Use a report

Last update: July 25, 2023

CREATED FOR:

  • Beginner
  • Intermediate
  • Admin
  • Developer
  • User

Use reports in Adobe Commerce Intelligence to help you answer business questions - whether you simply want to see this month’s revenue compared to last year or understand your acquisition costs for your latest Google AdWords campaign.

What does that path from question to answer look like, exactly?

To help you visualize this process, that route is mapped out below. This topic sheds some light on both how you approach an analytical question, and the backend logistics required to get you the data you need.

Starting with the question

You know that you are constantly asking questions to improve your business, from increasing customer satisfaction to cutting supply costs. You focus on how to translate your questions into analyses that help you drive decisions.

For this example, assume that you want to answer the following question:

  • How fast do my new registrants convert?

Identifying a measurement

It is time to identify a list of possible analyses and measurements to help answer the question. For this example, focus on the following metric:

  • Average time from registration to first purchase date per use.

This reveals the average time that lapses between registration date and the users’ first purchase date and give an idea on how users behave at this final step in the conversion funnel.

Finding the data

Understanding what to measure only gets us part of the way there. To assess the average time from registration to first purchase date per user, you need to identify all the data points that your measure is comprised of.

Break down your measure into its core components. You must know the count, or number, of people that registered, the count of people that made a purchase, and the time that elapsed between those two events.

At a higher level, you need to know where to find this data in the database, specifically:

  • The table that records a row of data every time someone registers
  • The table that records a data row that every time someone makes a purchase
  • The column that can be used to join or reference the purchase table to the customer table - this allows us to know who made a purchase

At a more granular level, you need to identify the exact data fields that are used for this analysis:

  • The data table and column that contain a customer’s registration date: for example user.created\_at
  • The data table and column that contain a purchase date: for example order.created\_at

Creating data columns for analysis

In addition to the native data columns outlined above, you also need a set of calculated data fields to enable this analysis, including:

  • Customer's first purchase date which returns a specific user’s MIN(order.created_at)

That is then used to create:

  • Time between a customer's registration date and first purchase date, which returns a specific user’s time lapsed between registration and first purchase date. This is the basis for your metric later.

Both of these fields need to be created at the user level (for example, on the user table). This enables the average analysis can be normalized by users (in other words, the denominator in this average calculation is the count of users).

This is where Commerce Intelligence steps in! You can use your Commerce Intelligence Data Warehouse to create the above columns. Contact the Adobe analyst team and provide us with the specific definition of your new columns for creation. You can also use the Column Editor.

It is a best practice to avoid creating these calculated data fields in your database directly as it puts an unnecessary burden on your production servers.