What is price elasticity and why does it matter for markdowns?
By Tom Summerfield on September 3, 2025Price elasticity. It sounds like something you might vaguely remember from economics class — an abstract concept involving curves, formulas and hypothetical consumers.
But in retail, elasticity is anything but theoretical. It’s the invisible force behind every markdown that works and every one that doesn’t.
Despite this, in most retail environments, it’s completely misunderstood — or worse, entirely ignored.
Retailers are still making pricing decisions based on gut feel, fixed discount increments, or old seasonal calendars. And when a product doesn’t sell, the default response is always the same: cut the price. But by how much? And at what cost to margin?
That’s where understanding real-world elasticity comes in. Not in theory, but in practice. Not for economists, but for trading, pricing, and merchandising teams who live and die by the numbers each week.
So let’s break it down. No jargon, no confusing curves, just real-world application.
What is price elasticity in retail?
At its core, price elasticity is a measure of how sensitive customer demand is to changes in price. In plain English:
If I lower the price, how much more will I sell — and is that enough to make up for the lower margin?
That’s the big question. If the answer is yes, great. You’ve run a profitable promotion or markdown. If the answer is no, then you’ve just given away profit for no meaningful gain.
However, there’s one thing that you need to keep in mind, and that’s that not all products behave the same.
Some are highly elastic, meaning a 10% drop in price might drive a 40% increase in volume. Others are inelastic; you could slash the price by 50% and see barely any movement. That difference is where money is made or lost.
Real-world examples: what elasticity actually looks like
Let’s move past the theory and look at three examples that highlight how elasticity plays out in a typical retail setting:
Example 1: fashion t-shirts vs. outerwear jackets
Fashion t-shirt: elastic product. At full price ($20), it’s selling modestly. But at 30% off, demand surges — perhaps because shoppers view it as non-essential or easy to impulse-buy.
Outerwear jacket: inelastic product. High value, high consideration. Even a 20% discount doesn’t drive much additional volume. Shoppers are comparing features, brand value and timing (e.g., winter seasonality) more than price alone.
Takeaway: One-size-fits-all markdowns across both categories will over-discount one and under-discount the other. Margin is lost either way.
Example 2: same product, different channels
Online: A home appliance priced at £199 sees a strong uptick in conversions when dropped to £179. Price comparison is immediate, and digital customers are sensitive to psychological price breaks.
In store: The same product, with the same markdown, barely moves. Footfall is low, and the visual merchandising doesn’t draw attention to the discount.
Takeaway: Channel elasticity matters. Pricing strategies should vary by channel, not just product.
Example 3: markdown timing on a seasonal line
Swimwear in early June: Elastic. A small markdown (say, 15%) triggers a demand spike because customers know they’ll use it imminently.
Swimwear in mid-August: Inelastic. Even at 50% off, sell-through is low. The season’s psychologically over.
Takeaway: Elasticity isn’t static — it shifts based on season, stock cover and shopper mindset.
There’s one thing that you need to keep in mind, and that’s that not all products behave the same.
Why most retailers get elasticity wrong
Despite these clear patterns, most retail pricing strategies remain sub-optimal:
- Flat 30% markdowns across an entire category
- Discounting based on weeks-on-sale, not sales velocity
- Promotions planned three months in advance, not based on real-time data
This is because calculating true elasticity across thousands of SKUs, channels and time periods is hard. It requires:
- Clean data (sales, pricing, inventory)
- Strong attribution logic (e.g., isolating price impact vs. marketing impact)
- Statistical modeling that can scale
Most teams don’t have the tools, or the time, to do this manually. So they fall back on rules of thumb, not rules of science.
The AI advantage: surfacing elasticity at scale
Here’s where a modern approach, driven by artificial intelligence (AI) can help. AI pricing solutions don’t just automate pricing. They illuminate elasticity in ways that teams can immediately act on.
1. Elasticity curves by product
Advanced tools can model how each product behaves at different price points — visually surfacing where demand surges, plateaus, or drops off.
For example, say a handbag shows strong demand between £59–£69, but no uplift at £49. That lower price might signal lower quality to the customer — hurting, not helping, conversions.
2. Scenario planning
Elasticity data powers simulations:
“What happens to profit if we drop this line by 10%?”
“What’s the revenue gain at 30% off, and what margin are we leaving on the table?“
This lets teams make deliberate trade-offs, rather than discounting blindly.
3. AI-driven recommendations
Instead of asking teams to digest complex elasticity curves, smart platforms simply surface ranked markdown options. These options can be ordered by your most important objectives, whether it’s to maximize profit, clear stock quickly or find the right balance between the two.
Each option comes with clear projections — expected revenue, margin, units sold — so you can choose based on real context, not guesswork.
Why elasticity should be the foundation of every markdown decision
Because it’s the only way to answer the three questions every pricing team should be asking:
- Should we reduce the price?
- If so, by how much?
- What outcome will that deliver — and is it better than doing nothing?
Without elasticity insight, these answers are speculative at best. With it, they become strategic.
The commercial impact: real numbers, not theory
From AI pricing projects I’ve led at Peak across fashion, home and beauty categories, here’s what using elasticity modeling unlocks:
- 30–50% fewer deep markdowns (many products clear with a shallower cut)
- 200–500 bps improvement in margin on promoted lines
- Faster sell-through with more predictable demand curves
- Fewer clearance “fire sales” at end of season
In one apparel business, AI recommendations showed that 20% of markdowns were completely unnecessary — sales would have cleared without any discount at all. That’s a huge margin recovery opportunity hiding in plain sight.
Making elasticity actionable for your team
You don’t need to invest in new tools or software immediately. Here’s how to embed elasticity thinking into your pricing process, even before rolling out AI:
Segment your SKUs: Group products by type, seasonality, price band and channel
Analyze past performance: Look at how demand shifted with different discounts last season
Run markdown experiments: A/B test different discount levels in small cohorts to estimate demand response
Build guardrails: Set rules: e.g., no markdown below 30% margin unless elasticity justifies it
Educate your teams: Make elasticity a commercial skill, not a data science secret
The future lies in tools that automate all of this — but the mindset starts now.
Final thought: elasticity is a lens, not a lever
Price elasticity isn’t just a number, more a way of seeing and interpreting the market. It helps you understand your products, your customers and your commercial model more deeply.
In a world of rising input costs and tighter margins, understanding elasticity is no longer a “nice to have,” but a competitive imperative.
Because if you don’t know how your prices shape demand, you’re not really in control of either.
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