At a glance
The percentage of Vortex IQ-filed Gorgias tickets that the merchant team has actually closed in the last 90 days. Designed to answer the merchant question: “is the audit programme working, or am I paying for findings that nobody actions?” Gorgias is the ecommerce-native helpdesk: order context appears inline in every ticket and AI auto-responses handle the high-volume “where is my order” tail. That changes how this card reads: a Gorgias-specific resolution rate above 80% is normal because the AI closes a chunk of tickets without human triage, so the human-action portion of the rate matters more than the headline number.
| What it counts | closed_findings_90d / (closed_findings_90d + open_findings_90d) × 100, expressed as a percent of Vortex IQ-tagged tickets created in the last 90 days now in closed status. |
| Numerator | Tickets with tags.name=vortex_iq AND status:closed AND created_datetime >= now-90d. |
| Denominator | All tickets with tags.name=vortex_iq AND created_datetime >= now-90d, regardless of state. |
| Status filter | Closed counts as resolved; Open counts as unresolved. Gorgias has a simple two-state model (Open / Closed) with no Pending state, so the rate is unambiguous in a way Zendesk and Freshdesk are not. |
| Issue type filter | All Vortex IQ-tagged tickets included. The category sub-tags (vortex_iq:catalogue, vortex_iq:checkout, vortex_iq:performance, vortex_iq:seo) do not filter the rate; they exist for breakdown views. |
| Project / board scope | Single Gorgias account. Gorgias does not have multi-brand/multi-Mailbox separation in the same way Zendesk does (one Gorgias = one storefront, typically). Multi-store brands use multiple Gorgias accounts and connect them as separate Vortex IQ instances. |
| AI auto-resolved tickets | Counted as resolved. Gorgias AI Auto-Responder closes tickets when its confidence threshold is hit (configurable per Macro). On a Vortex IQ ticket the AI rarely matches because findings have technical body text, but if the merchant has trained a Macro on vortex_iq history, AI closures are legitimate and count. |
| Reopened tickets | If a ticket is closed then reopened (the customer replies after closure), it counts as unresolved at the moment of measurement. |
| API endpoint | GET /api/tickets?filter[tag]=vortex_iq&filter[created_datetime][gte]={{now-90d}}&filter[status]=closed for the numerator, then a second call for total population. Gorgias REST API uses cursor pagination at 100/page; rate limits are 40 req/min on Starter, higher on Pro/Advanced. |
| Order-context fields | Gorgias enriches tickets with linked Shopify/BigCommerce orders. The card does NOT use order context for the rate calculation, but the inline order data is what makes the worked example per-ticket reasoning so much faster on Gorgias compared to other helpdesks. |
| Time window | 90D rolling. Anchored on the filing date; an old finding closed today does NOT count if it was filed more than 90 days ago. |
| Alert trigger | <50%. At 50% you are leaving as many findings open as you close, the point at which the backlog grows faster than the team clears it. |
| Sentiment thresholds | Good >= 75%, warn 50-75%, critical <50%. |
| Time zone | Account timezone in Settings -> Account -> Time zone. |
| Roles | owner, operations |
Calculation
Calculated automatically from your Gorgias data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
A US DTC supplements brand on Shopify Plus running Gorgias Pro with AI Auto-Responder enabled for shipping/order-status macros. Snapshot taken on 02 May 26 at 10:25 ET, looking back over the rolling 90 days from 02 Feb 26.| Bucket | Count | Notes |
|---|---|---|
| Vortex IQ tickets filed in window | 96 | Higher than non-Gorgias peers; ecommerce-native means more findings get filed in helpdesk vs Slack |
| Closed by human agent | 64 | Bulk of the resolution work |
| Closed by AI Auto-Responder | 4 | Rare; mostly tagged-as-shipping-related findings AI can answer |
| Closed by Workflow rule | 7 | Auto-close after 28 days no reply |
| Still open | 21 | Active findings |
| Resolution rate | (64 + 4 + 7) / 96 = 75 / 96 = 78.1% | Green, healthy |
- 78% is the upper-end-normal for Gorgias. Ecommerce-native helpdesks tend to score 5-10 percentage points higher than enterprise helpdesks (Zendesk, Freshdesk) because the inline order context lets agents close tickets faster. A Gorgias resolution rate below 65% is a red flag in a way that the same number on Zendesk would not be.
