6 Strategic Elasticity Curves for Apparel Buyers in 2025

Are you over-discounting bestsellers or underestimating demand for outerwear in shoulder seasons? Learn how elasticity can become your secret pricing weapon.

This article breaks down six crucial price–demand elasticity curves and what they reveal about T-shirts vs. outerwear in the 2025 apparel market. Expect data-backed insights and tips to protect your margins, clear slow movers, and negotiate smarter tiered pricing with suppliers. By understanding how different apparel categories react to price changes, Australian wholesalers and retailers can strike better deals and optimize their inventory strategies.

Key Features Across Tools:

• Predictive elasticity analysis: Forecast demand changes before you adjust prices.
• SKU-level price sensitivity modeling: Drill down to how each product responds to price tweaks.
• Seasonal demand forecasting: Anticipate peak vs. off-season sales to time discounts right.
• Visual dashboards for buy-planning: Get clear charts and competitor benchmarks to guide line plans.

Tools Covered:

1. EDITED – Price optimization with elasticity and competitor tracking edited.com. Uses AI to spot where you can raise or drop prices while benchmarking against rivals' assortments.
2. StyleSage – Real-time product elasticity insights and trend analytics. Provides competitive assortment benchmarking and price trend visualization for data-driven decision-making.
3. Omnia Retail – Dynamic pricing for multi-channel brands. Excels at season-aware pricing in highly competitive markets, adjusting prices with market and climate shifts.
4. Intelligence Node – AI for wholesale and retail pricing. A markdown & elasticity engine that predicts how shoppers respond to price changes and recommends timely discounts to maximize profit.
5. Pricefx – B2B-focused pricing software (CPQ). Offers custom elasticity modeling for quote negotiations and integrates with enterprise ERPs to optimize complex pricing and rebates.
6. Retviews (Lectra) – Enterprise-level competitive intelligence. AI-powered category elasticity tracking helps pinpoint optimal pricing strategies and reduce costly markdowns.

Quick Comparison Table:

Why Elasticity Curves?

Understanding elasticity is vital for Australian apparel SMEs balancing staple stock vs. seasonal buys. In practical terms, a core staple like a basic T-shirt often has inelastic demand – shoppers will buy it at steady rates regardless of small price changes, so heavy discounts only erode margin. In contrast, seasonal items like winter outerwear see demand spike then plunge, making them highly elastic by end-of-season when a lower price significantly boosts clearance sales. Knowing these curves helps wholesalers set tiered pricing that protects margin on staples and clears seasonal inventory efficiently.

This approach aligns with Australia's push for digital pricing tools. The ATO's recent SME technology investment boost (a 20% bonus tax deduction for digital investments) encouraged small businesses to adopt advanced analytics. And it's timely: post-COVID consumers have become extremely price-savvy, often comparing options and only paying a premium for truly compelling products. High inflation and interest rates in 2023 even led to a brief retail downturn in Australia, underscoring the need for smarter pricing now that conditions are stabilizing. In short, an elasticity-driven strategy lets you respond to these new spending patterns with precision – raising prices where you won't lose sales and offering deals where shoppers are most sensitive.

Below, we break down six strategic elasticity "curves" – and the tools that help harness them – in the T-shirt vs. outerwear context. From protecting your perennial sellers to timing markdowns on winter coats, these insights will sharpen your buy planning and negotiations.

1. EDITED – Elasticity Meets Competitor Benchmarking

For fast-fashion and basics-heavy retailers, EDITED provides a clear look at how pricing tweaks might play out against the market. This platform connects competitive data with your own sales and inventory numbers in one place. Buyers can visualize their T-shirt prices versus competitors' and see where there's room to charge more or less without hurting demand. EDITED's AI automatically flags products that could tolerate a price increase (for instance, a hot-selling tee that's underpriced relative to similar items) or that might need a markdown to stay competitive.

Elasticity Curve: Inelastic Staples. EDITED helps identify staple items (like a flagship T-shirt line) that have low price elasticity – meaning customers will keep buying even if you nudge the price up. By analyzing competitors and sell-through, it might reveal that your $10 basic tee could be $12 with minimal volume loss, thus boosting margin. Conversely, if a normally steady seller starts to see demand slip when priced above the market, the tool alerts you to adjust downward.

Real-World Example: Fashion retailer Mango used EDITED to monitor pricing across 115 global markets, a task that previously took hours per country. With automated competitive tracking, Mango spotted pricing opportunities and grew e-commerce sales by 50% while maximizing margins. For an Australian SME, similar intelligence means you won't accidentally underprice your core range or over-discount a bestseller. Instead, you base pricing on data – ensuring your year-round items maintain healthy profits.

