SKU-Level Transaction Data: Definition, Sources & Datasets to Buy

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Eugenio Caterino
Editor & Data Industry Expert

What is SKU-Level Transaction Data?

SKU-Level Transaction Data refers to detailed information about individual product sales within a retail or e-commerce setting. It includes data points like product identifiers (SKUs), quantity sold, price, date and time of sale, and customer details. This data offers insights into specific product performance, helping businesses analyze sales trends, manage inventory, and understand customer preferences.

Best SKU-Level Transaction Databases & Datasets

Here is our curated selection of top SKU-Level Transaction Data sources. We focus on key factors such as data reliability, accuracy, and flexibility to meet diverse use-case requirements. These datasets are provided by trusted providers known for delivering high-quality, up-to-date information.

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SKU-Level Transaction Data is essential for a wide range of business applications, offering valuable insights and driving opportunities across industries. Below, we have highlighted the most significant use cases for SKU-Level Transaction Data.

What are Examples of SKU-Level Transaction Data?

Examples of SKU-level transaction data include details about individual products, such as their identifiers, quantities sold, prices, and purchase dates. This data is used for analyzing sales patterns, managing inventory, forecasting demand, and making informed business decisions.

  • Quantities Sold: The number of units sold in a specific period. For example, “50 units sold in May 2024”.
  • Sale Prices: The prices at which products are sold. For example, “Price: $29.99”.
  • Dates and Times of Purchases: When the products were sold. For example, “Sold on May 15, 2024, at 2:30 PM”.
  • Customer Information: Data about the customers who purchased the products. For example, “Customer ID: CUST1234, Name: John Doe”.
  • Sales Channels: Where the sales occurred. For example, “Online Store, In-Store Purchase”.
  • Inventory Levels: The amount of stock available. For example, “Current Stock: 200 units”.
  • Product Returns and Exchanges: Details about returned or exchanged products. For example, “Returned: 5 units on May 20, 2024”.

SKU-Level Data Attributes

  • SKU Code: A unique identifier assigned to each product. For example, “SKU12345”.
  • Product Name: The name of the product associated with the SKU. For example, “Wireless Mouse”.
  • Category: The category to which the product belongs. For example, “Electronics”.
  • Brand: The brand of the product. For example, “Logitech”.
  • Model Number: The model number of the product. For example, “M325”.
  • Specifications: Detailed product specifications. For example, “Color: Black, Connectivity: Wireless, Battery Life: 3 months”.
  • Supplier Information: Details about the supplier of the product. For example, “Supplier: ABC Electronics”.

How SKU-Level Transaction Data is Collected?

Collecting SKU-level transaction data involves using Point-of-Sale (POS) systems, e-commerce platforms, and inventory management software. These systems record transactional data each time a sale happens.

  • POS Data: Contains and captures SKU-level data at purchase.
  • E-commerce Platforms: Record detailed sales data for online transactions.
  • Inventory Management Software: Tracks inventory levels and movements, providing additional context to SKU-Level Data.

Uses Cases for SKU-Level Transaction Data

SKU-Level Data is crucial for various business purposes:

  • Inventory Management: Maintain optimal inventory levels to prevent shortages and overstock.
  • Sales Analysis: Understand product performance and identify sales trends.
  • Customer Insights: Analyze buying patterns to tailor marketing efforts.
  • Pricing Strategies: Optimize pricing based on sales performance and market trends.
  • Demand Forecasting: Predict future sales and adjust inventory accordingly.

SKU-Level Data Benefits

The benefits of using SKU-Level Data include:

  • Better Decision-Making: Make informed choices based on detailed sales data.
  • Increased Efficiency: Streamline operations by optimizing inventory and reducing waste.
  • Higher Sales: Identify high-performing products and focus marketing efforts.
  • Customer Satisfaction: Understand customer preferences and enhance their shopping experience.
  • Cost Savings: Reduce holding costs by maintaining optimal inventory levels.

How to Categorize SKU-Level Data?

Categorizing SKUs can help streamline inventory management and improve sales analysis:

  • By Product Type: Group similar products together.
  • By Sales Volume: Classify SKUs into high, medium, and low sales categories.
  • By Profit Margin: Categorize based on profitability.
  • By Seasonality: Group products that are seasonal or have varying demand throughout the year.

What is a Good SKU-Level Data Format?

A good SKU format is vital for effective inventory management and sales tracking. Here are some tips for creating an effective SKU format:

  1. Consistency: Use the same format for all SKUs to avoid confusion.
  2. Simplicity: Keep the SKU format simple and easy to understand.
  3. Descriptive: Include relevant information such as product type, size, color, and other characteristics.
  4. Distinctive: Make sure each SKU is unique to avoid duplication and errors.

What are the Types of SKU-Level Data?

There are several types of SKUs, each serving a specific purpose in inventory management and sales tracking:

  • Style SKU: Identifies a product style.
  • Size SKU: Specifies the size of a product.
  • Configuration SKU: Refers to products with different configurations, such as sizes or colors.
  • Bundle SKU: Identifies a combination of multiple products.
  • Serialized SKU: Uniquely identifies each item within a product line.
  • Matrix SKU: Combines different characteristics of a product, such as size and color, into one SKU.

How to Track SKU-Level Data?

Tracking SKUs effectively involves several steps:

  • Use Inventory Management Software: Implement software that allows for real-time tracking of SKUs.
  • Regular Audits: Conduct regular inventory checks to ensure accuracy.
  • Barcode Scanning: Utilize barcode scanning technology to speed up the tracking process.
  • Data Integration: Integrate SKU data with other business systems for a comprehensive view.

What is SKU-Level Analysis?

SKU-level analysis involves examining detailed transactional data for individual products to gain insights into sales performance, inventory management, and customer preferences. This type of analysis helps businesses make data-driven decisions that can lead to increased efficiency and profitability.

What is SKU-Level Forecasting?

SKU-level forecasting involves predicting future sales and inventory needs for individual products based on historical SKU-level transaction data. This type of forecasting helps businesses prepare for demand fluctuations and maintain optimal inventory levels.

Frequently Asked Questions

How Can I Get SKU-Level Transaction Data?

You can get SKU-level transaction data through various delivery methods, depending on your needs. Historical data can be downloaded in bulk and delivered via an S3 bucket. For time-sensitive use cases, real-time data APIs, feeds, and streams are available to provide the most current information.

Where Can I Buy SKU-Level Transaction Data?

Data providers and vendors on platforms like Datarade offer SKU-level transaction data products and samples. Notable products include Granular SKU-Level Transaction Data by Measurable AI, SKU-Level Purchase Data by Consumer Edge, Consumer Transaction Data of FMCG Products by Snapbizz, and Grocery Retail Transaction Data by DecaData.

What Are Similar Data Types to SKU-Level Transaction Data?

SKU-level transaction data is similar to Consumer Transaction Data, Sales Transaction Data, and B2B Transaction Data. These data types are often used for pricing strategy and transaction data enrichment.

Eugenio Caterino

Eugenio Caterino

Editor & Data Industry Expert @ Datarade

Eugenio is an editor and data industry expert with over a decade of experience specializing in B2B data marketplaces and e-commerce platforms. He has a strong background in data analytics, data science, and data management. Eugenio is passionate about helping companies leverage data and technology to drive innovation and business growth, ensuring they can easily and efficiently access the solutions they need.

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