Most stores price products based on costs or competitors. But the best price isn't about what it costs you—it's about what customers are willing to pay.
Pricing is one of the most powerful levers in e-commerce. A small change in price can have a bigger impact on profit than changes to costs or volume. Yet most merchants set prices using methods that ignore the most important factor: what customers are actually willing to pay.
These approaches are common because they're simple. But simplicity comes at a cost.
"My cost is $10, so I'll sell it for $15." Simple, but ignores what customers will actually pay.
The problem:
"Competitor X charges $20, so I'll charge $19." Feels safe, but leads to a race to the bottom.
The problem:
"If inventory is above 100 units, reduce price by 10%." Many apps call this "optimization," but it's really just automation.
The problem:
Value-based pricing sets prices based on what customers are willing to pay—not what the product costs you, and not what competitors charge.
The core insight: for any product, there's a relationship between price and how many people buy it. If you understand that relationship, you can find the price that maximizes your revenue or profit.
Higher prices mean fewer sales. But the relationship isn't linear—and the optimal point depends on your specific customers.
Uses your actual sales history to understand how customers respond to price changes. No guessing.
Each product gets its own optimal price based on its unique demand characteristics. No blanket markups.
Finds the price point where you make the most money—balancing higher margins against lower volume.
You can see exactly why a price is recommended—the demand curve, the trade-offs, the expected outcome.
Price sensitivity (or "price elasticity") measures how much sales volume changes when you change the price. This is the key to finding the optimal price.
When price increases by 20%, sales volume drops by 30%—this product is price sensitive.
A small price increase causes a large drop in sales.
Common for: products with many alternatives, luxury items, discretionary purchases
A price increase has little effect on sales volume.
Common for: essential products, unique items, products with loyal customers
Price sensitivity isn't a fixed property of a product—it changes depending on the current price. At higher prices, customers tend to be more sensitive to increases. At lower prices, they're less sensitive. This is why finding the optimal price requires analyzing the full demand curve, not just labeling products as "elastic" or "inelastic."
A demand curve shows the relationship between price and sales volume. From this, you can derive revenue and profit curves—and find the price that maximizes either.
Hover over the charts to see how demand, revenue, and profit change at each price point. Tap the charts to explore different price points.
Shows how many units sell at each price. Higher prices mean fewer sales—but the relationship isn't linear.
Revenue = Price × Volume. There's a sweet spot where you balance higher prices against lower volume.
Profit = (Price - Cost) × Volume. The peak shows your optimal price—often higher than max revenue.
The more historical data, the better. Ideally, you'd have data that includes some price variation—times when prices were higher or lower. Even a few months of sales data can provide useful insights, though recommendations become more accurate with more data.
If a product has always been sold at the same price, we have less information about how customers would respond to different prices. In these cases, recommendations are more uncertain. The app will show you the confidence level for each recommendation.
Yes, higher prices typically mean lower volume. But the question is whether the extra margin per sale outweighs the lost sales. Value-based pricing finds the balance—and shows you exactly what trade-off you're making. You always control how aggressive the recommendations are.
A/B testing requires running experiments where different customers see different prices—which can be legally and ethically tricky, and takes time. Value-based pricing analyzes historical data to estimate demand without running experiments. It's faster and uses data you already have.
It depends on your goals. Optimizing for revenue maximizes your top line—useful if you're focused on growth or market share. Optimizing for profit maximizes your bottom line—useful if you're focused on profitability. Most established businesses should optimize for profit.
Every recommendation comes with a confidence level. More data and more price variation in your history means higher confidence. You can also set a "certainty threshold"—choosing to only see recommendations where we're confident the outcome will be positive.
Good question. Demand can change based on season, marketing campaigns, or market conditions. The model accounts for time-based patterns in your data, but significant external changes may require re-evaluating recommendations. This is why regular re-analysis is valuable.
Sell Smart Price Optimization brings smart pricing to Shopify merchants. See exactly what your optimal prices are—and how much you could gain.
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