2. StyleSage – Trend-Driven Elasticity Insights

When it comes to trend-led categories and seasonal shifts, StyleSage shines. Now part of Centric Software, StyleSage is known for its visual dashboards that map out pricing and product trends in real time. Imagine being a buyer planning outerwear for the trans-seasonal "shoulder" months. StyleSage can show how similar jackets are priced across the market, how quickly they're selling at those prices, and even which styles are trending on social media. This helps you gauge how elastic demand might be for a new $200 jacket versus a $150 one – before you place your order.

Elasticity Curve: Trend-Elastic Demand. For outerwear that's fashion-forward (say a neon puffer coat that's "in" this winter), demand can be very elastic – if you drop the price slightly, you might dramatically increase sales because many consumers want the trend but have a price threshold. StyleSage's competitive intelligence lets you visualize the pricing mix of competitors and where your product would sit. If data shows consumers are shelling out for premium puffers, you might price high; if not, a small discount could capture hesitant buyers. The tool's easy interface and AI-driven analytics mean you get these insights without crunching spreadsheets (users praise how intuitive and reliable the platform is.

Real-World Scenario: Let's say an Aussie brand sees that mid-weight jackets sold out early last autumn at full price, indicating less price sensitivity in that category. StyleSage could confirm this by showing, for example, all competitor mid-weight jackets were 90% sold through before winter clearance. That curve suggests you can safely negotiate a smaller wholesale discount on similar jackets (protecting your margin) because demand holds at regular price. On the flip side, if it's a highly trendy item that every retailer will markdown at season's end, StyleSage's trend analytics help you plan a promotion calendar to stay ahead of the pack.

3. Omnia Retail – Dynamic Pricing for Seasonality

Multi-channel retailers and brands with thousands of SKUs often turn to Omnia Retail for dynamic pricing automation. Omnia is particularly adept at handling seasonal pricing nuances: it allows you to set rules that adjust prices based on real-time demand, inventory, and even weather changes. In a competitive and seasonal market with a large catalog, dynamic pricing isn't just nice to have – it's necessary. Omnia's system can, for example, automatically markdown outerwear as winter winds down, or raise prices on a scarce style that's selling fast.

Elasticity Curve: Seasonal Peak vs. Off-Season Elasticity. Outerwear demand is highly time-sensitive. During winter's peak, a $250 coat might sell strongly even if you edge the price up (inelastic during peak cold). But once temperatures rise, demand becomes extremely elastic – even a small price difference can make or break a sale in late season. Omnia's season-aware pricing engine accounts for this, helping you maximize revenue over the product's life cycle. It might recommend keeping prices firm through July (mid-Australian winter) then progressively applying deeper discounts in August as demand naturally wanes. By automating these moves, you protect margin when demand is hot and clear stock when it cools off.

Negotiation Edge: If you're a wholesale buyer, you can leverage Omnia's insights when negotiating with suppliers. For instance, data might show that you sell 80% of your summer T-shirts at full price by mid-season (low elasticity), so you push for a smaller volume discount on those from the supplier – they'll still move without big price cuts. Conversely, if raincoats notoriously require 30% off by end of spring to sell out, you'd negotiate a lower initial cost or a markdown allowance, armed with data that those units must be cheaper later. Omnia helps quantify these patterns, strengthening your case with hard numbers rather than hunches.

4. Intelligence Node – Markdown Optimization Engine

For wholesalers and retailers juggling both staple products and promotions, Intelligence Node offers an AI-driven pricing platform that covers the full spectrum from competitive price monitoring to markdown optimization. Its system analyzes how shoppers respond to different price points and helps you make timely markdowns or price hikes in response to market shifts. Essentially, it's like a price elasticity radar – detecting when a slight discount could unlock a big jump in demand, or when you can hold price because your competitors are out of stock.

Elasticity Curve: Promotional Elasticity Spikes. Think about a slow-moving outerwear item, like last season's parka. Intelligence Node might simulate that a 15% drop in price could double the sales velocity – indicating a strong elastic response at that threshold. Its "what-if" scenarios let you test such outcomes. The platform can then auto-trigger that markdown at the optimal time, say right before winter ends, to beat competitors to the sale. On the flip side, for an in-demand T-shirt that all rivals have sold out, the tool may suggest you increase price or at least avoid discounting (since shoppers can't find it elsewhere – an opportunity to capture more margin).

Case in Point: A mid-sized Australian streetwear retailer used Intelligence Node to manage pricing during promotional periods like Boxing Day. They discovered some staple hoodies were actually inelastic – a 10% off promo didn't significantly boost units sold, so it was wasted margin. However, a niche outerwear style had very elastic demand – a small discount unlocked a flood of purchases. By applying Intelligence Node's recommendations, they scaled back discounts on the former and slightly increased them on the latter, yielding a higher overall profit and cleaner sell-through. The key learning is that not all products should be treated equal during sales; elasticity data can inform a very targeted promo strategy.

