Measuring the price elasticity of its catalog: Excel: Data Team or Tool?
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.
Everyone agrees that customers' price sensitivity matters. Very few companies actually measure it, product by product, and use that information to set their prices.
This article is intended for pricing, sales, and category management teams that are considering implementing a system to measure the price elasticity of their product catalog.
He compares three options—Excel, an in-house data team, and a dedicated solution—and explains what a tool must demonstrate before it is selected.
Measuring Price Elasticity in a Catalog: What Are We Talking About?
Measuring price elasticity for a catalog involves estimating, for each SKU, how much its sales vary when its price changes, while controlling for factors other than price: promotions, out-of-stock situations, seasonality, and cross-product effects.
It also means keeping these estimates up to date, since a customer's price sensitivity changes based on the market, competitors, and product assortment.
The formula itself fits on a single line, and our guide to price elasticity of demand—including its definition and calculation— explains it in detail with a numerical example.
Our professional perspective:The challenge isn't calculating a single elasticity value; it's keeping thousands of them up to date and using them to make decisions.
A product manager knows how to calculate the elasticity coefficient for three products. No one does this by hand for twenty thousand SKUs every month, taking into account the competitor's promotions.
Why Price Elasticity Becomes More Complex at the Catalog Level
For a single product with its own sales history, the analysis is a simple exercise. For an actual product catalog, however, three challenges arise simultaneously, and any one of them is enough to skew the decision.
Promotions and stockouts are causing confusion
A surge in sales volume during a promotion says nothing about a product's price sensitivity at regular retail prices. Mixing the two gives the impression that a product is more price-sensitive than it actually is.
Conversely, a disruption causes sales to plummet, regardless of price. If this is not detected, the model concludes that a price increase drove customers away.
These data pitfalls are discussed in detail in our article on robust price elasticity models using AI. They account for most of the anomalous elasticities we see in existing datasets.
The products influence each other
Lowering the price of a product often shifts sales away from its neighboring product on the shelf. This isknown ascross-elasticity, and it’s what turns a price cut that’s profitable on paper into a loss for the category.
Measuring these effects requires cross-referencing items two by two. The number of pairs increases exponentially as the catalog grows, as demonstrated by our method for analyzing cross-elasticity, cannibalization, and the halo effect.
Elasticity diminishes with age
Price elasticity measured before a period of inflation, the arrival of a competitor, or a product line change is no longer very meaningful six months later.
The true cost of the process, therefore, lies in its repetition: re-estimating, verifying, and correcting—for each product and at the start of each new sales period.
Only 11% of retailers actually incorporate consumer price sensitivity into their practices, even though 83% recognize its importance.
(Diamart Group, 2026 pricing study among French retail decision-makers, February 2025)
This gap between belief and practice is not due to a lack of will. It is a matter of tools and capabilities, which each option addresses to a greater or lesser extent.
Excel, an in-house data team, or a dedicated solution: what does each option offer?
There are three approaches available to a pricing or sales department that wants to measure the price elasticity of its product catalog. They differ in scope, cost, and their impact on decision-making.
| Criterion | Excel | In-house data team | Dedicated Solution |
|---|---|---|---|
| References Covered | A few dozen, tracked manually | Thousands, depending on the models built | The entire catalog, item by item |
| Cross-Elasticities | Out of Reach in Practice | Possible, but only after extensive development | Calculated using cannibalization |
| Sales and Out-of-Stock Items | Hand-cleaned, often overlooked | Processed if the history is documented | Isolated before each estimate |
| Update | One-time recalculation, quickly outdated | Depends on the team's availability | Continuous recalculation with each new data point |
| From Measurement to Decision-Making | Separate calculation of the margin | Template to link to pricing tools | Margin Simulation and Business Rules |
| Main Cost | Team Times and Errors | Rare Profiles and Sustainable Maintenance | Subscription; proof required in the margin |
Measuring Price Elasticity in Excel: Useful for Understanding, Not for Management
A spreadsheet remains the best tool for understanding how a few flagship products work, or for verifying an order of magnitude before negotiations.
