Pricing software:

Which features are truly essential?

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The Pitfall of 40-Feature Matrices

When faced with an initial pricing project, the natural instinct is to draw up a long list of criteria and rate each vendor against them. The problem is that an overly broad evaluation grid dilutes what really matters and leads you to choose the tool that scores highest on paper rather than the one that solves the actual problem. Our comparison of pricing tool families breaks down the major categories in the market; this article delves deeper into a single aspect: within a pricing analytics tool, which features are truly non-negotiable.

The right question isn't "How many features does this tool offer?" but "Given my volume of SKUs and my margin challenges, which of these features actually influence pricing decisions?"

The Essential Foundation: Five Non-Negotiable Functions

• Price/margin performance analysis. Without visibility into where margin is being gained or lost—by reference, by store, by category—no other feature has a foundation to build upon. This is the starting point, not an advanced option.
• Modeling elasticity. Understanding how demand responds to a price change, point by point, is what turns a recommendation into an informed decision rather than a gamble. Without elasticity, a tool merely shuffles numbers around.
• Pre-deployment simulation. The ability to test the impact of a pricing scenario on revenue, margin, and inventory before actually implementing it is what sets a management tool apart from a simple retrospective dashboard.
• Anomaly detection. A price that slips quietly—a data entry error, a forgotten update after a promotion, or inconsistencies across channels—is costly precisely because no one notices it before the customer does. This feature transforms manual monitoring into an automatic safety net.
• The explainability of recommendations. A tool that suggests a price without explaining why will never truly be adopted by field teams—see below.

The impact of this foundation is directly reflected in the bottom line: according to a McKinsey analysis of Global 1200 companies, a 1% price increase—at constant volumes—generates an average of 11% more operating income. A tool that fails to account for price elasticity or runs simulations on only a fraction of the product catalog leaves this lever untapped, precisely where it would matter most.

What we think is essential, but isn't always

• A spectacular dashboard. A well-designed interface helps drive adoption, but it can never replace the quality of the data or the relevance of the underlying models. A beautiful dashboard based on miscalculated elasticity is still a big mistake.
• 100% automation. A tool that sets all prices without human validation for high-stakes SKUs isn’t any more “advanced”—it’s an additional risk if the business rules aren’t perfectly calibrated from the start. Targeted human oversight isn’t a sign of immaturity; it’s a safeguard.
• Comprehensive, competitive coverage. Tracking all competitors across all product lines may sound reassuring, but it dilutes your focus. It’s better to have reliable tracking of truly strategic product lines (KVI) than to collect a massive amount of data that’s not very actionable.

Feature by Feature: The Business Impact

Here is a breakdown, function by function, of what each one actually contributes and the risk involved if it is missing.

Explainability: The criterion that determines adoption in the field

This is the most underrated feature in selection grids—and the most critical once the tool is in production. According to the McKinsey survey on the state of AI in business (2024), 40% of respondents identify explainability as a key risk in the adoption of generative AI, but only 17% say they are actively working on it.

This discrepancy is directly reflected in pricing: a category manager who cannot understand why the tool recommends lowering the price of a sensitive SKU by 3% will not follow the recommendation—they will quietly bypass it, and the tool will lose its value without any alert to indicate it. Explainability is therefore not merely a user-interface convenience: it is the prerequisite for other features (such as elasticity and simulation) to actually be useful once deployed.

What This Means in Practice: The U Cooperative Case

At Coopérative U (more than 1,700 stores, several million prices managed each year), the challenge was not a lack of tools, but the growing number of complex trade-offs between competitiveness, profit margins, and national pricing consistency—all of which had previously been subject to limited simulations. Frédérique Gautier, purchasing manager, summarizes the concrete benefits of the simulation features and category-level granularity:

"The ability to simulate different scenarios and take into account the specific characteristics of each category is a key factor in ensuring the success of our business strategies."

It wasn't the number of features that made the difference, but the ability to test a decision before implementing it, category by category.

The Checklist Before Signing

Five Questions to Ask Before Making Any Commitment.

• Does the tool model elasticity by reference, or does it apply generic rules?
• Can I simulate a pricing scenario before applying it to my own scope?
• Are price anomalies detected automatically, or do you have to look for them?
• Is each recommendation explained, or am I supposed to just trust it without understanding why?
• Can a person approve or reject a recommendation before it goes to checkout?

A demo is better than a 40-cell grid. Contact us for an audit of your current foundation.

Frequently Asked Questions

The questions we're asked most often before getting started.

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Sources: McKinsey & Company, “The Power of Pricing” (Global 1200 analysis) — mckinsey.com · McKinsey & Company, State of AI in Business Survey, 2024 — mckinsey.com · Booper × Coopérative U Business Case (customer case study published by Booper).