End-of-season clearance with AI: channels, timing and brand protection
AI can model markdown depth, timing and channel choice before a season ends, but clearance still depends on clean stock data, pricing rules and decisions about brand value.
KEY TAKEAWAYS Summary by the editors
- AI supports clearance by simulating markdown phases, depth and timing against sales forecasts and inventory risk, so teams can compare scenarios instead of relying on spreadsheets and judgement alone.
- A markdown tool recommends within the rules a business sets: floor prices, channel limits, and brand guardrails remain human decisions that the model cannot infer from sales data.
- Clearance channels differ in speed, margin and brand exposure: own stores and web shops, outlets, wholesale closeouts and off-price retailers each trade price recovery against brand protection.
- Under the EU Ecodesign for Sustainable Products Regulation, in force since 18 July 2024, destroying unsold textiles and footwear is banned and large companies must disclose annual information on discarded unsold products.
- The most useful first step is rarely the algorithm: it is a single, trusted view of stock by size, location and channel, plus an agreed markdown calendar.
AI helps with end-of-season clearance mainly by simulating many markdown paths quickly: it estimates how much stock each style will still sell at each price step, and flags the styles most at risk of being left over. It does not decide your brand strategy. Depth, timing and channel limits still have to be set by people, and the model works inside those limits.
This guide explains what AI changes in clearance, which channels exist for leftover stock, how to time markdowns, and how to protect the brand while doing it. It is written for merchandisers, planners and wholesale teams who must clear stock without eroding full-price sales.
What does AI actually do in end-of-season clearance?
A markdown tool for fashion typically combines a sales forecast, a view of current stock and a set of business rules. FashionUnited's 2025 report on the markdown assistant Markmi describes the pattern: recommendations draw on sales data and inventory risk, teams can simulate successive markdown phases, and rules and objectives can be configured by product category, collection type and region. The tool is presented as a replacement for spreadsheet analysis and gut feel.
The same report gives an idea of the scale of computation involved. For one retailer, G-Star EU, it says the tool handled markdowns for 1,866 products, running 14.5 million calculations across 21 scenarios in five days. These are figures published by the tool's developers, as are the headline claims of up to 10% higher sales and 5% better margins, and they should be treated as vendor claims rather than independent benchmarks. The same report lists C&A, G-Star, Zizzi and Torfs as retailers using the tool.
What the model adds is not a better instinct but breadth: it can test many combinations of depth and timing for thousands of styles, which a planning team cannot do by hand. What it cannot add is information that is missing. If stock by size is wrong, or if returns and in-transit goods are not visible, the recommendation will be precise and wrong.
Which channels can take clearance stock, and what do they cost the brand?
Leftover stock can be sold in several places, and each one has a different effect on price perception. The table below compares the common routes in qualitative terms. It deliberately avoids numbers, because recovery rates vary by brand, category and market.
| Channel | Speed | Price recovery | Brand exposure | Typical control |
|---|---|---|---|---|
| Own web shop and stores | Fast | Medium, depends on depth | Visible to core customers | Full control of timing and depth |
| Outlet stores | Medium | Medium | Contained, if separate from the main brand experience | Own assortment and pricing rules |
| Wholesale closeouts to retail partners | Fast, in bulk | Lower per unit | Depends on partner positioning | Contract terms and customer list |
| Off-price retailers | Fast, in bulk | Lower per unit | Weaker control over the context | Selection of buyers and categories |
| Donation, recycling or resale partners | Slow | Low or none | Can support brand values | Partner selection and documentation |
Off-price retail is an established route: chains such as TJ Maxx, Marshalls, Ross Dress for Less and Nordstrom Rack sell known brands at reduced prices, according to the Wikipedia overview of off-price retailers. For a brand, the trade-off is simple: bulk disposal is quick, but the brand gives up control over where and how its products appear next to full-price stock.
When should markdowns start, and how deep should they go?
