The AI markdown playbook: five levers to unlock hidden profit in your inventory

The five levers of AI-powered markdown optimization

Each lever solves a specific problem. Together, they move a retailer from reactive firefighting to proactive margin management.

1. Timing: Act sooner, not deeper

Old reality: Retailers wait too long to mark products down, then are forced to go deeper than needed to shift inventory.

With AI: AI models continuously monitor sell-through velocity, inventory cover, forecast variance, and external factors (e.g. weather, price movements, demand signals). When a product shows early signs of underperformance, the system triggers a recommendation before it becomes a problem.

Result: Smaller, earlier markdowns that protect margin and reduce the need for end-of-season clearance. Think of it as breaking the emergency markdown cycle before it starts.

2. Depth: Use data to set the right discount

Old reality: Blanket 30%, 40%, or 50% markdowns are chosen based on precedent or guesswork.

With AI: Using historical data and real-time demand signals, AI estimates the price elasticity for each product. It then simulates different discount levels and their likely impact on units sold, margin recovered, and stock remaining.

Instead of guessing, you get options like:

• 10% markdown → clears 40%, retains 38% margin
• 20% markdown → clears 75%, retains 32% margin
  
• 30% markdown → clears 90%, but drops to 20% margin

Result: You choose the trade-off that fits your objective, with clarity, not uncertainty. No more “gut-feel” discounting.

3. Segmentation: Stop treating all products the same

Old reality: Blanket markdowns are applied across categories or entire seasons.

With AI: Smart pricing tools segment products based on their role, seasonality, elasticity, and performance. Each segment gets its own guardrails and strategy:

• Protect margin on bestsellers
  
• Push volume on low-elasticity stock
• Clear through long-tail SKUs or obsolete sizes

Result: More personalized pricing decisions that reflect product role, not product location on a planner’s spreadsheet.

4. Simulation: Model before you move

Old reality: Retailers launch promotions or markdowns with limited visibility of what impact they’ll have.

With AI: Before any markdown goes live, AI engines simulate potential outcomes based on live data:

• What will sell-through look like?
• What’s the profit recovered at each discount tier?
• What are the risks of going too early, or too late?

Result: This new approach means that trading and pricing teams don’t just make faster decisions, but confident ones. Fewer surprises,  better post-campaign reviews, and stronger pricing governance. Now, every markdown becomes an intentional move — not a reactive one.

5. Learning: Get smarter over time

Old reality: Each season starts with a blank slate. No structured way to learn from past markdowns.

With AI: Every pricing decision becomes data for the model to learn from. Over time, the system improves:

• Better elasticity estimates
• Tighter forecasting
• Improved store/channel segmentation
• Faster flagging of slow-moving stock

Result: Your markdown strategy becomes self-improving, and even your misses become assets. Your team gets smarter every week, because the system does, too.

Building the playbook into your business

This isn’t a theory, it’s a toolkit. And it’s one that lean teams can embed without hiring armies of data scientists or replacing every legacy system. Here’s a lightweight rollout roadmap for integrating this playbook in the next 90–180 days:

0-30 days: Diagnose

Review markdown decisions from the last two seasons. Where did you go too deep? Where did you wait too long?

30-60 days: Pilot and segment

Choose one or two high-volume categories. Segment SKUs and assign markdown strategies (clear, protect, optimize). Begin testing AI-led simulations.

60-120 days: Operationalize

Embed markdown dashboards and scenario tools into trading meetings. Replace static calendars with guardrail-driven triggers.

120-180 days: Expand and automate

Extend AI recommendations across more categories and integrate into broader promo planning. Build learning loops to inform next season’s strategy.

The key is to start with the decision, not the dashboard. Focus firstly on how your team trades. From here, you can build the system that empowers faster, smarter choices that are going to be of value.

How much value are we talking about?

Based on the AI-powered pricing projects we’ve delivered at Peak, anything is possible. Here’s a quick look at some of the standout results that we’ve achieved for some of our retail customers:

On top of these kind of commercial statistics, let’s not forget other benefits — such as tighter price perception, better customer trust and generally improved full-price sell-through.

For a $100m category, even a 1% margin gain is $1m in recovered profit. Multiply that across multiple categories, and you’re looking at a fundamentally stronger, leaner business.

Final thought: profit lives in the detail

The next five years in retail won’t be about who has the loudest promotions or the flashiest campaign calendar.

• They’ll be won by the retailers who:
• Understand their inventory better
• Know how to price for behavior, not just clearance
• Use AI not to replace decision makers, but to superpower them

Markdowns will always be part of the game. But with the right tools, they no longer have to feel like defeat.

They can be a source of precision, aglity, and — most importantly — profit. Markdowns will always be part of the game. But with the right tools, they no longer have to feel like defeat.
They can be a source of precision. Agility. And—most importantly—profit.