Retail markdown strategy is broken — here’s how to fix it

The traditional markdown playbook is outdated and overexposed

Retailers have long relied on a calendar-based markdown structure:

• XX% off at six weeks
• A further 20% off if the stock hasn’t moved by week eight
• Final clearance before the new range lands

Sound familiar? These static triggers are comforting, yet predictable and wildly inefficient.

This traditional markdown method assumes all products perform the same, all channels behave identically and demand patterns are linear. The reality? They aren’t, they don’t and it’s not. As a result, retailers are often stuck between two dangerous extremes:

• Markdowns applied too early, eroding profit on products that would have sold through at full price with just a little more time or visibility
• Markdowns applied too late, leaving aging stock to gather dust in the warehouse, requiring even deeper discounts to shift, if it shifts at all

Both paths lead to margin erosion, inventory write-offs and poor customer price perception. But worst of all, they create a cycle of overbuying and reactive pricing. And this is a dangerous loop many retailers are struggling to escape from.

Four reasons retail markdown strategy is broken

1. Decisions are based on lagging indicators

Most markdown decisions today are made using reports pulled weekly or even monthly. By the time a trend is spotted — like slow-moving stock, competitor activity or channel underperformance — the window to act profitably has often closed.

Retail needs real-time visibility in order to move from reactive to responsive.

2. Blanket, one-size-fits-all approaches

Most markdowns are still being applied en masse, ignoring key differentiators such as:

• Channel elasticity (online vs. in-store)
  
• Product attributes (seasonal, replenishable, premium)
  
• Store size and profile
  
• Region- or climate-specific demand
  

Treating all inventory equally is like treating all customers the same; it ignores the nuance that drives value.

3. Organizational silos prevent coordinated action

Buying, merchandising, pricing and supply chain teams often operate in disconnected silos, rarely relaying key information to one another. This means that markdowns become last-minute decisions made in isolation, and aren’t always aligned to broader commercial objectives like customer lifetime value or stock productivity.

4. Markdowns are too manual

Many pricing and merchandising teams still run markdown processes manually. Spreadsheet gymnastics, version control chaos, pricing decks, approval chains. It’s exhausting, error-prone and slow.

More importantly, it eats into strategic thinking time. Teams are caught firefighting, unable to step back and ask: Why did we get into this position in the first place? How can we do better next season?

So, what should a modern markdown strategy look like?

Retailers who are winning today are approaching markdowns differently. They treat them not as a desperate last act but as a strategic profit lever. Here’s what that involves:

Data-rich decision making

Modern markdown strategies leverage real-time data: sales velocity, traffic trends, competitor pricing, inventory age, weather forecasts — even macroeconomic indicators or things like US tariff adjustments.

And this data doesn’t just describe performance, but predicts it. With the right systems, it can prescribe the best actions to take, too.

Product-level elasticity modeling

Rather than assuming an arbitrary 30% or 50% discount will move stock, artificial intelligence (AI) models can estimate how much a specific product needs to be discounted to trigger a meaningful increase in demand — and how that discount will impact gross margin.

This allows retailers to identify the optimal discount that clears stock and protects profitability.

Guardrails and scenarios

Modern systems allow merchants to set business-specific guardrails, like minimum margin thresholds, price floors or even brand guidelines.

From there, you can simulate multiple markdown options, ranked from most profitable to most aggressive, allowing decision makers to choose the best scenario with full visibility of the potential trade-offs.

Optimizing markdown strategy at scale with AI

AI doesn’t just have the potential to automate markdowns, but completely transform them. Here’s how:

Continuous learning

AI algorithms ingest performance data daily (sometimes even hourly), adjusting their understanding of product velocity, seasonal decay curves and demand shifts in real time. This enables dynamic markdowns that are not rigid, but responsive.

Price elasticity curve visualization

AI solutions can now surface elasticity curves for each product, allowing retailers to see how much demand is unlocked at each potential price point. This provides clarity on whether to push for margin or sales volume.

Pattern recognition across SKUs

AI can identify markdown opportunities across thousands of SKUs that no human could spot, whether that’s dead stock hidden in a long tail or a halo effect that suggests keeping a key SKU full price longer.

Strategy embedded into workflow

The best AI pricing tools embed recommendations directly into the workflow of buyers, merchandisers and traders — making it fast and easy to act on insights without switching between systems or hunting through reports.

AI for markdown optimization: the results speak for themselves

From the AI-driven projects I’ve delivered at Peak, we’ve delivered some game-changing results for some of the world’s leading retail brands. Some of the typical stats we usually see include:

• Gross margin uplift of 200–500bps on markdown sales
• Reduction in stock write-offs by 10–20%
• Time savings of 30–40% for pricing and merchandising teams
• Higher sell-through at shallower discounts
• Better price perception by customers

In a world of tight budgets and fragile consumer confidence, this isn’t just nice to have, but a matter of retail survival.

Ready to get going? Start with the problem, not the tech

This isn’t a pitch for selling software. It’s a call for merchandisers to rethink how you approach margin recovery.

The retailers who’ll thrive over the next five years won’t just be the ones who “adopt AI” — they’ll be the ones who challenge legacy processes, rebuild their commercial decision making with data, and give their teams tools to trade smarter, faster and with more confidence.

It starts with a simple question:

Is your markdown strategy really working for you? If the answer’s no, you know what to do.