 48 hrs
- from signal to a test proposal
- Signals are turned into test proposals with a stated hypothesis, evidence and validation step.
Situation
The chart showed where people left.
A drop-off chart says where people leave, but not why. Reading every exit as the same problem can lead a team to redesign a page when the real issue is an unclear requirement, a broken interaction or a mismatch in expectations.
Challenge
It could not explain why.
A broken interaction, a confusing requirement and a mismatch in expectations can all look like abandonment. The analysis needed to check tracking and functionality, connect relevant feedback and keep inferred intent separate from observed behavior.
What we built
Behavior and feedback read together.
The analysis agent maps permitted events and research feedback to journey steps, then groups recurring patterns. It separates observed behavior from inferred intent and checks for instrumentation problems before suggesting explanations.
Each finding includes the affected segment, supporting evidence and a validation step, so the team can move from an anomaly to a testable question.
- 01
Map journey steps
Define expected actions and the events that represent them.
- 02
Audit event quality
Detect gaps, duplicates and tracking changes before analysis.
- 03
Cluster behavior
- Behavior clustering
Identify repeated hesitation, errors and abandonment patterns.
- 04
Layer in buyer context
Connect relevant feedback without assuming it explains every session.
- 05
Form hypotheses
Separate observed friction from possible underlying intent.
- 06
Choose the validation
Recommend research, a functional fix or an experiment.
Evals and guardrails
Check unusual patterns. Support each explanation.
Detection evals
Measure missed funnel problems and false alerts against checked examples. Test missing events, small customer groups and broken interactions.
Evidence evals
Check explanations against events and survey responses. Test unsupported guesses about intent and whether alternative causes are considered.
Guardrails
Check data quality and sample size. Block guesses about sensitive personal traits and require analyst review before accepting an explanation.
Results
Drop-offs become testable questions.
The analysis brings event patterns and buyer feedback together, reducing the time analysts spend locating issues in the funnel. Analysts investigate more friction hypotheses, with affected segments and supporting evidence attached to each.
Signals are turned into test proposals with a stated hypothesis, evidence and validation step.
- less time locating funnel issues
- 76%less time locating funnel issues
- more friction hypotheses investigated
- 3×more friction hypotheses investigated
- from signal to a test proposal
- 48 hrsfrom signal to a test proposal
From User Yield
“We needed our optimization tooling in-house rather than rented. Now we build and test client funnels on our own system, and the testing keeps running between projects.”
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