This is an actively managed, high-quality data product that consolidates every order-created event from the company’s sales systems, providing a complete and accurate view of customer purchases. It includes personally identifiable details such as email, phone number, and invoice address, enabling deep insights into customer behavior, repeat purchasing patterns, and fulfillment efficiency. The data helps business teams answer questions like which customers are most valuable, which products drive the highest order totals, and how often customers place follow-up orders after cancellations. It supports strategic initiatives such as revenue forecasting, churn reduction, and customer satisfaction analysis. The dataset consists of two linked tables: - Orders - Line Items where each order can contain multiple line items representing purchased products or services. Example query: sql SELECT o.ORDER_ID, o.CUSTOMER_ID, o.ORDER_DATE, o.ORDER_TOTAL, COUNT(li.LINE_ITEM_ID) AS line_item_count FROM ANALYTICS.ORDERS_V2.ORDERS o LEFT JOIN ANALYTICS.ORDERS_V2.LINE_ITEMS li ON o.ORDER_ID = li.ORDER_ID WHERE o.ORDER_DATE >= DATEADD(day, -30, CURRENT_DATE()) GROUP BY 1, 2, 3, 4 ORDER BY o.ORDER_DATE DESC; This query summarizes customer activity over the last 30 days, showing each order’s total value and number of purchased items. Business teams can use this to track sales trends, identify high-value customers, or detect changes in product demand, all while maintaining appropriate data privacy controls for PII fields.

The Customers data product includes both registered users and guest buyers from the online shop. It contains all customer records collected since the launch of the platform, offering: - PII and non-PII versions - Latest state and full historical datasets This product supports downstream analytics, customer segmentation, and personalization initiatives.

All search queries and results since 2020.

A PowerBI report that combines revenue, cost, and margin to deliver actionable insights into the profitability of each article. It is designed to help decision-makers identify high-performing products, uncover underperforming items, and allocate resources more effectively.

A list of articles with no sales in last 6 months

PowerBI semantic model created from Customers