Pricing in the Age of AI
- George Boretos
- 1 day ago
- 14 min read
Updated: 20 hours ago
Optimise your pricing in a dynamic market, leveraging Predictive AI
In today’s volatile and competitive business landscape, pricing is more than just a number - it’s a strategic lever for driving growth, boosting profitability, and strengthening market position. While increasing volume or cutting costs can help, even a small price increase can yield disproportionately higher returns.
Yet, pricing decisions are notoriously complex, influenced by everything from market conditions and customer behavior to product value and competitive dynamics.
This is where AI, especially Predictive AI, steps in. By analyzing historical data and identifying hidden patterns, Predictive AI enables businesses to optimize pricing, generate actionable insights, and plan ahead with greater confidence.
In this article, we explore how harnessing AI-powered insights leads to smarter, faster, and more impactful pricing decisions.
Why is pricing so important? [1]
Imagine you are the CEO of a company selling specialized machinery for farmers. Your business is soaring after introducing TractX, a highly sophisticated tractor using multiple sensors for precision farming [2]. After some accelerated growth, sales have stabilized at a high of 1,000 tractors per year! With a price of $52,000 per tractor, your company generates $52m in sales per year, which is a pretty good performance for a single product! But there is a glitch. With a cost of $40,000 per tractor, you have an overall gross profit of $12m per year, which is lower than expected.
Your challenge as a CEO is to increase your gross profit by 5%, and the question is where you should focus the most: increasing volume, reducing cost, or improving your pricing.
Let’s see what happens with your gross profit if you improve any of these by 5%.
Volume: By increasing your sales volume by 5%, you get an equal increase of 5% in your gross profit and, therefore, achieve your target!
Cost: But wait a minute. If you reduce your costs by 5%, you’ll get an even more significant increase of 17%. Wow, this is what you should do then!
Price: But what about price? What would happen if you managed to increase the price by 5%? Well, in this case, your gross profit would increase by, wait for it, 22%!
It can be shown that even if you increase your volume and reduce your cost simultaneously by a certain percentage (e.g., 5%), you would always get the same result by just increasing your price by the same percentage! 😮 [3]
Pricing is the strongest profitability driver! But it doesn’t stop there since pricing also influences sales volume and market shares through price elasticity and affects cost through increased volume and economies of scale.
But pricing is not only a significant business driver but also a highly complex and intellectually challenging task. Just consider the many parameters that you must take into consideration:
Business environment: First, you need to analyze the overall Business environment, which includes macroeconomic conditions such as inflation, spending capacity, exchange rates, and tariffs, as well as market conditions such as competitors’ pricing, new technologies, or supply shortages.
Product-market fit: Second, you need to consider the product and how well it fits with the market you are trying to target, or in other words, Product-Market fit. This includes product features, specifications, bundles or packs, quality (or perceived quality), customer segments, and best-fit customer characteristics.
Let’s summarize:
Pricing is one of the most critical drivers of Business performance. It strongly impacts your business at so many different levels and can influence your growth, profitability, perceived quality, and overall performance.
Pricing has many dependencies. It can be affected by several factors, from market and macro conditions to the product and the customer.
This is exactly where AI gets into the picture!
Yes, we know that the probability to buy improves as price goes down, perceived quality goes up, you target the right audience, and macro, country, or market conditions become more favorable.
But what we don’t know is how much it improves and the degree to which each factor affects the outcome.
This is precisely where AI gets into pricing and price optimization. And because Pricing is so important, the benefit of leveraging AI in pricing translates into significant business gains!
Why is AI more relevant than ever?
It’s not a secret that AI is here to stay. As ChatGPT took the world by storm following its wide release in late 2022, numerous AI tools followed, making the use of AI a must for business or other applications.
Because of the vast and rising popularity of ChatGPT and Generative AI, many people considered 2022-2023 to be the birth point of AI. They couldn’t be more wrong.
Even without knowing it, most people have been using AI in various forms and levels of sophistication for many years. For instance, an AI component is hidden behind the scenes, performing voice recognition and task scheduling when using Siri or Alexa. And even the simplest spell-checking tool requires the use of AI, in this case, in the form of Natural Language Processing (NLP).
AI has evolved into a vast field with all sorts of flavors and degrees of sophistication. For instance, chatbots range from simple - and sometimes simply frustrating and almost useless - to highly advanced systems like ChatGPT or Gemini! Compared to traditional bank or support portal chatbots, the upgrade is so obvious that you don’t need to be an expert to spot the difference.