Markdown timing is a balance between selling through before the season ends and not training customers to wait for discounts. A model can estimate sell-through under different start dates and depths, but the commercial rules come from the business. A sensible structure is a staged plan rather than a single deep cut.
- Set the target: sell-through by a date, margin floor per style group, or a cash target.
- Segment styles by risk: core, fashion and trend items behave differently, and so do sizes that remain only at the ends of the run.
- Plan phases: a first light step, a second step for slow movers, and a final step with defined channels.
- Define guardrails: minimum price, items that are never discounted, and markets where discounts are restricted.
- Review weekly with the planner, comparing the model's recommendation with what actually sold.
A staged plan also gives the model something to learn from. Each phase produces data on price response that is more useful than a single end-of-season cut. Where promotions run in parallel, their effects on each other should be modelled, which is covered in our explainer on promotion forecasting.
How do you protect the brand while clearing stock?
Brand protection is mostly a set of rules written down before the season starts, not an algorithm setting. Typical rules include: which collections may enter outlets, which customers may buy closeouts, whether labels may be removed, and how long a style must have been in full-price distribution before discounting. Wholesale-led brands in particular need clear terms for partners, because a partner's own markdown timing can undercut a brand's plan.
Macro conditions raise the stakes. The 2026 State of Fashion report by the Business of Fashion and McKinsey names tariffs as the top hurdle cited by executives, says 46% of executives expect conditions to worsen in 2026, and notes that 26% of fashion executives expect to raise prices by more than 5%. Higher input costs and cautious consumers make over-ordering more expensive, which puts more weight on a disciplined clearance plan.
What does the regulation say about unsold stock?
Clearance is no longer only a commercial question in Europe. According to the European Commission, the Ecodesign for Sustainable Products Regulation (ESPR), in force since 18 July 2024, introduces a ban on destroying unsold textiles and footwear, described as the first such measure in the EU. It also requires companies to disclose annual information on unsold consumer products on their website, including the number and weight of products discarded and the reasons. The disclosure duty applies to large companies and, eventually, to medium-sized companies. The Commission page does not set out every detail of scope, so legal teams should check the text and any implementing acts for their situation.
The practical consequence is that a clearance plan needs a documented route for every unit, including what happens to goods that do not sell even at the lowest price. That favours early decisions: the earlier a style is flagged as a clearance risk, the more channels remain open.
What are the limits and risks?
- Price response is estimated from the past, so a new colour or a changed customer mood can break the pattern.
- Vendor claims on sales and margin uplift are rarely independently audited, so run a controlled test on a subset of styles before a full rollout.
- Heavy automation can hide a planning problem: if clearance volumes grow every season, the root cause is usually buying, not markdown timing.
- Models optimise the target they are given, so a sell-through target can reduce margin and train customers to wait.
The sober conclusion is that AI makes clearance faster and more systematic, but it does not remove the need for a clear policy on brand, channels and legal duties. Teams that start with data quality and guardrails usually get more from the tool than those that start with the algorithm.
Frequently asked questions
How does AI help with markdown optimisation in fashion?
It combines a sales forecast, current stock and business rules to simulate markdown phases and compare outcomes. Published descriptions of tools such as Markmi say recommendations use sales data and inventory risk and can be configured by category, collection and region. Teams still set the rules and review the output.
When should a fashion brand start end-of-season markdowns?
There is no universal date. The right start depends on sell-through targets, risk by style group and the channel mix. A staged plan with a light first step, tracked weekly, usually protects price perception better than one deep cut late in the season.
Can EU companies destroy unsold clothing?
According to the European Commission, the Ecodesign for Sustainable Products Regulation introduces a ban on destroying unsold textiles and footwear, and requires large companies to disclose annual information on discarded unsold products. Companies should check the legal text and implementing acts for their own scope.
Which clearance channels protect the brand best?
Own channels and outlets give the most control over context, while wholesale closeouts and off-price retailers move volume faster with less control. The right mix depends on how much price recovery the brand needs against how much brand exposure it accepts.
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