E-Commerce Data Analytics
&Visualisation
&Visualisation
Overview
To sharpen my practical data skills, I took on a self-directed project using the Looker E-Commerce sample dataset in Google BigQuery. The dataset simulates real-world retail activity across users, orders, and product categories. The goal was to uncover actionable insights to support business decisions around market segmentation, seasonal trends, and product performance—using SQL for data exploration and Power BI (with DAX) for visualisation.
Goal is to practise end-to-end reporting by translating raw data into meaningful insights that can guide pricing, marketing, and regional sales strategies.
Approach
Goal is to practise end-to-end reporting by translating raw data into meaningful insights that can guide pricing, marketing, and regional sales strategies.
Approach
I began by exploring the raw dataset in Google BigQuery using SQL, filtering for relevant fields such as non-null user IDs and valid order records. Once cleaned and structured, I exported the query results into Power BI for further analysis.
In Power BI, I standardised column names and data types, handled some missing values, and resolved formatting issues—such as converting text-based date columns using Power Query. I also created a custom date table to support time-based breakdowns.
Using DAX, I developed measures including total sales, total orders, and revenue share by category. These were used to build dashboards that visualise year-over-year sales performance and order volume trends across 2023 and 2024. The visuals compare quarterly patterns and highlight category-level dynamics, enabling a clear snapshot of seasonal behaviour and revenue distribution.
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