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

Upload your data

Export your data as CSV or XLSX from Zendesk, Intercom, or whichever support platform you use and upload it directly to Querri. The file lands and an automatic data audit runs, 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 generates a high-level summary of the dataset (ticket volume, channel breakdown, date range, agent coverage) so you have a clear starting point before any analysis begins.

Compute FRT, filter out the noise

Ask Querri to compute FRT for each ticket as 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 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.

Find the easy answer first

Before you go hunting for sophisticated explanations, rule out the obvious one. When teams miss FRT targets, the first instinct is always the same: we need more agents. Sometimes that's right. Often it isn't. Either way, the data will tell you.

In this example, Querri finds essentially no correlation. Breach rate doesn't track with volume. That tells you something important: the team isn't drowning. Adding bodies wouldn't fix this. The cause is structural, and you'll have to look harder.

Then look at the time of day

If volume isn't the cause, the next thing to check is when the breaches are happening. Aggregated daily numbers wash out hour-by-hour patterns, and most FRT problems live inside those patterns.

In this example, Querri finds a sharp peak at 11am. About 17% of all breaches happen in that single hour. That's a lot of pain concentrated in a 60-minute window, and it reframes the whole coaching conversation. The question stops being "why are these agents slow?" and starts being "what's happening at 11am, and is it a staffing, routing, or handoff issue?"

The fix for an 11am peak is almost never coaching. It's almost always a scheduling change, a routing rule, or an upstream handoff that's quietly breaking. You wouldn't find any of that in an agent-by-agent FRT report.

Test the fix before you commit to it

You know where the breaches are. The next question is what fixing it would actually do. Test it before you commit. Ask Querri to model a simple redistribution: what happens to the overall breach rate if 50% of tickets shift from your highest-breach agents to your lowest?

Querri runs the scenario against your actual ticket data and returns a projected new breach rate. Now you have a concrete, defensible number to bring to a staffing or routing conversation instead of a gut feeling.

Run as many variations as you want. Try different redistribution percentages, specific agents, or channel-level rebalancing until you find a scenario that moves the number and is realistic to actually staff.

Let Querri write the recommendations

Most articles about improving FRT give you the same three suggestions: hire more agents, route smarter, train better. Useful in the abstract. Useless when you're staring at a specific 11am breach pattern and trying to decide whether the fix is a scheduling change or a coaching one. So skip the generic playbook. Just ask Querri.

Querri already knows where your breaches are concentrated, what time-of-day patterns exist, and what impact redistribution would have. So its recommendations aren't starting from scratch. They're tied directly to your data, and they're prioritized by the math, not by what sounds most defensible in a meeting.

Drill into any recommendation with follow-up prompts. Ask Querri to explain its reasoning, size the impact, or identify which team members or channels it applies to most.

Bring it to the QBR

Weekly FRT data tells you what's happening now. Quarterly data tells you whether anything you're doing about it is actually working, which is the question your CFO wants answered. Pull three months of ticket exports into Querri and ask for the QBR version:

Querri produces a structured presentation your team can take directly into a leadership review. 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. Leadership gets the context to make decisions on staffing, tooling, or SLA targets instead of reacting to complaints after the fact.