But that doesn’t mean that we have reached the end of the journey, which is Artificial General Intelligence (AGI) or human-equivalent intelligence. Even ChatGPT is not there. In all its glory, it is still nothing more than an advanced probabilistic tool that predicts the best next word or phrase based on its model’s training and user’s prompt. It literally does not know what it is talking about, although it does a great job of convincing us it does. The dream of AGI or Superintelligence, which is superior to human intelligence, is still far away and not within sight for the foreseeable future (or perhaps forever).
Our attempt to mimic human intelligence goes back to the 1950s when Alan Turing introduced his now famous test of machine intelligence, and the foundation of AI was set for the first time in history. Over the following decades, most modern machine learning, forecasting, and AI tools have been built, enhanced, evolved, and used with great success but also failures.
Admittedly, we’ve gone a long way from the early AI days, and to a large extent, we have ChatGPT and OpenAI to thank for!
Not so much because ChatGPT was the first AI tool that more closely than ever resembled human intelligence in so many different fields and applications, but primarily because by doing so, it allowed AI to reach the mainstream market for the first time, capturing the interest of a broader audience beyond just technology enthusiasts and visionaries. Despite its many limitations and issues, ChatGPT marked a significant milestone in the evolution of AI.
We live in exciting times as thousands of new AI tools enter the market, covering numerous business or other applications, gaining interest and user base almost instantly.
But how does AI get into the Pricing practice, and, most importantly, what is the value that it brings? In the following, we'll mainly focus on Predictive AI, which is the AI branch with the most applications in pricing, but also touch upon the benefits of using Generative AI in certain business applications.
Combining Predictive AI and Pricing for Success
Predictive AI, also referred to as Predictive Analytics, involves the use of artificial intelligence to predict and/or optimize future events or outcomes based on actual data and patterns.
This type of AI:
Applies statistical or econometric models on available data: It could be historical business data, such as actual sales, or prospective data, such as pricing research.
Recognizes important patterns: For instance, it could estimate the demand curve, which simulates how demand changes over price or a sales trend, estimating the evolution of sales over time.
Generates important predictive insights: These include predictions, such as forecast sales, or optimized suggestions, such as the best price for maximum revenue.
Of course, Predictive AI cannot reinvent economics or how pricing works, but it can quantify trends following economic, business, or other rules and identify hidden trends. Its objective is to help you make better or optimized pricing decisions with significant gains for growth, profitability, or other vital KPIs of your business.
Evidence suggests that Predictive AI, incorporating econometric modeling, can help optimize pricing and increase revenue and other important KPIs even 2-fold while improving strategic business foresight with great accuracy, reaching 97% [4].
Estimating the Probability to buy and Demand over Price [5],[6]
Pricing significantly impacts sales performance by affecting the customer’s probability of buying. Customers are happier when price levels decrease as they seek to minimize purchase costs. This is incorporated into the Demand curve (Fig. 1), a chart with the price on one axis and the probability to buy or demand (probability to buy x opportunity size in units) on the other.
As expected, the probability to buy/demand decreases as price increases (a few exceptions to this rule are out of scope for this high-level introduction), but the question is how much?
This is precisely the type of estimates Predictive AI can give us!
It does so by simulating the demand curve using a specialized AI-based Demand model and fitting the model to actual data. “Fitting,” for the non-data science familiars, means that the Predictive AI system tries to change the model (usually its key parameters) repeatedly until its output resembles as much as possible the actual data.
And how can you know if your model is good and you can trust it? By testing its prediction accuracy or how close the model’s estimate is to actual data, with the smallest possible margin of error. There are multiple error metrics and methods (e.g., in & out of sample, cross-validation), but again, this is out of scope for this introduction.
Optimum Price suggestions and other predictive Insights [5],[6]
Once you have discovered a great Predictive AI model that adequately simulates your demand curve, this opens the doors to generating multiple valuable insights (Fig. 2).
Imagine offering your product at varying prices adjusted through list price changes, promotional discounts, negotiated prices, or other methods affecting the final price observed by the customer. Assuming everything else, besides the price, remains the same (market conditions, competitors' pricing, etc.), you can use the demand curve to predict volume and revenue sales and optimize pricing.
So, you can use it to estimate your success rate, your sales performance, and your profitability (for the latter, you’ll need to factor in cost as well) at any given price level.
