How Price Optimization Works

Price Optimization is Pricen's AI-powered feature that suggests the best price for each product based on how your customers have responded to prices in the past.
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Instead of guessing what price will maximize profit, the system studies your historical sales, builds a model of demand, profit and revenue for each product, and recommends the price that best meets your chosen goal, whether that's more profit, more revenue, or more units sold.

Importantly, Price Optimization does not change your prices automatically by default. It generates suggestions that you review and approve. This article explains how those suggestions are produced, so you know exactly what you're looking at. If you're looking for setup steps instead, see How to Create a Price Optimization Strategy.

Every price suggestion passes through the same sequence of steps. Each step refines the number before it reaches you for review.

Each of these steps has its own dedicated article: business rules in Which Pricing Actions Apply During Optimization, limits in How to Add Safeguards to Your Pricing Strategy, grouping in Pricing Groups – User Guide, and endings in How Price Rounding Works.

For every product, the system builds three models from your historical sales data:

Together these capture the core trade-off in pricing: a higher price earns more margin per sale but usually sells fewer units, while a lower price sells more units at smaller margins. The best price is the point where these values balance for the goal you've chosen.

The optimizer tests a wide range of price points, from near cost level up to around three times the current price, and picks the one that best achieves your objective.

Because it explores such a broad range, the system can recommend a price increase as readily as a decrease. If a product's demand pattern shows customers will pay more without buying much less, the AI will suggest increasing the price. This is common for products that turn out to be underpriced.

Each product is treated on its own terms. A price-sensitive everyday item might lose a lot of sales from a small increase, while a premium product might adapt a larger increase with little impact. The optimizer respects these differences rather than applying blanket rules.

The optimizer's core output is a price multiplier, the suggested price expressed as a ratio of the current price:

When suggestions are ready, each product shows a full breakdown so you can see exactly how the number was reached:

This transparency lets you understand why each price was suggested and which rules shaped the final number. If a product shows a confidence level of rule_based, or you're wondering why it wasn't optimized, see Why Some Products Aren't Optimized.