Price optimization: definition and numerical example

Price optimization involves calculating, for each product, the price that best serves a specific objective set by the retailer (margin, volume, or price image) while respecting certain constraints. These constraints might include, for example, a minimum margin, a maximum price difference with a competitor, consistency between formats within the same product line, or round prices. The calculation relies on the price elasticity of each product to predict the impact of a price on sales. It's not about finding the highest or lowest price, but rather the best compromise. The comprehensive guide to price optimization details these methods.

Price optimization seeks the best price for a given objective, while dynamic pricing refers to the frequency with which prices change. A business can optimize its prices and change them only once a month, for example, in stores with paper price tags. Conversely, a website that changes its prices several times a day simply by following a competitor is practicing dynamic pricing without necessarily optimizing. In practice, the two are combined: optimization sets the target price, and dynamic pricing adjusts it to the market's rhythm.

The gain from price optimization is measured in margin points and depends heavily on the starting point. A retailer that sets its prices using uniform coefficients or on a case-by-case basis generally has greater potential for improvement than a retailer already equipped with these tools. The gains come from two sources: raising the prices of less price-sensitive products that customers don't compare, and investing in those that shape the price image. To measure the results unbiased, test stores or product categories are compared to a control group over several weeks, with a constant product assortment.

To optimize pricing, you need at least historical sales and pricing data for each product, purchase costs, and past promotions. You also need competitor pricing, gathered through price monitoring, and current stock levels. The more price fluctuations the historical data contains, the better the model measures customer sensitivity. The data must be accurate: an unreported stockout can lead to the false impression that sales have dropped due to price. For products without historical data, you rely on comparable products in the same category.

No, price optimization changes the work of pricing teams without eliminating it. Instead of arbitrating prices item by item, they define objectives by category, the rules to follow, and the exceptions. The engine then calculates price proposals for thousands of items and explains each one. The teams validate, correct any special cases (new product launches, supplier negotiations, local events), and monitor the results. This approach works if the recommendations are understandable: a price that no one understands will not be applied in stores.

Price optimization typically begins with a pilot category, chosen for its significant revenue and reliable sales history. A clear objective is set, existing rules are formalized, and the elasticity of key product lines is measured. The initial optimized prices are tested in a subset of stores, compared to a control group, and then rolled out more broadly if the results are positive. This phased approach minimizes risk and fosters team buy-in. Booper supports this deployment with its price optimization software .