In addition, you can identify the Optimum Price that maximizes your revenue, profits, or other objective. Or find the Penetration Price that allows you to maximize your success rates under certain restrictions of profitability or other KPIs.
What-if sales and pricing scenarios [5],[6]
Price is not the only aspect affecting sales performance. Several other factors are crucial in determining the Demand curve and should be considered when making pricing or other business decisions (Fig. 3).
For example, inflation or changes in the average spending income of a country can alter the perception of what is considered expensive or not. When competitors change their prices, this may influence the chances of winning customers and new deals. Also, the perceived quality of the product and the best-fit customer characteristics matter a lot. All these factors affect the customer’s probability to buy at any price point. In addition, sales performance can also be influenced by the target country and market size, product bundling, and customer size or type, just to mention a few critical factors.
A more advanced Predictive AI model can take into account some or all of these factors and shift the Demand curve to reflect their impact. This enables the creation of multiple what-if scenarios to reveal new predictive insights for varying country, market, product, or customer conditions (Fig. 4).
Price optimization across countries [7]
One of the benefits of having a Predictive AI model that understands, among other things, country dynamics is that you can get differentiated price suggestions and sales performance estimates across countries.
For instance, you can create heat maps (Fig. 5) indicating the differences among countries in terms of suggested best prices, expected revenue, penetration levels, or other vital indicators. And you can update these maps automatically when something significant changes, like inflation or the average spending capacity per country.
Supply - Demand dynamics [8]
The Demand/Customer dynamics examined so far is one side of the coin. It is a one-way problem, where the challenge is to predict the customer’s probability to buy, given an asked price from the supplier and the additional market or other factors mentioned in Fig. 3.
Assuming suppliers are willing to sell at a wide range of prices and have adequate supply availability, studying just the Demand curve would suffice. This may be true or partly true for several markets where the variable part of the cost is relatively low, allowing for more price flexibility, and supply is guaranteed at almost all desired levels by the customers. The Software as a Service (SaaS) business is one such example, but there are many more.
But what about markets like commodities and the energy sector, where there is a considerable variable cost and frequent supply shortages or fluctuations? If in doubt, remember what happened in 2022 with the energy sector. In such markets, Supply vs. Demand dynamics are of paramount importance as they can distort sales and pricing considerably.
But what initiates these dynamics?
Every day, buyers (the Demand side) “confront” sellers (the Supplier side), and the result is a successful or failed (i.e., not taking place) sales transaction. This happens because buyers and sellers have conflicting interests. The seller is happier when prices increase and seeks to improve or optimize a KPI, for instance, to maximize revenue or profitability. The buyer, on the other side, is happier when prices decrease and seeks to maximize the value derived from the purchased product or offering at the lowest cost possible. All these dynamics are famously reflected in the Supply-Demand curve (Fig. 6).
The role of Predictive AI here is to simulate the Supply curve on top of the Demand curve and generate additional predictive insights, and more precisely, what will happen if demand is higher than supply (supply shortage) or lower than supply (supply surplus).
Of course, you could argue that the Supply curve is easier to tackle because you know your company. Well, this may be true in many cases. However, there are many companies, especially larger ones with many salespeople, that are not in complete control of their pricing because of the complexity of their markets and products or because there is room for negotiation, so the final price is not a given. Also, supply availability may not be fully known depending on salespeople's access to warehouse information, changing replenishment schedules, or other factors. Moreover, although you may know, at least to some extent, your supply availability and willingness to sell at different price levels, you don’t have the same information about your competitors.
Predicting Price Trends
So far, we have mainly focused on how price and other factors influence sales performance. Based on that and Predictive AI modeling, optimum price suggestions and other important insights can be generated.
But pricing also works the other way around, as many factors can influence it. The previous section, referring to Supply vs. Demand dynamics (Fig. 6), indicates precisely this. If something happens that affects the Demand curve (for example, any of the factors mentioned in Fig. 3) or the Supply curve (for instance, a temporary supply shortage), then the equilibrium, i.e., the point where supply meets demand in Fig. 6, will change. This will result in a new probability to buy and sales volume but also a new equilibrium price.
Pricing is a two-way process with multiple iterations, leading to new temporary equilibria until some conditions change again. Although this is a much more complex task, Predictive AI can model these dynamics and predict price trends.
In its simplest form, you can use Predictive AI to estimate volume and revenue sales separately and then divide them to get an indication of the pricing trend. In its more sophisticated form, Predictive AI would simulate demand & supply dynamics (like in Fig. 6) and estimate price and volume shifts and their respective equilibria using more advanced econometric predictive models.