It breaks down as soon as it's time to clean up historical data, cross-reference entries, and recalculate every month. Formulas multiply, versions diverge, and no one knows anymore which elasticity was used at what price.
We detail these break-even points within Excel to help you manage your pricing.
An in-house data team: good models, but challenges with scaling
A data science team can build robust elasticity models, including cross-price elasticity models. The challenge comes after the prototype: feeding data into the system every week, monitoring deviations, and integrating the model with the tools used to set prices.
Only 26% of companies have the capabilities needed to move beyond proof of concept and derive tangible value from AI.
(BCG, “Where’s the Value in AI?”, October 2024)
These professionals are rare, sought after by other projects, and when they leave, they often take some of their knowledge with them.
A Dedicated Solution: Continuous Measurement—Provided It Can Be Proven
A specialized solution provides models that have already been validated against historical distribution data and are recalculated for each new data point across the entire catalog.
It comes at a subscription cost, which is only justified if it results in better, measurable pricing decisions. That’s what you should ask them to demonstrate—using your data, not just a theoretical example.
From Measurement to Decision: Where Profit Margins Are Determined
Flexibility that remains confined to a report is of no use. Its value becomes apparent when it is linked to the margin, the product's role, and the brand's guidelines.
The same price reduction does not cost the same amount depending on the product'Gross margin .
Three optimal prices, a choice guided by the category's role
At the Barbotteau Group, a multi-brand retailer in the French Caribbean, pricing was managed in Excel using a single " Markup " for the entire store.
The group moved directly to a hybrid approach combining business rules and AI. Each product now receives three optimal prices that maximize volume, revenue, or margin.
The role of the category (traffic, revenue, or margin) guides the decision. The result:a one- Markup -point increase in one quarter for the products tested, before the initiative was rolled out to all four retail chains.
The lesson lies less in the sophistication of the model than in its application: elasticity alone does not determine the outcome; rather, it sheds light on a trade-off that the retailer had already established.
Try it before you decide
At the group's garden center, the team is testing a price reduction before removing an item from the product lineup to gauge the actual impact on sales.
This is the right approach when the model is uncertain or historical data is lacking. Our article on how to test prices without losing customers outlines the conditions for a clear and understandable test.
Beyond the Store: B2B, Manufacturers, and Pharmacies
This issue also arises in B2B distribution, where prices are negotiated on a per-customer basis, and in retail pharmacies, where some prices are unregulated and others are regulated.
From the manufacturers' perspective, elasticity is used to estimate a price increase prior to negotiations, as we explain in the section on price elasticity in the agri-food industry.
Choosing a price elasticity tool: what it needs to demonstrate based on your data
In a demonstration, all the solutions display elegant demand curves. Four questions—based on your own historical data—do a much better job of distinguishing between the candidates.
The first one concerns signal purity. Ask how the solution distinguishes between promotional prices, clearance prices, and seasonal prices from the regular shelf price, and have them verify this using a product line you know inside and out.
The second concerns the effects between products. A solution that does not measure cannibalization will recommend price cuts that shift sales rather than generate new ones, as shown in our article on simulating a promotion.
The third point concerns explanation. A buyer will not apply a price they do not understand: the solution must outline the factors behind each recommendation—a point discussed in the section on the explainability of price recommendations.
The fourth point concerns decision-making. Elasticity should be used to power a simulation of sales, margins, and inventory—guided by your pricing rules—rather than just another spreadsheet to export.
The response is measured against an actual scope compared to a control group. This is the purpose of the pricing POC—to verify the value of a solution—and the broader framework is outlined in our guide to selecting and deploying a pricing tool.
What Booper Brings to the Table
Our AI-powered price optimization solution estimates simple and cross-price elasticities as well as cannibalization, and then simulates the impact of a price on sales, margin, and inventory.