- Gorgias AI closing 4 of 75 (5%) is realistic for Vortex IQ tickets. AI handles the “where is my order” tail brilliantly but struggles with technical-bug findings whose body text is unique. Do not expect AI to materially lift this rate; the rate climbs through better triage workflows, not better AI.
- The 7 Workflow-closures are honest. Gorgias’s auto-close-after-N-days rule is configurable per tag. Set it to 28 days for
vortex_iqtags so abandoned findings drop out of the active backlog into the closed bucket. The merchant trades a small numerator inflation for a more accurate denominator-shrink later when the rolling window moves on. - Pair with
shopify.refund_rate. Gorgias’s deep Shopify integration means refund spikes show up in the helpdesk immediately, often before they show in Shopify Analytics. If refund rate is steady AND this card sits at 78%, the audit programme is paying for itself. If refund rate is climbing despite the high rate, the team is closing the wrong findings. - The 21 open findings include the highest-value bugs. Gorgias agents typically close the easy stuff (mistagged variants, broken redirects) and flag the harder revenue-impact bugs (broken Klarna, broken Shop Pay) for engineering. So the 21 open is biased toward high-value bugs, the inverse of what a Zendesk merchant sees. Inspect each open ticket individually rather than treating the count as fungible.
- Reconcile with
bigcommerce.refund_rateif multi-platform. Brands on Shopify Plus + BigCommerce B2B sometimes have one Gorgias account aggregating both. The card aggregates the same way; if the BigCommerce side is dragging the rate, scope a Stacked Panel byvortex_iq.platform_filter:bigcommerce(custom config) for visibility.
Sibling cards merchants should reference together
| Card | Why pair it with Finding Resolution Rate | What the combination tells you |
|---|---|---|
| VortexIQ Findings Open | The unresolved-count counterpart. | Open count high + resolution rate low equals findings filed but never closed, the worst possible state. |
| Abandoned Findings (>14d no movement) | The “silent-leak” subset. | Abandoned rising while this rate falls equals the team is losing ground specifically on the audit queue. |
| Avg Time-to-Fix (days) | Cycle-time peer. The rate tells you whether findings close; this tells you how fast once they do. | Rate green + time-to-fix slow equals team eventually ships but late; rate red + time-to-fix fast equals team ships some quickly and abandons the rest. |
| Open Tickets (all) | Total Gorgias backlog context. | Both elevated equals CS team overloaded; rate dropping in isolation equals findings deprioritised specifically. |
| Avg Cycle Time | Triage-health peer for the whole queue. | Cycle time creeping up while this rate falls equals systemic triage breakdown. |
| Refund Rate (Shopify / BigCommerce) | The downstream truth metric an audit programme should protect. Gorgias’s Shopify integration makes refunds visible inline. | Refund rate flat or falling + rate at 70%+ equals the programme is paying for itself; refund rate climbing despite high resolution rate equals team is closing low-impact findings. |
| Customer Service Sentiment (Shopify) | The retention-side outcome of running this programme well. | Rate climbing + sentiment climbing equals the audit story works for the founder. |
| Total Revenue (Shopify) | Gorgias is ecommerce-native; the revenue card is one click away in the dashboard. | Revenue holding while findings close fast equals the Vortex IQ programme protected revenue against an issue the team would otherwise have shipped late. |
| Datadog Operational Health Score | Sibling-platform health score for technical findings. | Datadog health green + this rate green equals the team is keeping pace with both reliability and audit work. |
Reconciling against the vendor’s own dashboard
Where to look in Gorgias’s own dashboard: Gorgias does NOT provide a single “tag-scoped resolution rate” gauge, so this card is computed by Vortex IQ from the Tickets API. To verify it manually:Statistics -> Performance Overview filtered to theWhy our number may legitimately differ from Statistics’ number:vortex_iqtag and date range last 90 days. Read the percentage ofClosedagainstTotal tickets created. That is the same calculation this card runs. Views -> All tickets withtag:vortex_iqplus astatus:closedfilter, gives the numerator population at a glance. [Saved view -> “Vortex IQ findings, closed (90d)”] if you have set one up, gives the count without re-typing the filter.
