Your conversations generate a wealth of data that tells you how your team is performing, where customers are engaging most, and when demand peaks. The Chat Analytics report breaks down your messaging activity into clear, measurable metrics so you can make decisions based on evidence rather than gut feeling.
Conversation Metrics
The top section of the Chat Analytics page shows your core conversation numbers for the selected date range:
- Total Conversations — The number of conversations that were active (new or ongoing) during the period.
- New Conversations — Conversations initiated by customers for the first time.
- Resolved Conversations — Conversations that were marked as resolved or closed.
- Open Conversations — Conversations that remain active and unresolved.
These four numbers give you a quick read on your conversation pipeline. If new conversations consistently outpace resolved ones, your team is falling behind. If resolved numbers are healthy, you're keeping up with demand.
Message Volume Over Time
The message volume chart plots the number of messages sent and received over the selected time period — typically shown as a daily or hourly line chart.
What to look for:
- Upward trends — Growing message volume might mean your business is expanding, a campaign is driving traffic, or a service issue is generating complaints. Context matters.
- Spikes — Sudden jumps usually correlate with external events: a product launch, a broadcast campaign, a viral social media post, or an outage.
- Dips — Drops could indicate weekends (normal), a disconnected channel (investigate), or a seasonal slowdown.
Overlay your message volume data with your marketing calendar. If you see a spike on Tuesday, and your team sent a WhatsApp broadcast on Monday night, the cause-and-effect is obvious. Without that context, the same spike might trigger unnecessary alarm.
Response Time Analysis
Response time is one of the most impactful metrics in customer communication. Chatmo tracks several response time indicators:
| Metric | What It Measures |
|---|---|
| First Response Time | How long a customer waits from their first message until they receive a reply from your team. |
| Average Response Time | The typical wait time between a customer message and the next agent response, across all messages in a conversation. |
| Median Response Time | The midpoint response time — less affected by outliers than the average. |
| Longest Response Time | The worst-case wait time during the period. Useful for identifying extreme delays. |
The response time chart shows how these metrics trend over time. You'll want to see a downward slope (faster responses) or at least a stable line.
Response time is calculated based on business hours if you've configured them in your organization settings. Messages received outside business hours don't start the clock until the next business day begins.
Resolution Rates
Resolution rate tells you what percentage of conversations are being resolved versus remaining open or being escalated.
- Resolution Rate = (Resolved Conversations / Total Conversations) x 100
- First-Contact Resolution Rate = Conversations resolved in a single interaction without follow-up or escalation.
A high resolution rate means your team is effectively handling issues to completion. A low rate might indicate that conversations are being abandoned, left in limbo, or escalated too frequently.
Tracking first-contact resolution separately is important because it measures efficiency — resolving an issue in one interaction is better for the customer and cheaper for your business.
Channel-Wise Performance Comparison
If you're running multiple channels (WhatsApp, Instagram, Facebook Messenger, Telegram, web chat), the channel comparison section shows how each one stacks up:
| Channel | Conversations | Avg. Response Time | Resolution Rate |
|---|---|---|---|
| Shown per channel | Shown per channel | Shown per channel | |
| Shown per channel | Shown per channel | Shown per channel | |
| Shown per channel | Shown per channel | Shown per channel | |
| Telegram | Shown per channel | Shown per channel | Shown per channel |
| Web Chat | Shown per channel | Shown per channel | Shown per channel |
This table (and accompanying chart) helps you identify which channels need the most attention. Maybe your WhatsApp response time is excellent at 2 minutes, but Instagram messages sit for 20 minutes because fewer agents are assigned to that channel. The data makes the gap visible.
Don't just look at volume — compare the quality of metrics across channels. A channel with low volume but terrible response times deserves attention just as much as a high-volume channel.
Agent Performance Metrics
The agent performance section ranks your team members by their chat-related metrics:
- Conversations Handled — Total number of conversations each agent participated in.
- Messages Sent — How many messages each agent sent.
- Average Response Time — Per-agent response speed.
- Resolution Rate — What percentage of each agent's conversations were resolved.
This data is available as both a summary table and a comparative bar chart. You can sort by any metric to find your top performers or identify agents who might need additional support or training.
Agent performance metrics should be used for coaching and process improvement, not as a punishment tool. An agent with a high response time might be handling the most complex issues — context matters. Always pair numbers with qualitative understanding before drawing conclusions.
Peak Hours Analysis
The peak hours chart visualizes your conversation activity broken down by hour of the day and day of the week. It typically appears as a heat map where darker colors indicate higher volumes.
This analysis helps you answer questions like:
- When do most customers reach out? Schedule your strongest staffing for those hours.
- Are there dead zones? If volume drops to near-zero between 2 AM and 6 AM, you might not need overnight coverage (or an AI agent could handle those hours).
- Do certain days have different patterns? Monday mornings might spike as customers follow up on weekend issues. Friday afternoons might slow down.
Understanding your peak hours is essential for shift planning and ensuring you have the right number of agents online when demand is highest.
Customer Satisfaction Indicators
If customer satisfaction surveys are enabled in your organization, the Chat Analytics page includes a satisfaction section showing:
- Average Satisfaction Score — The mean rating across all collected feedback during the period.
- Score Distribution — A breakdown of how many customers rated each score level.
- Trend Over Time — How satisfaction is changing week over week or month over month.
- Low-Score Conversations — Conversations flagged with below-average satisfaction for review.
Low-score conversations are worth reviewing individually. They often reveal systemic issues — a confusing product, a broken process, or an agent who needs coaching — that aggregate numbers alone won't expose.
Satisfaction surveys are sent to customers after a conversation is resolved. The response rate depends on the channel and how the survey is delivered. Not every conversation will generate a satisfaction data point.
Using Chat Analytics Effectively
Raw data is only useful if it drives action. Here's how to turn insights into improvements:
- Set response time targets. Decide on a team goal (e.g., under 5 minutes for first response) and track progress weekly.
- Investigate anomalies. Don't just note that response time spiked on Thursday — find out why. Was the team short-staffed? Did a channel go down?
- Balance agent workload. If one agent is handling three times the conversations of another, redistributing workload improves both performance and morale.
- Match staffing to demand. Use peak hours data to adjust shift schedules. Having agents online during low-traffic hours is wasted capacity.
- Close the feedback loop. When satisfaction scores dip, review the flagged conversations, identify the root cause, and address it — whether that's training, process changes, or product fixes.
What's Next?
- Reports Overview — Return to the reports dashboard for a broader view of all available report types.