Cross-price elasticity

Cross-price elasticity measures how the demand for one product responds when the price of another product changes, revealing whether the two products are substitutes, complements, or unrelated in the eyes of customers making a purchase decision. It extends the idea of price elasticity beyond a single item to the wider set of products a customer is actually choosing between.

How cross-price elasticity works

Cross-price elasticity is calculated by dividing the percentage change in demand for product A by the percentage change in price for product B. A positive value means the products are substitutes - raising the price of one pushes customers toward the other, like two competing brands of soda sitting on the same shelf. A negative value means the products are complements - raising the price of one reduces demand for both, like coffee makers and coffee pods. A value near zero means the products are largely unrelated in customer decision-making, even if they happen to sit in the same store.

Retailers use this concept most heavily in category management, where the goal is not just to price each item well individually, but to understand how a change to one item's price will ripple across everything else customers might buy instead of it, or alongside it.

• Positive value: products are substitutes
• Negative value: products are complements
• Near zero: products are unrelated
• Calculated as percent change in demand A over percent change in price B

Example

A retailer running a routine quarterly price review raises the price of its store-brand orange juice by 10% and observes that sales of a competing national-brand orange juice rise by 6%, while sales of breakfast cereal in the same store fall by 2%. The orange juice brands show positive cross-price elasticity of 0.6, confirming they are substitutes, while cereal shows mild negative cross-price elasticity, suggesting some cereal purchases are paired with orange juice purchases and drop off when juice becomes more expensive.

Why it matters for retailers

Pricing decisions made in isolation, SKU by SKU, can miss how a price change ripples across related products. Raising the price on one item might quietly boost sales of a private-label alternative, or unexpectedly dent sales of a complementary product, both of which affect total category profit in ways a single-item view would never show. Category-level pricing strategy depends on understanding these relationships well enough to model the full impact of a change, not just the direct one. Retailers who ignore cross-price elasticity can end up optimizing individual items while the category as a whole underperforms, chasing a small win on one SKU while quietly giving up a larger one nearby.

How Retailgrid helps

Retailgrid's price optimization software can model how a price change on one SKU is likely to affect related products, not just the item itself. Competitor price analysis adds visibility into how rival products are priced in the same category, helping retailers anticipate substitution effects with the help of the AI workspace before a price change goes live rather than discovering them after the fact.