Finding Resolution Rate (90d), Gorgias

Metrics type: Key Metrics • Category: Project Management

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

The card reads 78%. Five observations:

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_iq tags 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_rate if 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 by vortex_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 the vortex_iq tag and date range last 90 days. Read the percentage of Closed against Total tickets created. That is the same calculation this card runs. Views -> All tickets with tag:vortex_iq plus a status:closed filter, 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.

Why our number may legitimately differ from Statistics’ number:

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.

Cross-connector reconciliation:

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_iq rules are still active.

A finding closed today but it was filed 95 days ago. Does it count? No. The window is anchored on the filing date, not the closure date. A ticket filed outside the 90-day window does not appear in the denominator at all, even if it just closed. This avoids “closed an old ticket today” gaming the metric. What is a healthy rate on Gorgias specifically?

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.

Why is the Gorgias healthy band higher than Zendesk’s (60-75%)? Gorgias’s inline Shopify/BigCommerce order context lets agents close tickets without context-switching to another tab. The same ticket takes 30-40% less time on Gorgias than on Zendesk-with-Shopify-app. So a Gorgias merchant should sustain a higher resolution rate; the same input produces a higher output. Should I optimise for a higher rate? On Gorgias, slightly more aggressively than on other helpdesks. Above 90% sustained for a quarter often means the audit thresholds are too generous (finding only obvious issues). Lower the audit threshold to surface more borderline issues; the rate will dip but the merchant outcome (lower refund rate, lower customer-service load) will improve. Gorgias AI Auto-Responder closed a Vortex IQ ticket. Does that count as resolved? Yes. AI Auto-Responder closes a ticket only when its Macro confidence threshold is hit. On a Vortex IQ ticket the AI rarely matches because findings have technical body text, but if it does close one, the underlying issue is genuinely addressed (the Macro ran the workflow, not just a canned reply). Accept those closures as legitimate. A reopened ticket dropped my rate by 1 point. Can I exclude it? A closed ticket that gets reopened is genuinely no longer resolved, so the rate correctly drops. If reopens are common (>5% of closed tickets), it points to a quality-of-fix problem, fixes are landing but not actually resolving the underlying issue. Gorgias’s auto-close-after-N-days rule is closing my Vortex IQ tickets. Is that gaming the rate? A little, yes. If you have set the global rule to 7 days, abandoned Vortex IQ tickets drop into the closed bucket fast and inflate the numerator. The honest configuration: set a separate rule for 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.

Tracked live in Vortex IQ Nerve Centre

Finding Resolution Rate (90d) is one of hundreds of KPI pulses Vortex IQ tracks across Gorgias and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. Start for free or book a demo to see this metric running on your own data.

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Finding Resolution Rate (90d), Gorgias

At a glance

Calculation

Worked example

Sibling cards merchants should reference together

Reconciling against the vendor’s own dashboard

Known limitations / merchant FAQs

Tracked live in Vortex IQ Nerve Centre

​At a glance

​Calculation

​Worked example

​Sibling cards merchants should reference together

​Reconciling against the vendor’s own dashboard

​Known limitations / merchant FAQs

​Tracked live in Vortex IQ Nerve Centre

At a glance

Calculation

Worked example

Sibling cards merchants should reference together

Reconciling against the vendor’s own dashboard

Known limitations / merchant FAQs

Tracked live in Vortex IQ Nerve Centre