Six Customer Segments
8 min read
TrustLens divides the 0–100 trust score into six segments. The segments are how you’ll think about customers day-to-day — segment is what shows on the orders list, on the dashboard, and in automation rule conditions. This guide describes each segment in detail, when customers move between them, and the operational treatment recommended for each.
The Six Segments #
| Segment | Score Range | Color | Default Treatment |
|---|---|---|---|
| VIP | 90–100 | Green | Reward, fast-track, protect from friction |
| Trusted | 70–89 | Blue | Normal processing — your reliable base |
| Normal | 50–69 | Gray | Normal processing — neutral, average customers |
| Caution | 30–49 | Yellow | Monitor; consider optional friction; no auto-block |
| Risk | 10–29 | Orange | Hold for review; require account creation |
| Critical | 0–9 | Red | Manual approval or block |
The thresholds are configurable via the trustlens/segment_thresholds filter, but the defaults match how TrustLens is documented throughout the admin UI.
You don’t have to memorize these. The Customers page includes a built-in “What do the segments mean?” legend — a collapsible glossary of all six segments with their score bands and a short description of each — and inline ? tips throughout the admin explain terms like “fingerprint” right where you encounter them.
VIP (90–100) #
Who’s In It #
The top tier. Customers reach VIP through one of two paths:
- Allowlisted. Toggling allowlist on locks any customer at exactly 100 and assigns VIP. This is a kill-switch — useful for customers you never want flagged, regardless of behavior.
- Earned. A customer can organically reach the 90–100 range through long tenure (+15 age bonus), high completed-order count, low refund rate, clean chargeback history, and no abuse signals across modules.
What VIPs Get #
- Green badge wherever they appear in the admin
- Card-Testing Defense bypass by default — VIPs skip velocity checks at checkout (configurable; default on)
- Excluded from many automation rules by default (you typically condition rules on “segment is not VIP”)
- Highlighted in dashboard Top Returners only if their refund value is exceptionally high — VIP doesn’t suppress the alert
Recommended Treatment #
- Reward loyalty: dedicated VIP discount tier, early access, free shipping
- Never apply friction (account-required, manual review) automatically
- Treat VIP refund requests as automatic approvals unless something extreme is happening
- Watch for VIPs dropping out of the segment — that’s a strong leading indicator something has changed
How Customers Leave VIP #
Either (a) you remove them from the allowlist, or (b) they accumulate enough negative signals to drop below 90 organically. The most common organic drops: a filed dispute, a sudden refund spike, or being detected as linked to a risky account. The allowlist insulates against all of these.
Trusted (70–89) #
Who’s In It #
Reliable repeat customers. Typically multiple completed orders, low single-digit refund rate, account tenure of three or more months, no abuse signals. Most healthy stores find 20–40% of their scored customer base lands here.
Recommended Treatment #
- Normal processing — no friction, no special handling
- Eligible for standard loyalty programs and email marketing
- Customers who frequently move between Trusted and VIP are good candidates for proactive allowlisting
How Customers Leave Trusted #
Either drift upward into VIP (with continued clean behavior plus accumulating age bonus) or drift downward into Normal (a single refund or dispute can drop a Trusted customer 5–20 points depending on severity).
Normal (50–69) #
Who’s In It #
The default segment. Three populations live here:
- Genuinely average customers. Some refunds, some orders, nothing exceptional in either direction.
- Customers below the minimum-orders threshold. Anyone with fewer than 3 orders (configurable default) stays at exactly 50 regardless of behavior, with the “Insufficient data” system signal recorded.
- Recovered customers. A customer who was previously in Caution and is rebuilding trust through clean orders may land in Normal.
Recommended Treatment #
- Normal processing
- The largest segment in most stores — should be the default in automation rules
- No automatic friction; no allowlisting needed
Distinguishing Sub-Populations #
To tell apart “average customer at 55” from “new customer pinned at 50,” check the signal breakdown on the customer detail page. The “Insufficient data” signal is the giveaway for sub-threshold customers.
Caution (30–49) #
Who’s In It #
The early-warning segment. Customers here have at least one meaningful negative signal — an elevated return rate (above 25%), a coupon-then-refund pattern, a country mismatch, an unusual order velocity. Nothing dispositive yet, but worth watching.
Recommended Treatment #
- Don’t block or require account creation by default in Free
- Optional: surface these customers in a weekly review queue
- In Pro, common automation patterns: fire a webhook alert (for example to Slack), flag the order with a note, or send an email to a manual review inbox
- Pay attention to direction — Caution trending toward Risk needs faster intervention than Caution trending back toward Normal
What Drives Customers Into Caution #
Most commonly:
- Return rate crossing 25% with sufficient order history (-10 signal)
- A first refund where the customer also used a first-order coupon
- Shipping address velocity (changes too frequently)
- Country mismatch between billing and shipping
- One linked account, no further signals
Risk (10–29) #
Who’s In It #
Multiple negative signals adding up. Either one severe signal (high return rate at 40%+, filed dispute, linked to a risky account) or several moderate signals compounding. The behavior pattern is consistent with active abuse.
