At a glance
The percentage of Vortex IQ-filed Help Scout conversations 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?” Help Scout’s customer-centric, shared-inbox philosophy makes this card especially readable for owner-led brands; “conversations” feel less industrial than “tickets” so non-engineering owners actually look at the gauge.
| What it counts | closed_findings_90d / (closed_findings_90d + active_findings_90d) × 100, expressed as a percent of Vortex IQ-tagged conversations created in the last 90 days now in closed state. |
| Numerator | Conversations with tag:vortex_iq AND status:closed AND createdAt >= now-90d. |
| Denominator | All conversations with tag:vortex_iq AND createdAt >= now-90d, regardless of state. |
| Status filter | Closed counts as resolved; Active and Pending count as unresolved. Pending in Help Scout means “snoozed waiting on customer reply”; on a Vortex IQ conversation it usually means waiting on a developer, so still unresolved. Spam and Deleted drop out entirely. |
| Issue type filter | All Vortex IQ-tagged conversations 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 | Cross-Mailbox. Aggregated across every Help Scout Mailbox the connector token can read. Help Scout’s Mailbox is the multi-tenant unit (one per brand or product line); set vortex_iq.mailbox_filter to scope to a single Mailbox. |
| Resolution counts | Help Scout has only one resolved state, closed, so the rate is unambiguous. There is no separate “archived” state to reconcile (Help Scout’s data model is simpler than Zendesk’s or Freshdesk’s). |
| Reopened conversations | If a conversation is closed then reopened (Help Scout supports this; the agent clicks “Reopen”), it counts as unresolved at the moment of measurement. |
| API endpoint | GET /v2/conversations?status=closed&tag=vortex_iq&modifiedSince={{now-90d}} for the numerator, then a second call for total population. Help Scout’s API is simpler than Zendesk’s; no separate count endpoint, but cursor-based pagination at 50/page scales cleanly to thousands of conversations. |
| Beacon-originated conversations | Vortex IQ does NOT file via Beacon (the in-app widget); findings always come in via API. Beacon-originated conversations are excluded from the population unless they happen to also carry the vortex_iq tag. |
| 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 Manage -> Account -> Settings -> Time Zone. |
| Roles | owner, operations |
Calculation
Calculated automatically from your Help Scout 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 owner-led DTC skincare brand on Shopify running Help Scout Standard (3 Mailboxes: Support, Press, Wholesale). Snapshot taken on 02 May 26 at 11:10 ET, looking back over the rolling 90 days from 02 Feb 26.| Bucket | Count | Notes |
|---|---|---|
| Vortex IQ conversations filed in window | 41 | Smaller volume than enterprise peers; SMB DTC scale |
| Closed (Support Mailbox) | 23 | Fast triage, owner is in the Mailbox daily |
| Closed (Press Mailbox) | 0 | No findings file there, expected |
| Closed (Wholesale Mailbox) | 4 | B2B-pricing fixes in last sprint |
| Still active across all Mailboxes | 14 | Active + Pending |
| Resolution rate | (23 + 0 + 4) / 41 = 27 / 41 = 65.8% | Amber, healthy mid-range |
- The owner-in-the-Mailbox effect is visible. Help Scout’s shared-inbox philosophy means small brands often have the founder reading conversations daily. 23 closed in the Support Mailbox out of an estimated 32 filed there = 72% on its own, which is enterprise-grade. The drag comes from the Wholesale Mailbox where the founder is less involved.
- 66% is healthy for an SMB DTC brand on Help Scout. The product attracts customer-centric brands with smaller teams; benchmark is 60-75%. Above 75% sustained for a quarter often signals the audit thresholds are too generous (lower them; more findings filed lifts the impact even if rate dips).
- The Wholesale Mailbox is dragging the rate. 4 closed out of an estimated 9 filed = 44% resolution. B2B has a different rhythm; the wholesale rep does not use Help Scout daily. Set up a Help Scout Workflow that auto-tags Vortex IQ Wholesale conversations and notifies the rep via Slack; that single change typically lifts the Wholesale resolution rate by 15-20 points within 4 weeks.
- Pair with
shopify.refund_rate. If refund rate is steady or falling AND this card sits at 66%, the audit programme is paying for itself, the unclosed findings are probably the lower-impact ones. If refund rate is climbing despite findings being closed, the team is closing the wrong findings. - Help Scout’s lack of Pending semantics is a small advantage. Unlike Zendesk and Freshdesk, Help Scout’s Pending only means “snoozed waiting on customer”. The card treats it as unresolved consistently with the other helpdesks but the merchant rarely encounters edge cases around Pending. The data is cleaner.
- No Beacon noise. Vortex IQ does not file via Beacon; the merchant’s customer-facing Beacon widget conversations are excluded from this card’s population unless tagged
vortex_iq. This keeps the audit-programme metrics decoupled from end-customer support volume, which is the right separation.
