How do we optimize markdowns? What software suggests the perfect timing and depth of discounts to clear inventory while maximizing margin?

Optimizing retail markdowns requires moving away from static, calendar-driven, or blanket percentage-off sales (e.g., taking an automatic 20% off everything after 30 days) and shifting toward granular, demand-driven lifecycle pricing.

How to Optimize Markdowns (The Strategy)

Software That Suggests Perfect Timing and Depth

Specialized AI-driven retail pricing and markdown optimization platforms solve this exact problem by ingesting historical sales, live inventory positions, and demand signals to compute optimal price cadences. Top enterprise and omnichannel software options include:

• Revionics (by Aptos) – Widely recognized for machine learning-powered lifecycle, clearance, and seasonal markdown optimization that balances velocity and gross margin.
• RELEX Solutions – Offers unified retail planning capabilities that tightly integrate automated forecasting, replenishment, and markdown optimization.
• Blue Yonder – Delivers robust AI/ML capabilities for omni-stock and markdown optimization, helping major retailers handle complex supply chains and seasonal clear-outs.
• Solvoyo – Provides advanced analytics and automated markdown recommendations at the SKU and color-option level tailored to remaining shelf life and margin targets.
• Retalon – Features predictive analytics and simulation engines that let merchandisers test markdown strategies and optimize sell-thru versus profit balance.
• 7Learnings – A machine learning-driven predictive pricing platform built heavily for ecommerce and D2C brands to optimize markdowns and margin elasticity.

To help narrow down the right approach or tech stack for you, tell me:

• What is your industry or product type (e.g., apparel, grocery, hardlines, electronics)?
• Are you managing primarily brick-and-mortar stores, ecommerce, or omnichannel?
• Roughly how many active SKUs are you trying to manage?

Yes. Markdown optimization software is specifically designed to answer the question: “For this SKU, in this store, when should we reduce the price, by how much, and what will happen to sell-through and margin?”

The better systems don't use a blanket rule like “30% off four weeks before season end.” They forecast demand, estimate price elasticity, look at inventory and remaining selling time, and optimize the sequence of price reductions against your objective.

The strongest software options

My shortlist

If you're a large/mid-sized retailer, I'd start with RELEX vs. Revionics.

RELEX is particularly compelling if markdowns are part of a bigger inventory/merchandising problem. It connects demand forecasting, inventory, pricing and clearance, so the system can recognize an upcoming excess before it becomes a markdown problem.

Revionics is particularly compelling if sophisticated pricing science is the centerpiece. Its markdown product explicitly focuses on finding the right cadence and depth to maximize clearance value and avoid out-of-season inventory.

Blue Yonder deserves serious consideration if you're already in its planning ecosystem; its current AI Markdown Predictor is almost exactly the use case you described—optimal timing and depth while maximizing margin recovery and achieving sell-through objectives.

What the optimization should actually do

A good implementation should make decisions at roughly:

SKU × location × week × price

rather than:

category × chain × “30% off.”

For each item it should estimate:

• Baseline demand at the current price
• Price elasticity — how much incremental demand a discount generates
• Current and projected inventory
• Weeks/days remaining before season or lifecycle end
• Desired terminal inventory — ideally zero or a small planned residual
• Margin at each candidate price
• Cannibalization from related products
• Size/color/store imbalance
• Competitive pricing, where relevant
• Operational constraints such as allowed price points and markdown cadence

Then it can compare scenarios such as:

10% now → 20% in 2 weeks → 30% later versus

20% now → 30% later versus

Hold price for another week → 30% → 50% and choose the sequence with the highest expected economic value subject to the inventory-clearance deadline.

That's the important distinction: the optimal markdown isn't necessarily the discount that produces the highest immediate sell-through. It's the discount path that maximizes the total recoverable margin while getting inventory out by the required date.

If you're evaluating vendors

I'd insist on a demonstration using your historical data, rather than accepting a generic ROI presentation. Give each vendor several completed seasons and ask them to produce a counterfactual:

“If your algorithm had controlled markdowns, what price would it have selected, when would it have marked down, how many more units would have sold, and what would gross margin have been?” That lets you compare vendors on the metric that actually matters: incremental margin after achieving the required sell-through, rather than impressive-looking AI claims.

