Understanding Unique User Visits

As a data analyst, it’s essential to track user interactions with your website or application. This can include page views, clicks, and other events that help you understand user behavior. In this article, we’ll explore how to count unique user visits grouped by quarter and year.

Problem Statement

Given a table of user visits with columns for id, user_id, link, and added_on, we want to:

1. Count the number of unique visits per user per link.
2. Find the number of different links visited by each user.
3. Calculate the total number of distinct visits (user - link) per quarter.

Querying the Data

The provided query uses a combination of aggregate functions, grouping, and ordering to achieve these goals.

Counting Unique Visits Per User Per Link

SELECT 
  CONCAT(QUARTER(added_on)," ",YEAR(added_on)) AS quarter_year,
  user_id,
  link,
  COUNT(id) AS number_of_visits
FROM t
GROUP BY 
  1,
  user_id,
  link
ORDER BY 
  1 DESC,
  user_id;

This query groups the data by quarter_year, user_id, and link. The COUNT(id) function counts the number of visits for each combination of these columns. Note that this includes both single visits and repeated visits.

Finding Different Links Visited by Each User

SELECT 
  CONCAT(QUARTER(added_on)," ",YEAR(added_on)) AS quarter_year,
  user_id,
  COUNT(DISTINCT link) AS number_of_links
FROM t
GROUP BY 
  1,
  user_id
ORDER BY 
  1 DESC,
  user_id;

This query uses the COUNT(DISTINCT link) function to count the number of unique links visited by each user. The DISTINCT keyword ensures that we only count each link once.

Calculating Total Distinct Visits Per Quarter

SELECT 
  quarter_year,
  SUM(number_of_links) AS distinct_visits
FROM (
SELECT 
  CONCAT(QUARTER(added_on)," ",YEAR(added_on)) AS quarter_year,
  user_id,
  COUNT(DISTINCT link) AS number_of_links
FROM t
GROUP BY 
  1,
  user_id
ORDER BY 
  1 DESC,
  user_id) q
GROUP BY quarter_year
ORDER BY quarter_year;

This query first calculates the number of unique links visited by each user in a subquery. It then groups this data by quarter_year and calculates the total sum of these counts.

Data Types and Terminology

• QUARTER: Returns the quarter of the year (1-4) based on the month.
• YEAR: Returns the full year.
• COUNT(DISTINCT): Counts unique values in a column.
• GROUP BY: Groups data by one or more columns.

Data Structure and Assumptions

The provided table structure assumes that:

• id is the primary key (unique identifier for each row).
• user_id uniquely identifies each user.
• link represents a specific URL visited by the user.
• added_on stores the timestamp of when the visit occurred.

Performance Considerations

When working with large datasets, it’s essential to consider performance and optimize queries accordingly. In this case:

• Using COUNT(DISTINCT) instead of COUNT(*) can improve performance by reducing the number of rows processed.
• Grouping data by columns that are not frequently used in queries can help reduce the size of intermediate results.

Example Use Cases

1. Website Analytics: Tracking user interactions helps website owners understand user behavior, identify areas for improvement, and optimize their content.
2. Application Development: Analyzing user interactions can inform application design decisions, such as improving navigation or reducing bounce rates.
3. Research Studies: Collecting data on user visits can help researchers analyze trends, patterns, and correlations in human behavior.

By understanding how to query and analyze user visit data, you can unlock valuable insights into your users’ behavior and make data-driven decisions to improve your application or website.

Last modified on 2023-07-08