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Customer Sales & Retention Analysis

SQL | BigQuery | Power BI | Customer Analytics

Includes a data-driven analysis of customer purchasing behaviour, retention trends, and revenue performance using SQL and BigQuery, with insights delivered through an interactive dashboard.

View Live Dashboard

View SQL & Data Pipeline

INTERACTIVE DASHBOARD

Explore the dashboard to analyse customer behaviour, revenue trends, and retention patterns across different segments.

Includes a live interactive dashboard, explore performance metrics, customer cohorts, and purchasing trends in real time.

OVERVIEW

This project focuses on analysing customer sales and retention to uncover patterns in purchasing behaviour, customer lifetime value, and churn risk.

By combining SQL-based data transformations in BigQuery with an interactive Power BI dashboard, the project provides clear visibility into how customers engage over time and where opportunities for growth and retention exist.

THE PROBLEM

Businesses often struggle to:

Understand which customers drive the most value
Identify early signs of customer churn
Track retention and repeat purchase behaviour
Connect transactional data to meaningful insights

Without structured analysis, it becomes difficult to optimise marketing strategies and improve long-term customer relationships.

KEY FEATURES

Customer segmentation by behaviour and value
Revenue tracking and performance trends
Retention and repeat purchase analysis
Cohort-based customer insights
Interactive filtering by customer group, time period, and product

THE SOLUTION

Developed an end-to-end analytics workflow to transform raw transactional data into actionable insights.

The solution integrates SQL-based data processing in BigQuery with an interactive dashboard, enabling clear tracking of customer behaviour, revenue performance, and retention metrics.

KEY INSIGHTS

A small percentage of customers drive a significant portion of total revenue
Repeat customers contribute more consistently to long-term growth
Certain segments show early signs of churn after initial purchase
Retention rates vary significantly across customer groups
Targeted engagement strategies can improve customer lifetime value

TOOLS USED

TECHNICAL APPROACH

Data was extracted, cleaned, and transformed using SQL within BigQuery to create structured datasets for analysis.
Key steps included:
Cleaning and standardising raw transactional data
Joining multiple tables (customers, orders, products)
Creating derived metrics such as revenue, frequency, and recency
Aggregating data for customer-level and cohort analysis
Preparing optimised datasets for dashboard visualisation

A small percentage of customers drive a significant portion of total revenue
Repeat customers contribute more consistently to long-term growth
Certain segments show early signs of churn after initial purchase
Retention rates vary significantly across customer groups
Targeted engagement strategies can improve customer lifetime value

DATA PIPELINE

This project follows a structured data pipeline approach, transforming raw data into clean, analysis-ready datasets before visualisation. This ensures accuracy, scalability, and consistency in reporting.

SQL SHOWCASE

SELECT
customer_id,
COUNT(order_id) AS total_orders,
SUM(order_value) AS total_revenue,
MAX(order_date) AS last_purchase_date
FROM orders
GROUP BY customer_id