Скачать или смотреть How to Identify Recurring Customers in PostgreSQL Using Boolean Columns

Discover an effective method to identify customers with consecutive purchases in PostgreSQL by using boolean columns.
---
This video is based on the question https://stackoverflow.com/q/73478494/ asked by the user 'Ueslei Sutil' ( https://stackoverflow.com/u/6872062/ ) and on the answer https://stackoverflow.com/a/73478842/ provided by the user 'The Impaler' ( https://stackoverflow.com/u/6436191/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Using Postresql, identify IDs if they have three or more boolean columns matching the premises

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Unlocking Customer Insights with PostgreSQL: Identifying Recurring Purchases

In the modern business landscape, understanding customer behavior is essential for success. One crucial aspect of this understanding is identifying recurring customers, especially those who consistently make purchases over consecutive months. If your data structure consists of boolean indicators that mark these purchases, you might face some challenges.

In this post, we will explore a straightforward solution using PostgreSQL that allows you to identify customers based on boolean columns indicating their purchase activity.

The Problem: Recurring Purchases and Boolean Columns

Imagine you have a table that tracks distinct customer IDs and records their purchase activity across multiple months using boolean values (0 for no purchase, 1 for a purchase). Here's a quick snapshot of such a data structure:

customer_id2020_032020_042020_052020_062020_072020_081234511100012346001110123470001011234801000012349000101To define a recurring customer, we need to identify those who have purchases in three consecutive months. In our example, that would mean identifying customers like 12345 and 12346, who meet this condition.

The question arises: how can you efficiently identify these customers in PostgreSQL?

The Solution: Using String Concatenation

To tackle this problem, you can leverage PostgreSQL’s capability to concatenate data columns into a single string and look for a specific sequence of characters. In our case, we want to search for the substring '111', which indicates three consecutive months of purchases.

Implementation Steps

Concatenate Boolean Columns: Use PostgreSQL to combine the boolean columns for each customer into a single string.

Search for the Pattern: Apply a LIKE condition to check if the constructed string contains the sequence '111'.

The SQL Query

Here's how you would structure your SQL query to achieve this:

[[See Video to Reveal this Text or Code Snippet]]

Explained

The '' || is used to concatenate all the relevant columns into a single string representation, creating a long binary-like number where 1 represents purchases.

The LIKE '%111%' checks for the presence of the sequence, effectively identifying if there are three consecutive months marked with 1.

Conclusion

By implementing the above query, you can efficiently identify customers who have made purchases in three consecutive months based on boolean columns. This method not only streamlines the analysis process but also provides you with the necessary insights to enhance your customer retention strategies.

If you're looking to delve deeper into SQL practices or need any assistance, feel free to reach out or explore further resources on database management! Happy querying!