Markdown: Optimize Your Markdowns and Speed Up Inventory Turnover

Artificial intelligence transforms markdown from an empirical decision into a predictive one: by cross-referencing sales history, inventory levels, seasonality, prices, and purchasing behavior, it estimates the actual impact of each markdown level before applying it, and identifies the timing and discount rate that maximize sales without sacrificing more margin than necessary.

Specifically, BOOPER MPS combines this predictive analysis with an operational search engine: the models do more than just observe past trends; they simulate multiple reduction scenarios (in terms of time and intensity) and project their impact on demand, remaining inventory, and margin— product by product , and store by store.

This approach corrects the common pitfalls of manual markdowns: markdowns applied too late, which tie up cash in inventory, or too early, which erode margins on products that would have sold at full price. The AI also refines its recommendations based on actual sales data observed after each round of markdowns, allowing the strategy to be adjusted mid-season rather than being bound by a rigid plan.

For a retailer, this predictive approach to markdowns has a direct impact on end-of-season profitability and inventory turnover: less leftover excess inventory, fewer last-minute markdowns decided in a rush, and better control over pricing strategy, since discounts are based on data rather than a generic schedule.

Markdown optimization software, also known as markdown software, bases each discount on its simulated effect on demand, inventory, and margin, rather than on a fixed percentage applied to an entire category. This is what the markdown module offers in BOOPER MPS, the platform from BOOPER, a French pricing software provider since 2014.

In practice, the solution identifies at-risk inventory, simulates several reduction scenarios (in terms of both duration and intensity), recommends gradual reductions on an item-by-item and store-by-store basis, and then evaluates each campaign to refine the next one. Your teams retain control over business rules, such as minimum margin thresholds and sales constraints.

When comparing solutions, three criteria are key: the level of detail in the calculations (by product and store, not just by category), the ability to run simulations before making a decision, and the integration with sales forecasts and already-planned promotions. Learn why you should consolidate pricing, promotions, and markdowns into a single platform.

BOOPER MPS relies primarily on five categories of data to optimize the " markdown ": sales history, inventory levels, current prices, promotional schedules, and store data (location, format, customer demographics). External factors such as the weather or competitors' prices may also be factored in to further refine the recommendations.

Sales history is the most critical data: it allows the model to learn the specific sales velocity of each product and each retail location, and thus to distinguish between an item that simply needs time and one that is truly at the end of its commercial life. When cross-referenced with inventory levels, it determines the relative urgency of each markdown decision. Promotional schedules, meanwhile, prevent a markdown from cannibalizing a sales promotion already planned for the same period.

BOOPER doesn’t require a perfect data foundation to get started: a Data Loader adapts to existing data flows (ERP exports, flat files, databases, APIs), allowing you to connect to already available sources without waiting for an IT system overhaul. Recommendations become more accurate as the historical data set grows, but the platform already produces actionable results using basic sales and inventory data.

For a pricing or category management team, the quality of the input data remains the key factor in ensuring the reliability of recommendations: the more seasons and promotional cycles the sales history covers, the better the model learns to anticipate atypical behaviors (end-of-season sales, competitor stockouts, weather-related fluctuations) rather than simply extending a past trend.

Manual markdowns are based on generic rules (for example, “-30% after 6 weeks without sufficient turnover”) and the teams’ intuition, applied relatively uniformly across an entire category. AI-driven markdowns, on the other hand, rely on predictive models that simulate the actual impact of each discount level— product by product , and store by store—before implementing it.

The difference lies primarily in the level of detail and foresight. A generic rule treats an item that is still selling well the same way as an item at the end of its commercial life in the same store, since it is impossible to analyze thousands of SKUs individually. The AI-driven approach, on the other hand, evaluates each product’s unique sales dynamics, inventory level, and seasonality to recommend a specific discount rate and timing.

The other difference lies in the correction loop: manual markdowns are rarely reassessed once decided, whereas AI-driven management readjusts its recommendations based on actual sales observed after each round of markdowns, allowing a course to be corrected before it becomes costly. The two approaches can coexist: teams retain control over business rules (minimum margin thresholds, sales constraints) while allowing the predictive engine to refine the optimal level within that framework.

For a multi-store retailer, this shift from manual markdowns to predictive markdowns primarily changes the scale at which detailed decisions can be made: it becomes possible to customize thousands of markdown decisions without increasing the teams’ workload, which has a direct impact on the overall margin retained at the end of the season.

Yes. BOOPER MPS identifies, for each product, the time frame during which a price reduction has the greatest impact on demand, in order to avoid two common pitfalls: a price reduction launched too early, which sacrifices margin on sales that would have occurred at full price, and a price reduction launched too late, which is no longer sufficient to clear inventory before the end of the season.

This timing is based on a cross-analysis of several indicators: the actual sales velocity of the product since its launch, the remaining inventory level relative to its expected shelf life, the seasonality of the category, and, when available, competitive data. The predictive engine simulates the likely evolution of demand week by week and triggers a recommendation as soon as the optimal trigger threshold is reached.