The models are designed by a team that includes four PhDs in AI and data science. Our clients reduce the time spent preparing prices by 70 to 90 percent and increase their margins by 0.5 to 3 percentage points in just a few months.
Would you like to measure the price elasticity of your catalog ?
Our experts estimate the price elasticity based on a sample of your product families and simulate the effect of a price change on your sales and margin.
Let's talk about your flexibilityFAQ: Measuring Price Elasticity in Your Catalog
By measuring elasticity item by item, based on historical data that excludes promotions and stockouts, and then linking it to the role of each category.
An average elasticity across a range masks the products for which a price increase results in a small change in volume and those for which it results in a large change.
Analysis is useful when it informs a decision: protecting a loss leader to maintain price competitiveness, raising the price of a product with low price sensitivity to boost margins, or testing a price reduction before making a final decision.
Yes for a few products, but no for managing an entire catalog.
The spreadsheet allows you to apply the formula and understand how it works for flagship products, based on two periods of price and sales data.
It becomes unreliable as soon as you need to clear the history of promotions and out-of-stock items, cross-reference SKUs, and recalculate the figures every month. Versions multiply, and the traceability between the applied price elasticity and the set price is lost.
A tool that covers the entire catalog, measures the effects between products, and links elasticity to a margin simulation.
The choice is based less on the sophistication of the model and more on four verifiable factors: the separation of price, promotions, and out-of-stock situations; the measurement of cannibalization; the explanation of recommendations; and the alignment with your pricing rules.
The safest approach is to compare the solutions using your own historical data, with both a test group and a control group.
Verify that each recommendation undergoes a margin simulation and remains within the parameters of your rules.
Price elasticity alone often leads to price cuts on sensitive products. However, a profitable increase in volume can erode margins depending on the product’ Gross margin .
The solution must therefore show the expected impact on sales, margin, and inventory, adhere to your minimum levels and margin thresholds, and allow your teams to approve it. It is this combination that safeguards profitability.
Ideally, two years of weekly sales data per SKU, with several price changes observed.
A two-year period allows us to distinguish the effect of price from that of the season. Price changes are essential: a product whose price has never changed reveals nothing about its price sensitivity.
When sales history is unavailable—for a new product or a slow-moving item—we rely on comparable products or a controlled price test.
Yes, she can build good models. The challenge comes after the prototype.
Data must be updated weekly, model deviations must be monitored, elasticities must be recalculated, and the results must be linked to the tools that buyers use to set prices.
This industrialization effort requires the long-term commitment of rare talent, who are often in high demand for other projects. Before choosing this path, quantify the maintenance costs over several years and the risk associated with the departure of the people who designed the models.
Yes, with adjustments specific to each sales model.
In B2B distribution, prices are often negotiated on a per-customer or per-segment basis: price elasticity is analyzed by product family and customer type, and helps determine discounts that do not erode margins.
In pharmacies, only a portion of the product lineup is subject to unregulated pricing, such as over-the-counter products and non-reimbursable medications. It is within this scope that measuring price sensitivity helps balance attractiveness and profitability.
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 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 to be examined; their depth and level of detail depend on the objective, and competitor data becomes essential as soon as the test focuses on price positioning.
Before calculating profit, the recalculated revenue, volumes, and margin must be reconciled with those from management accounting, using aligned definitions and jointly established tolerances. To compare multiple software vendors, the same data and the same protocol are required.
The model is the easy part. A recommendation engine can be up and running in a few months; what takes years is the product catalog, product matching against competitors, the rule set, explainability, and compliance. According to Exclaimer (2025), 71% of in-house development projects are abandoned, and 83% in regulated industries: the breaking point is almost never deployment—it’s maintenance.
The hidden cost is a cost of continuity: an in-house tool relies on two or three people, and if they leave, the asset becomes a liability. The build remains useful within a narrow and stable scope, as a complement to a solution rather than a replacement for it. The deciding factor isn’t the team’s expertise; it’s their ability to last ten years.