Selecting and implementing a pricing tool: From Requirement to Production Deployment
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.
Spreadsheets are no longer keeping up, margins are shrinking, and management has approved the idea of implementing a pricing tool. Now we just need to choose which one—and, more importantly, make it work.
This guide is intended for sales, pricing, and procurement teams that are launching this project or reviving an existing one.
It covers the four stages of the project—from identifying the need to going live—and provides a link to the article that covers each topic in depth.
Choosing and Implementing a Pricing Tool: What Are We Talking About?
Selecting and implementing a pricing tool is a process that begins with the realization that spreadsheets are no longer sufficient and ends with your teams making their first pricing decision using the new solution, based on reliable data.
It involves four stages: defining the need, selecting the solution, demonstrating its value in a real-world setting, and then deploying it. What the tool itself does is described in our definition of the pricing tool.
Based on our experience supporting about 50 companies since 2014, we’ve come to believe thatthe choice of solution matters less than the context surrounding it.
Projects that fall short rarely choose the wrong software. They either defined the problem poorly from the start or underestimated the data and the teams involved later on.
| Moment | The Issue to Be Decided | The Most Common Pitfall |
|---|---|---|
| Frame | What is the cost of this decision today, and what is its scope? | Start with a list of features |
| Choose | Which solution explains its recommendations and accepts your data? | Judging Based on a Demonstration |
| Prove | How much does it contribute to your product line? | Generalizing without a control group |
| Deploy | Who sets the price, and what are the rules? | Underestimating data and teams |
This guide serves as a starting point. Each topic links to the article that covers it in detail.
Start with the problem to be solved, not the publishers' catalog
Many consultations begin with a list of features copied from a previous request for proposals. This results in responses that all look alike and do not set any one apart.
A good place to start is with the decisions that are costing us today: a margin that’s eroding without us knowing where, days spent consolidating files, and promotions that are renewed without being evaluated.
Put this way, the requirement already indicates what kind of solution to look for and the scope within which to test it.
What You're Really Replacing
A pricing tool rarely replaces a single file. Instead, it replaces a chain of spreadsheets, implicit rules, and decisions made from memory—and we’ll detail the limitations of Excel when it comes to managing prices.
Nor does it replace the PIM, which manages product records but does not determine prices: the distinction is explained in our article “PIM or Dedicated Pricing Solution.”
There is still the temptation to build it in-house. It looks good on paper, but much less so once you factor in maintenance costs and the departure of the people who designed it: consider the true cost of an in-house pricing tool.
A tool just for pricing, or for the entire sales cycle?
Regular prices, promotions, and markdowns are often determined using three different tools, each with its own version of the margin. Should you use a single integrated tool or several separate tools? This is one of the most frequently asked questions on this topic.
Our position: a single pricing and margin framework, even if the modules are rolled out one after another. We support this by consolidating pricing, promotions, and markdowns into a single tool.
The same logic applies to general-purpose AI assistants: they help with analysis, but they don’t replace an engine that understands your rules, costs, and elasticities. See General-Purpose AI or Dedicated Pricing Solution.
Choosing Pricing Software: What Really Sets Solutions Apart
In a demo, the solutions all look similar: dashboards, alerts, recommendations. What sets them apart is evident in your data and in the hands of your teams.
Three questions make all the difference. Can the solution explain why it recommends a price? Does it accept your data as-is, or does it require preliminary work? Does it let your business rules have the final say?
The last question is discussed in detail in our guide to automating your pricing without losing control. The other criteria are listed among the truly essential features of pricing software.
Compare Publishers
To provide context for the different categories of solutions and their uses, our comparison of pricing tools sets the general framework.
Three guides then list the software providers:alternatives to Booper, alternatives to Optimix, PricingHUB, and Mercio, and alternatives to Pricefx, Competera, and Revionics.
The deployment model is just as important as the features. The benefits of the hosted model are detailed in the advantages of a SaaS pricing management platform.
