Build custom analytics with natural language

The 17 pre-built dashboards cover most reporting needs. For everything else, the custom analytics builder lets you ask a question in plain English and get a chart back.

Behind the scenes the builder generates SQL against your tenant’s data warehouse and a chart spec for visualization. You don’t write either — just describe what you want.

Before you start

• Admin permissions on Analytics
• A clear question — vague prompts produce vague charts

Steps

1. Open Analytics → Custom Analytics.
2. Click New module.
3. In the chat panel, describe the metric or question: > “Average first-response time on email, by team, last 30 days”
4. The builder responds with:
   The SQL it generated (you can review it)
   A chart specification (which chart type, dimensions, measures)
   The rendered chart inline
5. Iterate. If the chart isn’t what you wanted, refine: > “Same chart but break it down by week” > “Switch to a line chart” > “Add a filter for VIP contacts only”
6. When the chart is right, click Save module. Give it a title and description.
7. The module appears in your personal Custom Analytics dashboard, ready to revisit.

Prompt patterns that work well

• “X by Y” — Average resolution time by channel, last 30 days
• “X over time” — Reopen rate over the last 90 days, weekly
• “X compared to Y” — Compare CSAT in chat vs email, last quarter
• “Top N by X” — Top 10 agents by conversations resolved this month
• “What’s driving X?” — What's driving the increase in resolution time over the last 14 days? (the builder picks an appropriate breakdown)

The cleaner the question, the better the chart. “Last 30 days” beats “recently”; “by team” beats “broken down somehow.”

What the builder can do

• Aggregate metrics: count, average, median, percentile, sum
• Time-bucketed dimensions: by hour, day, week, month
• Categorical breakdowns: by channel, team, agent, country, tag, custom field
• Filters: any dimension or metric, with comparators
• Combinations: average X, broken down by Y, over time, filtered to Z

What the builder can’t do (yet)

• Cross-tenant comparisons (your tenant only)
• Predictions (“forecast next quarter”) — analytical, not predictive
• Joins to external systems (your data warehouse for that)
• Edits to the SQL after it’s generated (you can re-prompt for a different version, but can’t hand-edit)

Sharing modules

Modules you save are personal by default — only you see them in your Custom Analytics view. To share with the team:

• Export the chart as an image (right-click → save) for ad-hoc shares
• Discuss in your team’s reporting cadence with the saved module open
• (Future) tenant-shared modules — not currently available

For team-wide standardization, prefer the 17 pre-built views or the export-to-CSV flow into a shared BI tool — see Export analytics data.

When to use this vs the pre-built views

• Your question maps to one of views 1–16 — Your question doesn’t fit any view
• You want filtering and drill-down — You want a single saved module
• You want AI Insights and chat — You want full chart control
• Your team standardizes on the dashboards — You’re investigating a one-off question

A productive pattern: start in the pre-built views, drill down with filters and AI Insights. If you find yourself re-filtering the same way every Monday morning, that’s a candidate for a custom analytics module.

Troubleshooting

• Symptom: The chart shows zero data even though the metric exists. Fix: The filter is too narrow, or the date range excludes the data. Re-prompt with a wider date range.
• Symptom: The builder generates a chart but it’s not what you asked for. Fix: Re-prompt with more specifics. Specify the metric (average resolution time), the dimension (by team), and the time period (last 30 days) explicitly rather than relying on inference.
• Symptom: SQL the builder generated looks weird. Fix: SQL inspection is for understanding, not editing. If the result is wrong, re-prompt; if you want full SQL control, your data warehouse is the right tool, not Custom Analytics.