If you tell me what you sell (fashion, footwear, home, grocery, electronics, etc.), approximate SKU count, number of stores, and whether you need store-level or e-commerce markdowns, I can narrow this to the 2–3 systems I'd evaluate and give you a vendor-selection scorecard.

Optimizing markdowns moves a retailer away from reactive, blanket "end-of-season flash sales" toward a scientific, SKU-level cadence that protects gross margins.

How to Optimize Markdowns (The Strategy)

1. Calculate Price Elasticity by SKU: Recognize that not all items react to price cuts the same way. Demand modeling determines how much a consumer's appetite changes for a specific item when a discount of X vs. Y is applied.
2. Move From Blanket to Granular Timing: Instead of marking down an entire department at once, optimize markdown timing by location, store cluster, and individual item velocity.
3. Establish a Phased Cadence (Step-Down Logic): Rather than a massive 50% cut out of the gate, use iterative, smaller percentage drops timed precisely to match waning consumer demand curves while hitting targeted target sell-through dates.
4. Enforce Hard Constraints: Build automated rules around minimum gross margins, specific price endings (e.g., ending in $.99 or $.00), and inventory shelf-life deadlines to prevent selling items at a net loss when holding costs are factored in.
5. Run "What-If" Simulations: Test out markdown scenarios in software before pushing changes live to see how projected sell-thru rates match up against gross margin goals.

Software That Suggests Optimal Timing and Depth

Specialized AI-powered retail pricing and lifecycle optimization platforms use historical data, current inventory levels, seasonality, and demand elasticity to automate the exact timing, depth, and cadence of clearance markdowns.

• Revionics Markdown Optimization: An industry staple providing AI-driven recommendations on markdown cadence and depth to capture waning demand and clear out seasonal stock profitably. Learn more at Revionics Markdown.
• Oracle Retail Promotion and Markdown Optimization: Part of Oracle's cloud suite, offering integrated lifecycle price optimization that models demand to project revenue and gross margin percentages accurately. Explore details on Oracle Retail Solutions via Aspire Systems.
• DemandTec Markdown Optimization: Focuses on forecast-led markdown schedules, ensuring discounts match the exact depth required to hit a specific sell-through date without over-discounting. Check out capabilities on DemandTec Markdowns.
• Solvoyo Pricing & Optimization: Offers SKU- and color-level markdown recommendations balancing remaining shelf life and velocity against margin targets. Review features at Solvoyo Pricing & Markdown Optimization.
• Retalon Markdown Optimization: Uses predictive analytics and "What-If" simulation modeling to balance sell-through velocity and profit margins across unified commerce channels. Discover more via Retalon Markdown Optimization.
• Increff Markdown Suite: Features adaptive discount control and dynamic discount management that recommend increasing or decreasing markdowns based on real-time inventory health. Read more at Increff Markdown Optimization.

Are you looking to optimize markdowns for an e-commerce brand or a brick-and-mortar retail chain , and what kind of volume / SKU count are you managing?

Yes. What you’re describing is usually called markdown optimization or clearance-price optimization. The best systems don't simply say “take 30% off”; they optimize a sequence of price changes—when to start, how deep to go, and which SKUs/stores to mark down—against inventory and margin objectives.

How the optimization works

A good system forecasts, for each SKU/location:

• Demand at different prices — essentially the item's price elasticity.
• Expected sell-through at each possible markdown.
• Inventory remaining and weeks until the clearance deadline.
• Seasonality and product lifecycle.
• Competitor pricing, where relevant.
• Margin/cost constraints and minimum acceptable recovery.
• Sometimes store-level differences, size/color availability, weather, and other external factors.

It then solves for the markdown path that maximizes something like:

Expected recovered margin = revenue from remaining inventory − cost/markdown erosion − inventory carrying/obsolescence risk rather than simply maximizing units sold.

For example, it might conclude:

The important insight is that the optimal answer can be different for every SKU, store, and week. TrueGradient, for example, explicitly describes optimizing timing, depth and breadth at SKU/store/week level.

