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Funnel Chart

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Terakhir diperbarui: 2026-05-13 19:12:04
A funnel chart is suitable for business processes with one single flow direction and path. It collects the statistics of each stage and uses a trapezoidal area to represent the business volume difference between two stages.

Operation Steps

1. Log in to the CLS console, choose Dashboard > Dashboard List, and then select the dashboard you want to view.
2. Click Add Chart to create a chart, and select Funnel plot as the chart type.
3. Configure the chart as needed.

General Configuration

Configuration Item
Description
Basic Info
Chart Name: Set the display name of the chart, which can be left empty.
Legend
Set the chart legends. You can control the legend styles and positions and configure the data to be displayed as legends.
Standard Configuration
Set the unit of all metric-type fields in the chart. For more information, see Unit Configuration.
Interaction event
Custom interaction event feature. Clicking chart elements can trigger interactions such as opening the search and analysis page, navigating to a dashboard, or accessing a third-party URL. For details, see Interaction Events.

Statistical Analysis Fields

For a funnel chart, a metric field must be selected. When a data mismatch occurs, you need to manually select the appropriate statistical analysis fields.
Configuration Item
Description
Dimension Fields
Classification field. By default, fields with the string data type are selected. If no string field exists, you must manually specify a field.
Metric Fields
Numerical field. By default, fields with the number data type are selected. If no number field exists, you must manually specify a field.

Funnel Chart Configuration

Configuration Item
Description
Chart Style
Sort By: Control the sorting order of values in the funnel chart. By default, the values are displayed in the order of the query results.
Displayed Content: Set the label display form of each stage in the funnel chart, which can be Value or Conversion rate.
Tag: Configure the display content of the funnel chart tags.
Tag location: Configure the position of tags in the chart.
Conversion rate: Set the calculation method of the conversion rate, which can be Percentage of the first stage or Percentage of the previous stage.
Metric Aggregation: This is an advanced configuration. It aggregates the results of identical metrics before plotting. For example, if a range of values containing results from multiple time points is returned for a metric, the system aggregates the values from all those time points before plotting. By default, no aggregation is performed. You can set a limit on the maximum number of metrics to be displayed. Any metrics beyond this limit are hidden.
Maximum Number of Display Layers in a Funnel Chart: Set the number of rendered stages in the funnel chart, which can be up to 20.

Statement Example

Calculate the conversion rate for each node in the user's access path:
* | select url, count(*) as pv group by url limit 5



Calculate User Conversion Rate:
event:(Advertising OR EnterTheMall OR BrowserTheList OR Order OR Payment) |
select count(distinct ip) as "user_count", CASE
WHEN event = 'Advertising' THEN 'Go to the activity page'
WHEN event = 'EnterTheMall' THEN 'Open the mall'
WHEN event = 'BrowserTheList' THEN 'View product details'
WHEN event = 'Order' THEN 'Confirm order'
WHEN event = 'Payment' THEN 'Confirm payment'
ELSE 'other' END as event group by event limit 1000



Calculate the 7-day retention rate for registered users:
* | select
count(distinct (CASE WHEN count < 1 THEN ip END)) as "Day 1 Retention Rate",
count(distinct (CASE WHEN count = 1 THEN ip END)) as "Day 2 Retention Rate",
count(distinct (CASE WHEN count = 2 THEN ip END)) as "Day 3 Retention Rate",
count(distinct (CASE WHEN count = 3 THEN ip END)) as "Day 4 Retention Rate",
count(distinct (CASE WHEN count = 4 THEN ip END)) as "Day 5 Retention Rate",
count(distinct (CASE WHEN count = 5 THEN ip END)) as "Day 6 Retention Rate",
count(distinct (CASE WHEN count = -1 THEN ip END)) as "Day 7 Retention Rate"
from
(
select t1-t0 as count,table1.ip from
( select extract( day_of_year from cast(__TIMESTAMP__ as timestamp)) as t0 ,ip where event='SignUp' group by t0 ,ip limit 10000 )
as table1
left join
( select extract( day_of_year from cast(__TIMESTAMP__ as timestamp)) as t1 ,ip where event='EnterTheMall' group by t1 ,ip limit 10000 )
as table2
on table1.ip = table2.ip
)




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