Findings sat in the backlog with no status change for two weeks, these are the ones losing money silently.
At a glance
Live count of Vortex IQ findings sitting in your Zendesk queue with no status change for 14 days or more. The merchant question this answers is: “how many revenue-leak fixes have I forgotten about?” This is the silent drain gauge. Every ticket in this count was important enough to file but has not been touched, which usually means it has dropped off the team’s mental backlog and is quietly costing money on every shopper session.
| What it counts | Tickets in the zen_vortexiq_findings_open population whose updated_at timestamp is older than now() - 14 days. The 14-day threshold is the inflection point at which a backlog item statistically stops getting picked up without escalation. |
| API endpoint | GET /api/v2/search.json?query=type:ticket tags:vortex_iq status<solved updated<14days_ago. Same Search API as the parent card with an additional updated< clause. |
| Ticket-state filter | New + Open + Pending + On-hold (Solved/Closed excluded). Same as the parent. |
| What counts as “movement” | Any change to updated_at: a comment (public or internal), a status change, an assignee change, a tag change, an SLA update. A view or an open-without-edit does NOT update updated_at, by Zendesk design. |
| Bot-handled tickets | If your Answer Bot leaves an automated reply, that updates updated_at and resets the 14-day clock. Bot-handled findings are unlikely to appear here unless the bot has gone silent. |
| Multi-brand aggregation | Aggregated across every brand the connector reads. |
| Time window | RT. Refreshed every 60 seconds. |
| Alert trigger | Warn at >5, Critical at >15. The thresholds are absolute counts, not percentages, because a single abandoned finding can leak meaningful revenue. |
| Sentiment | Threshold gauge: 0-5 healthy, 6-15 warn (amber), 16+ critical (red). |
| Time zone | Account timezone. The 14-day boundary is computed against the connector’s UTC clock and offset by account zone for display consistency. |
| Roles | owner, operations |
Calculation
Calculated automatically from your Zendesk data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
The same US home-and-kitchen brand from the parent card. Snapshot taken on 02 May 26 at 09:40 PT. Of the 23 open Vortex IQ findings, 7 have not seen any status change for two weeks or more.| Finding | Age | Last activity | Estimated revenue cost |
|---|---|---|---|
| Size-chart broken on 4 PDPs (US) | 16 days | Filed, not assigned | £900/week (1.4% conversion drop on 4 PDPs that drive 8% of US revenue) |
| Mobile checkout button below fold on iPhone 12 | 21 days | Assigned, no comment | £1,400/week (12% mobile cart abandonment rate higher than desktop) |
| Wholesale pricing tier missing on 22 SKUs | 12 days | Comment “looking into it” | Unknown; quoted manually so visible cost is zero |
| Refund “where is my refund” macro responds in 4h not 30min | 18 days | Filed | NPS impact, no revenue cost |
| Algolia search returns 0 results for “tongs” | 30 days | Filed | £200/week (search-to-purchase conversion) |
| Free-shipping threshold widget renders £0 instead of £50 | 14 days | Status changed once on day 2 | £600/week (AOV down 4% on cart-aware sessions) |
| Klaviyo abandoned-cart email links 404 | 22 days | Filed | £300/week (recovered-revenue gap) |
- The composite weekly leak is roughly £3,400. Add the estimated revenue costs across the seven abandoned findings: that is the silent monthly drain the merchant has accepted by not triaging. £3,400/week is more than most agency retainers, by itself a justification for an audit-triage SLA.
- The 30-day-old Algolia search finding is the highest-priority one despite the smallest absolute revenue. Search returning zero for a category term is a leading indicator of a much bigger catalogue or indexing issue. If “tongs” returns zero, “spatulas” probably does too. Pull the
shopify.search_zero_resultssibling on the commerce side; if zero-result rate is up across categories, this single finding is masking a 5-10% sitewide search regression. - Two of the seven are NPS-impacting, not revenue-impacting. The macro response time and the Klaviyo 404 do not directly drop revenue this week; they erode trust over months. Pair with
shopify.customer_service_sentiment. NPS-only findings should still close, just on a different SLA than direct revenue findings. - The wholesale finding is 12 days old, just under the 14-day cutoff. It will tip into Abandoned tomorrow if no one comments. The cheapest action: a one-line internal note from the wholesale rep (“quoting manually, fix scheduled for next sprint”) resets the clock and removes it from the abandoned list. Visible activity, not just intent, is what this card watches.
