Chart Types

Kubit's Report Builder supports 12 chart types across five categories: Time Series, Distribution, Composition, Correlation, and Metrics. Each chart type has its own set of required and optional configuration parameters.

This guide covers what each chart type does, when to use it, and exactly what configuration it needs — both in the UI and when prompting Kubit through MCP.

Common Parameters (All Chart Types)

Every chart type requires these baseline parameters:

Optional parameters available to all chart types:

Time Series Charts

Time series charts plot data over time. They support granular time units (Hour, Day, Week, Month, Quarter, Year) in addition to All Time.

Bar

A vertical bar chart showing values over time periods. Best for comparing discrete time buckets.

Line

A line chart connecting data points over time. Best for spotting trends and patterns across continuous time periods.

Stacked

A stacked column chart where each bar is divided into segments by a breakdown dimension. Best for showing part-to-whole relationships over time.

Combo

A combination chart that overlays bars and lines on the same time axis. Best for comparing two measures with different scales (e.g., revenue as bars, conversion rate as a line).

Distribution Charts

Histogram

A histogram showing the distribution of a measure's values across buckets. Best for understanding how values are spread (e.g., "How are session durations distributed?").

Steps to build your Histogram Analysis

1. Change the Mode in Query to Histogram.

2. Select the Metric you want to measure that is going to be impacted. Typically these are Sum, Count Event, or a Compound Measure , showing % per User.

3. Select the Subject you want to analyze, this will be the "thing we are counting" that would perform the measure you create in Step 2.

4. Choose your 'Count Subject' by in the y-axis

5. Choose your 'Group By' in the x-axis (Binning)

   1. You are able to use a pre-built Range if it's applicable or create a custom one.

   2. These Bins will be related to the expected values from your Measure in Step 2.

      1. Ex. If you are counting events performed and a user typically only can do that action 10 times in a day then the max number of your Range would likely be 10.

6. Apply any necessary filters

   1. You cannot perform Breakdowns in Histogram Mode

7. Select the date range

8. Execute!

Composition Charts

Pie

A pie chart showing proportional breakdown of a single measure. Best for quick part-to-whole snapshots when there are few categories.

Treemap

A treemap showing hierarchical proportional data as nested rectangles. Best for visualizing relative sizes across many categories, especially with hierarchical breakdowns.

Waterfall

A waterfall chart showing how individual components add up to a total. Best for understanding incremental contributions (e.g., which event types contribute most to total count).

Correlation Charts

Scatter

A scatter plot mapping three measures to X-axis, Y-axis, and bubble size. Best for spotting correlations between two metrics, with a third metric as context.

Heatmap

A heatmap plotting two dimensions on X and Y axes with color intensity representing measure values. Best for finding patterns across two categorical dimensions (e.g., activity by day-of-week and hour).

Metric Charts

KPI

A single large number (or set of numbers) showing a key metric with optional trend. Best for dashboards and at-a-glance performance indicators.

Impact

An impact chart measuring how a specific trigger event affects a metric over time. Best for evaluating the effect of product changes, feature launches, or campaigns.

Within Impact Analysis the main use case is: "What is the impact to Metric A after a user engages with Event A?" More practically, here are a few examples from your favorite digital products:

• What is the impact to Total Time Watched after a user is shown a Content Recommendation?

• What is the impact of Total Sessions after a user signs up?

• What is the impact of Customer lifetime value (LTV) after they purchase their first premium item?

Steps to build your Impact Analysis

1. Change the Chart Type in Query to Impact Analysis.

2. Choose the Metric you want to measure that is going to be impacted. Typically these are Sum, Count Event, or a Compound Measure, showing percent per User.

3. Apply any necessary filters or breakdowns.

4. Define the date range.

5. Select the Event that is the Impact or "trigger" event. This will be the thing that you believe will Impact your metric you built in Step 1.

   1. Examples include Campaign Impression, Content Recommended, Signed In, etc.

6. Execute the analysis!

Interpreting Results of Impact Analysis

When you see the results what you see is the following:

• A Day 0 indicator in the center of the chart showing that is was the first time in the period you selected that these users triggered the "Add to Cart" tool execution.

• A negative and positive Day counter on either side.

  • This shows the days less than or after the Day 0 event date. It could be different for each user as you're selecting a date range so we normalize them for you.

• A line indicating the Metric (or Measure) you defined in Step 1.

  • This will show IF the metric was impacted by the event you selected.

What we can see in our example is that in general "Add to Cart" had a positive impact on Order placement for our users during that time period.

Quick Reference: Chart Type Capabilities