Tickets we created from audit findings that haven’t been resolved yet.
At a glance
Live count of Gorgias tickets that Vortex IQ filed on your behalf from audit findings (broken checkout flows, refund spikes, performance regressions, ad-on-OOS, missing SEO fields) and which still sit in an unresolved state. Gorgias is ecommerce-native, so Vortex IQ findings appear in the same inbox as live shopper queries with full Shopify/BigCommerce order context inline. The merchant question this answers is: “how many revenue-protecting fixes have I asked Gorgias to triage that haven’t been picked up yet?”
| What it counts | Gorgias tickets created by the Vortex IQ integration (tagged vortex_iq in Gorgias) whose status is open. Gorgias has only two terminal states (open and closed), so this card is simpler than its multi-state peers (Zendesk, Freshdesk). |
| API endpoint | GET /api/tickets?tags=vortex_iq&status=open&limit=100 paginated with cursor-based pagination via next_token. Gorgias caps API requests at 40 per second; the connector batches accordingly. |
| Ticket-state filter | open only. closed excluded. Gorgias has no separate Pending or On-Hold states; long-pause tickets stay Open. |
| Bot-handled tickets | Included. Gorgias’s Auto-Respond and the Automate AI agent can reply automatically on common queries (refund status, shipping ETA, cancel-order); these replies do NOT close the ticket by default. The ticket only drops out when an agent or rule sets status: closed. |
| Multi-channel scope | All Gorgias channels (Email, Chat widget, Facebook, Instagram, WhatsApp, SMS, Voice, Contact Form, API). Vortex IQ-created tickets always come in via API (channel: api). |
| Multi-store aggregation | Gorgias supports multiple Shopify/BigCommerce stores under one account via Integrations. The card aggregates across all integrations the connector token reads. Set vortex_iq.store_filter to scope by Shopify domain. |
| Time window | RT, refreshed every 60 seconds. |
| Alert trigger | > 20 open. |
| Anomaly detection | If the count grows by >5 in 60 minutes, Vortex IQ raises an “Audit findings backing up” alert. |
| Time zone | Account timezone in Settings → Account → Timezone. |
| 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 apparel brand on Shopify Plus running Gorgias Advanced with the Automate AI agent. Two storefronts (US main + DTC outlet) under one Gorgias account. Snapshot taken on 02 May 26 at 13:55 ET, mid-afternoon promotional push.| Storefront | Open Vortex IQ findings | Notes |
|---|---|---|
| US main | 11 | Mostly checkout and Shopify-app integration findings |
| DTC outlet | 8 | All catalogue/pricing inconsistencies from a recent migration |
| Total | 19 | One short of the 20-ticket alert threshold |
- Gorgias’s tight Shopify integration changes how findings should be triaged. Every ticket here has the customer’s Shopify order history, AOV, lifetime value, and current cart visible in the right rail. Open the highest-LTV-customer-impacting findings first. The Vortex IQ integration writes the affected SKU IDs into the ticket body; combined with the customer panel, the agent can see “this finding affects a $400 order from a 3x-repeat buyer” at a glance. That is what no other helpdesk does and is the single biggest argument for Gorgias-on-Shopify-merchants.
- The Automate AI agent should NOT auto-close Vortex IQ tickets. Automate is excellent at refund-status, shipping-ETA, and cancel-order replies on customer queries, but Vortex IQ findings need a human or developer judgement. Configure the Automate rule with an exclusion:
IF tag CONTAINS 'vortex_iq' THEN do_not_handle. Otherwise the AI may “respond” to a finding with a generic reply and reset the SLA clock without actually fixing anything. - The DTC outlet has 8 catalogue findings concentrated post-migration. Migrations are the highest-yield audit period for ecommerce-native helpdesks. Cross-reference
shopify.catalogue_driftfor the outlet store; if drift score is elevated, the audit is doing its job and the team should batch-fix rather than triage one-by-one. - 19 open is healthy in absolute terms but Gorgias’s typical ticket volume is much lower than Zendesk. A typical Gorgias merchant runs 50-200 daily tickets total (vs Zendesk merchants at 500-2000). 19 Vortex IQ findings is therefore a higher share of the inbox. Pair with
gor_open_tickets. If 30%+ of the inbox is Vortex IQ findings, the team will perceive the audit programme as noise unless framed as revenue protection. - Mid-afternoon promotional pushes are when commerce-native helpdesks light up. The 19 count was 12 this morning. Ad spend driving traffic surfaces audit issues fast. Pre-empt: run audits before promo windows, not during.
