Markmi, a pricing technology company founded in 2025, has emerged as a specialist in one of fashion retail’s most stubborn problem areas: markdowns. In the apparel industry, final-price decisions still rely heavily on spreadsheets, intuition, and locally improvised rules. Markmi has built an AI-driven platform that reframes markdowns and pricing as a predictive, profit-focused discipline. Its work with leading European fashion retailers such as C&A, Zizzi, Torfs, G-Star RAW, and others demonstrates not only technological sophistication, but also clear commercial impact.
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This combination of rigorous science, practical workflow design, and measurable financial uplift has led to Markmi’s recognition in the 2025 Just Style Excellence Awards with the Innovation award for Pricing Optimization and the Product Launches award for its Markdown Assistant.
Innovation in fashion pricing optimization
Markmi’s innovation lies in how it treats markdowns. Instead of viewing them purely as a cost to clear seasonal stock, the company positions them as a controllable profit lever. Its platform evaluates how different discount levels affect demand, revenue, margin, and residual inventory across the product lifecycle. This turns questions such as “what should we mark down, when, and by how much?” into a quantified optimization problem rather than a subjective debate.
The system is built specifically for fashion. Its models are trained on apparel data and reflect seasonality, short lifecycles, and multi-wave markdown strategies. It understands that a winter coat follows a different demand curve than a basics T-shirt and that phased reductions behave differently from a single deep cut. This allows retailers to move from fragmented, local rules of thumb to consistent, cross-market markdown strategies across countries, channels, and assortments.
Speed is another key advantage with Markmi. Traditional markdown planning can take days of manual analysis for large assortments and multiple discount waves. Markmi forecasts sales at each discount level and generates optimized recommendations, compressing decision time to minutes. That makes pricing a real-time lever in critical trading periods instead of a slow, once-per-season exercise locked into static spreadsheets.
The models are built for real-world retail conditions. Markmi reports forecast accuracy of up to 95% for sales projections up to 24 weeks ahead. Clients see single-digit percentage margin improvements during sales and clear P&L uplift on multi‑million‑euro businesses. These results, achieved in different markets and formats, suggest the system performs reliably beyond controlled pilots and test cases.
From gut feel to predictive profit systems
Markmi’s impact is as much organizational as it is technical. The platform embeds forecasting and optimization into a structured workflow, changing how merchandising teams work. Instead of checking thousands of rows in Excel, teams define objectives, such as maximize margin, clear stock, or balance both, and configure rules by category, region, or brand. The system then quantifies scenarios and proposes markdown sets that align with those objectives.
This reframes internal discussions. Pricing conversations shift from “what discount feels right?” to “which strategy best fits our targets and constraints?” Decision-makers can compare, for example, a margin-protective scenario versus an inventory‑clearance scenario and see the modeled impact on revenue, sell‑through, and leftover stock. With shared dashboards and common forecasts, pricing becomes more transparent and less vulnerable to ad hoc decisions.
Over time, this turns markdowns from a reactive, often chaotic process into part of a broader predictive profit system. Once markdowns are modeled in this way, the same approach can extend to promotions and full‑price management. Markmi’s roadmap points toward that direction: It aims to become a comprehensive pricing optimization platform for fashion, with markdown optimization as the first deployed capability.
AI-powered Markdown Assistant built for fashion
The Product Launches award recognizes Markmi’s Markdown Assistant, the operational product that delivers this capability to users. The Assistant replaces manual spreadsheet work with a guided workflow that mirrors how merchandising teams actually plan markdowns. It connects to ERP or POS systems, ingests historical sales, inventory, pricing, and product data, and applies fashion‑specific machine learning models to forecast sales at different discount levels for each item.
On top of this forecasting layer, the Assistant supports scenario simulation. Merchandisers can design alternative markdown strategies such as conservative versus aggressive discounting, or faster versus slower stock reduction and immediately compare their projected impact. This “what‑if” analysis lets teams align on a chosen path before prices change in stores or online, reducing the need for mid‑season course corrections driven by guesswork.
Crucially, the tool is not a black box. Users define the rules, including maximum discount thresholds, brand guardrails, regional nuances, operational limits related to reticketing, and channel‑specific constraints. The Assistant returns recommendations along with expected outcomes on margin, revenue, and remaining stock. Human decision-makers remain in control, but with a stronger analytical base and consistent logic.
Turning markdown planning into a repeatable, scalable process
Markdown decisions have traditionally been hard to standardize. Each season is influenced by different stakeholders, time pressures, and inventory challenges. Markmi’s Markdown Assistant turns this into a repeatable process: import data, forecast outcomes, simulate scenarios, choose the strategy, execute, and feed back results. This structure supports governance and makes it easier to explain and defend pricing decisions internally.
The same workflow also enables better omnichannel and multi‑region control. For fashion retailers running shared stock across physical stores and e‑commerce, consistent pricing is critical to customer perception and to inventory management. With a single analytical engine driving markdowns across channels and markets, the Assistant helps maintain price coherence while allowing for channel or regional tuning where justified by data.
The operational impact is significant. Retailers report cuts of up to 95% in time spent on markdown planning, freeing teams from manual data assembly and reconciliation. They can instead focus on interpreting scenarios, challenging assumptions, and aligning commercial, finance, and operations stakeholders. A continuous learning loop then uses real outcomes to refine future forecasts, improving accuracy and stability from season to season.
Financially, the product has shown clear returns. Markmi cites margin improvements during critical sales periods of up to around 7%, alongside the time savings and more controlled inventory run‑down. Better sell‑out planning also reduces overstock and markdown waste, supporting both profitability and sustainability objectives. This combination of speed, consistency, and measurable impact underpins Markmi’s recognition in this year’s Product Launches category.
Company Profile
Markmi is the AI-powered markdown assistant for fashion retail. Built specifically for the fashion industry over four years of direct collaboration with merchandising teams, Markmi helps retailers make faster, smarter, and more profitable markdown decisions. By replacing error-prone spreadsheets with fashion-specific AI, Markmi delivers measurable results: up to 7% margin improvements, up to 95% time savings, and up to 95% forecast accuracy (24 weeks ahead).
Based in Ghent, Belgium, with clients including C&A, Torfs, and Schuurman Schoenen, Markmi is expanding across Europe. Learn more at markmi.ai.
Contact Details
For Media Inquiries, contact Nils Roelandt, Head of Marketing & Communications
E-mail: nils@markmi.ai
Tel: +32 485 136 038
For General Inquiries, email hello@markmi.ai
Links
Website: https://markmi.ai/
LinkedIn: https://www.linkedin.com/company/markmi/
Instagram: https://www.instagram.com/markmi.ai/
