The Role Of AI In Predictive Dynamic Pricing

This article was contributed by Tomi Grönfors, founder of Pricen. Shero partners with platforms like Pricen to support merchant pricing strategies and help brands stay competitive in fast-moving markets.

Let’s start with a truth: Pricing is the only part of your business that directly impacts profit. Change it, and the impact hits your bottom line today, not next quarter.

It used to be enough to review prices monthly or run a few simple rules in a spreadsheet. Today, that’s a liability. Shoppers are faster, more informed, and more price-sensitive than ever. They carry a price-comparison engine in their pockets. And your competitors? They’re adjusting prices hourly, not seasonally.

Price is no longer a static tag; it’s a moving target. And it moves faster than ever.

To keep up, businesses turn to data. And while there’s more data available than ever, that doesn’t mean pricing has gotten easier. In fact, the opposite is true. More data often means more noise, and more ways to get it wrong. Competitor tracking tools and rule-based repricers are a step in the right direction, but they often leave teams reacting instead of anticipating. You’re constantly playing catch-up, adjusting prices after the market moves rather than before.

This is where AI-driven predictive pricing changes the game.

What is predictive dynamic pricing?

Let’s break it down:

Dynamic pricing means your prices change based on real-time conditions: demand, inventory levels, competitor moves, time of day, you name it. Maybe a competitor drops the price on a product you also sell. Maybe you’re nearly out of stock and don’t need to compete. A dynamic pricing engine adjusts for that, and fast.

Predictive is the real unlock. It’s where AI gets involved. Instead of reacting to what just happened, predictive models estimate what’s likely to happen next. If you raise the price, how will that affect a purchase? If you discount, will it drive more sales, or just cannibalize margin?

Dynamic pricing is already widely used in e-commerce and retail. Most companies track competitor prices and adjust their own accordingly. But this is a somewhat reactive method, which often fails to optimize margins or sales in a meaningful way.

AI pricing introduces a proactive element. It doesn’t just react to competitors; it predicts what will happen if you change a price based on competitor prices.

At the heart of this is price elasticity – a way to measure how demand changes when price moves. Hotels and airlines do this in near real-time as they sell high amounts of tickets every minute. But in retail, many products don’t sell frequently enough to get reliable elasticity data instantly.

That’s where cross elasticity becomes invaluable. It measures how changes in the price of one product affect demand for others. This includes:

• How competitor price drops affect your sales
• How complementary or substitute products influence what customers buy

For example, a competitor drops the price of a shirt similar to yours, and your sales dip shortly after. Your AI pricing system spots this pattern, learns the connection, and uses that insight to recommend smarter pricing moves, like gently matching their discount only when it actually impacts your sales.

The smartest platforms use cross-elasticity data to craft pricing strategies that keep you competitive without throwing away margin.

A Shopify store example

Imagine you run a clothing Shopify store, and one of your best sellers is a linen shirt. Let’s say:

• Your competitor drops their similar linen shirt from €99 to €79.
• Within 48 hours, your sales start to dip.
• Your AI-powered system sees this pattern repeating from previous months.

Rather than just matching the €79 blindly, the system:

• Sets the price to 81,90€. That’s how much customers are actually willing to pay for your brand.

This is predictive dynamic pricing with cross elasticity in action. Instead of matching every competitor move, it reacts only when it matters to your sales.

When to use AI vs. basic dynamic pricing

Basic dynamic pricing tools (e.g. PriceShape, Price2Spy, Prisync) are great if you want to monitor competitor prices and react quickly. If your catalog is small or you’re just starting with dynamic pricing, these tools are fast to set up and cost-effective. They automate rule-based decisions like matching the lowest price or adjusting prices based on set margins.

But if you want to level up, AI-powered platforms like Pricen, Competera, PriceFX, or Relex take things several steps further:

• They forecast demand shifts before they happen
• They understand how pricing influences both margin and volume
• They optimize for multiple goals at once (e.g., sell-through and profitability)

If you’re managing a large catalog or several stores, scaling at an industrial level is crucial. In that case, choosing a larger player that handles complexity at scale will be the optimal choice.

What kind of data do you need?