Estimating pricing trends can be very useful, for instance, when we want to estimate the average price in the market and how this may change as a response to rising inflation, tariffs, an economic slowdown, or a competitor changing prices.
Using Generative AI in Pricing
Although Generative AI is the talk of the town today and enjoys rising popularity, Predictive AI has the most applications in Pricing. This doesn't mean, though, that other types of AI cannot be used as well.
For instance, one of the most critical challenges in pricing is collecting relevant data found in an unstructured form. Anyone with the slightest selling experience knows that salespeople include internal comments on their CRM indicating that they lost a case to a particular competitor at a specific price. Similar data can be found online when customers or potential customers post reviews, product assessments, prices, or information about competitors. The problem is that this information is not structured and, therefore, is challenging to get and analyze properly to aid decision-making.
But this is where Generative AI is strong!
You can apply Generative AI models to unstructured content from internal or external sources and ask to look for specific information and structure it. The same applies to scraping websites that contain valuable online pricing info.
Epilogue - Using AI in Pricing for Success
AI is here to stay for a reason. It can augment your professional efforts by offering actionable predictive insights to inform decision-making in pricing or elsewhere.
Pricing is one of the most critical drivers of business performance, having a substantial impact on growth, profitability, perceived quality, and other KPIs. It is also very complex and has many dependencies. This is why we need AI and especially Predictive AI, to analyze data, offer optimum price suggestions, identify essential pricing trends and patterns, and estimate how these affect sales and overall business performance.
Using AI can help answer many important questions regarding pricing and sales performance, such as:
Macro environment impact: What will happen if inflation, spending capacity, exchange rates, tariffs, or other macro indicators change?
Market impact: What will happen if competitors' pricing, market size, or other market conditions change?
Product impact: What will happen if I offer more value with my products or improve the perceived quality of my offering?
Customer impact: What will happen if I target customers of different sizes or characteristics and address specific market segments?
Key drivers: Which of the above are the most important drivers of growth, profit, or other important indicator for my company?
Price optimization: What is the best price to optimize revenue, profits, or other KPI, considering all or some of the aforementioned drivers?
Price discrimination: Should I differentiate prices across countries, different market segments, or per customer?
Dynamic pricing: How to adjust pricing in real-time to reflect changes in demand vs. supply, competitors' intelligence, customer’s profile or history, or other parameters?
Competition-based pricing: How should I adjust my pricing in response to competitors' pricing or actions?
Pricing and packaging: Should I pack or bundle my offering in a certain way, including certain specifications or characteristics, to boost my sales performance and product acceptance?
Pricing trends: How does the average price in my market evolve, and are there any emerging trends that I should be aware of?
Market intelligence: What is the value and trend of key market indicators like churn or market shares against key competitors?
AI can tremendously improve your pricing and planning ahead, boosting your sales and business! Although you should be mindful of the challenges and risks of using AI, you should also be aware of the bigger risk of not using it when everyone else does, including your competitors.
So don’t be afraid or feel intimidated by it because, more than anything else, AI is an exciting field and an excellent opportunity to supercharge your business!References
[1] Content, with the exception of reference 2, is based either on material or reproduced partly or fully from: https://www.futureup.io/post/please-pay-attention-to-your-pricing
[2] Although the example is different, it is based on the underlying logic described in much more detail in Danilo Zatta’s excellent book “The Pricing Model Revolution: How Pricing Will Change the Way We Sell and Buy On and Offline”, 2022, Wiley
[3] Check here for a more elaborate analysis of why this happens and its limitations under elasticity effects: https://www.futureup.io/post/the-secrets-of-maximizing-profits-with-price-elasticity-ai-significance
[4] Check out more AI success stories and evidence of Predictive AI’s positive contribution here: https://www.futureup.io/news/categories/success-stories
[5] Content is based either on material or reproduced partly or fully from: https://www.futureup.io/predict
[6] You can see more information regarding a relevant customer case here: https://www.futureup.io/post/combining-predictive-ai-with-neuroscience-to-identify-the-best-price-across-countries
[7] You can see a video with an actual price heat map for a relevant customer case here: https://youtu.be/x7o-rkn-cp8
[8] You can find more information here: https://www.futureup.io/post/pricing-inflation
Interested in learning more about AI-Powered Price Optimization and Strategic Forecasting?