Fashion merchandisers have long relied on familiar heuristics to guide their markdown decisions: don't discount too early, preserve margin first, then clear inventory later. These rules feel safe and proven. But our new analysis reveals they come at a hidden cost. While rule-based approaches may hit sell-through targets, they systematically over-discount products that could have sold with smaller markdowns, leaving significant value on the table in today's competitive fashion retail environment.
You're a merchandiser preparing for markdown season. Full-price sales in several key categories are slowing, and pressure is mounting to hit sell-through while protecting margin.
Do you motivate your team to stay calm: "Don't go too deep too early; hold back discounts at first, then ramp them up later to clear stock."
Or do the numbers start to nag at your gut? Pushing you toward bigger markdowns, faster?
That tension, between holding your ground and reacting fast, is what makes markdowns so challenging. You're constantly walking a tightrope between selling now at a lower price or holding out for higher margins and risking leftover stock.
Every decision feels like a trade-off. Go too shallow, and you're stuck with racks of unsold inventory. Go too deep, and you hit your targets but eat into profit.
And just when you think you've found the right balance, something shifts: a drop in demand, a competitor's flash sale, a last-minute push from marketing.
Yet the tools to make these critical decisions haven't kept pace with the complexity. Most merchandising teams still rely on spreadsheets, piecing together fragmented data from multiple systems, manually calculating what-if scenarios, and debating the numbers in endless email threads.
Without clear foresight into how different markdown levels will actually perform, teams struggle to quantify the trade-offs. Will a 30% discount clear enough stock to justify the margin hit? Should you go deeper in one region and hold back in another?
These aren't just operational questions. They're strategic decisions that shape profitability. But when merchandisers sit down with leadership to make the call, they often lack the hard numbers needed to substantiate their recommendations. The conversation becomes based on gut feel and past experience rather than concrete projections of sales uplift, margin impact, and inventory outcomes across different scenarios.
It's no wonder many merchandisers turn to rules of thumb, familiar shortcuts that make tough calls feel faster and safer, even if they come at a cost later.
When time is short and the stakes are high, rules of thumb feel like a lifesaver. They're simple, familiar, and easy to explain to the team. After all, no one wants to risk being the one who "went too deep too soon." Most merchandisers have built their playbook over years, learning from experience, intuition, and what's worked before.
These instincts often translate into heuristic business rules, simple frameworks for deciding on markdown levels. They offer a sense of order in the chaos.
But here's the problem: over time, you will for instance start grouping products together, assuming each group will behave the same when discounted. A certain category, a price band, or even a color range becomes a "type" that gets treated by the same rule.
The reality is more complex. Every single item in your catalog follows its own rhythm. Some behave exactly as experience predicts. Others don't. They move faster or slower than expected, react differently to discounts, or get influenced by subtle shifts in weather, availability, or online visibility.
And that's where margin slips away, not because the logic was wrong, but because the world changed faster than your rules could keep up.
Sure, rule-of-thumb markdowns may get the job done, but they steadily erode margin.
The optimal markdown strategy requires product-level data in combination with the appropriate data intelligence, not a one-size-fits-all approach. Each product behaves differently, and effective discounting needs to consider far more than sales rotation or weeks of cover. Seasonality, performance history, inventory levels, price elasticity, expected uplift, and the residual value of unsold stock all play a role.
When these factors aren't considered, rule-based approaches still deliver sell-through targets but at a hidden cost. Products that might have sold with smaller discounts are often over-markdowned, reducing profitability without improving results. The outcome looks fine on paper (strong sales uplift), yet discounts were used inefficiently, leaving value on the table.
Our analysis shows margins improve by 3-7% when moving from rule-based to optimized approaches. An optimized markdown strategy, one that captures each product's unique behavior, achieves higher turnover uplift from the same overall discount level. Sticking to simple, rule-based strategies leaves significant turnover unrealized, a serious loss in today's highly competitive market.
Merchandising teams often celebrate hitting sell-through goals. But what's often overlooked is that the same result could have been achieved with better financial outcomes.
The key is precision: targeting the right products with the right discounts. Retailers that do this achieve stronger margins while maintaining the same markdown intensity, proving that smarter, data-driven markdowns don't just sell more, they sell better.
The fundamental challenge isn't that merchandisers lack experience. It's that the volume and velocity of decisions now exceeds what any team can process manually. When you're managing thousands of items across multiple channels and regions, each reacting differently to discounts, the margin impact of small miscalculations compounds fast.
The patterns that once felt predictable now shift weekly across categories, channels, and regions. What used to be safe is now leaving money on the table. In an environment this dynamic, experience alone isn't enough. Merchandising teams need tools that can sense, learn, and adapt faster than the market.
A new generation of AI-enabled price management platforms has emerged to meet this challenge. General retail pricing platforms like Invent Analytics and PricingHub have proven AI's value in retail optimization. But fashion demands something different: tools that understand seasonality, newness, product lifecycle dynamics, and the specific challenges that make fashion unique.
That's where a tool like Markmi comes in. Built specifically as the markdown management tool for fashion retail, Markmi applies AI intelligence tailored to the unique rhythms of fashion: seasonal lifecycles, each product's role within the assortment, markdowns in various phases, scenario analysis, etc.
Markmi gives merchandisers something even more valuable than recommendations: confidence. By simulating scenarios across your full assortment (testing margin protection against aggressive clearance, or regional variations against unified strategy), teams can see exactly what each path delivers before committing. The platform provides highly accurate predictions of sales uplift and profit impact for each scenario, then translates those insights into actionable, item-level recommendations.
Instead of second-guessing which path will perform best, teams know and therefore act with confidence, finally leaving those rules of thumb behind.