EoS pricing: why a solid pricing plan is not enough

Over the past year, we’ve seen the same pattern among fashion brands . Seasonal planning - specifically for End-of-Season (EoS) sales - looks solid, until execution begins.

On paper, everything aligns. But a week before launch, reality intervenes. Legal questions whether the “50% off” headline is compliant. Merchandising sees that stock will not clear evenly across categories. The Board asks for an additional 5% revenue uplift without reducing margin.

Under this kind of deadline pressure, optimisation quickly turns into compromise. Static plans in a moving market

The structural issue is that plans are built at a fixed moment in time, while the market continues to shift. Between planning and launch:

• • A competitor may start discounting earlier.
• • Weather patterns may alter category demand.
• • Inventory distribution may change due to logistics constraints.

When these assumptions change, the original plan loses validity. In many organisations, recalculating a single alternative scenario across hundreds or thousands of SKUs can take hours or days in Excel. That recalculation window limits flexibility. As a result, teams move forward with the most defensible option rather than the most optimal one. The constraint is not strategy, but rather speed. The operational cost of "good enough"

This execution gap has measurable consequences. When teams are forced to make late-stage adjustments:

• • Margin erosion increases because deeper markdowns are applied broadly rather than selectively.
• • Excess inventory carries over into the next season, tying up capital and warehouse space.
• • Discount architecture becomes inconsistent across channels.
• • Internal alignment weakens as Finance, Merchandising, and Legal defend different priorities. Most decisions are not irrational; they are constrained by time and recalculation capacity. In these moments, “good enough” becomes the only viable option.

How speed improves EoS pricing decisions

When testing no longer takes days but minutes, the decision dynamic changes. AI-driven simulation removes this bottleneck. Instead of recalculating one markdown path at a time, teams can model multiple scenarios simultaneously using historical sales data, current inventory, and estimated price elasticity.

• • Board targets are translated into quantitative constraints.
• • Margin floors are embedded as rules.
• • Category priorities are explicitly modelled.

The value is not automation itself; it is the ability to evaluate trade-offs quickly. If projected demand drops or legal constraints change, alternative paths can be tested in minutes. The discussion shifts from debating assumptions to comparing quantified outcomes.

Most decisions are not irrational; they are constrained by time and recalculation capacity.

Results from AI-driven pricing simulation

In a recent pilot with a European fashion retailer, markdown planning moved from manual Excel recalculations to rapid scenario simulation.

The team reduced recalculation cycles from roughly two days to minutes. Instead of planning each markdown wave separately, the entire sales period was evaluated as an integrated strategy. Within three months, revenue increased by 8% and margin improved by 2 percentage points compared to the previous approach. More importantly, internal discussions became structured around scenario comparison rather than opinion. The model expanded the team's ability to test alternatives before committing.

Experience it yourself: The Pricing Game

The Pricing Game is a simulated EoS safe space where Merchandising, Pricing, and Finance teams can test scenarios without risk.

You step into the decision process of a live campaign. When one assumption changes (e.g. demand, discount depth, or legal constraints), you immediately see the financial impact. It is about experiencing how different the discussion becomes when recalculation takes minutes instead of days.

Try the Pricing Game now.