Sibling cards merchants should reference together
| Card | Why pair it with VortexIQ Findings Open | What the combination tells you |
|---|---|---|
| Abandoned Findings (>14d no movement) | The “silently leaking” subset. | Open count steady but Abandoned rising means findings filed faster than triaged. |
| Open Tickets (all) | Total Gorgias backlog. | If both elevated together, the inbox is overloaded and findings get buried under live shopper queries. |
| Avg First-Response Time | Gorgias’s headline merchant metric. Vortex IQ findings count toward this unless excluded. | If FRT is elevated, your finding tickets are dragging the average. Configure a separate Gorgias View for vortex_iq tag and a different SLA. |
| Finding Resolution Rate (90d) | Throughput counterpart. | Open high + resolution low equals findings filed but never closed. |
| Refund Rate (Shopify / BigCommerce) | Refund spikes drive most audit findings. Gorgias’s customer panel makes the link visible inline. | Refunds up + findings flat equals audit blind spot; findings up + refunds dropping equals programme working. |
| Ecommerce Conversion Rate (Shopify) | The single metric Gorgias merchants care most about. | Open findings climbing + conversion rate dropping equals direct cost evidence. |
| Customer Service Sentiment (Shopify) | NPS-side outcome. | Findings dropping + sentiment rising equals proof the audit programme protects retention. |
| Datadog Operational Health Score | Technical findings should map to Datadog signals. | Vortex IQ “checkout error” finding open + Datadog steady = possible false positive. |
Reconciling against the vendor’s own dashboard
Where to look in Gorgias’s own dashboard:Tickets list (app.gorgias.com) with the tag filterFor multi-store accounts, use the Integration filter at the top of the Tickets list to scope by Shopify domain. Why our number may legitimately differ from Gorgias’s view:vortex_iqand the default Open status filter. Views, build a saved View fortags:vortex_iq AND status:openand pin it for the team. Statistics → Tickets with the same tag filter for trend visibility.
| Reason | Direction | Why |
|---|---|---|
| Time zone | Boundary days off | Gorgias Statistics honour the account timezone; agent UI honours each agent’s profile zone. The card uses account timezone for the alert calculation. |
| Ticket-channel scope | Ours equals theirs | Both include all channels by default. If you scope a Gorgias View to “Email only” or “Chat only”, that view is a subset. |
| Archived / deleted tickets | Ours equals theirs | Gorgias’s Trash is excluded from GET /api/tickets by default and from most UI views. |
| Status simplicity | Ours equals theirs | Gorgias has only open and closed, no Pending/On-Hold ambiguity. The card and any Gorgias View should match exactly on the status dimension. |
| Multi-store (multi-integration) aggregation | Either | Card aggregates across all integrations; per-integration Views are subsets. |
| API rate limit lag | Ours lower briefly | Gorgias caps API requests at 40/sec. Accounts with very large open Vortex IQ counts may see a 30-60 second refresh lag during peak hours. |
| Card | Expected relationship | What causes the divergence |
|---|---|---|
shopify.refund_rate / bigcommerce.refund_rate | Refund spikes drive most findings (24-72h lag). Gorgias makes the link inline-visible per ticket. | Refunds up + findings flat = audit blind spot; findings up + refunds dropping = programme working. |
shopify.ecommerce_conversion_rate | When findings stack up unaddressed, conversion typically dips within 2-4 weeks. | The lag is shorter on Gorgias-using brands because they tend to be Shopify Plus / high-traffic where regressions show up fast. |
shopify.customer_service_sentiment | Inversely correlated. | Open findings >30 for 2+ weeks predicts CSAT drop of 3-5 points. |
Known limitations / merchant FAQs
Why does my Gorgias open count differ from your card by 2 to 3 tickets? Almost always one of these. (1) Your Gorgias View has an additional channel filter the card does not. The card is channel-agnostic; your saved View may be Email-only. (2) Multi-store aggregation: the card sums across all integrations connected to your Gorgias account; your Gorgias View may be scoped to a single Shopify domain. (3) API rate-limit lag: Gorgias caps requests at 40/sec, accounts with very large open counts may see a 30 to 60 second refresh lag. Wait for the next refresh and re-check; usually self-resolves. The Automate AI agent replied to one of my Vortex IQ findings. Does that count as resolved? No. The Automate reply does not change the ticket status; the ticket stays Open until an agent or rule explicitly setsstatus: closed. So the count is still accurate. However, an Automate reply on a Vortex IQ finding is bad behaviour: Automate’s stock replies (refund status, shipping ETA) are not appropriate responses to a finding like “checkout error rate spiked 18%”. Configure the Automate rule with an exclusion: IF tag CONTAINS 'vortex_iq' THEN do_not_handle. The Vortex IQ-Gorgias connector docs cover this in the setup guide.
