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A high app rating can coexist with serious trust stress. Market Experience measures what users report when systems fail - scams, stale listings, wrong locations, and the support path that follows.
MEI is a consumer-surface view. It complements (but does not replace) internal fraud, support, and NPS analytics.
The MEI chart below is the ‘trust gap’ in its simplest form: the prevalence of complaint themes across the cohort.
In practice, the gap emerges when a portal optimizes for conversion and volume while the consequence system (support, escalation, verification, refund rules) does not scale at the same pace.
AI increases the importance of this layer because it can amplify both scams (through cheap persuasive content) and detection (through anomaly detection) - see AI & the Trust Gap (signal).
In the MEI consumer cohort (n=20 portals), the most prevalent complaint themes are UX gaps (65.0%), scams (45.0%), and stale inventory (40.0%). These are topic‑presence signals, not incident rates.
Source: GPPI MEI (Market Experience) consumer dataset, 2025 cycle. Topic prevalence indicates presence of theme in the evaluated window where available.
Star ratings compress complex outcomes. Trust risk often sits in long-tail events: scams, disputes, misrepresentation, and ‘support failure when it matters’. MEI treats those as signals, not anecdotes.
In 2025, ‘trust’ behaves like infrastructure. It is not a marketing claim; it is a system property that regulators, advertisers, and boards increasingly expect to be evidenced.
Three infrastructure questions dominate:
• Identity & provenance: can you prove who is behind a listing and where it originated?
• Consequence path: what happens when a user reports a scam or misrepresentation - and how quickly?
• Transparency: can you explain paid placements, ranking, and AI-generated content in a way that reduces disputes rather than creates them?
Data Notes