AI engines don’t work magic without fuel. To make predictive dynamic pricing effective, you need to feed them rich, structured data, such as:

• Historical sales data: By product, day/week/month, including returns
• Competitor pricing history: Ideally over time, so patterns emerge
• Inventory levels and movements: Critical for time-sensitive or seasonal goods
• Clickstream and conversion data: To track consumer interest and dropout points
• Product attributes: Brand, color, size, category, seasonality tags
• External factors: Weather data, promotions, macroeconomic signals

How AI works in pricing

Here’s what happens behind the scenes when AI suggests your next price move:

1. Data ingestion: AI pulls in millions of data points, structured and unstructured. We’re talking everything from your Shopify sales and competitor prices to weather patterns, customer click behavior, and beyond.
2. Pattern recognition: The algorithm digs deep, spotting connections that would take a human forever to find. For example, it might notice that your wool coats sell better two days after the temperature dips below 5°C.
3. Forecasting: Based on these patterns, AI estimates what will likely happen if you change a price. Think: “A 5% price bump might lower sales by 3%, but boost margins by 8%.”
4. Optimization: Finally, it suggests the best price, aligned with your goals. Whether that’s maximizing profit, speeding up sell-through, or keeping customers loyal.
   

Here’s a quick example of what it looks like with Winter boots:

• Current price: €79.90
• AI recommended price: €84.90
• Forecast with AI-recommendation:
  • Sales volume: ↓ 6%
  • Gross margin: ↑ 12%
  • Inventory turnover: ↑ 9%
    

Why AI beats traditional pricing

Let’s be honest: most traditional pricing methods are stuck in the past. They either

change too slowly or not at all, and neither will cut the rate of the market today.

• Static pricing: Prices are fixed until someone manually updates them.
  • Problem: You miss windows of opportunity and end up sitting on outdated prices. And unmoving inventory.
    
• Cost-plus pricing: You take your costs, add a margin, and call it a day.
  • Problem: It completely ignores what customers are willing to pay.
    
• AI-based predictive pricing:
  • Reacts in real time based on relevant market signals
  • Prioritizes profitable reactions
  • Reduces risk by forecasting before acting

Real benefits of AI-driven pricing

So what do you actually get when you put AI behind your pricing?

• Higher margins: because you’re charging the price people are willing to pay, not just the one that “sounds about right”
• Faster stock turnover: especially for products that can’t afford to sit around (seasonal and perishable items)
• Precision promotions: target discounts where they’ll move the needle, not where they’ll just burn margin
• Product lifecycle clarity: Launch, optimize, discount, and exit efficiently

Who should be using AI pricing?

If you’re a retailer and you’re in a fast-moving category like apparel, electronics, or groceries, AI pricing isn’t a nice-to-have. It’s essential. Your products turn fast, your customers brand-switch quickly, and your competitors aren’t waiting around.

The same goes for D2C brands. You’re up against bigger players with deeper pockets and broader reach. Your edge has to come from speed and precision, knowing when to push, when to pull back, and how to stay just competitive enough without killing your margins. AI gives you that edge.

And if you’re part of a pricing team still buried in spreadsheets? You already know the pain. Thousands of products, multiple markets, pricing rules layered on top of each other, and updates that need to happen now. AI helps scale the decision-making and lets your team focus on strategy, not just firefighting.

Getting started with predictive pricing and cross elasticity

1. Audit your current pricing workflow: What’s manual? What’s reactive? Identify where your team is still relying on delayed decisions.
2. Define your pain points: Are you overstocked? Missing sales windows? Shrinking margins? Clarity here sets your direction.
3. Collect clean data: You’ll need historical sales, inventory levels, competitor pricing, and customer behavior. Structured, consistent, and recent.
4. Test with one category or campaign: Start small. Prove the impact. A narrow pilot gives you learnings without overwhelming your ops team.
5. Choose your platform: Whether it’s Pricen or another AI-driven solution, pick a tool that fits your business complexity and can grow with you.
6. Scale and iterate: AI improves with volume and feedback. The more data it sees, the sharper it gets. Every pricing cycle becomes smarter than the last.

Final thought: Predictive means profitable

In a world where prices change faster than customer attention spans, predictive pricing isn’t a luxury- it’s survival.

Let algorithms handle the complexity. Let your team focus on strategy.

ABOUT PRICEN

The article is written by Tomi Grönfors, the CEO & Co-Founder of Pricen, an AI-driven dynamic pricing solution for retail and eCommerce companies globally.