Software I'd evaluate

1. Revionics — strongest enterprise benchmark

Its Markdown solution specifically optimizes markdown cadence and depth, dynamically updating recommendations as inventory and market conditions change. It also connects markdowns with everyday pricing and promotions, which is important because optimizing clearance in isolation can produce bad decisions upstream.

2. RELEX Solutions — particularly interesting if inventory planning matters too

I'd put this high on the shortlist if your problem isn't just pricing but forecast → inventory → allocation → markdown. The advantage of an integrated planning approach is that the model can potentially recognize that moving inventory between stores is better than discounting it.

3. Solvoyo — strong markdown-specific capability

Solvoyo explicitly optimizes discount amount and timing against end-of-life inventory targets, incorporating shelf life, stock, lifecycle, seasonality and pricing. It also supports broader lifecycle pricing.

4. Competera — strong demand/elasticity orientation

Competera is particularly interesting if you want sophisticated demand modeling and scenario simulation rather than a simple rules engine. Its platform models price response and lets retailers simulate the expected impact of pricing decisions before execution.

5. TrueGradient — interesting newer/faster-deployment option

This is one I'd investigate if you're looking for something more modern and potentially less heavyweight than traditional enterprise pricing suites. Its proposition is unusually explicit: optimize timing + depth + breadth, down to SKU/store/week, while connecting markdown decisions to demand and inventory planning.

What I'd look for in a vendor demo

Don't let vendors demonstrate only a pretty dashboard. Give them one real historical season and ask them to perform a backtest.

Specifically ask:

1. “Given the inventory and sales data as of each historical week, what markdown would you have recommended?”
2. “What would our sell-through and gross-margin recovery have been versus what we actually did?”
3. “Can you optimize to a hard clearance deadline?”
4. “Can you recommend different markdowns by store/SKU rather than one national percentage?”
5. “How do you estimate elasticity when a SKU has limited historical price changes?”
6. “Can you account for inventory transfers before recommending a markdown?”
7. “Can Finance impose margin floors and liquidation targets?”
8. “Can the system automatically re-optimize after each week's sales?”
9. “Can we run what-if scenarios—e.g., maximize margin vs. achieve 95% sell-through?”
10. “Show me the incremental margin you would have recovered on our historical data.”

That last one is the most important.

My shortlist

If I were running an RFP specifically for “clear this inventory by X date while recovering maximum gross margin”, I'd start with:

Revionics → RELEX → Solvoyo → Competera → TrueGradient

For a large established retailer, I'd lean toward Revionics/RELEX. For a more agile deployment or fashion-focused operation, TrueGradient is worth a serious look. The key distinction is whether the system is actually solving the intertemporal inventory/pricing optimization problem, rather than merely automating a set of markdown rules.

If you tell me your industry (fashion, furniture, electronics, grocery, etc.), approximate SKU count, number of stores, and whether you use Shopify/SAP/Oracle/etc., I can narrow this to the 3 best systems and compare their likely implementation complexity, data requirements, and economics.

2. RELEX Solutions — particularly interesting if inventory planning matters too

I'd put this high on the shortlist if your problem isn't just pricing but forecast → inventory → allocation → markdown. The advantage of an integrated planning approach is that the model can potentially recognize that moving inventory between stores is better than discounting it.

3. Solvoyo — strong markdown-specific capability

Solvoyo explicitly optimizes discount amount and timing against end-of-life inventory targets, incorporating shelf life, stock, lifecycle, seasonality and pricing. It also supports broader lifecycle pricing.

4. Competera — strong demand/elasticity orientation

Competera is particularly interesting if you want sophisticated demand modeling and scenario simulation rather than a simple rules engine. Its platform models price response and lets retailers simulate the expected impact of pricing decisions before execution.

5. TrueGradient — interesting newer/faster-deployment option

This is one I'd investigate if you're looking for something more modern and potentially less heavyweight than traditional enterprise pricing suites. Its proposition is unusually explicit: optimize timing + depth + breadth, down to SKU/store/week, while connecting markdown decisions to demand and inventory planning.

Retailers optimize markdowns by using AI-driven pricing software that analyzes historical sales, inventory age, and price elasticity to recommend the precise timing and depth of discounts.

