But is it protecting margin?

That question is becoming more important as retailers operate under increasing profitability pressure. A recent BCG analysis of 55 North American retailers found that weighted-average operating margin declined from 6.7% in 2021 to 5.9% in the latest twelve-month period, while gross margin fell from 26.1% to 24.9%.

Markdown decisions sit directly inside that pressure.

Used well, markdowns help retailers release excess inventory, protect working capital, and preserve the remaining selling window. Used too broadly or too late, they quietly give away margin.

McKinsey, on the other side, has reported that markdown optimization can improve margin rates by 400 to 800 basis points when retailers build more disciplined pricing capabilities.

The opportunity here lies actually in making markdown decisions more precisely instead of just discounting more aggressively.

Price Elasticity Is Not One Number

Price elasticity measures how demand changes when price changes.

The concept is simple. Retail behavior is not.

Products within the same category rarely respond to markdowns in the same way. One SKU may benefit from an earlier, modest intervention. Another may need a deeper price reduction later in the season. A third should remain untouched because demand is healthy and margin should be protected.

The same product may also behave differently across stores, markets, and channels. Customer profile, local competition, stock depth, footfall, seasonality, promotion overlap, and remaining selling time all influence the result.

This is why AI-driven price elasticity should not be treated as a static coefficient applied across a category.

For retailers operating across large portfolios and store networks, it is better understood as a decision system: one that connects demand behavior, inventory exposure, commercial constraints, timing, and planner judgment.

The relevant question is not simply:

Will a lower price increase demand?

It is: Which markdown path is most likely to improve sell-through while protecting margin within the time still available?

Why Static Markdown Rules Underperform

Many retailers still manage markdowns through fixed discount ladders.

A product moves from full price to 5%, then 10%, then 20%, based on predefined dates, category-level rules, or stock thresholds. These approaches are easy to manage and simple to communicate.

They also assume that demand behaves predictably enough for the same logic to work across products and locations.

That assumption becomes expensive at scale.

A broad markdown rule can discount healthy products unnecessarily. It can intervene too late for products whose selling window is already closing. It can apply the same discount depth to stores with completely different inventory positions. It can increase units sold while quietly weakening gross margin.

McKinsey has described this as a “peanut butter” approach: applying the same pricing strategy across products despite differences in item- and store-level performance.

The problem is not that rules are useless.

The problem is that rules alone cannot interpret enough context.

At scale, pricing decisions sit across multiple dimensions:

SKU, store, day, price band, inventory position, margin floor, seasonality, promotion, channel, and remaining selling window.

That is where spreadsheet-based markdown planning begins to struggle.

A BeeBI Case: Daily Markdown Recommendations Across a 90-Day Horizon

For a €20B+ global sportswear company, BeeBI developed an AI-supported markdown optimization engine for outlet operations.

The goal was to make pricing decisions more precise at SKU-store level while protecting margin and improving sell-through.

The system generates daily markdown recommendations across a 90-day planning horizon. Rather than applying the same discount logic across an entire category, it evaluates the expected financial impact of different pricing paths for each SKU, store, and day.

The solution combines price-elasticity modeling, demand forecasting, scenario simulation, and a configurable rule engine. Recommendations must respect business constraints such as minimum advertised price, margin thresholds, discount increments, seasonal restrictions, and blackout periods.

The result is not simply a more advanced forecasting model.

It is a controlled pricing workflow that allows planners to compare commercial outcomes before taking action.

Value at a glance
Daily SKU-store recommendations
90-day planning horizon
Millions of candidate pricing combinations evaluated at SKU-store-date level
Nightly model retraining and monitoring
Configurable margin, discount, MAP, and seasonal rules

The Modeling Challenge: Sparse and Uneven Retail Demand

Retail pricing data contains more ambiguity than it first appears.

A zero-sale day is a good example.

If a product has inventory but records no sales on a particular day, the signal may indicate weak demand. But it may also reflect low store traffic, limited visibility, timing, or a local seasonal pattern.

Treating every zero in the same way can distort elasticity estimates.

In the BeeBI solution, a separate classifier was introduced to model the probability of sale. This created a more realistic distinction between two questions:

Will a sale occur?

and:

How much demand can be expected once the product is in play?

That separation supported more robust zero-adjusted demand curves.

The price-elasticity model also combined mixed-effects panel regression with ARMA-based time-series smoothing. The mixed-effects approach helped capture variation across SKUs and stores, while the time-series component supported residual adjustment over time.

The optimization layer then simulated financial outcomes across price bands and returned the most efficient markdown path at SKU-store-day level.

This matters because the commercially important cases are rarely the obvious bestsellers or obvious slow movers.

The value often sits in the products where the signal is ambiguous and the decision still matters.

From Forecasting to Financial Simulation

Forecasting tells planners what may happen.

Simulation helps them compare what could happen under different decisions.

That distinction is central to markdown optimization.

