Anonymization & cleaning
We remove personally identifying fields before analysis. Published reports use aggregated cohorts, so individual buyers are never exposed.
Deep Trust
Methodology
We publish decisions, not vibes. This page is the long version: how we score, how the data is treated, what models do the work, and who is accountable for the calls.
Our Process
How We Score
Verified Purchase Data
We start with real transaction signals — not search trends or brand claims.
40,000+ Public Reviews Analyzed
We systematically analyze public user feedback from Reddit, retail platforms, and buyer communities.
No Brand Influence
We do not accept sponsored placements. Brands cannot pay to appear in our reports.
Our editorial process: identify category → collect public review corpus → extract verified signal → remove outliers → produce scored matrix.
E-E-A-T · Trust
Data Ethics & Transparency
SheStat uses purchase and review signals to reduce checkout risk, not to sell brand narratives. We separate commercial links from editorial decisions and preserve user privacy by publishing cohort-level patterns only.
No brand sponsorships
We do not accept sponsored placement, paid reviews, or pay-to-rank arrangements. Brands cannot purchase inclusion or position.
Conflict-of-interest disclosure
When a retailer link may earn commission, the page discloses it. Affiliate status never changes Buy / Avoid calls.
E-E-A-T · Expertise
Algorithm & Processing Models
Each report turns noisy buyer feedback into a short decision path: define the cohort, isolate recurring failure modes, compare product fit against those failures, then update the call as category evidence changes.
Cohort & RFM analysis
We segment historical buyers with a Recency / Frequency / Monetary frame, then layer return-risk and repeat-purchase signals.
Public review ingestion
We review public buyer discussions, retail reviews, and fit-specific communities. Reports state sample windows and refresh timing when available.
Score derivation
The S-score blends fit certainty, durability signal, and return-risk delta on a 0-10 scale. Category weights are reviewed as new reports ship.
E-E-A-T · Authoritativeness
Team & Authors
SheStat reports are edited by a small decision-intelligence team responsible for data sourcing, claim review, and final Buy / Avoid calls.
Jason Yang
Founder / Chief Architect
Jason leads the SheStat decision engine, report architecture, and editorial review process. He is accountable for turning buyer evidence into concise consumer decisions.