> For the complete documentation index, see [llms.txt](https://help.zaapi.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.zaapi.com/analytics/support-dashboard.md). # Support dashboard **Volume, response, and resolution health across all your conversations.** This is your day-to-day overview page: how much came in, how fast you replied, how much got resolved, and how that splits across channels. Everything here is calculated for the **date period you select** and respects the filters at the top — so the numbers always reflect the slice you're looking at. If you haven't already, read How we calculate analytics first; it explains the period model, time zones, and the rules every metric on this page follows. *** ### Filters and how to read the cards Three controls sit at the top of the page: * **Date period** — e.g. *Last 7 days · May 6 – May 12, 2026*. Sets the window every metric is calculated for. * **Channel** — e.g. *All channels (4)*. Narrows to one or more channel types (Facebook, Instagram, WhatsApp, Email). * **Agent** — e.g. *All agents*. Scopes metrics to specific agent(s). Each headline card shows three things: the **value** for the period, a **trend line** (the in-period movement, day by day), and a **comparison to the previous period** of equal length (the 7 days before your 7 days). For volume metrics, the comparison shows the percentage change. For time metrics, it's shown as **"faster"** or **"slower"** — an increase in response or resolution time is *slower*, and is flagged in red because higher is worse. *** ### How human and AI replies are counted on this page Zaapi is AI-first, so several metrics deliberately separate work the AI Agent handled from work your human team handled. The model across this page: | Metric | Basis | | --------------------------------------------- | ---------------------------------------------------------- | | Avg First Response Time, Avg Response Time | **AI Agent /** **Human agent** replies | | One Touch Tickets | **Human agent** replies only | | Zero Touch Tickets | **AI Agent / automation** resolutions, with no human reply | | Avg Resolution Time, Created / Closed Tickets | **All** tickets, however they were resolved | So a ticket the AI fully resolves with no human involved counts as **Closed**, has a **Resolution Time**, and lands in **Zero Touch** — but it has **no First Response Time** (there was no human reply to measure). *** ### Volume Four headline counts for the period. #### Created Tickets The number of tickets created during the period. Counts when the ticket was **created.** #### Closed Tickets The number of tickets closed during the period and still closed at period end. Counts when the ticket was **closed**. Closed can be **higher** than Created in the same period (in the example, 1,015 closed vs 895 created) — that's normal when your team is working down a backlog carried in from earlier. The two aren't meant to tie out. #### Messages Sent The total number of messages sent **to customers** during the period, excluding internal notes. Counts each message by its **sent** time (so this is a message count, not a ticket count). #### Messages Received The total number of inbound messages **from customers** during the period. Counts each message by its **received** time. *** ### Customers #### Customer Messages by Time of Day A heatmap of **when customers reach out**, with day of week across the top and three-hour blocks down the side. Each cell is the number of inbound customer messages that you received in that day-and-time slot, shaded darker as volume rises. Times use your **workspace time zone**. Use it for staffing — it shows your real demand curve across the week so you can put coverage where the messages actually are. #### New vs Returning Customer Tickets A split of the period's tickets by whether the customer is **new** (their first-ever message to you happened during this period) or **returning** (they messaged you before this period). The donut shows the total with the new/returning breakdown and each side's share. #### Unique Customer Interactions Per Day The number of **unique customers** (Contacts) who messaged you per day during the period based on "opened at". A customer who opened five tickets within one day counts once. *** ### Response & Resolution Three averages describing speed. All three are **medians** (not averages) and measure **elapsed clock time**, including nights and weekends — see the calculations page. #### Median First Response Time (FRT) The median time from a customer's first message to the **first human agent reply**, across tickets that got their first human reply during the period. Automated acknowledgements don't count, and tickets resolved by the AI with no human reply have no FRT (they show up under Zero Touch instead). #### Median Response Time The median time a customer waits for a reply across **every** human agent response in the period — not just the first. Where FRT measures the opening reply, this measures ongoing responsiveness throughout the conversation. #### Median Resolution Time The median time from a customer's first message to the ticket's **final closure**, across tickets closed during the period. This includes AI-resolved tickets. As on the calculations page: reopened tickets use the final closure time is included (it doesn't shorten resolution time). *** ### Resolution Effort How much resolution happened with little or no human touch — the core efficiency story for an AI-first inbox. #### Zero Touch Tickets Tickets **resolved by the AI Agent or automation with no human reply at all**, shown as a count and as a share of resolved tickets. This is your automation/deflection metric: the higher it is, the more your team is freed from routine work. #### One Touch Tickets Tickets **resolved with exactly one human agent** reply, shown as a count and share. One efficient human reply, done. Internal notes don't count toward the "one." *** ### Performance by Channel A **per-channel breakdown** repeating every metric above, sliced by channel type, and it **respects the current filters**. The bottom **Total** row matches the headline cards. | Column | Metric (defined above) | | ----------------- | ------------------------------ | | Created | Created Tickets | | Closed | Closed Tickets | | Msg Sent | Messages Sent | | Msg Recv | Messages Received | | Median FRT | Median First Response Time | | Median Resp | Median Response Time | | Median Resolution | Median Resolution Time | | Zero Touch | Zero Touch Tickets (count + %) | | One Touch | One Touch Tickets (count + %) | Two things worth knowing when you read it: * The time columns (Median FRT, Median Resp, Median Resolution) are **medians, so they don't add up**. The Total row is the blended median across all integrations, not the sum of the rows above it. *** ### Good to know * **Why don't these numbers match the Live page?** Support reports on a date range and includes closed tickets; Live is a real-time snapshot of only open tickets. Different questions, different numbers. * **Why is Closed higher than Created?** You're clearing older backlog — see Closed Tickets above. * **Do AI replies count?** See *How human and AI replies are counted* above — FRT, Response Time, and One Touch are human-only; Zero Touch is the AI/automation view; Resolution Time and the volume counts include everything. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://help.zaapi.com/analytics/support-dashboard.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.