https://github.com/thenazar9/user-behavior-email-campaign-analysis-sql
Analysis of user behavior and email campaign performance using BigQuery and Looker Studio, focusing on account creation trends, email engagement, and user segmentation.
https://github.com/thenazar9/user-behavior-email-campaign-analysis-sql
analytics bigquery data-analysis data-visualization etl looker-studio sql structured-query-language
Last synced: 8 months ago
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Analysis of user behavior and email campaign performance using BigQuery and Looker Studio, focusing on account creation trends, email engagement, and user segmentation.
- Host: GitHub
- URL: https://github.com/thenazar9/user-behavior-email-campaign-analysis-sql
- Owner: thenazar9
- Created: 2025-05-22T20:58:02.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-22T21:33:05.000Z (about 1 year ago)
- Last Synced: 2025-06-07T13:01:35.903Z (about 1 year ago)
- Topics: analytics, bigquery, data-analysis, data-visualization, etl, looker-studio, sql, structured-query-language
- Homepage:
- Size: 429 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# User Behavior and Email Campaign Performance Analysis (SQL & Looker Studio)
This project analyzes user behavior and email campaign performance using an e-commerce dataset in BigQuery. The main goal is to create a dataset that helps track account creation dynamics, user activity with emails (sent, opened, clicked), and evaluate user segmentation based on sending intervals, account verification status, and subscription status.
## 🔍 Project Goals
- Analyze user account creation trends and email activity by date and country.
- Segment users by sending interval, account verification (`is_verified`), and subscription status (`is_unsubscribed`).
- Calculate key metrics including:
- Number of accounts created (`account_cnt`)
- Number of emails sent (`sent_msg`)
- Number of emails opened (`open_msg`)
- Number of email link clicks (`visit_msg`)
- Rank countries by total account creation and total emails sent to identify top markets.
- Combine account and email metrics in one dataset using SQL `UNION`.
- Filter results to include only top 10 countries by accounts or emails sent.
## 📊 Tools & Skills Used
- **BigQuery SQL**: complex queries with CTEs (Common Table Expressions), window functions for ranking, grouping and aggregations.
- **Looker Studio**: interactive dashboards and data visualizations.
- **Data analysis**: user behavior, email campaign effectiveness, segmentation, ranking.
## 📁 Dataset Structure
The final dataset includes the following fields:
- `date` — account creation date or email sent date
- `country` — user country
- `send_interval` — email sending interval set by user
- `is_verified` — account verification status
- `is_unsubscribed` — subscription status
- `account_cnt` — number of accounts created
- `sent_msg` — number of emails sent
- `open_msg` — number of emails opened
- `visit_msg` — number of email link clicks
- `total_country_account_cnt` — total accounts created per country
- `total_country_sent_cnt` — total emails sent per country
- `rank_total_country_account_cnt` — rank of countries by accounts created
- `rank_total_country_sent_cnt` — rank of countries by emails sent
## 📄 Deliverables
- SQL query with detailed comments explaining logic and structure.
- Looker Studio dashboard visualizing:
- **Email engagement trends (sent, opened, clicked messages) over time.**
- **Total accounts created by country (map visualization).**
- **Country rankings by total account creation and total emails sent.**
- **Account information by country, including subscription and verification status.**
## 📈 Visualization of Results
Below is the graphical representation of the analyzed data, showing key trends and metrics from the user behavior and email campaign performance analysis.