Prove its value before rolling it out on a large scale
A demonstration shows what a tool can do. It doesn't tell you how it enhances your product lineup compared to your competitors.
Only 16.5% of retail and consumer packaged goods (CPG) executives can quantify the return on investment (ROI) of AI, even though 75% list it as their top strategic priority.
(Deloitte, State of AI Adoption in Retail and CPG, June 2026)
Our position is simple: we do not apply a solution broadly if it has not been tested in a real-world setting and compared to a control group. That is the purpose of the pricing POC.
A POC is only as good as its data. Our article on the data to prepare for a pricing POC explains why spectacular results are often a sign of poorly controlled data.
What the Field Shows
The Barbotteau Group, a multi-brand retailer in the French Caribbean, managed its pricing using a single Excel- Markup .
The group first tested a platform combining AI and business rules on a selection of products in its supermarket. The result:a one- Markup -point increase in one quarter for the products tested.
The solution was then rolled out to the supermarket and subsequently to the group's four retail chains. The order matters: proof first, expansion second.
The question remains: what exactly are we measuring? The time savings are immediately apparent: our customers reduce the time spent preparing quotes by 70 to 90 percent.
Profit margins require greater discipline, as explained in our article “ROI of a Pricing Solution: Time Savings or Profit Margin Gains.”
Implementing a pricing tool: where projects are won
Once the contract is signed, the nature of the risk changes. It is no longer technical; it is human and organizational.
70% of the challenges in deploying AI at scale stem from people and processes, compared with just 20% from technology.
(BCG, “Where’s the Value in AI?”, October 2024)
Data: Start with What You Have
Waiting for perfect data before getting started means you’ll never get started. Instead, you need to know which data points are critical and which ones can be corrected along the way: make sure your data is reliable before deploying a pricing tool.
At Booper, you can get started using existing exports (inventory, pricing, catalog) without needing a connector. The available integration levels are described in “Integrating Your Pricing Data Without a Major IT Project.”
The Teams: A Decision That Changes Hands
A pricing tool shifts a decision that buyers and product managers had previously made on their own. Without clear roles, the recommendation stays on the screen, and the old file is brought back.
We address this topic in “Successfully Managing Change During a Pricing Rollout” and “Role Allocation in Structuring a Pricing Organization.”
Booper also supports this aspect on its own, through its change management services.
The timeline: first the scope, then the expansion
When you have multiple brands or business units, it’s tempting to launch everything at once. We recommend the opposite approach: start with one brand or product family, measure the results, and then expand.
The complete project workflow is covered in our article “Implementing a Pricing Tool.” The transition from spreadsheets is discussed in “Moving from Excel to a Dedicated Pricing Solution.”
For a national chain, our case study of a food retailer demonstrates how pricing consistency is maintained across hundreds of retail locations.
Switch tools without reopening the same project
Not all projects start with Excel: some replace an initial solution that didn't live up to expectations. The risk, then, is that the new tool will replicate the very reasons why the previous one failed.
Before comparing pricing tools, it’s important to understand what went wrong: that’s the focus of our article— how to switch pricing tools without repeating those mistakes.
And before signing, you need to know what you'll be able to retrieve when you leave: rules, history, and settings. Consider the reversibility of a pricing solution.
If you're not sure where to start yet, a price diagnostic can help you identify your initial margin levers before choosing a solution.
Would you like to define the scope of your pricing tool project?
Our experts help you define the business need, select the scope of testing, and measure the value before rolling out the solution on a larger scale.
Let's discuss your pricing projectFAQ: Choosing and Implementing a Pricing Tool
Start with the decision that's difficult to make today, not with a list of features.
A shrinking margin, days spent consolidating files, or promotions that have never been tracked don't call for the same tool.
With that need in mind, compare the solutions based on three criteria: their ability to explain a recommendation, to accept your data as-is, and to let your business rules have the final say.
Next, test the selected solution in a real-world setting, comparing it to a control group, before rolling it out on a larger scale.
Ask to see the solution running on your own exports, not on a demo set.
