> ## Documentation Index > Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt > Use this file to discover all available pages before exploring further. # First-Response Time by Team, Intercom > Median minutes to first human reply, broken out per Intercom team. How to read it, why it matters, and how to act on it. **Card class:** [Non-Hero](/nerve-centre/overview#card-classes-explained) • **Category:** [Support](/nerve-centre/connectors#connectors-by-type) > Median minutes from `created_at` to the first admin reply (`statistics.time_to_admin_reply`), grouped by the team a conversation is assigned to. ## At a glance > **First-Response Time by Team** is a Support Performance metric tracked from your Intercom workspace. It takes the same first-response clock that drives your headline response-time pulse and splits it by team — so you can see whether the Orders team, the Returns team, or general triage is the one keeping customers waiting. For Blitz, where a sports shopper chasing a delayed kit order behaves very differently from one asking about sizing, knowing *which* queue is slow is the difference between hiring, re-routing, or rewriting a macro. Read it alongside the workspace-wide [Median First-Response Time](/nerve-centre/kpi-cards/intercom/median-first-response-time) and the [First-Response SLA Attainment](/nerve-centre/kpi-cards/intercom/first-response-sla-attainment) gauge. | | | | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | For each Intercom team, the median minutes between a conversation being created and the first reply from an admin on that team. Derived from `conversation.statistics.time_to_admin_reply` and the conversation's `team_assignee_id`. | | **Sample type** | API-derived from the Intercom Conversations endpoint, filtered to conversations created in the window and grouped by assigned team. | | **Why it matters** | A workspace average hides the queue that is actually failing customers. Splitting by team tells you whether slowness is a staffing problem, a routing problem, or one understaffed specialty. | | **Reading the value** | Lower is better. Compare each team's bar against your target and against the other teams. A single tall bar points to one queue to fix; uniformly tall bars point to a workspace-wide capacity gap. | | **Currency** | number | | **Time window** | `7D` | | **Alert trigger** | `>240 min` | | **Sentiment key** | `first_response_time` | | **Roles** | owner, operations | ## Calculation Calculated automatically from your Intercom data. Vortex IQ pulls conversations created in the trailing 7 days, reads each conversation's `team_assignee_id` and its `statistics.time_to_admin_reply` (seconds from creation to the first admin reply), converts to minutes, then takes the median per team. Conversations with no admin reply yet are excluded from the median — they are still waiting and belong to the [Unanswered (awaiting first reply)](/nerve-centre/kpi-cards/intercom/unanswered-awaiting-first-reply) card. The card renders one horizontal bar per team; the alert fires when any team's median exceeds 240 minutes. ## Worked example *A representative reading for Blitz across a typical trading week.* Over the last 7 days the card shows three bars: **Orders & Delivery** at 38 minutes, **Sizing & Product** at 52 minutes, and **Returns** at 271 minutes. The Returns bar trips the `>240 min` alert. Because the workspace-wide median sits at a comfortable 61 minutes, the slow Returns queue would have been invisible on the headline card. Drilling in, you find Returns is staffed by one part-time agent who only works mornings, so afternoon return requests sit for hours. The fix is a routing rule or a second pair of hands on that queue, not a workspace-wide hiring push. To trace which conversations dragged the median up, open Vortex Mind on the Returns team; to ask "which Returns tickets waited longest yesterday?" use Ask Viq. ## Sibling cards merchants should reference together | Card | Why merchants reach for it | | ------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- | | [`ic_first_response`](/nerve-centre/kpi-cards/intercom/median-first-response-time) | The workspace-wide first-response median this card decomposes. | | [`ic_sla_attainment`](/nerve-centre/kpi-cards/intercom/first-response-sla-attainment) | Turns the same first-response clock into a percent-within-target gauge. | | [`ic_workload_by_team`](/nerve-centre/kpi-cards/intercom/workload-by-team) | Pairs slow teams with how much volume they are carrying. | | [`ic_resolution_by_agent`](/nerve-centre/kpi-cards/intercom/resolution-time-by-agent) | Drops from team-level to agent-level to find the individual outlier. | | [`ic_busiest_hours`](/nerve-centre/kpi-cards/intercom/conversation-volume-by-hour) | Shows whether a team is slow because work arrives when nobody is staffed. | ## Reconciling against the vendor's own dashboard **Where to look in Intercom's own dashboard:** Open **Reports → Team performance** (or **Reports → Conversations** and add a *Team* breakdown). Intercom's "Median time to first response" metric is the closest equivalent. You can also see per-team backlog and response figures from each team's Inbox view. **Why the Vortex IQ value may legitimately differ:** | Reason | Direction | What to do | | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- | ----------------------------------------------------------------------------- | | **Business hours vs calendar time.** Intercom can report first response against *office hours* only; Vortex IQ measures wall-clock minutes by default. | Vortex IQ higher | Match Intercom's report to "calendar time," or note the office-hours setting. | | **Team reassignment.** A conversation moved between teams is attributed to its *current* `team_assignee_id` in Vortex IQ; Intercom may credit the team that actually replied. | Variable | Check for conversations that changed teams mid-thread. | | **Bot / auto-replies.** Intercom may exclude or include Operator/bot first touches differently from Vortex IQ's "first *admin* reply" definition. | Variable | Confirm whether bot replies count as first response in both tools. | **Cross-connector reconciliation:** read this card with [Median First-Response Time](/nerve-centre/kpi-cards/intercom/median-first-response-time) for the blended figure. For divergence investigations, use Vortex Mind. ## Known limitations / merchant FAQs **Q: How often does First-Response Time by Team update?** The card refreshes on the standard data refresh (typically every 30-60 minutes for live integrations). For a real-time read, force a manual refresh from the dashboard. **Q: Why does my Intercom report show a different number?** The two most common reasons are the business-hours setting (Intercom can measure office hours only) and team reassignment (Vortex IQ attributes to the current assigned team). Match those before assuming a real divergence. **Q: A team has no bar — why?** Either no conversations were assigned to that team in the window, or none of its conversations have received a first admin reply yet (those are counted on the Unanswered card instead). **Q: Can I customise the alert threshold?** Yes. The `>240 min` trigger is configurable per profile in the Sensitivity tab. Set it to your published first-response target rather than the generic default. *** ### Tracked live in Vortex IQ Nerve Centre *First-Response Time by Team* is one of hundreds of KPI pulses Vortex IQ tracks across Intercom 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](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.