Translate SQL queries in Commerce Intelligence

Last update: 2023-07-25
  • Created for:
  • Beginner

Ever wondered how SQL queries are translated into the calculated columns, metrics, and reports you use in Commerce Intelligence? If you are a heavy SQL user, understanding how SQL is translated in Commerce Intelligence enables you to work smarter in the Data Warehouse Manager and get the most out of the Commerce Intelligence platform.

At the end of this topic, you find a translation matrix for SQL query clauses and Commerce Intelligence elements.

Start by looking at a general query:

a, Report group by
SUM(b) Aggregate function (column)
FROM c Source table
d IS NOT NULL Filter
AND time < X

AND time >= Y
Report time frame
GROUP BY a Report group by

This example covers most translation cases, but there are some exceptions. Dive in, starting with how the aggregate function is translated.

Aggregate functions

Aggregate functions (for example, count, sum, average, max, min) in queries either take the form of metric aggregations or column aggregations in Commerce Intelligence. The differentiating factor is whether a join is required to perform the aggregation.

Look at an example for each of the above.

Metric aggregations

A metric is required when aggregating within a single table. So for example, the SUM(b) aggregate function from the query above would most likely be represented by a metric which sums column B.

Look at a specific example of how a Total Revenue metric might be defined in Commerce Intelligence. Look at the query below that you attempt to translate:

SUM(order_total) as "Total Revenue" Metric operation (column)
FROM orders Metric source table
email NOT LIKE '' Metric filter
AND created_at < X

AND created_at >= Y
Metric timestamp (and reporting time range)

Navigate to the metric builder by clicking Manage DataMetricsCreate New Metric, you first must select the appropriate source table, which in this case is the orders table. Then the metric would be set up as shown below:

Metric aggregation

Column aggregations

A calculated column is required when aggregating a column that is joined from another table. So for example, you may have a column built in your customer table called Customer LTV, which sums the total value of all orders associated with that customer in the orders table.

The query for this aggregation may look something like the below:

c.customer_id Aggregate owner
SUM(o.order_total) as "Customer LTV" Aggregate operation(column)
FROM customers c Aggregate owner table
JOIN orders o Aggregation source table
ON c.customer_id = o.customer_id Path
WHERE o.status = 'success' Aggregate filter

Setting this up in Commerce Intelligence requires the use of your Data Warehouse manager, where you build a path between your orders and customers table then create a column called Customer LTV in your customer’s table.

Look at how to establish a new path between the customers and orders. The end goal is to create a new aggregated column in the customers table, so first navigate to the customers table in your Data Warehouse, then click Create a ColumnSelect a definitionSUM.

Next, you need to select the source table. If a path exists to your orders table, simply select it from the dropdown. However if you are building a new path, click Create new path and you are presented with the screen below:

Create new path

Here you need to carefully consider the relationship between the two tables you are attempting to join. In this case, there are potentially Many orders associated with One customer, therefore the orders table is listed on the Many side, whereas the customers table selected on the One side.


In Commerce Intelligence, a path is equivalent to a Join in SQL.

Once the path has been saved, you can create the Customer LTV column! See below:

Now that you have built the new Customer LTV column in your customers table, you are ready to create a metric aggregation using this column (for example, to find the average LTV per customer). You can also group by or filter by the calculated column in a report using existing metrics built on the customers table.


For the latter, anytime you build a new calculated column you must add the dimension to existing metrics before it is available as a filter or group by.

See creating calculated columns with your Data Warehouse Manager.

Group By clauses

Group By functions in queries are often represented in Commerce Intelligence as a column used to segment or filter a visual report. As an example, let us revisit the Total Revenue query that you explored previously, but this time segment the revenue by the coupon\_code to gain a better understanding of which coupons are generating the most revenue.

Start with the query below:

SELECT coupon_code, Report group by
SUM(order_total) as "Total Revenue" Metric operation(column)
FROM orders Metric source table
email NOT LIKE '' Metric filter
AND created_at < '2016-12-01'

AND created_at >= '2016-09-01'
Metric timestamp (and reporting time range)
GROUP BY coupon_code Report group by

The only difference from the query you started with before is the addition of the ‘coupon_code’ as the group by._

Using the same Total Revenue metric that you created previously, you are now ready to create your report of revenue segmented by coupon code! Look at the gif below which shows how to set up this visual report looking at data from September to November:

Revenue by coupon code


Sometimes, a query may involve multiple aggregations in order to calculate the relationship between separate columns. For example, you could calculate the average order value in a query through one of two ways:

  • AVG('order\_total') OR
  • SUM('order\_total')/COUNT('order\_id')

The former method would involve the creation of a new metric which performs an average on the order\_total column. However the latter method could be created directly in the report builder assuming you already have metrics set up to calculate the Total Revenue and Number of orders.

Take a step back and look at the overall query for Average order value:

SUM(order_total) as "Total Revenue" Metric operation (column)
COUNT(order_id) as "Number of orders" Metric operation (column)
SUM(order_total)/COUNT(order_id) as "Average order value" Metric operation (column) / Metric operation(column)
FROM orders Metric source table
email NOT LIKE '' Metric filter
AND created_at < '2016-12-01'

AND created_at >= '2016-09-01'
Metric timestamp (and reporting time range)

Now assume you already have metrics set up to calculate the Total Revenue and Number of orders. Since these metrics exist, you can simply open the Report Builder and create an on-demand calculation using the Formula feature:

AOV forumula

Wrapping Up

If you are a heavy SQL user, thinking about how queries translate in Commerce Intelligence enables you to build calculated columns, metrics, and reports.

For quick reference, check out the matrix below. This shows a SQL clause’s equivalent Commerce Intelligence element and how it can map to more than one element, depending on how it is used in the query.

Commerce Intelligence Elements

SQL Clause Metric Filter Report group by Report time frame Path Calculated column inputs Source table
SELECT X - X - - X -
FROM - - - - - - X
WHERE - X - - - - -
WHERE (with time elements) - - - X - - -
JOIN...ON - X - - X X -
GROUP BY - - X - - - -

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