Availability: Async messaging metrics are available for Genesys Cloud customers using asynchronous messaging channels (such as chat, SMS, email, or social messaging). If you are unsure whether your account includes this data, contact us at support@brightmetrics.com.
Async messaging metrics let you report on the speed and volume of message exchanges in digital and asynchronous conversations — including response times, message counts, and conversation turn activity. These fields are available on Conversations, Conversation Metrics, Queue, and Agent reports. Use them to evaluate channel performance, compare responsiveness across queues, and identify where customers or agents are experiencing delays.
- Async messaging metrics reflect entire conversations, not individual agent sessions.
- When a conversation involves multiple agents — such as a transfer or escalation — metrics like response times and message counts combine data across all participants and do not indicate individual agent performance.
- If you need agent-specific insight, use Agent Activity data and apply a filter to the Number of Agents in Conversation field to isolate conversations handled by a single agent.
1. Why these metrics are conversation-level, not agent-level
Genesys emits async messaging data at the conversation level — metrics are calculated across the entire interaction and are not broken out by individual agent session. This is a Genesys platform behavior, not a Brightmetrics limitation.
In practice, if a conversation is transferred from one agent to another, or if multiple agents participate at any point, the metrics you see in Brightmetrics reflect the combined activity of everyone involved. There is no way to attribute a response time or message count solely to one agent when the conversation passed through more than one.
A customer sends a chat message. Agent A responds quickly, then transfers the conversation to Agent B, who takes longer to reply. The Time - Agent Message Response value for that conversation reflects the combined response pattern of both agents — not Agent A or Agent B individually.
If you report on Agent B's performance and include this conversation, the data includes Agent A's faster responses, which may make Agent B appear more responsive than they were. The reverse is also true.
2. Available fields and what they measure
All fields below are sourced from the agent session unless otherwise noted.
Response times
| Field | What it measures |
|---|---|
| Time - Agent Message Response | The total time agents spent responding to messages across the conversation. |
| Time - Agent Message Response Avg | The average agent message response time across message sessions in the grouping (total response time divided by message session count). |
| Time - Average Agent Message Response | The per-conversation average of agent response times, as reported by Genesys. |
| Time - Average Agent Message Response Avg | The average of the per-conversation agent response averages across the grouping. |
| Time - Average Customer Message Response (from the customer session) | The per-conversation average of customer response times — how long customers took to reply to agent messages. |
| Time - Average Customer Message Response Avg | The average of per-conversation customer response averages across the grouping. |
Use these for: Evaluating how quickly agents and customers are responding within messaging conversations. Useful for identifying queues where response times are lagging, or comparing responsiveness across channels or time periods.
First response and engagement
| Field | What it measures |
|---|---|
| Time - Agent First Message Response | The time from when the customer first sent a message to when the agent sent their first reply. |
| Time - Agent First Message Response Avg | The average first response time across conversations in the grouping. |
| Time - To First Message Engagement with Agent | The time from conversation start to the first substantive message exchange between the customer and agent. |
| Time - To First Message Engagement with Agent Avg | The average time to first engagement across qualifying conversations in the grouping. |
Use these for: Measuring the customer experience at the start of a conversation. Long first response times are often the highest-impact area for improving customer satisfaction in async channels.
Message volume
| Field | What it measures |
|---|---|
| Count - Messages Total | The total number of messages sent in the conversation (by all participants). |
| Count - Messages Avg | The average number of messages per conversation in the grouping. |
| Count - Message Turns Total | The total number of back-and-forth exchanges (turns) in the conversation. A turn represents one side sending one or more messages before the other side responds. |
| Count - Message Turns Avg | The average number of message turns per conversation in the grouping. |
| Count - Message Sessions | A count of agent sessions that include any of the above messaging metrics. Used as the denominator in average calculations. |
Use these for: Understanding conversation complexity and volume. High message or turn counts may indicate that issues are taking many exchanges to resolve — a signal to review queue workflows, agent training, or self-service options.
3. Getting accurate data with filters
Because these metrics reflect entire conversations, filtering is the key to getting meaningful agent-level results. The most important filter is Number of Agents in Conversation, which lets you isolate conversations handled entirely by one agent from start to finish.
Set Number of Agents in Conversation = 1 whenever you are evaluating individual agent performance. This removes transferred and multi-agent conversations from the data so the metrics reflect a single agent's work.
Additional filters that can improve accuracy for queue-level or channel-level analysis:
| Goal | Filters to apply |
|---|---|
| Agent-level performance | Number of Agents in Conversation = 1 |
| Queue responsiveness | Queue Name, Date range |
| Channel comparison | Media Type (to isolate chat, SMS, etc.) |
| Trend analysis over time | Date Group - Week or Month, plus Number of Agents in Conversation = 1 if agent-focused |
4. Useful examples
Compare first response times across queues
| Rows | Queue Name |
| Values | Time - Agent First Message Response Avg, Count - Message Sessions |
| Filter | Date range, Media Type = messaging channel |
Surface which queues are slowest to send that first reply. Queues with high first response averages are candidates for staffing adjustments, routing changes, or auto-acknowledgment messages to set customer expectations while an agent becomes available.
Identify agents with high message turn counts on single-agent conversations
| Rows | Agent Name |
| Values | Count - Message Turns Avg, Count - Messages Avg, Time - Agent Message Response Avg |
| Filter | Number of Agents in Conversation = 1, Date range |
Agents with high turn counts relative to peers may be resolving issues through many small exchanges rather than clear, complete responses. Pairing this with response time data helps distinguish agents who are thorough but fast from those who are struggling to move conversations to resolution.
You must apply the filter to the Number of Agents in Conversation field in order to accurately evaluate individual agent performance. See the note at the top of this article.
Track messaging channel responsiveness over time
| Rows | Date Group - Week |
| Values | Time - Agent First Message Response Avg, Time - Agent Message Response Avg, Count - Message Sessions |
| Filter | Queue Name, Date range |
Trend response times week-over-week to evaluate whether staffing changes, schedule adjustments, or routing updates have improved messaging channel performance. A sustained drop in first response average is a strong signal that a change is working. Use Count - Message Sessions alongside the time fields to make sure you are comparing weeks with similar conversation volumes.
Next steps
Once you have a working report, consider saving it as a favorite or scheduling it to run automatically so your team has a consistent view of messaging performance over time. As you build familiarity with the data, you can layer in additional dimensions — such as Queue Name or wrap-up code — to refine your analysis.
Have questions or need help? Email support@brightmetrics.com.