This proactive approach also avoids the classic “bottleneck” effect, where several product families reach the end of their commercial life at the same time and are marked down simultaneously—a situation that dilutes customer attention and increases pressure on the period’s overall margin. By spreading out the markdowns according to each product’s actual lifecycle, the platform smooths out the markdown workload over time.

For pricing and purchasing teams, this ability to anticipate the right moment transforms the “ markdown ” from a reactive decision—often made under pressure at the end of the season—into a continuous management process, with a direct impact on margin preservation and inventory turnover, two metrics closely monitored by every sales department.

By offering discounts earlier rather than deeper: slow-moving inventory identified early sells off at moderate discount levels, whereas a discount offered late in the season must be much deeper to move the same amount of inventory.

The schedule is developed product by product based on actual sales velocity since launch, remaining inventory relative to expected shelf life, and the seasonality of the category. The first phase is triggered as soon as the gap from the target widens, and the discount is then adjusted based on observed sales, store by store. For an item that is significantly behind schedule, transferring it to stores that sell it better may be less expensive than offering an additional discount.

Truly dormant inventory requires a separate decision: either selling it to targeted customer segments or finding a supplier solution. See our schedule and markdown depth chart.

Avoiding across-the-board markdowns requires calculating each discount at the most granular level (SKU, store, or even region) rather than applying a uniform discount rate to an entire category. BOOPER MPS determines the appropriate discount level based on the actual sales potential of each SKU, ensuring that discounts are applied only where necessary.

Specifically, the platform combines artificial intelligence and operational research to simulate—before implementation—the effect of a given markdown level on inventory turnover, store by store. A product that is still selling well at one retail location does not need the same discount as the same item nearing the end of its life cycle elsewhere: a uniform markdown, calculated based on a national average, ignores these differences and sacrifices margin on items that did not need it.

This level of granularity draws on sales history, remaining inventory levels, seasonality, and—when available—competitive data to suggest product by product the discount rate that maximizes sales without unnecessarily eroding margins. Teams retain control over the scenarios and can adjust the parameters based on their current priorities (margin, turnover, or price image).

For a multi-store retailer, the stakes go beyond line-by-line savings: more precise markdowns prevent the cumulative effect of thousands of unjustified discounts across an entire network, protect the overall margin, and limit the need for deep discounts decided in a rush at the end of the season due to a lack of sufficient foresight.

An AI-powered Markdown optimization solution delivers a rapid return on investment by addressing three directly measurable factors: reducing excessive markdowns, improving inventory turnover, and cutting the time teams spend on manual markdown decisions, product by product.

The first lever (fewer excessive markdowns) directly translates into preserved margins: every percentage point of discount avoided on a product that would have sold at full price (or with a smaller discount) remains as retained margin. The second lever, inventory turnover, reduces capital tied up at the end of the season and limits the need for last-minute deep discounts. The third lever frees up time for the pricing and category management teams, who were previously tied up in low-value-added “ product by product ” trade-offs.

These gains typically become apparent as early as the first markdown campaigns managed by the platform, since the mechanism does not require an organizational overhaul: BOOPER integrates into the existing markdown schedule and gradually refines its recommendations as actual sales confirm or adjust the forecasts.

However, the actual ROI depends on the volume of SKUs involved, the maturity of the available historical data, and the frequency of each retailer’s markdown campaigns: a retail chain with several sales periods per year and a rich sales history will see a faster return on this predictive approach than an organization with limited historical data.

Yes. BOOPER MPS is designed for multi-country, multi-store, and multi-category networks, with centralized markdown management at headquarters while allowing for the flexibility needed to accommodate the specific local conditions of each market or point of sale.

In practice, this means that markdown rules (minimum margin thresholds, sale schedules, and country-specific regulatory requirements) are defined and managed centrally, while the calculation of discount levels remains granular—store by store and category by category—to account for differences in sales velocity from one region to another.

This architecture is particularly well-suited to multi-brand groups: a network structured across multiple countries or comprising dozens of brands can apply a single management methodology while complying with the legal requirements of each market (such as regulated sales periods) and the unique business dynamics of each region.

For a centralized pricing department, the challenge is to maintain a consolidated view of the “ markdown ” across the entire network (marked-down volumes, margin impact, inventory turnover) without imposing a one-size-fits-all rule that is ill-suited to markets with different purchasing behaviors—which remains the primary source of margin loss when markdowns are managed too uniformly.

By making decisions on a per-item and per-store basis, based on three variables: projected remaining inventory, sales rate relative to the target, and discount depth.

An early, measured price reduction allows for more weeks of sales than a late discount, which must be larger to move the same amount of inventory. Items that are ahead of schedule are not marked down; those that are significantly behind schedule are marked down earlier or transferred to stores where they sell better.