Price elasticity in the agri-food sector:Estimate the cost of a rate increase before negotiating
A manufacturer that raises its list price does not set the price on the shelf. Between the negotiated list price and the displayed price lie the retailer, its markup, and its promotions. What is measured is the price elasticity of each product, retailer by retailer.
In 2026, food manufacturers requested an average price increase of 3.5% from major retailers. At the end of the negotiations, the average net price rose by only 0.05%. A year earlier, the gap was already clear: a 5.7% increase was requested, but only 1.5% was granted. These results illustrate a simple point: a price increase is not decided unilaterally. It is negotiated, it can be scaled back, and the negotiated price is not the price the consumer pays.
The industrial rate does not determine the shelf price
In a B2B2C supply chain, the manufacturer sells to the retailer at a transfer price. The retailer then sets the price displayed in stores and online. Consumers respond only to this second price—the one on the label—even though the manufacturer has no control over it.
A 3% increase in the wholesale price can therefore result in a 0%, 1%, or 3% increase on the shelf. It all depends on how much the retailer passes on, how much it absorbs into its margin, and how much it offsets elsewhere—through a promotion or another product in its lineup.
The relevant question for a manufacturer is therefore not “by how much can I raise my price?” It is framed differently: how much volume will I lose if the shelf price rises by a certain amount, store by store, and what margin will I retain once negotiations are complete?
Average elasticity is not enough
A price elasticity calculated across the entire product line is rarely the deciding factor. It varies depending on the product, its position on the shelf (an entry-level product does not react the same way as a leading brand), the retailer, and the season. The average masks the products where a price increase results in only a small loss of volume and those where it results in a significant loss. Yet that is precisely where the trade-off lies.
To measure it accurately, you need at least two years of price history and average weekly sales data by SKU and by retailer. You also need the variables that explain the rest of the demand: seasonality, holidays, competitors’ promotions, and weather for certain categories. Without them, the model attributes to a price increase what is actually due to a month of high demand.
Calculating price elasticity lays the groundwork, and the cluster on elasticity by product and by store shows how to measure it on a per-SKU basis.
Separate the regular stock from the sale items
An increase in volume during a promotion says nothing about the product’s price sensitivity at the regular price. We need to distinguish between three things: the baseline—that is, the sales that would have occurred without the promotion; the promotional effect; and the truly incremental volume. Part of this volume often comes from another product in the line, which loses sales at the same time (cannibalization). Another portion is simply a shift in sales from one week to the next.
Without this distinction, a price increase appears less costly than it actually is. The promotional volume inflates apparent demand, and the manufacturer makes a decision based on a signal that does not exist at the normal price.
The regulatory framework is also more restrictive in the food sector. The Egalim Act regulates promotions for these products, which limits promotional leverage and makes the baseline all the more important.
Simulate the impact, store by store
There are three possible scenarios.
The impact is full when the shelf price matches the list price.
It is partial when the company absorbs part of the increase.
It's worthless when the franchisee keeps the entire amount, which also happens when prices are lowered.
Each of these cases results in a different break-even point for the manufacturer.
A simulation that shows only the volume lost on the manufacturer’s side will not convince the buyer. The retailer thinks in terms of profit margins by category. It is therefore necessary to quantify both sides for each scenario: the manufacturer’s volume and margin, and then the retailer’s margin.
A good simulation highlights the price ranges where a price increase results in minimal volume loss. That is where a price increase is justified, and that is where it is worth focusing trading efforts. The platform’s simulator compares various price levels and their estimated effects on sales, inventory, and margin before a price is set.
What a tool must demonstrate before being selected
A forecast accuracy figure announced without its metric or scope doesn't mean much. A good test is to ask for a demonstration using a real-world example from your product line and to check five points:
The last point often causes more problems than the others. Many manufacturers have market data under contract but aren't sure whether that contract covers their use of the data in a third-party tool. It's best to ask this question before exporting a file for the first time.
Sources
- Trade Negotiation Observatory, 2026 Report, press release dated April 23, 2026: average wage increase of 3.5%, net price increase of 0.05% as of March 1, 2026, approximately 42 billion euros in revenue.
- Trade Negotiation Observatory, 2025 Report, April 22, 2025: requested increase +5.7%, achieved increase +1.5%.
- Egalim Act of April 14, 2025, regulations on promotional offers, DGCCRF fact sheets: economie.gouv.fr
Last updated: October 6, 2026.
A manufacturer that raises its list price does not set the price on the shelf. Between the negotiated list price and the displayed price lie the retailer, its markup, and its promotions. What is measured is the price elasticity of each product, retailer by retailer.
The volume sold during a promotion does not indicate whether it created value: part of it comes from similar products (cannibalization), and another part from purchases that were simply brought forward.
Each promotional scenario must be costed out prior to launch using the same metrics: base sales, actual incremental sales, cannibalization, carryover, halo effect, net margin for the category, and cost per unit actually gained.
Margin-volume arbitrage can then be explained: a stated objective, visible forecasting factors, constraints adhered to, and a documented validation process.
A national food retailer with more than 1,700 stores and several million price points per year: With Booper, its pricing teams simulate the impact of each decision on margins, competitiveness, and price perception before implementing it.