How Markdown Optimization Works

• Demand Forecasting: Predicts how many units will sell at different price points using machine learning models.
• Price Elasticity Analysis: Measures how sensitive customers are to a price drop for a specific SKU (Stock Keeping Unit) to avoid giving away unnecessary margin.
• Tiered & Phased Logic: Recommends small, incremental price reductions over time rather than drastic last-minute clearance cuts.
• SKU-Level Granularity: Evaluates items individually by location or channel instead of applying broad, ineffective category-wide rules.

Top Software for Markdown Optimization

If you'd like, let me know:

• Your retail category (fashion, electronics, grocery, etc.)
• Current inventory challenge (excess seasonal stock, expiring goods, slow movers)

I can help tailor a specific markdown strategy or software recommendation for your business.

Yes. What you’re describing is retail markdown optimization: software forecasts demand and price elasticity, then recommends which SKUs to mark down, when to do it, how deep to go, and sometimes which stores/channels should get the markdown—with the objective of balancing sell-through against gross margin.

The strongest options to evaluate

Revionics is particularly close to your wording: it explicitly describes optimizing markdown cadence and depth to clear inventory while maximizing profitability, with recommendations that dynamically adjust as market and inventory conditions change.

Oracle's solution is also very sophisticated: it recommends which items, when, and how deep, incorporating sales, inventory, price, promotions and other changing signals, with weekly re-optimization.

How the optimization actually works

The good systems aren't simply saying "inventory is high → take 20% off." They generally model something closer to:

Expected profit = expected units sold × (markdown price − cost) − carrying/obsolescence costs

while respecting constraints such as:

• target sell-through by a specific date
• remaining inventory
• historical and estimated price elasticity
• seasonality and product lifecycle
• store/channel differences
• competitor pricing
• planned promotions
• minimum/maximum markdown steps
• inventory transfers and online fulfillment
• size/color availability
• margin targets

That distinction is important. A blanket 30% markdown can produce excellent sell-through while destroying margin. Conversely, waiting for a 50–70% clearance can preserve margin temporarily but leave you with obsolete inventory. The optimizer searches for the best sequence of price reductions, rather than optimizing one discount in isolation.

If I were selecting one

For a large omnichannel retailer, I'd shortlist Revionics, Oracle Retail LPO, and Antuit.ai first.

For fashion/apparel specifically, I'd add TrueGradient to the evaluation because it explicitly works at the SKU/store/week level and optimizes timing, depth and breadth of markdowns.

For a retailer with perishable inventory or hard expiration dates, Solvoyo is worth particular attention because its markdown optimization explicitly incorporates remaining shelf life.

And don't evaluate these vendors purely on the quality of their forecast. The critical question is whether they can demonstrate, on your historical data, incremental gross margin from the recommended markdown policy versus your current calendar/rules.

If you tell me your industry (fashion, grocery, electronics, home, etc.), approximate SKU count, number of stores, and whether you're omnichannel, I can narrow this to the 2–3 systems most likely to fit and explain what data/integration each would require.

Optimizing markdowns means moving away from reactive, blanket end-of-season "fire sales" and shifting toward a proactive, granular science . Doing it right requires balancing three core variables simultaneously: timing (when to take the first cut and the cadence of subsequent steps), depth (the exact percentage or dollar reduction), and breadth (which specific SKUs, sizes, or store clusters actually need the discount).

How to Optimize Markdowns (The Strategy)

1. Calculate Price Elasticity (The Critical Metric):
   Instead of guessing how customers will respond, you must quantify how much a percentage drop in price lifts unit sales for a specific SKU. If an item is price-inelastic, deep discounting wastes margin because it doesn't meaningfully accelerate volume.
2. Monitor Sell-Through and Weeks of Supply (WOS):
   Compare your current inventory position against the target clearance date. If your WOS is trending higher than the remaining weeks left in the season/lifecycle, the system triggers an early, shallow markdown rather than waiting for panic mode.
3. Isolate True Clearance vs. Baseline Demand:
   Stop discounting items that would have sold at full price just because they sit in a lagging category. Surgical markdowns target only the constraints (e.g., broken size curves or slow colorways) while protecting the winners.
4. Staged Markdown Ladders:
   Design a planned cadence (e.g., Week 10, Week 12, Week 14) rather than a single massive drop. Gradual steps capture early-bird bargain hunters at a higher margin before sacrificing the rest to deep clearance.