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 on the audit queue specifically. |
| 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 Help Scout 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 / Adobe) | The downstream truth metric an audit programme should protect. | Refund rate flat or falling + rate at 60%+ 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. The “Vortex IQ pays for itself” narrative. |
| 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 Help Scout’s own dashboard: Help Scout does NOT provide a single “tag-scoped resolution rate” gauge, so this card is computed by Vortex IQ from the Conversations API. To verify it manually:Reports -> Conversations withFor multi-Mailbox accounts, switch the Mailbox selector at the top of the inbox or filter by Mailbox in Reports. Why our number may legitimately differ from Reports’ number:tag = vortex_iqfilter. Set the date range to last 90 days. Read the percentage ofClosedagainstTotal. That is the same calculation this card runs. Inbox -> Conversations -> Filters withtag:vortex_iqplus astatus:closedfilter, gives the numerator population at a glance. [Saved Search] in the inbox, save the filter for spot-checks.
| Reason | Direction | Why |
|---|---|---|
| Time zone | Boundary days off | Reports 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” | Help Scout’s API replication is typically faster than Zendesk’s (5-30 seconds), so the gap is small but non-zero on freshly-closed conversations. |
| Reopened conversations | Ours lower | If a conversation was closed then reopened in the 90-day window, we count it as unresolved. Some Reports views count “ever closed” rather than “currently closed”. |
| Multi-Mailbox aggregation | Either | We aggregate; per-Mailbox Reports views are subsets. |
| Tag inclusion | Ours stricter | We require literal tag:vortex_iq. Legacy tags (vortex, vortexiq_v1) on older conversations drop out of our population. |
| Spam / Deleted | Ours lower | We exclude Spam and Deleted conversations entirely. Reports has an option to include them. |
| Beacon-originated conversations | Ours stricter | We exclude Beacon-originated conversations unless they carry the vortex_iq tag. Some Reports views include all sources by default. |
| Card | Expected relationship | What causes the divergence |
|---|---|---|
shopify.refund_rate / bigcommerce.refund_rate / adobe_commerce.refund_rate | Inverse correlation. Higher resolution rate should reduce refund rate over a 4-8 week trailing window. | 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 70%+ resolution rate predicts CSAT lift of 2-4 points within a quarter. Help Scout customers tend to over-index on CSAT; the lift is larger than on enterprise helpdesks. |
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. The denominator grew faster than the team’s closure rate. Often resolves itself within 2-3 weeks.
- Capacity loss. A senior agent on holiday, a key engineer focused on a feature deploy, or temp churn. Pair with Open Tickets (all); if global backlog is also up, capacity is the answer.
- Workflow drift. A Help Scout Workflow that auto-tagged or auto-routed Vortex IQ conversations turned off. Open Manage -> Workflows and confirm the
vortex_iqrules are still active.
- 75%+ : healthy. Audit programme is working; team has slack.
- 60-75% : normal for a maturing programme. Acceptable for owner-led brands.
- 50-60% : warn. Team is straining; investigate triage process.
- <50% : critical. Findings are filed faster than they are closed.
closed. The rate calculation is identical in spirit but Help Scout merchants do not have to think about archive transitions. One fewer edge case.
Should I optimise for a higher rate?
Not necessarily. A rate 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.
A reopened conversation dropped my rate by 1 point. Can I exclude it?
A closed conversation that gets reopened is genuinely no longer resolved, so the rate correctly drops. If reopens are common (>5% of closed conversations), it points to a quality-of-fix problem, fixes are landing but not actually resolving the underlying issue.
Beacon (the in-app widget) drove a customer to file a Vortex IQ-style complaint. Does the conversation count?
Only if it carries the vortex_iq tag. Vortex IQ files findings via API and tags them automatically. Beacon-originated conversations from end customers are excluded from this card unless someone manually adds the tag (which is rare and usually accidental).
My account spans 3 Mailboxes (one per brand). Why does the gauge show a single number?
The card aggregates by default. To break out by Mailbox, build a Stacked Panel in the Vortex IQ Nerve Centre with multiple instances, each scoped via vortex_iq.mailbox_filter. Alternatively, use Help Scout’s Reports Mailbox filter for the same answer.
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, when finding volume legitimately exceeds team capacity by design.
Help Scout is owner-led-friendly. Should the founder watch this card directly?
Yes, especially for brands with <5 employees. Help Scout’s shared-inbox philosophy is built around the founder being in the Mailbox; the resolution-rate gauge is the perfect single-number translation of “is the audit programme paying its way?”. For brands with dedicated CS managers, route the card to the CS lead and the engineering lead jointly. For founder-led brands, surface it on the executive dashboard.
My team uses tags for category breakdown. Does that affect the rate?
No. The card filters on tag:vortex_iq (the parent tag); category sub-tags (vortex_iq:catalogue, vortex_iq:checkout) are additive metadata that do not narrow the population. Use them for breakdown views in Reports, not for rate calculation.
Help Scout AI Assist replied to a Vortex IQ conversation. Did that close it?
No. AI Assist drafts replies; it does not move status to closed unless a human (or a Workflow rule) does so explicitly. The conversation stays Active until someone confirms.