5. Pricefx – B2B Pricing and Tiered Deals

When negotiating large wholesale orders and B2B contracts, Pricefx is a go-to solution for price optimization. It's tailored to scenarios with complex pricing tiers, rebates, and customer-specific terms (think supplier agreements where price per unit drops if you order 1000+ units). Pricefx's analytics include elasticity modeling in the CPQ (Configure Price Quote) process, meaning it can calculate how a volume discount might affect demand and profitability. It also integrates seamlessly with big ERPs like SAP or Oracle for real-time data on costs and inventory.

Elasticity Curve: Volume Elasticity in Wholesale. This is about how price breaks influence order size. For example, as a wholesaler, if you cut the price of blank T-shirts by 5% at the 500-unit tier, will the retailer order significantly more? Pricefx can analyze past data and similar client profiles to predict that. If the demand is elastic with respect to wholesale price, offering that tiered discount could lead to a disproportionately large order (good for you, provided margin still holds). If it's inelastic, you're just giving away margin for little gain. By understanding this, you protect margins on staples – you might find that retailers would have bought 300 units of your black tees even without a discount, because they're core stock. So instead of reflexively granting a big price cut at 300 units, you hold your pricing until, say, 500 units where they truly need an incentive.

Strategic Use: Pricefx's insights can also guide trade promotions and rebate structures. Let's say outerwear is a category where retailers often over-order and then return or markdown unsold pieces. You could structure a deal where the wholesale price is higher (protecting your margin upfront), but you offer a rebate on any sell-through above 90% by end of season. This encourages the retailer to push sales and only rewards them if the demand was strong (and if not, your margin covered the risk). Pricefx would help model these outcomes – factoring in multi-source data from suppliers to stores – so you can craft data-driven agreements. In 2025's climate of cautious buying, such smart pricing partnerships build trust and profit for both sides.

6. Retviews (Lectra) – AI Market Intelligence at Scale

For larger brands and enterprise retailers, Retviews brings AI-powered competitive intelligence to the table. Now part of Lectra's suite, Retviews doesn't set prices for you; instead, it feeds you live market data so you can make the call. It tracks competitors' pricing, new product launches, and markdowns in real time. The tool is brilliant for category managers who need to understand elasticity on a broader scale – e.g., how price-sensitive is the women's outerwear category this winter in Australia? Retviews will show if all major retailers are slashing jacket prices early (signaling weak demand or high elasticity), or if full-price sell-through remains high (signaling robust, inelastic demand at the initial price point).

Elasticity Curve: Category-Wide Elasticity Trends. This is the macro view of elasticity. Retviews might reveal, for instance, that across dozens of brands, winter parkas consistently needed 30% markdowns by July to clear – consumers as a whole wouldn't buy at full price past mid-season. That trend is a curve every buyer should know: it suggests baking expected markdowns into your margin calculations. Alternatively, it could show that basic crew-neck T-shirts at value retailers barely go on sale at all (confirming those as inelastic essentials). Armed with these insights, you tailor your strategy: allocate budget to more inelastic items for steady profit, and be cautious with deep buys on items that history shows will require heavy discounting.

Enterprise Perspective: Large Australian brands using Retviews have noted improved strategic [decision-making and margin protection(https://www.lectra.com/en/fashion/products/retviews#:~:text=Alerts). One example is a department store chain's womenswear team that monitored competitors via Retviews and realized they could delay their winter outerwear markdown by two weeks – because no one else had dropped prices yet, indicating consumers were still buying at full price. That extra sell-through at high margin meant tens of thousands in additional profit. On the flip side, a fast-fashion player noticed through Retviews alerts that a key competitor suddenly slashed denim jacket prices by 40% in early autumn; they responded by accelerating their own promotion to not be caught with overpriced inventory. In both cases, the tool's AI alerts and data helped teams act quickly and confidently in tune with market elasticity.

Conclusion – Turning Insight into Action

In 2025, Australian apparel retailers and wholesalers face a market where data rules. Embracing these elasticity insights can be the difference between boosting your bottom line or getting stuck with excess stock. The six "elasticity curves" – from steady T-shirt staples to volatile outerwear – highlight that one-size-fits-all pricing is a thing of the past. By leveraging the right tools, even small and mid-sized businesses can apply the same savvy techniques as the retail giants: predictive analytics, real-time market intel, and responsive pricing strategies.

Ready to apply elasticity analysis to your pricing strategy? Cybergarden can help you get started. We specialize in helping Australian fashion SMEs implement the latest pricing tools and data-driven methodologies. Whether you need a lightweight solution for competitive tracking or a full-scale AI pricing engine, our team will guide you through selecting and integrating the right platform for your business. Don't leave money on the table – contact us today to future-proof your pricing and turn those curves into profit curves.

(Cybergarden is here to assist with tailored solutions in retail technology – from pricing analytics to full digital transformation for your fashion business.)