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The dashboard can be found under Insights > Dashboard (or by pressing ⌘+K and searching “Dashboard”). At the top are built-in charts you get out of the box, like call volume and agent activity. At the bottom of that page you can create custom charts based on your own personalized data about your calls. Charts let you track metrics over time, compare categories, and filter to specific subsets of calls. You can set up custom Analyzers and Parameters for you calls from the Calls page. These extract structured data from each call — things like call_status, refund amount, customer sentiment, and more. Custom charts are built on top of this data.

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Custom Charts

Charts are built from three building blocks: a measure, a dimension (optional), and a filter (optional).

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Measure

A measure is the value you want to aggregate. Each datapoint on the chart represents a bucket of calls. If you group by daily, each datapoint is a single day in which you may have received many calls. The measure defines how those calls are reduced to a single number. For example:
  • SUM(.refund_amount) — total refund amount for that time period
  • AVG(.satisfaction_score) — average satisfaction score for that time period
  • COUNT(.call_status) — number of calls that had a call_status value
  • COUNT_DISTINCT(.call_status) — number of unique call_status values (e.g. “completed”, “voicemail”, etc.)
  • COUNT(*) — total number of calls, regardless of any specific field. Useful when you just want to count calls that match a filter.

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Dimension

A dimension splits your measure into separate series. Instead of one line on your chart, you get one line per distinct value of the dimension. For example, adding .call_status as a dimension turns a single “total refund amount” line into separate lines for completed, voicemail, no_answer, etc. There is a special dimension Agent which splits your data into a series per call agent Dimensions are optional. Without one, your chart shows a single series. If a call does not have a value for the dimension field, it is grouped under a special __NONE__ series so it still contributes to the chart rather than being silently dropped. Here is an example of the same data no dimension, a dimension with display “Stacked Bar”, and a dimension with display “Line”
Dashboard overview

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Filter

A filter restricts which calls are included before any aggregation happens. For example, “only include calls where .customer_sentiment is positive.” Filters narrow the data — they do not create additional series. You can combine multiple filters and they are ANDed together.

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Data Sources

Charts pull from two data sources:
  • Params — custom parameters you on each call
  • Analyzers — values extracted by your post-call analyzers
Each chart uses one source. You cannot mix params and analyzers in a single chart.

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Chart Types

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Timeseries

Timeseries charts show how a measure changes over time. The x-axis is time (divided into buckets like hours or days), and the y-axis is the aggregated value. Use timeseries charts when you want to answer questions like:
  • “How many calls are we getting per day?”
  • “What is the average satisfaction score per week?”
  • “How does refund volume compare across call_statuss over time?”
You choose the bucket size (e.g. 1 week, 1 day) and date range when viewing the chart.

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Distribution

Distribution charts show the breakdown of a single value. The x-axis is categories (for strings and booleans) or histogram bins (for numbers), and the y-axis is count. Use distribution charts when you want to answer questions like:
  • “What percentage of calls end in each call_status?”
  • “What does the call duration distribution look like?”
  • “How many calls were transferred vs not?”