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What is SKU Level Data? [2024 Update]

2 Minutes

In this article, we will cover what SKU-level data is and a framework to effectively manage your sku level data to improve your business operations and decision-making. 

sku level data

What is SKU Level Data?

SKU-level data refers to detailed information about individual Stock Keeping Units (SKUs) within a product inventory. It typically includes unique identifiers, product attributes, pricing, and inventory quantities.


Here are examples of SKU-level data: 

Inventory Levels: How many units of the SKU are in stock both in a central warehouse and perhaps in retail locations. 

Sales Data: How many units of the SKU were sold during a specific time period and the revenue generated from those sales.

Price: Current pricing of the SKU. This might also include historical pricing or promotional pricing.

Product Attributes: Specifics about the product such as its color, size, weight, material, and other characteristics.

Location Data: Where the SKU is stored or displayed in retail locations.

Supply Chain Information: Lead times, supplier information, restocking levels, etc.

Return or Defect Rates: How often this SKU is returned or found to be defective.

Promotions: Data about any promotions or discounts that were applied to the SKU.

Customer Reviews: Feedback or ratings from customers specifically about this SKU.

sku-level data

Framework to Effectively Use SKU-Level Data

Below we have provided our custom framework to help you harness the power of SKU-level data:

1. Data Collection & Standardization: 

Gather all SKU-level data from various sources. Ensure data is clean, consistent, and in a standardized format.

Sources: Point of Sale, e-commerce, inventory systems.

Tasks: Data cleaning and normalization.

2. Segment & Analyze: 

Group SKUs for a more structured view. This aids in identifying trends, anomalies, or patterns.

Criteria: Product categories, regions, time frames.

3. Performance Metrics: 

Evaluate how each SKU is performing. Determine top sellers, slow movers, and profitability.

Metrics: Sales volume, inventory turnover, margins.

4. Operational Efficiency: 

Use SKU data to optimize operations. This includes inventory management, store layout, and supply chain.

Goals: Reduce overstocks, optimize store placement, streamline supply.

5. Customer Insights: 

Delve into customer behavior and feedback for each SKU. Understand purchase patterns and product feedback.

Data points: Purchase combinations, reviews, return rates.

6. Forecasting & Predictions: 

Use past data to anticipate future SKU performance. This aids in better inventory and marketing decisions.

Techniques: Demand forecasting, price optimization.

7. Strategic Action: 

Make informed decisions based on SKU data insights. Introduce, promote, or discontinue products as needed.

Decisions: Product lifecycle, inventory procurement, targeted promotions.

8. Continuous Monitoring: 

Regularly track SKU-level metrics and adjust strategies as needed. Utilize dashboards for real-time data visibility.

Tools: Business Intelligence platforms, real-time dashboards.

what is sku level data


Let’s apply our framework to the company, “FashionForward” which aims to address high return rates for dresses, optimize inventory management, and enhance profitability in this product category. 

1. Data Collection & Standardization:

FashionForward gathers SKU-level data from their point of sale system, e-commerce platform, and inventory systems. They ensure the data includes product names, descriptions, pricing, SKU codes, and return data for dresses.

2. Segment & Analyze:

They segment SKU data by product categories (e.g., dresses, shoes, handbags), regions (e.g., North America, Europe), and time frames (e.g., monthly, quarterly). For dresses, they look into specific styles and sizes.

3. Performance Metrics:

They evaluate dress SKU performance, focusing on return rates, sales volume, inventory turnover, and margins for different dress styles and sizes. For example:

  • SKU: Dress_XYZ (Size M, Style A)
  • Return Rate (last quarter): 15%
  • Sales Volume (last quarter): 200 units
  • Inventory Turnover (last month): 4 times
  • Profit Margin: 30%

4. Operational Efficiency:

FashionForward uses SKU data to optimize dress inventory management. They identify that a particular style and size combination (e.g., Dress_XYZ, Size M, Style A) has a high return rate. To reduce returns, they decide to stock fewer units of this SKU and focus on styles and sizes with lower return rates.

5. Customer Insights:

They analyze customer behavior and feedback for dresses. They find that customers often return Dress_XYZ (Size M, Style A) due to sizing issues. They also note that customers provide feedback on the dress's length being shorter than expected.

6. Forecasting & Predictions:

FashionForward uses past SKU-level data to forecast future dress SKU performance. They predict that if they adjust the length of Dress_XYZ (Size M, Style A) based on customer feedback, it may reduce returns and increase sales.

7. Strategic Action:

Based on insights from SKU data, FashionForward decides to take the following actions:

  • Adjust the design of Dress_XYZ (Size M, Style A) to address the sizing and length issues mentioned in customer feedback.
  • Promote dresses with lower return rates to encourage sales.
  • Continue monitoring and possibly discontinue Dress_XYZ (Size M, Style A) if returns do not improve.

8. Continuous Monitoring:

FashionForward regularly monitors SKU-level metrics using business intelligence platforms and real-time dashboards. They track the impact of their actions on return rates, sales, and profitability for dress SKUs.

We hope that you now have a better understanding of what SKU-level data is and how to effectively use it to enhance inventory management and your business decisions.

If you enjoyed this article, you might also like our article on SKU-level or SKU count.

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