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Price Optimization Machine Learning & AI: Unlocking Growth

August 15th, 2023 | 10 min. read

By Sylvain Rougemaille

In today’s rapidly evolving business environment, optimizing prices for products and services has become a key factor for companies looking to gain a competitive edge. The advent of price optimization machine learning (ML) and artificial intelligence (AI) has revolutionized the way businesses approach pricing strategies, enabling them to unlock valuable insights and opportunities that were previously impossible to identify. For example, can you imagine trying to do price optimization for your car parts company with hundreds of thousands of products in an Excel spreadsheet? Ouch, what a nightmare! Clearly, Excel is not the ideal tool for price optimization. With hundreds of thousands of assorted products to analyze, Excel would quickly become unwieldy and impractical, leading to errors and inconsistencies in the optimization process. As a result, in such cases, automated software with advanced algorithms and data processing capabilities will be necessary for accurate and efficient price optimization. 

Pricefx, a provider of ML & AI in price optimization software solutions for over a decade, is perfectly positioned to discuss how the technology works to unlock business growth and unearth business opportunities that your company may not have realized possible. pricing software. The technology allows for crystal-clear insights into pricing, driving price optimization by enabling more attractive prices, better product and customer groupings, and more alluring offers. It also predicts market changes and customer reactions to price changes, allowing companies to optimize their operations and portfolios to reflect what customers want, and break “value-based pricing” into play by urevealing each customer’s “willingness to pay.” It is an approach that allows companies to offer the right product, at the right time, and at the right price for each customer, maximizing sales and building profitability. 

In this article, we will explore how price optimization through both machine learning and artificial intelligence works together in synchronization in price optimization software to help organizations unlock growth insights and business opportunities. But let’s start by taking a precursory look at the fundamentals of price optimization. 


The Basics of Price Optimization 

Price optimization is the process of determining the ideal price for a product or service that for the most part, maximizes revenue or profits but can be fine-tuned to optimize other business goals (whatever your company uniquely sees as its priorities).  

The outcomes that price optimization can include, but are not limited to: increased margins, profits, volume, revenue, or market share, driving customer behavior, finding the right balance from a supply and demand perspective, etc. 

It is about playing with price to optimize your chosen function/s, which could be anything from maximizing sales to minimizing cost to serve

The price optimization process requires careful analysis of a range of factors, including market demand, competition, consumer behavior, and production costs. 

Traditional methods of price optimization relied on statistical analysis and human expertise to determine optimal prices. However, these methods were often subjective and prone to errors, resulting in missed revenue opportunities and decreased profitability. 

Optimization in Excel or with Pricing Software?   

Many companies do their pricing in Excel and can build very simple optimization or linear programming models that allow them to find a solution to a particular problem. While Excel presents some constraints, it’s not rocket science. So yes, technically you can optimize in Excel. But…  

The Excel optimization process is limited and not scalable.  

You simply cannot physically input a labyrinth of Excel formulas, collate information from numerous data points, clean the data to make it usable, iterate many times over until you find the right range of prices that work for one product in one segment, and then attempt to do the same thing repeatedly for your company’s entire global price list. 

Excel vs. Pricing Software: Which is best for you? 

The scalable option of price optimization powered by ML and AI will simply allow you to do more, more often and with greater insight to unlock your company’s profit potential. And this is why price optimization software allows you to level up in pricing.  

Most price optimization software solutions (including our own Optian solution) come wrapped in an intuitive design with easy-to-customize dashboards to easily level up to that standard of pricing sophistication. 

Let’s look at how both ML and AI make that level of price optimization sophistication possible.  

Machine Learning (ML) in Price Optimization Software 

Many people confuse ML and AI and automatically assume that the two are the same thing. But’s that is an over-simplification and what is more, it’s not entirely accurate. 

ML is actually a subset of AI that involves the use of algorithms and statistical models to analyze data and make predictions or decisions without being explicitly programmed. In the context of price optimization, ML algorithms can analyze vast amounts of data to identify patterns and trends that can inform pricing decisions.


Machine learning algorithms can be trained using historical data on sales, pricing, and consumer behavior to identify correlations between a range of factors and sales performance. This data can then be used to generate predictive models that can forecast future sales and revenue under different pricing scenarios.

For example, a machine learning algorithm can provide prices which are the most probable (from past observation) The algorithm can then generate price recommendations based on this analysis, considering factors such as seasonality, competition, and consumer behavior. 

What that means is that ML focuses on allowing a system to learn using statistical methods to improve with experience without human intervention.  

In the price optimization software environment, ML is used to set up the predictive model that AI will apply in building a price optimization model for a company’s unique set of products and required business outcomes.

Pure ML algorithms will tell you what to expect from a pricing you design but they won’t tell you which price to choose in order to achieve your business goals. 

However, if you want to take on more than one of your organization’s business objectives simultaneously, AI-informed price optimization software will provide the level of multiple additional insights and potential growth that your business is craving.  