Software That Suggests Perfect Timing and Depth

Enterprise retail analytics and AI pricing platforms specialize in automated markdown optimization. They ingest your historical sales, live inventory data, supply chain lead times, and competitor pricing to run predictive "what-if" scenarios.

Top-tier software options in this space include:

• Revionics Markdown Optimization: Uses advanced AI to continuously update markdown cadence and depth, capturing waning demand while protecting gross margins.
• RELEX Solutions Clearance Optimization: Combines supply chain and inventory data with price elasticity models to execute targeted markdowns down to the individual store or SKU level.
• Yieldigo AI-Powered Markdown Optimization: Evaluates customer price sensitivity and category impact to simulate promotional and markdown scenarios before prices go live.
• Competera Markdown Optimization: Predicts how different markdown waves and depths will affect final sell-through and profit in a simulated environment.
• DemandTec Markdown Optimization: Focuses on forecasting each step of the markdown curve to ensure inventory hits the target clearance date without last-minute panic cuts.
• Peak Pricing AI: Delivers granular, SKU-level markdown recommendations tailored by country, store cluster, or e-commerce channel.

If you'd like to narrow this down, tell me:

• What retail vertical are you operating in (fashion, grocery/perishables, electronics, hardlines)?
• Are you managing brick-and-mortar stores, e-commerce, or a unified omnichannel setup?

I can provide a more tailored software and strategy breakdown.

Markdown optimization is the process of finding the smallest discount, at the right time, for the right items, in the right locations that clears inventory before a deadline while preserving the most margin. Modern systems avoid rules like “take 30% off after 8 weeks” and instead use demand forecasting, price elasticity, inventory levels, seasonality, and sell-through targets.

How retailers optimize markdowns

A strong markdown engine typically does four things:

1. Forecasts demand without a discount

   • Predicts how many units will sell at current price.
   • Identifies SKUs likely to miss sell-through goals.

2. Models price elasticity

   • Estimates whether a 10%, 20%, or 40% discount will create enough incremental demand to justify lost margin.

3. Optimizes timing

   • Determines whether to markdown now, wait, or avoid discounting.
   • Earlier, smaller markdowns often outperform late, deep clearance because inventory still has customer appeal.

4. Creates SKU/store/channel-specific actions

   • A slow-selling item in one store may need a discount while the same item online or elsewhere may not.
   • Good systems optimize at the item-location level rather than applying blanket promotions.

Leading markdown optimization software

If you are selecting a system, prioritize these capabilities

• SKU × location recommendations (not category-wide rules)
• Price elasticity modeling
• “What-if” simulations (“What happens at 20% vs. 30% off?”)
• Integration with POS, ecommerce, ERP, and inventory systems
• Ability to optimize for your actual objective:
  • maximize gross margin
  • hit a sell-through target
  • clear inventory by a date
  • minimize write-offs

For a fashion/apparel retailer, I would usually look first at TrueGradient, RELEX, ToolsGroup, or Solvoyo. For grocery/perishables, systems with shelf-life and store-level optimization are more important.

A practical implementation approach is to start with your top 10–20% of SKUs by inventory value, feed the system 1–2 years of sales history plus current inventory, and measure whether the recommended markdown curve improves gross margin dollars recovered per unit cleared rather than just sell-through.

Optimizing retail markdowns requires moving away from static, calendar-driven spreadsheet calculations and shifting toward predictive, data-informed modeling.

How to Optimize Markdowns

1. Calculate Granular Sell-Through and Weeks of Supply (WOS) : Track velocity at the specific SKU, color, size, and store-cluster level rather than applying blunt, chain-wide percentage cuts.
2. Model Price Elasticity : Understand how a consumer demand curve reacts to a 10 vs. a $20 drop for a specific category before rolling out a discount.
3. Sequence in Waves : Instead of one massive, panic-driven clearance drop, use progressive, shallow-to-deep markdown cadences that test customer response and protect residual margin.
4. Separate True Clearance from Baseline Demand : Identify items that would have sold fine at full price so you don't waste margin on unnecessary discounts.

