Customer 360
A customer-level view that brings together order history, revenue, purchase behavior, and lifecycle signals in one analytical foundation.
Public case study
Synthetic Ecommerce SQL & Looker Studio Analysis
A reporting and analysis project that turns a relational synthetic ecommerce dataset into documented SQL sources and Looker Studio reports for customer, lifecycle, and sales questions.
Project overview
From raw relational data to reusable reporting views
The project demonstrates a practical analytics workflow: model the business logic in SQL, keep definitions close to the source, and use Looker Studio to make customer and commercial patterns easier to explore.
RFM segmentation
Recency, frequency, and monetary-value measures that make customer segments easier to compare and prioritize.
Purchase patterns
Purchase intervals, repeat behavior, and cohort-style views that support customer-retention and lifecycle analysis.
Sales performance
Sales measures based on item-level revenue, with cancelled orders excluded so commercial reporting is consistent.
Live reports
Explore the Looker Studio report suite
Each dashboard focuses on a distinct commercial or customer question while sharing the same documented SQL analysis foundation.
Executive report
A high-level view of ecommerce performance and the key commercial signals in the dataset.
Open reportCustomer portfolio and lifecycle
Customer composition, value, and lifecycle patterns to support portfolio-level analysis.
Open reportNew vs. returning purchasing customers
A comparison of acquisition and repeat-purchase behavior across purchasing customers.
Open reportCustomer cohorts analysis
Cohort views that make retention and subsequent purchasing behavior easier to examine.
Open reportCustomer experience and delivery reliability
Delivery and experience signals that connect operational reliability to customer outcomes.
Open reportPurchase cadence and reactivation opportunities
Purchase timing and reactivation views to identify customers who may need attention.
Open reportAnalytics workflow
Metric logic that is visible and traceable
The SQL is organized as focused reporting sources rather than one opaque query, helping each dashboard answer a defined question and making its measures easier to inspect.
01
Start from a relational synthetic ecommerce dataset with orders, order items, customers, and supporting dimensions.
02
Define reporting logic at the correct grain: customers from orders and revenue from joined order items.
03
Keep metric logic focused in separate SQL sources so each Looker Studio report has a clear, reviewable foundation.
04
Use dashboards to turn the prepared data into decision-oriented views of customer behavior and sales performance.
Technical stack
Designed for analytics engineering and BI practice
The case study pairs SQL transformation and metric definition with a lightweight reporting layer, mirroring the handoff from analytical model to business-facing dashboard.
SQLBigQueryLooker StudioCustomer analyticsRFM analysisCohort analysisEcommerce analyticsData modeling
Data context
Built for original, transparent analytics practice
The analysis uses fully synthetic data from the accompanying ecommerce dataset project. It is intended to demonstrate analytical methods, data modeling, and reporting design—not to represent real customers, sales, or market performance.