A good solution starts with existing files (inventory, pricing, catalog) and then builds up integration step by step, without requiring any prior IT work.
When it comes to customization, make sure your business rules (price tiers, margin thresholds, competitive alignment) can be configured by your teams without any special development, and that they take precedence over the calculated recommendation.
A single price and margin reference system, even if the modules arrive one after another.
With three separate tools, each team calculates its own margin, and decisions regarding regular prices, promotions, and markdowns are made without a shared understanding.
A single platform doesn't require you to roll everything out at once: you can start with the regular price and add promotions or markdowns later, all within the same database.
Allow a few weeks to get started in an initial area, and a few months to see an increase in profit margin.
The Barbotteau Group tested its platform on a selection of products in its supermarket and saw a one- Markup -point increase in one quarter, before expanding the solution to its four retail chains.
The total duration depends primarily on the condition of the data and the number of teams involved, much more so than on the tool itself.
Start with a brand or product line, assess it, and then expand.
Opening up the entire scope at once increases the number of rules to configure, teams to train, and data discrepancies to correct—without producing any clear results.
A successful initial implementation serves as a benchmark for subsequent ones: common rules, clearly defined roles between the pricing department and local teams, and shared metrics.
Start by translating your current strategy into rules in the tool, and then ask it to optimize.
Competitive positioning, price tiers, and margin thresholds: these guidelines govern all recommendations, which are ultimately approved by your teams.
The tool thus begins by making what you already do more reliable and faster. The discrepancies it highlights then become explicit decisions made by management, rather than changes imposed on you.
Yes, provided that the gain is measured based on actual data rather than assumed.
Booper's customers see their profit margins increase by 0.5 to 3 points within a few months, and a 70 to 90 percent reduction in the time spent preparing prices.
Gains rarely result from a blanket increase: they come from better-adjusted prices product by product, more targeted promotions, and corrected inconsistencies. That is why it is important to compare a test group with a control group before rolling out the changes across the board.
Rarely, unless pricing is your core technology business and you can assign a dedicated team to it over the long term.
The cost of an in-house tool doesn't end with development: maintenance, regulatory changes, models that need to be recalibrated, and the departure of the people who designed it all add up each year.
The true cost is therefore measured over several years, taking into account maintenance hours and the risk of relying on a few key individuals.
A pricing assessment is an audit of a company's pricing policy based on its actual data: sales, margins, pricing schedules, promotions, and competitors' prices.
It examines four key areas—price consistency, market positioning, price image, and promotions—to determine which discrepancies warrant action and what the expected return is.
The most common issues include inconsistent pricing tiers, overpriced benchmark products subsidized by non-comparable products sold at excessively low prices, promotions that are extended despite being unprofitable, and exceptions that have become the rule.
Each recommendation is costed and presented in a roadmap that distinguishes between “quick wins” that can be implemented immediately and structural initiatives.
A pricing proof of concept (POC) is a test that is limited in both time and scope, conducted using a retailer’s actual data, which compares the pricing decisions recommended by a solution to a baseline scenario measured in the same way. It provides evidence when five elements are established before the test begins: the scope, the baseline scenario, the metrics, the duration, and the resources allocated.
The method described here distinguishes between three categories of indicators that are too often conflated (model performance, economic gains, and team usage), explains how to test multiple vendors using the same dataset, and explains why a proof of concept (POC) should be conducted without altering the existing price-dissemination tool.
A SaaS pricing management platform centralizes a retailer’s prices, rules, competitor data, and sales forecasts in a browser-based tool—with no servers to install or versions to maintain. For the retail industry, its benefits include the ability to respond quickly to market changes, measurable margin gains, built-in compliance with French regulations, a predictable subscription cost, and scalability across thousands of retail locations.
Booper, a French pricing software provider since 2014, exemplifies this model with a platform that manages more than 4,000 retail locations for food retailers, home improvement chains, and specialty retail networks, and aims to increase margins by 0.5 to 3 percentage points within a few months.