How to Track and Improve First Response Time by Channel and Agent

Upload your data

Export your data as a CSV or XLSX from Zendesk, Intercom, or whichever support platform you use and upload it directly to Querri. As soon as the file lands, Querri runs an automatic data audit — checking for missing values, duplicate records, and inconsistent formats across timestamps, agent names, and channel labels.

Querri surfaces everything it finds in a plain-language report so you can see exactly what's there. If it spots issues it can fix — like normalising date formats or deduplicating rows — it'll ask for your permission before making any changes.

Once the data is clean, Querri automatically generates a high-level summary of the dataset — ticket volume, channel breakdown, date range, and agent coverage — so you have a clear starting point before any analysis begins.

Compute FRT per ticket and filter out noise

Ask Querri to compute FRT for each ticket — that's first reply timestamp minus ticket creation timestamp. Before it does, apply the critical sanity check: exclude any tickets where no first reply exists. Bot-handled and auto-closed tickets with no human response will otherwise inflate your performance numbers and make slow agents look better than they are:

Querri flags and removes the noise rows automatically, so your FRT calculations only reflect genuine human responses going forward.

Analyze root cause issues by digging deeper

Once you have the breach picture, ask Querri to go a level deeper — identifying whether the breaches are driven by workload spikes, time-of-day patterns, or other structural factors:

In this example, Querri finds little to no correlation between ticket volume and breach rate — meaning the problem isn't simply that agents are overwhelmed. What it does surface is a clear time-of-day pattern: breach rate peaks at 11 am, with approximately 17% of all breaches occurring in that single hour window.

This kind of finding reframes the coaching conversation entirely. Instead of asking "why are these agents slow?", the question becomes "what's happening at 11 am — and is it a staffing, routing, or handoff issue?"

Go deeper with your analysis

Once you have your breach data, ask Querri to find correlations between time of day and breach rate. This surfaces patterns that averages hide — like a consistent spike at a specific hour that points to a staffing gap rather than an agent performance issue.

Querri plots breach rate across every hour of the day and flags where it spikes. A peak at 11 am, for example, may indicate that incoming ticket volume outpaces available agents at that time — a resourcing insight, not a coaching one.

Use this to separate structural problems from individual performance issues before deciding where to intervene.

Run what-if scenarios

Once you know where the breaches are concentrated, test what fixing it would actually look like. Ask Querri to model a simple redistribution: what happens to the overall breach rate if 50% of tickets are moved from your highest-breach agents to your lowest-breach agents?

Querri runs the scenario against your actual ticket data and returns a projected new breach rate — giving you a concrete, defensible number to bring to a staffing or routing conversation rather than a gut feeling.

You can run multiple variations — different redistribution percentages, specific agents, or channel-level rebalancing — to find the scenario that's both impactful and operationally realistic.

Ask Querri for recommendations

By this point, Querri has the full picture — the breach data, the root cause analysis, and the what-if modeling. That context is what makes its recommendations genuinely useful. Rather than generic advice, Querri draws on everything it's already found to surface specific, prioritized actions tied directly to the gaps and opportunities in your data:

Because Querri already knows where the breaches are concentrated, what time-of-day patterns exist, and what impact redistribution would have, its recommendations aren't starting from scratch — they're grounded in your specific data. The result is a prioritized action list your team can actually work from, not a list of things you already knew.

You can drill into any recommendation with follow-up prompts — asking Querri to explain its reasoning, size the impact, or identify which team members or channels it applies to most.

Make it part of your QBR

Weekly FRT data tells you what's happening now. Quarterly data tells you whether anything is actually changing. Pull your last three months of ticket exports into Querri and ask it to generate a QBR-ready presentation — complete with your findings, key takeaways, and the recommendations it surfaced earlier in the analysis:

Querri produces a structured presentation your team can take directly into a leadership review — with the breach trends, root cause summary, what-if scenario outcomes, and a prioritized action plan all in one place. No manual formatting, no starting from a blank slide.

Bringing FRT into the QBR shifts it from an operational metric to a strategic one — giving leadership the context to make decisions on staffing, tooling, or SLA targets rather than reacting to complaints after the fact.