Utilize our Costco products dataset to gain a comprehensive view of new products, categories, pricing, and customer feedback. You can acquire the full dataset or tailor it to fit specific requirements.

Popular use cases include identifying inventory shortages and pinpointing high-demand items, analyzing customer sentiment, and crafting pricing strategies by comparing similar products and categories with your competitors.

The Costco dataset may include these data points: category, product name, description, images, features, price, specifications, review count, review score, review texts, and more. Subsets are available by categories and specific data points.

How high is the brand awareness of Costco in the UK?When it comes to grocery store customers, brand awareness of Costco is at 72% in the UK. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Costco in the UK?In total, 16% of UK grocery store customers say they like Costco.What is the usage share of Costco in the UK?All in all, 15% of grocery store customers in the UK use Costco.How loyal are the customers of Costco?Around 10% of grocery store customers in the UK say they are likely to use Costco again.What's the buzz around Costco in the UK?In June 2024, about 7% of UK grocery store customers had heard about Costco in the media, on social media, or in advertising over the past three months, meaning at the time of the survey there was little to no buzz around Costco in the UK.

I scraped grocery data from Costco's online marketplace.

Features:

Sub Category: - This column categorizes the grocery items into subcategories, providing a detailed classification for easier analysis and organization.

Price: - Represents the monetary value of the grocery item, indicating its cost or retail price in the specified currency.

Discount: - Reflects any discounts or promotional offers applicable to the respective grocery item, providing insights into pricing strategies.

Rating: - Indicates the customer satisfaction or product quality based on user ratings, offering a measure of the overall perceived value of the grocery item.

Title: - Describes the name or title of the grocery item, providing a concise identifier for easy reference and understanding.

Currency: - Specifies the currency in which the prices are denominated, facilitating proper interpretation and comparison of monetary values.

Feature: - Includes features or characteristics of the grocery item, offering additional information about its unique attributes or selling points.

Complete dataset: Costco Wholesale's 2 incidents, security score trends, compliance status, and comparative benchmarks against 15,710 industry peers.

The King’s Pantry: A Fantasy Grocery Dataset Built Like a Real ERP System

By Ardonna Cardines (Mercury Musings)

🌟 Overview

The King’s Pantry is a fantasy grocery dataset inspired by Westeros — but built like a real-world ERP and retail system.

Every table, relationship, and field was designed to mirror how mid-market retailers manage their data — from products, vendors, and pricing to customers, sales channels, and transactions.

It’s fictional in theme, but realistic in structure. Think “King’s Landing meets Costco.”

⚙️ Dataset Architecture

The dataset was engineered in phases to teach different layers of analytics and database design:

Phase 1: The Vision

Built to simulate the systems I’ve worked with in pricing, supply chain, and retail analytics. Every decision — from product hierarchies to tax logic — came from real-world business rules.

Phase 2: Schema Design (Star + Snowflake Hybrid)

A hybrid schema that serves two purposes:

-Star Schema: Flattened for Power BI — optimized for visuals and faster queries.

-Snowflake Schema: Normalized for SQL practice — great for relational modeling and integrity checks.

🧩 In real analytics teams, the BI layer and data warehouse layer often co-exist — this dataset lets you practice both.

Phase 3: Product Strategy & Scale

-540 SKUs across 8 major categories (Apothecary, Produce, Beverages, etc.)

Includes 3 private label tiers (like real retailers):

-Smallfolk Essentials → Value

-The King’s Pantry Select → Premium

-Crown Reserve → Luxury

Private label data allows you to explore pricing tiers, category margins, and brand vs. private performance.

Phase 4: Pricing, Margin & Costing

Each product includes:

-Purchase Cost

-Landed Cost (with Freight)

-Base Price

-Margin %

📊 Perfect for learning margin analysis, COGS calculations, and pricing strategy in SQL or Power BI.

Phase 5: Transactional Layer

-10K+ sales orders and 50K+ order lines

-Modeled after real retail order logic

Each channel behaves differently:

🦅 Raven Prime → eCommerce

🏪 Market Stall → In-store / POS

⚙️ Guild Supply → B2B / Wholesale

🍽️ Court Catering → Institutional

🔢 Includes realistic prefixes (e.g. RP-001043) for pattern-based queries and order tracing.

Phase 6: Documentation & Learning Design

The /docs or references_files folder includes:

-Tax & Discount Rules

-Category Weighting Logic

-Segmentation + Channel Mapping

Because behind every great dataset is even better documentation.

🧠 Learning Opportunities

You can use this dataset to practice: ✅ SQL joins, aggregations, and subqueries ✅ Time intelligence and sales performance metrics ✅ Margin and pricing analysis ✅ Data modeling and relational integrity ✅ Power BI dashboard building ✅ Documentation and naming standards

🎯 Ideal Use Cases

Building dashboards (Power BI, Tableau)

-Practicing SQL joins, CTEs, and window functions

-Learning category management and product analytics

-Running pricing or margin simulation models

-Teaching database design or data storytelling

Creator Notes

This dataset started as a creative side project — but evolved into a full data learning ecosystem as I reflected on everything I have seen and learned so far in my data career. I built it to help analysts understand not just how to query data but why it’s structured the way it is.

💬 If you build something with it, tag me or share your work! I’d love to feature community dashboards and analyses on my blog.

🔗 Blog Post: Behind the Scenes: Engineering The King’s Pantry Dataset https://mercurymusings.blog/2025/10/08/behind-the-scenes-engineering-the-kings-pantry-dataset?utm_source=chatgpt.com

🧩 Collection Methodology

The King’s Pantry Dataset was built through a mix of research, modeling, and synthetic data generation to reflect how real grocery and retail ERP systems operate.

Aside from my personal experience with pricing, retail, and distribution, additional research was conducted. The process included research into:

1.Real-world product assortments and category distributions across grocery retailers to estimate SKU mix and hierarchy weighting.

2.Private label strategies from brands like Costco (Kirkland Signature) and Sam’s Club (Member’s Mark) to design realistic brand tiers and ownership logic.

3.Retail pricing and costing structures, including landed cost, freight inclusion, and margin targets, to model product-level profitability.

4.Common ERP and data warehouse schemas used in retail analytics (inspired by systems such as Microsoft Dynamics, Epicor, and Prophet 21) to shape relational integrity and table design.

All transactional data (10K+ orders, 50K+ order lines) was synthetically generated using weighted probabilities to simulate realistic sales behavior across channels — eCommerce, in-store, B2B, and institutional.

While the dataset is ficti...