Скачать или смотреть How to Track User Subscription Activity with SQL Queries in Snowflake

Discover how to effectively analyze user subscription activities over time using SQL queries in the Snowflake cloud data platform.
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This video is based on the question https://stackoverflow.com/q/64527735/ asked by the user 'Paul Anthony' ( https://stackoverflow.com/u/14517940/ ) and on the answer https://stackoverflow.com/a/64534659/ provided by the user 'Daniel Zagales' ( https://stackoverflow.com/u/14400998/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Understanding User Subscription Activity in SQL

In the world of data analytics, understanding user behavior is crucial for businesses looking to optimize their services and target their customers more effectively. One such important aspect is tracking user subscription activity. This post delves into how to create a query that identifies and summarizes subscription activities from a user transaction table. The goal is to turn complex transaction data into a concise report that highlights user activity over several months.

The Problem

Imagine you have a transaction table containing data for various users, including:

customer_id: The unique identifier for each customer

created_at: The timestamp of the transaction

merchant_name: The name of the merchant involved in the transaction

transaction_amount: The amount of the transaction

Your objective is to generate a new output table that shows whether a user has transacted with a specific merchant for at least three consecutive months. The output will be in the following format:

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In this table:

1 indicates that the user transacted with that merchant at least once in a month within a string of at least three consecutive months.

0 indicates no such activity.

The Solution: Building the SQL Query

To achieve this, we will leverage SQL window functions and date manipulation techniques to analyze the transaction data effectively. Here's how to construct the SQL query step-by-step.

Step 1: Create a Temporary Table

First, you need to create a temporary table to store sample data that will be used for analysis:

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Step 2: Generate Filler Dates

To check for consecutive months efficiently, it’s useful to generate filler dates for previous months:

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Explanation of the Query Steps

Data Ingestion: The temporary table captures transaction data for analysis.

Filler Dates Creation: This step generates a list of dates to compare against transaction dates.

Data Join: The main data is joined with the filler dates to identify periods of activity.

Consecutive Activities Check: A window function checks for the last three months of transactions to determine whether the value is ‘1’ or ‘0’.

Conclusion

By utilizing window functions and structured queries, you can easily track user subscription activities over time. This approach will allow businesses to analyze user engagement effectively, providing valuable insight into customer behaviors and patterns in transaction data.

As you further apply this knowledge, remember that adapting SQL queries can greatly enhance your analytical capabilities and lead to more informed decision-making in your business endeavors.