Recommended Treatment #
- Hold orders for review — common automation action in Pro
- Require account creation at checkout — adds friction without blocking
- Manual moderation before issuing further refunds
- Tighter coupon eligibility (block first-order coupon use)
- In Pro, automation rules typically fire alerts when a customer enters Risk
What Drives Customers Into Risk #
- Return rate crossing 40% with sufficient orders (-25 signal)
- Wardrobing pattern (90%+ full refunds with 3+ refunds, -10 added to existing return penalty)
- Multiple first-order coupon usages (-15 to -25 from Coupons module)
- Linked to one risky account (-25 from Linked Accounts module)
- A filed but pending dispute
- Compound effects across two or three modules
The Reversibility Question #
Risk-tier customers can return to Normal if they place several clean orders without further negative events. The account-age bonus continues to accumulate, and as more orders dilute the return rate, the Returns signal weakens. This recovery typically takes 3–6 months of clean behavior.
Critical (0–9) #
Who’s In It #
Confirmed multi-module abuse. Typically:
- Very high return rate (60%+) plus wardrobing pattern
- A lost dispute plus existing return abuse
- Fraud-ring members linked to multiple already-Critical accounts
- Card-testing-attack-tied fingerprints with subsequent customer activity
Internally these customers often score significantly below 0 (e.g. -45) before being clamped to 0–9. The clamp means you can’t distinguish “bad” from “very bad” by raw score in this range — look at the signal breakdown for the magnitude.
Recommended Treatment #
- Block at checkout (requires the master checkout-blocking toggle enabled)
- Manual approval required for any future orders
- No coupon eligibility
- Refund denials supported by the signal breakdown as evidence
- In Pro, generate a Dispute Evidence Report if a chargeback is filed — the report bundles the trust score, all signals, full order history, and behavioral analysis for processor submission
The False Positive Question #
Critical is the tier where false positives matter most. Before blocking a Critical customer, always:
- Open the Customer Detail page and read every signal
- Confirm the signals are real (e.g. the linked-accounts link is to a customer you’d also flag)
- Check whether the customer is actually a known high-value customer who triggered an unusual edge case
If you find a legitimate customer in Critical, allowlist them — the allowlist locks them at 100 and prevents future false positives. Don’t just unblock; that doesn’t fix the underlying score.
Segment Transitions #
When a customer crosses a threshold, TrustLens fires the trustlens/segment_changed action with the old and new segments. Pro automation rules subscribe to this to trigger segment-change behaviors.
Promotion (Upward Transitions) #
| From | To | Typical Trigger |
|---|---|---|
| Caution | Normal | Refund rate falls back below 25% as new clean orders dilute the rate |
| Normal | Trusted | Crossing 5+ orders with low refund rate, or 3-month tenure bonus kicks in |
| Trusted | VIP | 1-year tenure bonus pushes score above 90, or allowlist toggled on |
Demotion (Downward Transitions) #
| From | To | Typical Trigger |
|---|---|---|
| VIP | Trusted | A refund or coupon abuse signal in a non-allowlisted VIP |
| Trusted | Normal | A dispute, or refund rate spiking |
| Normal | Caution | Return rate crossing 25% threshold |
| Caution | Risk | Return rate crossing 40%, or a second negative signal |
| Risk | Critical | Dispute filed, or linked-account fraud-ring detection |
Segment Distribution Targets #
There’s no universal “right” distribution — it depends on your industry, return policy, and customer base. But a healthy store usually looks roughly like:
| Segment | Healthy Range | If Significantly Higher |
|---|---|---|
| VIP | 2–10% | You may be over-allowlisting; check for over-broad rules |
| Trusted | 20–40% | Excellent — strong repeat base |
| Normal | 40–70% | Mostly fine; can indicate min-orders threshold is too high |
| Caution | 5–15% | Tune thresholds if very high — may be over-sensitive |
| Risk | 1–5% | If > 5%, you have either real abuse or false positives — investigate samples |
| Critical | < 1% | If > 2%, suspect a fraud ring, a leaked coupon, or a wardrobing community |
The Dashboard’s segment distribution row shows your current percentages. Comparing them against these ranges is a quick health check.
Industry Variations #
| Industry | Common Distribution Quirks |
|---|---|
| Apparel / Shoes | Higher Caution share (often 15%+); raise return-rate thresholds since high return rates are baseline for the industry |
| Electronics | Lower refund rates but higher dispute exposure; chargeback signals dominate |
| Beauty / Cosmetics | Coupon abuse dominant; new-customer fraud rings common |
| Home goods | Lower velocity; long-tenure VIPs common |
| Digital / subscription | Refund signals weaker; chargeback and card-testing dominate |
Per-Segment Automation Patterns (Pro) #
Common Pro automation rules per segment:
| Segment | Common Rule |
|---|---|
| VIP | If order placed, tag as “VIP order” for fulfillment fast-track |
| Trusted | Usually no rules — normal processing |
| Normal | Usually no rules |
| Caution | If order placed, add internal note; send digest summary to ops daily |
| Risk | If order placed, hold for manual review; fire a webhook alert (e.g. to Slack) |
| Critical | If order placed, hold + alert + require manual unhold; block customer if dispute filed |
See Automation Overview for the full rules engine.
