Blog: Pricing & AI—Retail Analytics and Strategies

Managing your pricing strategy with a tool involves combining a volume-weighted competitive index, customer perception, and the internal consistency of the product line. None of these three sources is sufficient on its own.

A robust solution must identify the products that shape the brand image, explain how the index is weighted, and link a price change to a measurable effect on perception. You cannot manage a price image using an average price difference or based solely on the lowest price.

Measuring price elasticity for a catalog does not involve any complex calculations. The challenge lies in isolating promotions, out-of-stock situations, and cross-product effects, and then keeping track of thousands of elasticity values and linking them to margin.

Excel is enough to understand how the mechanism works for a few products. A data team builds good models but struggles to scale them. A dedicated solution covers the entire catalog on an ongoing basis, provided it proves its value using your own data.

When choosing sales forecasting software, there are five key points to check before conducting any tests: demand hidden by stockouts, the forecast granularity, an explanation of each figure, the relationship with prices and promotions, and an honest assessment of accuracy.

A demonstration doesn't prove anything on these points. Only a test using your own data, compared to a simple baseline, will show whether the forecast truly improves your decisions.

A promotional management tool is judged by its ability to measure the actual profit margin generated by a promotion—after accounting for cannibalization, deferred purchases, and early purchases—and to forecast that margin before committing to the promotion.

He must also review each transaction against a non-promotional reference price and, before the promotion begins, verify the 30-day reference price, the break-even threshold, and the Egalim price caps.

Choosing a markdown tool means, first and foremost, calculating the cost of a poorly planned markdown: giving up margin too early, applying markdowns too uniformly, leftover inventory, wasted team time, and poorly maintained 30-day reference prices.

Excel is sufficient for a small scope; the internal tool holds its own with a dedicated data team. Beyond that, a dedicated solution must demonstrate that it runs simulations, makes decisions on a per-store basis, explains its recommendations, and ensures compliance on its own.

Reviewing a sale ad involves verifying, prior to publication, its list price, compliance with the no-loss resale threshold, and promotional caps. At the network level, this review can no longer be done manually.

A pricing tool shifts control to the moment the price is set: price history organized by channel, calculated price ranges for each product, and a record of every decision. However, it must natively incorporate French regulations.

The no-loss resale threshold prohibits reselling a product for less than its actual purchase price.

For food products, it is increased by 10% through April 15, 2028, and discounts are capped at 34% by value and 25% by volume; for household goods, cosmetics, and personal care products, the cap is 40% by value.

These rules establish a minimum threshold that adjusts based on each purchase condition and limit promotional mechanisms. They must be integrated into the pricing engine as thresholds calculated on a per-product basis, rather than being enforced retroactively.

The reference price is the lowest price charged in the 30 days prior to a price reduction.

Any advertisement for a discount must display the original price, and following a September 2024 ruling by the Court of Justice of the European Union, the discount percentage must be calculated based on that price.

A temporary price drop therefore becomes the benchmark for one month. Successive price reductions and perishable products are subject to specific rules. Applying the rule requires a dated price history, organized by product ( product by product ) and by channel.

Price compliance refers to the legal rules that a price and its display must follow.

In France, four rules apply: the reference price for discount advertisements, the prohibition on reselling at a loss, the EGalim promotional caps, and price disclosure requirements.

These rules are simple to state but difficult to apply to thousands of products and multiple channels. Most discrepancies stem from incomplete data at the time the price is set, not from any intention to mislead.

Selecting and implementing a pricing tool is a four-step process: defining the need, choosing the solution, demonstrating its value in a real-world setting, and then implementing it with the data and teams.

Projects that fall short rarely choose the wrong software. Instead, they define the problem poorly from the start, overgeneralize without restraint, or underestimate the data and the teams’ buy-in down the line.

A pricing analysis is an audit of a company’s pricing policy based on its actual data: sales, margins, pricing schedules, promotions, and competitors’ prices. It examines the consistency of prices, market positioning, price image, and promotions.

It identifies which discrepancies warrant action, along with the expected return, and organizes them into a detailed roadmap. An average “ Price Index ” is often misleading: when weighted by sales, it shows that just a few dozen benchmark products determine the price image.

A pricing proof of concept (POC) does not require perfect data, but rather data that is appropriate for what it needs to demonstrate, with known limitations. The product catalog, sales data, prices and promotions, purchase costs, and inventory levels form the foundation; the granularity and depth of this data depend on the test’s objective.

Before calculating a profit, the recalculated revenue, volumes, and margin must match those in management accounting. A known and documented error does not skew the comparison; a hidden error does.

A pricing POC is a limited test, conducted using a retailer’s actual data, that compares a solution’s pricing decisions to a baseline scenario measured in the same way. It proves nothing if the scope, baseline, metrics, duration, and resources are not established beforehand.

Three categories of metrics should not be confused: model performance, economic gains, and team usage. And the gain measured within a selected scope is a ceiling, not an average: it should be extrapolated with caution, based on documented assumptions.

A SaaS pricing management platform centralizes a retailer’s prices, rules, competitor prices, and forecasts in a browser-based tool. In the retail sector, it delivers responsiveness, measurable margin gains, native compliance with French regulations, predictable costs, and scalability.

Its limitations are just as clear: it does not correct poorly matched data, outdated rules, or an organization lacking leadership. Booper, a French software company founded in 2014, manages more than 4,000 retail locations and aims to increase margins by 0.5 to 3 percentage points within a few months.

A franchisor cannot impose a minimum resale price: doing so is punishable by law; the franchisor may only recommend a price or set a maximum. A franchisee’s margin is therefore governed by a documented target, defined by network segment and applied uniformly across each segment.

The best indicator of this governance is not adherence to the suggested retail price, but the rate of compliance with it by segment. A suggested retail price that franchisees in a given area do not follow often signals a poorly set price, not a lack of discipline.