Structured Data: The Foundation for Effective AI in Retail
September 5, 2025
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The plug and play retail analytics data warehouse and reporting platform.
The plug and play retail analytics data warehouse and reporting platform.
For retailers managing seasonal products, particularly in fashion, home, and leisure, an effective markdown strategy is essential. These products have limited shelf lives, and timing is critical to maximise margin and minimise inventory risk.
Yet in many cases, markdowns are handled reactively. Discounts are introduced too late, applied too aggressively, or executed without a clear framework. This often leads to reduced margins, surplus stock, and poor sell-through.
The good news? A structured, data-driven markdown process is not only achievable, it’s scalable and repeatable.
Step 1: Define Sell-Through Benchmarks
Before implementing any markdown activity, establish clear, category-specific benchmarks that define success. These benchmarks should be based on:
These benchmarks serve as a reference point, enabling teams to assess early performance and take corrective action when necessary.
Step 2: Calculate Markdown Uplift Factors
Historical sales data can be used to estimate the expected uplift at various discount levels. This provides clarity on how pricing adjustments influence demand—critical for deciding how deep a markdown needs to be.
Break this into clear discount bands by category:
Markdown % | ||||||
Markdown Uplift Factor | up to 20% | up to 30% | up to 40% | up to 50% | up to 60% | up to 70% |
Dresses – Spring Summer | 50% | 60% | 65% | 90% | 120% | 150% |
Dresses – Aus Winter | 40% | 55% | 60% | 75% | 90% | 105% |
Trousers | 35% | 50% | 55% | 65% | 70% | 90% |
Shorts- Spring summer | 60% | 75% | 80% | 110% | 150% | 170% |
If your team needs a starting point for uplift assumptions, The Retail Score can assist by drawing on past data across comparable categories.
Step 3: Establish a Markdown Review Calendar
Create a markdown cycle for each product category, aligned with the natural lifecycle of the product. A well-structured cadence typically includes at least three key checkpoints:
Step 4: Conduct the Initial Demand Review
Around weeks 4–6, assess performance against the benchmarks. Classify products accordingly:
💡 Acting early helps preserve margin and can reduce the need for aggressive markdowns later 💡
Step 5: Execute First Markdown Checkpoint
Now it’s time to determine what sales uplift is needed to reach your sell-through goal. Use this formula:
Target Uplift = Current Weeks of Stock / Remaining Weeks in Lifecycle
EXAMPLE:
Then, consult your uplift table to determine the appropriate markdown percentage to achieve this goal.
Step 6: Execute Final Markdown Review
As the product approaches the end of its lifecycle, repeat the process. If the initial markdown hasn’t delivered sufficient results, reassess uplift needs and consider a deeper markdown, based on updated performance data.
To operationalise this strategy:
To implement this process effectively, you’ll need detailed, historical data—ideally covering at least two years—at the item/store level:
⚠️ Promotional activity must be tracked separately from markdowns for accurate analysis ⚠️
You’ll also need to:
If you’re working with limited reporting tools (or still relying on spreadsheets) The Retail Score can help.
We consolidate and structure your sales, stock, and discount data so you can:
Let us help you turn markdowns from a reactive process into a strategic lever for protecting margin and improving sell-through.