Artificial Intelligence (AI) in Price Optimization Software 

The biggest difference between Machine Learning and AI in price optimization software is that AI is more powerful and allows for more pricing functions, solving a broader set of problems across a broader set of objectives and constraints. ML tells you what to expect, AI tells you what to do (from predictive to prescriptive) 

AI can do that because it is a computer system designed to think the way human intelligence does. AI goes beyond the type of repetitive tasks that Machine Learning performs and is capable of analyzing and contextualizing data to provide information or automatically trigger actions without human interference.  

In the context of price optimization, AI algorithms can analyze vast amounts of data to identify hidden patterns and insights that humans may not be able to identify. 


AI algorithms can incorporate a range of factors beyond sales data, such as customer sentiment, social media trends, and economic indicators, to generate more accurate pricing recommendations. For example, an AI algorithm can analyze customer reviews and social media mentions to identify trends in consumer sentiment that can inform pricing decisions. To date, it is the ultimate in price optimization. 

To underline that claim, AI algorithms can also incorporate real-time data on market conditions, such as changes in competitor pricing or shifts in consumer behavior, to generate dynamic pricing recommendations. This allows companies to adjust their prices in real-time to optimize revenue, profitability or whatever their unique set of business objectives happen to be.  


Combining the Power of Machine Learning and AI in Price Optimization Software 

Price optimization software that incorporates both machine learning and AI can provide businesses with a powerful tool for unlocking growth insights and opportunities. These software solutions can analyze vast amounts of data from multiple sources to generate pricing and product mix recommendations that are both accurate and dynamic. 

By leveraging machine learning algorithms, price optimization software can generate predictive models that forecast sales and revenue under different pricing scenarios. This allows businesses to identify the optimal price range for a particular product or service that maximizes revenue and profitability. 

But stepping it up a notch, AI algorithms can further enhance price optimization software by incorporating real-time data on market conditions and consumer behavior to generate dynamic pricing recommendations. This allows businesses to adjust their prices in real-time to respond to changing market conditions and consumer preferences. 

In real use cases, for example, you could increase prices when you know your competitor’s stocks are running low. For customers who want their products immediately, if they go to one website and find it unavailable, they will be more than likely happy to buy it from your store for a higher price, if they know they can get the item sooner.  

It also helps your business gain advantages over your competitors. When your conversions are low, you can use AI-informed price optimization to lower prices to increase your conversions to match your company’s overarching business goals. 

Of course, the benefit of price optimization through ML and AI working together is that it is scalable, automatic, and flexible enough to meet whatever your organization’s business objectives are from maximizing profit, volume, or revenue through to increasing market share and much more.

Think about eCommerce for a moment. The biggest companies like Amazon can and do sometimes change their prices several times per hour. On the other hand, smaller companies without the turnover of Amazon may not be able to match that rate of price change. However, for Amazon it works – they might not have the highest profit margins but across many products they do enjoy the majority market share – meaning that they can afford to change their prices regularly without taking overall profit hits. 

Learn more in this handy article below about the very cool things that you can do with Pricing AI: 


How to Know if ML or AI-Powered Price Optimization (or Both) Are Right for Your Business? 

The short answer to that question is probably; ‘it depends.’ However, as with any other business decision or opportunity, you need to carefully examine the feasibility of implementing pricing optimization tools for your business.  

For example, it could be that ML price optimization software like that offered by some first-generation pricing software vendors (also known as ‘legacy vendors’ in some circles) could conceivably be best for your organization’s needs. If your company is on the lookout for a pure single variable price optimization tool (particularly smaller companies or those without a critical mass of complexity in terms of their customer or product segments), you may have less requirement to illuminate your price optimization decision-making. 

On the flip side, if you are looking for a complete price optimization tool than can manage the input of several variables simultaneously, along with multiple associated objectives and provide a fully explained transparent price optimization outcome, then next-gen price optimization software like Pricefx that takes the best of both ML & AI working together could be more what your organization needs.

If you are still unsure of the total needs that your business will require, check out our recent blog article to learn all there is to know about price optimization software here:



Sylvain Rougemaille

Senior Product Manager , Pricefx

Sylvain Rougemaille PhD is Senior Product Manager at Pricefx based in France. He has 15 years of experience in the IT industry and AI. He obtained his PhD on Software Engineering applied to AI in 2008. Since then, he has participated the creation of two startups aiming at the diffusion of AI to solve complex industrial problems as aircraft optimization, genomic simulation, and ultimately price optimization. In 2015 he co-founded Brennus Analytics where he occupied the position of Chief Product Officer. The purpose of it was to bring the PO&M software market unrivalled optimization capabilities thanks to Multi-Agents’ AI. Since 2020 and its acquisition by Pricefx he is pushing pricing science even further as the Price Optimization and Science Manager.