When you sell clothing and apparel, new collections aren’t the exception, they’re the rhythm of the business. And with every new drop comes a familiar task: setting prices for products that have never been sold before.
That first number? It’s what we call the base price (also known as the initial price). It’s the price you assign before discounts, promotions, or markdowns enter the picture – and it sets the tone for everything that follows: perceived value, profit margins, markdown strategy, and your brand’s positioning in the market.
Get that number wrong, and you could be leaving margin on the table. Too low, and there’s no room to move. Too high, and the product stalls on the rack. And the kicker? You’re often making that decision without any sales data, months before launch.
This post is about how to get base pricing right, from classic methods like cost-plus to more advanced, AI-powered approaches that work even at the design stage – before anything’s been produced.
Why base pricing matters in fashion
Base price is not just a number, it’s a strategic anchor.
It determines how your product is perceived, how much profit you can make, how competitive you’ll be when collections hit the floor, and how flexible you’ll be when markdown season rolls around. In short, it influences:
Your brand’s market positioning: Premium, mass, sustainable; value—price is a signal.
Your competitive edge: Especially during new launches or trend-led drops.
Your margin potential: A strong base price gives you room to maintain healthy margins, without needing to fall back on aggressive discounts.
In a category defined by trend volatility and shorter product lifecycles, the base price becomes your first (and sometimes only) shot at setting a product up for success.
Traditional approaches to base pricing
1. Cost-plus pricing
Cost-plus pricing is still the go-to for many brands. It’s simple: calculate the full cost to make and market the product, then add your desired markup.
What’s typically included:
- Production costs: Fabric, trims, labor, factory overhead
- Operational costs: Shipping, warehousing, packaging
- Commercial overhead: Marketing, photography, influencer fees, platform commissions
For example, if your product costs €30 to produce and you want a 60% margin, your base price is €75.
It works because it’s easy to calculate and it guarantees coverage of costs and target margins.
But it ignores what customers are willing to pay, it doesn’t consider competitive context, and such a method is not adaptable across diverse categories and brand tiers.
2. Market-aligned pricing
The market-aligned approach sets your base price based on what similar products in the market are charging. Think competitive benchmarking, keystone markup rules, or channel-based price ladders.
Tactics include:
- Competitive benchmarking: Tracking price points of similar styles in your category
- Channel-based pricing: Setting different base prices for e-commerce, wholesale, and retail (note; as omni-channel alignment becomes the norm, price variation can create friction or confusion)
- Brand positioning alignment: Reflecting aspirational or accessible brand values through price
The upside is that it keeps you in step with market expectations, but the issue is that you’re reacting to the market – not leading it. Such a tactic relies too much on external benchmarks and not internal performance data.
NYDJ elevates their pricing with strategic partnership with Pricen
“Our collaboration with Pricen signifies a pivotal moment for NYDJ, aligning seamlessly with our mission to provide comfortable and flattering jeans that fit women of all shapes, sizes and ages at compelling prices.”
Tara Ragan, Executive Vice President of Planning and Operations at NYDJ
The real challenge: pricing before the product exists
This is where things get interesting – and risky.
In fashion, pricing often happens during the early design or line planning phase, long before a product sees the light of day. At this point:
There’s no customer feedback
No performance/sales data
No visibility into how demand will shape up: you risk setting a price that’s too low to profit or too high to convert
You’re making decisions based on projected costs, past assumptions, and a moodboard.
And if you’re wrong, the ripple effects show up months later in markdowns, margin erosion, and unsold inventory.
So how do you bring more precision to a phase that’s historically run on gut?
A smarter way: AI-powered base pricing
At Pricen, we’ve built a model that helps you set optimal base prices, at the exact moment when you need the most help: before launch.
It combines a large language model (LLM) combined with historical pricing and elasticity data to recommend realistic, high-confidence price points – before the product is even finalized.
How it works:
Start with product context
Feed in structured info: category, materials, target customer, brand tier, seasonality, etc.Model interpretation
The LLM reads and understands the product, then matches it to similar items in your catalog.Data enrichment
It pulls relevant historical data: price points, demand curves, seasonality trends, and elasticity indicators.Pricing suggestion
The model recommends a price based on real-world performance of similar products. Not just gut feel or a standard markup.
Example:
You’re pricing a linen summer dress. Cost-plus suggests €79, competitive analysis says €99. The AI model? It recommends €89.90 – because that’s the sweet spot where similar dresses converted best, based on past seasons and actual demand signals.
It’s not magic. It’s machine-backed memory and pattern recognition, aligned with real-world buyer behavior.
Why AI helps (a lot)
- Make pricing decisions earlier and with more confidence
- Reduce pricing errors that lead to excessive markdowns
- Align pricing with customer expectations from the start
- Unify pricing logic across teams and channels
- Ensure consistency across categories, collections, and regions
- It gets smarter over time: the more you use it, the better the recommendations get
Comparison: traditional vs AI-assisted base pricing
Method | Pros | Cons | Best for |
Cost-plus | Simple, margin-driven | Ignores demand and market dynamics | Entry-level pricing discipline |
Competitive-based | Competitive, brand aware | Lacks internal performance data | Brand & product positioning |
AI-assisted | Data-driven, predictive, scalable | Needs clean historical data and good product tagging | Strategic planning & optimization |
Getting started with AI-assisted base pricing
You don’t need a full AI transformation to start pricing smarter. Here’s how to begin:
- Structure your product data: Use consistent tags for season, style, materials, our customer segments etc.
- Organize historical pricing and performance: The richer your dataset, the better the predictions.
- Run a pilot: Start with a product line in development and compare AI-suggested pricing to traditional estimates.
- Test and learn: Track which method leads to better outcomes across margin, sell-through, and returns.
Over time, you’ll build a pricing system that thinks ahead – and fits your business like a custom-tailored garment.
Conclusion
In fashion, your base price is a signal. It tells the market what you stand for, what your product is worth, and what kind of margin story you’re trying to write.
Traditional methods still have their place. But if you want to price with confidence, earlier in the process, and based on real behavioral data – not just costs or competitors – AI has changed the game.
With Pricen’s AI-powered base pricing, fashion brands can now set smarter prices during the design phase. This means before a product hits the line sheet, before the budget locks in, before you’re forced to markdown your way out of a mistake.
That’s how you price to win.
