“P2P and On-Ramp are live, everything looks fine.”
Cool story. What exactly are you looking at - CPU usage? 😅
If you’re running P2P or fiat→crypto flows in production, infra metrics alone won’t save you. You need product-level telemetry that maps directly to user pain and revenue leaks.
1. Core Health: Can People Actually Get Funds In? 💸
For On-Ramp, your primary heartbeat is:
- Payment success rate per provider / method / country
- Median & p95 time to credit (from “pay” to “crypto received”)
- KYC/KYB drop-off rate per step (document upload, selfie, proof-of-address)
Red flags:
- One provider with “okay” uptime but 20–30% failure on card attempts
- Users waiting 20+ minutes for fiat confirmation while your UI says “usually 1–3 min”
2. P2P Reality Check: Are Trades Completing or Just Rotting? 🔄
For P2P, “orders created” is vanity. You want:
- Completion rate (created → paid → released)
- Time to completion segmented by payment method
- Dispute rate (% of trades that escalate)
- Auto-cancel rate (timers, no-shows)
These should be broken down by:
- taker vs maker
- country pair
- risk tier
If your dispute rate spikes after a UI change or new payment method—you just shipped a problem.
3. Risk & Abuse: Don’t Wait for Compliance to Ping You 🚨
Some unsexy but critical metrics:
- Volume & count per user / per 24h (velocity)
- % of volume from top X users (whales or wash?)
- Sudden payment method mix shifts (e.g., 80% jump in one bank or PSP)
You’re looking for behavior that says “this is no longer normal user activity”.
4. User Experience Metrics: Feelings, but Quantified 😏
UX-ish, but dev-owned:
- Error code distribution (by step, by provider)
- Retry rate per flow step
- Session abandonment point (where they rage-quit the funnel)
These metrics turn “users say it’s buggy” into “95% of drop-offs happen after PSP redirect timeout”.
5. Go Deeper: P2P vs On-Ramp Behavior
If you want to dive into how P2P vs On-Ramp compare in terms of user behavior, risk and UX trade-offs, there’s a separate article that breaks this down nicely - worth a read.
Until then: if your dashboards don’t tell you who failed, where, and why - you’re not monitoring, you’re just watching graphs wiggle 🚀
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