Lens 1
Credibility gap
The distance between reported traffic and traffic supported by enough evidence to be counted.
Sample output
What an evidence report looks like
A BuyerRecon evidence report surfaces what reported performance is actually made of — so teams can see where signal is strong, where it is weak, and where optimisation is compounding error.
No integration required. Results in 48 hours.
Why teams use this
Before optimising, see what the signal is really made of
Most commercial reporting presents traffic and conversions as if they were homogeneous. An evidence layer separates credible activity from activity that should be excluded, reviewed, or held before it influences spend and optimisation.
What the report shows
Four evidence lenses
Lens 2
Reason factors
Why specific sessions, campaigns, or sources fall outside the credible range — device patterns, timing, attribution inconsistencies.
Lens 3
Waste exposure
A measurable estimate of spend or attributed outcomes that are currently attached to low-credibility activity.
Lens 4
Next actions
Concrete, evidence-bounded moves: what to exclude, what to review before scaling, and where reporting should be tightened.
Example evidence summary
What one report looks like
A fictional but representative sample for a mid-sized e-commerce property across a single reporting period.
- Total sessions observed: 18,420
- Evidence-supported sessions: 11,960
- Review zone: 2,180
- Waste exposure: ~35% of attributed spend attached to low-credibility activity
⚠ Conditionally Valid — Review Before Scaling
Segment credibility
Confidence: 72%
Signals
- Repeated device usage patterns across sessions
- Compressed timing between conversion events
- Clustered conversion behaviour in narrow windows
Recommended action
Optimise with caution. Exclude high-risk segments before scaling spend.
Start with a lightweight evidence layer
Designed for evidence-first adoption
A BuyerRecon evidence review does not require replacing existing analytics, changing attribution, or committing to a broader rollout. It sits alongside current reporting and produces a readable evidence layer the team can act on — or not.
Teams that find the report useful typically extend verification into paid acquisition, retailer media, and eventually delegated or AI-assisted work.
Related verification
The same logic applies to work itself
Where commercial traffic needs evidence before spend is optimised, delegated work needs evidence before acceptance or payment. Time-to-Point extends the same verification logic to distributed and AI-assisted execution.
See the work verification demo →Run one on your traffic
See what the evidence layer surfaces in your own reporting. No heavy integration. No commercial commitment required.
Evidence-first. No heavy integration required.