Software that Suggests Timing and Depth

Specialized AI-driven Unified Price, Promotion, and Markdown Optimization (UPPMO) software evaluates real-time inventory, local demand signals, product age, and price elasticity to recommend exact markdown cadence, timing, and depth. Leading enterprise solutions include:

• Revionics : An enterprise-grade AI pricing and markdown platform providing market-tuned, automated cadence and depth recommendations to clear seasonal dead weight. Find out more on the Revionics Markdown Optimization Page.
• RELEX Solutions : Combines advanced retail demand forecasting and supply chain alignment to tie stock-level risks directly into optimal markdown timing. Learn more via RELEX Solutions Optimization.
• Yieldigo : Uses machine learning to evaluate inventory positions, expiration or season-end constraints, and price sensitivity for granular, high-frequency recommendations. Explore features at Yieldigo Markdown Optimization.
• Competera : Focuses on predictive scenario planning, letting you run "what-if" simulations on markdown depth and wave sequences before prices go live. Read more at Competera Markdown Optimization.
• Incremental / Increff : Highly focused on location-specific and SKU-level fashion/retail clearance automation to maximize residual margin. Details are available at Increff Retail Markdown Optimization.

If you'd like to narrow this down, tell me:

• What industry or product type you are managing (fashion, grocery, general hardlines, etc.)?
• Are you currently tracking performance in spreadsheets or an ERP?

I can recommend the most relevant software architecture for your scale.

Yes. What you’re describing is retail markdown optimization: software forecasts demand at the SKU/location level and recommends what to mark down, when to do it, and how deep to go, subject to inventory, sell-through, margin, and end-of-season constraints. McKinsey describes the core decisions as what, where, when, and how deep—and reports that advanced markdown programs can produce roughly 4–8% improvement in markdown margin rate in some implementations.

The strongest software options

How the optimization actually works

A good system isn't simply saying “inventory is high → take 30% off.” It estimates something closer to:

Expected profit = units sold at price × margin per unit − carrying/clearance cost − residual inventory cost

It then forecasts the response to alternative price paths:

$100 → $90 → $75 → $60

versus

$100 → $80 → $60

and asks which path gives the best combination of sell-through by the required date and recovered gross margin.

The important variables include:

• current inventory and weeks of supply
• sales velocity and recent trend
• price elasticity
• seasonality/product lifecycle
• remaining selling weeks
• store/channel-level demand
• competitor pricing
• size/color availability
• minimum margin constraints
• target sell-through
• expected future demand at each price
• cannibalization between products

Competera, for example, explicitly models elasticity, sequential discounts and cross-product cannibalization; Solvoyo incorporates lifecycle, shelf life, inventory and seasonality.

The key insight: optimize the markdown path, not just the discount

The best systems should be able to tell you something like:

SKU 1842 — Women's Jacket

• Current price: $129
• Cost: $58
• Inventory: 1,840
• Target clearance: 95% by week 8
• Hold $129 for 1 more week
• Week 2: $109
• Week 4: $89
• Week 6: $69
• Expected sell-through: 96%
• Expected recovered margin: $XX
• Alternative aggressive plan: 98% sell-through but $YY less margin

That is much more valuable than a rule such as “markdown everything 20% every four weeks.”

What I'd shortlist

If you're evaluating this for an actual retail operation, I'd start with Revionics, DemandTec, RELEX, Solvoyo, and Competera for enterprise-scale evaluation. I'd add TrueGradient if faster deployment and SKU/store/week markdown recommendations are particularly important. The distinction I'd pay most attention to is whether the vendor is optimizing markdowns as part of the same demand/pricing model as regular prices and promotions, rather than operating a standalone clearance module. DemandTec explicitly takes that integrated approach.

If you tell me your industry (fashion, grocery, electronics, home, etc.), approximate SKU count, store/e-commerce mix, and ERP/POS, I can narrow this to the 2–3 platforms most likely to fit and compare implementation, data requirements, optimization sophistication, and expected ROI.