The Experience Impact report shows how your staffing and schedule adherence affect what customers actually feel: wait times, missed chats, and satisfaction. It combines data you already collect into one 0 to 100 Experience Index.

What the Report Shows

Pick a date range (and optionally a team). You get a summary, an interval chart, a per-agent table, and plain-language insights. The summary lists Adherence, Service Level, Missed Chats, Average Wait, CSAT, and the Experience Index.

How the Experience Index Is Built

The index is a weighted score from 0 to 100:

• Service level: 30 points. Share of chats answered within your service level threshold.
• Schedule adherence: 25 points. How closely agents followed their scheduled shifts.
• CSAT: 25 points. Average satisfaction from post-chat surveys.
• Missed chats: 20 points. Fewer missed chats scores higher.

If a signal has no data (for example, no survey responses), the remaining weights are used so the score stays fair.

Reading the Interval Chart

Each interval compares scheduled agents with agents actually online, next to chat volume, missed chats, wait time, and CSAT. Look for intervals where actual agents drop below scheduled and missed chats or waits rise at the same time. That is a staffing gap, not a demand problem.

Reading Insights

Insights are short sentences ranked by severity, such as a time of day where adherence dips and missed chats climb. Treat them as a starting point: open the interval or agent they mention to confirm. Insights only appear when there is enough data to support them.

Where CSAT Comes From

CSAT is the average 1 to 5 score from two sources: ticket CSAT replies, and survey ratings from post-chat, workflow, or standalone surveys. A survey question counts when it is a 1 to 5 rating (a CSAT or star rating question). The Coverage panel shows how many responses each source contributed.

Response Rate and Minimum Responses

Response rate is rated survey submissions divided by all survey submissions in the range. CSAT needs at least 5 responses before it counts toward the Experience Index; below that it is shown but excluded.

Why CSAT Is Zero or Empty

The report explains it in a reasons list. Common causes: no CSAT survey is configured; a survey exists but nobody answered in the range; responses arrived but the survey has no 1 to 5 rating question; fewer than 5 responses; or the range is before the survey was enabled. Fix by adding a star or CSAT question to your post-chat survey, turning on ticket CSAT requests, or widening the range.

Backfilling Older CSAT

If chats were surveyed before but the scores do not appear, a manager can run the CSAT backfill. It fills missing survey ratings from data already stored. It only fills survey ratings, never ticket CSAT. It previews first ("would fill N") and writes nothing until you apply, and running it twice is safe.

• Report banner: when the report detects unrated surveys, use the backfill button in the banner, review the preview, then apply.
• CLI: velaro report-backfill csat --from 2026-09-01 --to 2026-09-30 previews; add --apply to write.
• MCP: report_backfill with type, from, to; apply defaults to false.
• Velaro staff: velaro staff backfill --site <id> --type csat --from --to [--apply], or the staff MCP tool backfill_for_site.

Per-Agent View

Each agent row shows adherence, CSAT, chats handled, average handle time, and an agent index. Use it for coaching conversations, and compare against the team rather than a single day.

Bots vs Humans

The Bots vs Humans table splits chats by who handled them. Bot only means the bot resolved the chat without an agent. Bot to human means the bot started the chat and handed it to an agent. Human only means an agent handled it from the start. Each row shows chats, average wait, missed chats, and CSAT with its response count. Bot containment is the share of chats the bot touched that it resolved without a human. Waits for handed-off chats are measured from the moment the chat entered the agent queue.

Sample Data

If your account has sample data for the selected range, a Show demo data toggle appears above the report. Turn it on to see only the sample data, which is handy for exploring the report before you have much real traffic. Turn it off to return to your real conversations. Sample data and real data are never mixed. The CLI uses --include-demo and the MCP tool uses includeDemo.

Access and Requirements

Requires Work Schedules to be configured (see the Schedule Adherence guide). Available to managers and admins with report access. Ask Moshky, use the velaro experience-impact CLI command, or the experience_impact_report MCP tool for the same data.

Using the Report From the API, CLI, or Your AI Assistant

The same numbers are available everywhere, so you can automate a weekly check or ask Moshky in plain language.

• REST: GET /Reports/experience-impact?from=2026-09-01&to=2026-09-30 (add &teamId= to narrow). Dates must be YYYY-MM-DD; anything else returns 400.
• CLI: velaro experience-impact --from 2026-09-01 --to 2026-09-30 (add --json for raw output).
• AI assistant: ask "How did schedule gaps affect customers last week?" and the velaro_get_experience_impact skill answers from the same data as the report.
• MCP: the experience_impact_report tool returns the report for AI clients.

Managers see the whole site or a team. Agents see only their own rows.