Sibling cards merchants should reference together
| Card | Why pair it with Abandoned Findings | What the combination tells you |
|---|---|---|
| VortexIQ Findings Open | The full backlog. Abandoned is a subset. | Abandoned ÷ Open is your “stale ratio”. Above 30% means triage is broken; below 10% means the team is healthy. |
| Finding Resolution Rate (90d) | Throughput. If resolution rate is high, abandoned should stay low. | Abandoned rising while resolution rate is steady means the team picks easy findings and lets hard ones sit. Inspect category mix. |
| Avg Time-to-Fix (days) | The average resolution latency. Abandoned is the long tail of this distribution. | If avg is 5 days but abandoned count is 15, you have a bimodal distribution: most close fast, some never close. Surface the long tail with a Zendesk view filtered by tags:vortex_iq priority:low updated<14days. |
| Top Assignees Overloaded | Capacity bottleneck. Abandonment usually concentrates on overloaded assignees. | If 60%+ of abandoned tickets have the same assignee, reassign or add capacity. |
| Refund Rate (Adobe / Shopify / BigCommerce) | The downstream revenue cost of letting findings sit. | When abandoned count exceeds 10 and refund rate is creeping up, the abandoned findings are the likely cause. Direct evidence for adding triage capacity. |
| Customer Service Sentiment (Shopify) | NPS-side cost of unfixed friction. | Long-tail of UX findings (slow response, broken email links) primarily impact this metric, not revenue directly. |
| Datadog Operational Health Score | If abandoned findings are technical, they should be visible in Datadog too. | An abandoned “checkout error rate up” finding should also show in Datadog’s error rate. If it does, escalate to engineering on-call; if not, the finding may be stale and closeable. |
Reconciling against the vendor’s own dashboard
Where to look in Zendesk’s own dashboard:Agent Workspace → Views, build a view:Why our number may legitimately differ:Tags = vortex_iqANDStatus < SolvedANDHours since update > 336(336 = 14 × 24). Explore → Tickets dataset with the calculated metricD_COUNT(Tickets) WHERE updated < DATE_SUB(NOW(), INTERVAL 14 DAY) AND tags CONTAINS 'vortex_iq' AND status < SOLVED. Search withtags:vortex_iq status<solved updated<14days_ago.
| Reason | Direction | Why |
|---|---|---|
| Time zone | Boundary days off | The 14-day cutoff is computed in UTC by Vortex IQ; Explore views may use the dataset timezone. A ticket on the boundary will tip into Abandoned at different moments in the two views. |
| Ticket-channel scope | Ours equals theirs | Both include all channels. If you scope Explore to a single channel, that view is a subset of the card. |
| Archived / deleted tickets | Ours lower | Tickets in Recycle Bin are excluded from the Search API. Explore may still surface them depending on archive settings. |
| Multi-brand aggregation | Either | Card aggregates across brands; per-brand Explore views are subsets. |
| What counts as movement | Ours stricter on internal-only changes | Vortex IQ uses Zendesk’s updated_at, which captures internal notes. Some Zendesk views look only at last_public_comment_at, which would put more tickets into Abandoned. |
| Card | Expected relationship | What causes the divergence |
|---|---|---|
adobe_commerce.refund_rate / shopify.refund_rate / bigcommerce.refund_rate | Abandoned findings often correspond to chronic refund drivers. | When refund rate is up and abandoned findings >10 simultaneously, the leak is being measured but not fixed; this is the core “audit programme broken” alert. |
shopify.customer_service_sentiment | NPS slowly degrades as abandoned count climbs. | The lag between abandonment and NPS dip is typically 4-8 weeks, depending on how shopper-facing the abandoned findings are. |
Known limitations / merchant FAQs
Why does our Zendesk Explore view say a different number? Three reasons:- Field choice. Explore lets you pick
last_updated_atorlast_public_comment_at. Vortex IQ useslast_updated_at, which counts internal notes as movement. If your Explore view uses public comments only, it will report more abandoned tickets. - Time zone. The 14-day cutoff is UTC for the card; Explore honours the dataset zone.
- Recycle Bin. Soft-deleted tickets stay searchable in Explore for 30 days but are excluded from the Search API the card uses.
updated_at, so the 14-day clock resets. We treat any update as “the team is still thinking about it”. Abandonment is silence, not lack of resolution.
Should bot replies count as movement?
Yes by default, because they update updated_at. If you would rather discount bot updates (the bot is keeping the clock alive on tickets nobody is actually triaging), set vortex_iq.exclude_bot_updates: true in the connector config and the card will only consider human or trigger-driven updates.
Why is the threshold 14 days, not 7 or 30?
Empirical. Across 200+ Vortex IQ-connected merchants, the probability that an open finding will eventually close drops sharply after day 14 and approaches zero after day 21. 14 is the latest day at which intervention still typically rescues the ticket. 7 would generate too many false alarms; 30 would let half the rescuable tickets fall through.
My team flags some findings as “won’t fix”. Do they count?
Yes, until the status moves to Solved or Closed. “Won’t fix” is a label inside Zendesk; the API still sees the ticket as Open or Pending. If you want won’t-fix tickets out of this count, configure a workflow that solves them automatically when a wont_fix macro is applied.
The count went from 5 to 0 overnight, what happened?
Most likely someone ran a bulk update (Zendesk → Edit → Bulk update tickets) and added a comment to all the matching tickets. That resets updated_at and removes them from Abandoned. This is fine if it was deliberate triage; suspicious if it looks like queue-grooming to make the metric look better. Audit by looking at the next-90-day Resolution Rate. If those tickets actually close, the bulk update was healthy; if they age into Abandoned again two weeks later, the team is gaming the metric.
Open count jumped, what should I check first?
Same playbook as the parent VortexIQ Findings Open card with one addition: if Abandoned jumped without Open jumping, you have a triage stoppage (someone left, on holiday, deprioritised) rather than a finding influx. Check Top Assignees Overloaded next.
Response time vs resolution time, which one is this card about?
Resolution-time-extreme. This card is the long tail of resolution time, not response time. A ticket can have a 30-second first response and still be abandoned if the conversation goes silent after that. The card’s purpose is to catch the abandoned-after-initial-response pattern.
Multi-brand aggregation: should I split this card per brand?
Yes if your brands have separate engineering teams. The merchant SLA on Vortex IQ findings should be set at the brand level. Use vortex_iq.brand_filter to scope per-brand panels and surface them on each brand’s tab in the Nerve Centre.