| Reason | Direction | Why |
|---|---|---|
| Time zone | Boundary days off | Statistics honours the user’s configured timezone; the card uses the account-level timezone for the rolling 90-day window. For a 90-day window the gap is usually <1%. |
| API replication lag | Ours lower for “just now” | Gorgias’s API replicates the database 5-30 seconds behind real-time ticket changes. A finding closed seconds ago may not be in the numerator yet. |
| Reopened tickets | Ours lower | If a ticket was closed then reopened in the 90-day window (the customer replied), we count it as unresolved. Some Statistics views count “ever closed” rather than “currently closed”. |
| Tag inclusion | Ours stricter | We require literal tag:vortex_iq. Legacy tags (vortex, vortexiq_v1) on older tickets drop out of our population. |
| Auto-Responder closures | Same | We count AI Auto-Responder closures the same way we count human closures (both move status to closed). Some Statistics views break them out separately. |
| Multi-store accounts | Either | If the Gorgias account is connected to multiple Shopify stores, we aggregate; per-store filters in Statistics are subsets. |
| Workflow auto-close | Same | Tickets closed by a Gorgias Rule (e.g. “auto-close after 28 days no reply”) count toward the numerator the same as agent closures. |
| Card | Expected relationship | What causes the divergence |
|---|---|---|
shopify.refund_rate / bigcommerce.refund_rate | Inverse correlation. Higher resolution rate should reduce refund rate over a 4-8 week trailing window. Gorgias’s inline order context makes this loop especially tight. | Resolution rate up + refund rate up equals team is closing low-impact findings; resolution rate down + refund rate down equals findings were duplicates or stale. |
shopify.customer_service_sentiment | Positive correlation, 2-4 week lag. | Sustained 75%+ resolution rate predicts CSAT lift of 2-4 points within a quarter. The retention-side payoff. |
shopify.total_revenue | Co-protection signal. Gorgias and Shopify are tightly coupled in this brand of merchant. | Revenue stable + resolution rate stable equals the audit programme is protecting the revenue line. |
datadog.dd_health_score | Independent peer; correlates only when the audit is dominantly technical. | Both green equals balanced engineering culture; technical findings closing fast and reliability holding. |
Known limitations / merchant FAQs
The rate dropped 15 points this week. What changed? Three usual causes, in order of likelihood:- Audit volume up. Vortex IQ filed more findings than usual (a big catalogue audit, a payment-funnel audit). The denominator grew faster than the team’s closure rate. Often resolves itself within 2-3 weeks.
- Capacity loss. A senior agent on holiday, or BFCM/launch peak swallowing all CS attention. Pair with Open Tickets (all); if global backlog is also up, capacity is the answer.
- Rule drift. A Gorgias Rule that auto-tagged or auto-routed Vortex IQ tickets turned off. Open Settings -> Rules and confirm the
vortex_iqrules are still active.
- 80%+ : healthy and expected. Gorgias’s inline order context makes triage fast.
- 70-80% : normal for a maturing programme. Acceptable.
- 60-70% : warn. Below typical Gorgias baseline; investigate triage process.
- <60% : critical. On Gorgias, this is a stronger red flag than on Zendesk. The ecommerce-native workflow advantage is being squandered.
vortex_iq-tagged tickets at 28 days minimum, longer than the customer-ticket auto-close rule, because findings need engineering time, not just a customer reply.
My Gorgias account has multiple Shopify stores connected. Why a single number?
The card aggregates by default. To break out per-store, build a Stacked Panel in the Vortex IQ Nerve Centre with multiple instances scoped via custom config. Alternatively, file findings with a per-store sub-tag (vortex_iq:storeA) and read the rate by sub-tag in Gorgias Statistics.
Why is the alert threshold 50% and not 70%?
50% is the breakeven point at which the team is closing one finding for every one filed. Below 50% the backlog grows mathematically; above 50% it shrinks. A 70% threshold would over-page in the first quarter of any new audit programme. For a mature Gorgias programme, you can manually raise the threshold to 65% in Vortex IQ -> Settings -> Alerts to match the higher baseline.
Does the inline Shopify order context affect this card’s calculation?
No. The calculation depends only on the ticket’s tag and status fields. Order context is what makes Gorgias merchants close tickets faster, but the resolution-rate math is the same as on every other helpdesk. The difference is in cycle time (covered in Avg Time-to-Fix).
My team uses Gorgias on the Shopify Plus integration. Is anything different?
The Shopify Plus integration adds a few enrichments (subscription order context, Shop Pay refund visibility) but does not change the API surface this card uses. The rate calculation is identical for Plus and non-Plus accounts.