A retailer should be able to compare the likely effect of several pricing paths before execution. A 10% markdown may protect more margin but move inventory too slowly. A 20% markdown may improve sell-through while preserving enough profitability. A deeper discount may clear inventory but destroy value unnecessarily.

The correct choice depends on context.

A product with eight weeks left in the season is not the same commercial decision as a product with two weeks left. A store with healthy stock cover is not the same decision as a store carrying excess exposure. A channel with strong organic demand should not automatically inherit the same discount logic as a weaker channel.

The objective is not to identify one mathematically attractive number.

It is to identify a commercially feasible pricing path.

The Technology Architecture Behind AI-Driven Pricing

AI-driven markdown optimization depends on more than a machine-learning model.

It requires a connected pricing architecture.

The BeeBI solution used Databricks for distributed computing, AWS S3 for cloud storage, and a React frontend for planner interaction. The architecture also included nightly retraining pipelines, schema validation, metadata tagging, monitoring, and audit-ready outputs.

Distributed processing matters because the number of candidate pricing combinations grows quickly when simulation operates across SKU, store, date, and price band.

The broader architecture may vary by retailer. Depending on the client environment, pricing optimization can sit across Databricks, AWS, Azure, Snowflake, Power BI, ERP systems, POS platforms, data warehouses, lakehouses, custom APIs, and planner-facing applications.

The technology stack is not the strategic point.

The strategic point is that pricing, inventory, product, promotion, store, channel, and margin data need to operate inside one decision-ready environment.

Without that foundation, even a strong model becomes difficult to trust and harder to scale.

Business Rules Belong Inside the Pricing Engine

A mathematically optimal markdown is not always a commercially valid markdown.

A recommendation may increase revenue while violating a margin threshold. It may suggest a discount that conflicts with minimum advertised price. It may ignore a seasonal restriction, blackout period, or channel policy.

That is why business rules belong inside the optimization layer.

In the BeeBI solution, a metadata-configurable rule engine allowed planners to enforce pricing constraints without rewriting the underlying software.

This creates an important balance.

The model brings precision.

The rule engine brings control.

Planner interaction brings commercial judgment.

The strongest pricing system connects all three.

Markdown Optimization Is a Margin Capability

It is easy to frame markdown optimization as a clearance exercise.

The stronger strategic perspective is margin.

A markdown can increase volume while weakening profitability. A late intervention can temporarily protect margin while creating more stock risk later. A broad category-level rule can clear inventory while discounting products that did not need help.

That is why the business should evaluate more than units sold.

A decision-ready pricing system should compare expected sell-through, expected revenue, expected margin, remaining stock exposure, timing, and business constraints before execution.

The goal is not simply to sell more inventory.

It is to sell inventory more intelligently.

From Pricing Optimization to Decision Intelligence

AI-driven price elasticity becomes more valuable when it operates inside a wider decision-intelligence workflow.

A forecasting model may estimate demand under several price points. A decision system helps planners understand which path is recommended, why it is recommended, which constraints were applied, and where human review is still needed.

This gives commercial teams a stronger operating model.

Instead of reviewing underperformance after it appears in a monthly report, planners can evaluate the trade-offs while the selling window is still open.

That shift matters.

Retailers do not need another dashboard explaining what went wrong.

They need earlier visibility into what can still be changed.

Where Agentic AI May Take Pricing Operations Next

Agentic AI may eventually make pricing workflows more continuous.

An AI agent could monitor inventory exposure, identify underperforming SKUs, compare pricing scenarios, check business rules, and escalate only the products that require human attention. In lower-risk workflows, it may prepare a pricing action for approval or open a review task automatically.

But autonomy should come after decision architecture.

Pricing agents need trusted data, transparent rules, approval paths, audit trails, model monitoring, and clear ownership.

The objective is not to automate price changes blindly.

It is to create a controlled operating model where the system can move faster without losing commercial discipline.

BeeBI’s View: Pricing Optimization Starts with the Decision Environment

At BeeBI, we see retail pricing optimization as a data, analytics, and decision-design challenge.

The work is not limited to training a model. It includes connecting POS, product, price, margin, inventory, promotion, store, and channel data; designing the right demand models; encoding commercial rules; building scenario simulations; and creating planner workflows that are transparent enough to trust.

Depending on the environment, this may involve Databricks, AWS S3, Azure, Snowflake, Power BI, ERP integration, POS data, lakehouse architectures, custom APIs, and AI/ML pipelines.

The strongest outcome is not a more complicated pricing engine.

It is a clearer commercial decision.

Ready to Protect Margin More Precisely?

Reach out to BeeBI Consulting and let’s turn pricing data, demand signals, inventory exposure, and commercial rules into decision-ready markdown optimization.

AI-Driven Price Elasticity for Retail Markdown Optimization yazısı ilk önce BeeBI üzerinde ortaya çıktı.