My team uses multiple Gorgias Views. Which one should I trust?
The card. Gorgias Views are user-saved filter combinations and can drift apart per agent over time. The card pulls live from GET /api/tickets?tags=vortex_iq&status=open, which is the canonical source. Use the card as the source of truth for ops decisions; use Views for daily agent workflow.
We have two Shopify stores under one Gorgias account. Why does the count include both?
Because Gorgias treats tags:vortex_iq as account-wide, not per-integration. To break out by store, set vortex_iq.store_filter in the connector config to scope the card to a single Shopify domain, or build a Stacked Panel in Vortex IQ Nerve Centre with one panel per store. Alternatively, build two Gorgias Views (one per Integration filter) for the same answer in your agent UI.
A Vortex IQ ticket was important but we never used Gorgias to track it; we fixed it directly. Do we need to close the ticket?
Yes. Mark the ticket status: closed once the work is done. Otherwise it stays in this card’s count and the abandoned-rate timer (14 days of no movement) starts ticking. Vortex IQ does not auto-close findings just because the underlying audit signal cleared; the human acknowledgement (closing the ticket) is the close signal.
Open count dropped suddenly. What happened?
Three usual causes, in order of likelihood. (1) Bulk close: an agent or admin selected multiple tickets and bulk-closed them in the Tickets list. Gorgias does not surface bulk-close events as audit-log entries by default; ask the team. (2) Tag drift: someone removed the vortex_iq tag from a batch of tickets (intentionally or via a misconfigured macro). The tickets still exist but no longer match this card’s filter. (3) Vortex IQ outbound paused: check the Vortex IQ connector status; an account-level pause stops new finding tickets from being created.
Why is the count higher today than the typical baseline?
Most common cause on commerce-native helpdesks is a promotional push. Ad spend driving traffic surfaces audit issues fast (broken checkout flows, OOS-on-ad, sale-price-drift). The mid-afternoon spike pattern is normal during promos. Pre-empt by running audits before promo windows, not during.
Should I configure a separate Gorgias SLA for Vortex IQ tickets?
Yes if your team’s volume warrants it. Gorgias’s headline Avg First-Response Time includes Vortex IQ tickets by default, which can drag the metric your team is measured against. Two practical options. (1) Build a Gorgias View for tags:vortex_iq with a separate FRT target (e.g. 24h vs the 4h target on shopper tickets); the team works the View on its own cadence. (2) Use the Gorgias Rules engine to apply a different SLA tag to Vortex IQ tickets, which Gorgias’s reporting honours.
My team uses Gorgias but you also have Jira connected. Will Vortex IQ duplicate findings into both?
No. Each finding is routed to one tool based on the connector setup’s outbound priority. Default for ecommerce-native merchants is Gorgias (operations / customer-experience finding). Override in Settings → Connectors → Routing if your engineering team owns audit findings and you want them in Jira instead.
Is Gorgias the right tool for managing Vortex IQ findings?
Gorgias shines when the merchant’s audit findings have a customer-impact framing (refund-driving, conversion-killing, retention-hurting). The customer panel and Shopify integration make the cost of each finding visible in the same view as the work to fix it. If your team treats audit findings as engineering tickets with cycle times measured in sprints, Jira or Linear is a better fit. If your team treats them as operations workflows with daily triage, Gorgias is the right home.
Today’s count looks volatile. Why?
At low volumes (<10 open) a single close or new finding moves the count by 10%+ which can register as noise. The 30-day average and 7-day rolling are the steadier reads for trend. The headline number is the live count.
Why is the alert threshold 20 and not 30 or 50?
20 reflects Gorgias’s typical inbox-size profile. Most Gorgias merchants run 50 to 200 daily total tickets, so a Vortex IQ count of 20 means roughly 10 to 30% of the inbox is findings, which is the threshold above which the team perceives the audit programme as noise. Tunable per organization in connector settings; engineering-led teams using Jira-or-Linear as their primary use a higher threshold (50+) because Gorgias is auxiliary.