While reviewing your orders, if you notice that many
customer\_id values are null or do not have a value to join back to the
customers table, this is indicative that your store allows guest orders. This means that your
customers table is most likely not inclusive of all of your customers.
This topic discusses the impact guest orders have on your data and what options you have to properly account for guest orders in your Commerce Intelligence Data Warehouse.
In the typical commerce database, there is an
orders table that joins to a
customers table. Every row on the
orders table has a
customer\_id column that is unique to one row on the
If all customers are registered and guest orders are not allowed, this means that every record in the
orders table has a value in the
customer\_id column. As a result, every order joins back to the
If guest orders are allowed, this means that some orders do not have a value in the
customer\_id column. Only registered customers are given a value for the
customer\_id column on the
orders table. Customers who are not registered receive a
NULL (or blank) value for this column. As a result, not all order records have matching records in the
To identify the unique individual that made the order, there needs to be another unique user attribute beside
customer\_id attached to an order. Typically, customer’s email address is used.
Typically, the Sales Engineer that implements your account takes guest orders into consideration when building the foundation of your Data Warehouse.
The most optimal way to account for guest orders is to base all customer-level metrics on the
orders table. This setup uses a unique customer ID that all customers have, including guests (normally customer email is used). This ignores registration data from the
customers table. With this option, only customers who have made at least one purchase are included in customer-level reports. Registered users who have not yet made one purchase are not included. With this option, your
New customer metric is based on the customer’s first order date in the
You may notice that the
Customers we count filter set in this type of setup has a filter for
Customer's order number = 1.
In a situation without guest orders, each customer exists as a unique row in the customer table (see Image 1). A metric such as
New customers can simply count the id of this table based on
created\_at date to understand New customers based on registration date.
In a guest orders setup where all customer metrics are based on the
orders table to account for guest orders, you must ensure that you are
not counting customers twice. If you count the id of the orders table, you are counting every order. If instead you count the id on the
orders table and use a filter,
Customer's order number = 1, then you are going to count each unique customer
only one time. This is applicable for all customer level metrics such as
Customer's lifetime revenue or
Customer's lifetime number of orders.
You can see above that there are null
customer\_ids in the
orders table. If you use the
customer\_email to identify unique customers, you can see that
firstname.lastname@example.org has placed three (3) orders. Therefore, you can build a
New customers metric on your
orders table based on the following conditions:
Operation table = orders
Operation column = id
Operation = count
Timestamp = Customer's first order date
Filter = Customer's we count (where Customer's order number = 1)