Dynamic Pricing Algorithms in 2026: Top 3 Models
Simple rule-based triggers require human approval for each price change, while agentic orchestration in modern dynamic pricing allows modules to independently adjust prices using their reasoning capabilities.
What is dynamic pricing?
Dynamic pricing, also called surge, demand, or real-time pricing, is a strategy in which prices adjust instantly based on real-time data such as supply, demand, and competitor activity. The concept has evolved from passive algorithms to two main standards:
- Mandatory transparency: Businesses must display the total price upfront and clearly state when a price is personalized using a customer’s personal data.
- Agentic intelligence: Modern systems use autonomous AI agents to analyze market changes, such as news or social trends, and adjust prices without human input.
What is a dynamic pricing algorithm?
A dynamic pricing algorithm, or algorithmic pricing, manages your pricing strategy. These algorithms have evolved from basic mathematical models to agentic systems that autonomously analyze market events and adjust prices in real time.
Key data inputs
Dynamic pricing algorithms use historical data from the following sources:
- Internal fundamentals include historical sales, production costs, and real-time inventory levels.
- External intelligence covers competitor prices, social media sentiment, and news feeds, which are detected using Agentic AI.
- Contextual variables include local weather patterns, regional holidays, and real-time carbon footprint data for sustainability-linked pricing.
What are the models of dynamic pricing algorithms?
Many organizations prefer Hybrid Agentic Frameworks over standalone models. These frameworks integrate traditional mathematical methods with Large Language Models (LLMs) to process both structured data, such as prices, and unstructured data, including news and social sentiment.
1. Bayesian model
In a Bayesian model, the user selects a prior value that reflects their initial belief about the possible price. Then, whenever a new data point is entered into the algorithm, the initial belief shifts either higher or lower.
This type of dynamic pricing model uses historical pricing data as its primary input to determine the final price, as in a typical pricing algorithm.
2. Reinforcement learning model
Reinforcement learning (RL) is a goal-directed dynamic pricing model which aims to achieve the highest rewards by learning from environmental data. An RL dynamic pricing model analyzes data regarding customers’ demand, taking into account seasonality, competitor prices, and the uncertainty of the market, to achieve a revenue optimal price.
A common method used in RL-based pricing systems is the policy gradient approach. It directly optimizes the pricing policy by adjusting parameters to increase the expected return.
3. Decision tree model
Decision trees are classification machine learning models that output a tree-like model of decisions and their possible consequences, including the possibility of a certain outcome, resource costs, and utility.
Decision tree dynamic pricing algorithms help businesses understand which parameters have the most effect on the prices and which of these price ranges predicts the highest revenues, and using this information, the algorithm predicts the best price range for each product.
What are examples of companies using dynamic pricing?
Google Ads
Google leverages dynamic pricing for optimizing the prices of Google Ads. The price for a Google ad changes according to what business competitors are bidding for the keywords, locations, industry, and audiences. For instance, creating an ad targeting competitive keywords or high-bidding keywords will cost more than targeting low-competition keywords. However, the potential ROI may also be higher.
Airbnb
Airbnb is one of the most popular home rental marketplaces. To determine stay prices for similar properties, Airbnb’s dynamic pricing algorithm inputs variables such as:
- Seasonality
- Supply and demand
- Day of the week
- Special events and festivals
- Number of days left to book
- Historical performance of users’ listings
- Competitor occupancy and prices
- Number and quality of property reviews
Airbnb offers its own dynamic pricing tool “Smart Pricing” to hosts to help them automate the pricing of their properties in a more profitable manner.
Airlines
Airlines are the earliest adopters of dynamic pricing. A ticket for the exact same flight with the same destination and at the same date can have a number of different prices for different customers.
Because airline sales moved online earlier than other categories and because airlines are expected to charge different prices for the same ticket bought on different days, it was easy and acceptable for airlines to move to dynamic pricing.
e-Commerce
Retailers, especially e-commerce companies like Amazon and eBay use dynamic pricing for personalized pricing. If you consistently buy from Amazon or another e-commerce website, prices will be higher. The dynamic pricing engine calculates the loyalty level of each customer and sets the price lower if a person is a newcomer.
Dynamic pricing is now used for almost every product and service. From the price of a concert ticket to the price of a hotel booking is calculated by dynamic pricing algorithms. Even Uber is using surge pricing.
How to choose the most suitable dynamic pricing algorithm?
To choose the best dynamic pricing algorithm, businesses need to take into account that the algorithm should be able to provide prices that:
1. Maximize revenue and profit
Dynamic pricing algorithms are designed to ensure that prices adjust in real time to dynamic market conditions, enabling businesses to capture maximum revenues and profits.
2. Minimize customer churn
An effective dynamic pricing algorithm should be able to analyze customer behavior and preferences in order to provide personalized prices which help reduce customer churn.
3. Compete with competitor prices
The algorithm should also be able to anticipate competitor prices and adjust your prices accordingly in order to stay competitive and attract customers.
4. Improves customer experience
Dynamic pricing algorithms should be able to provide customers with personalized prices, discounts, and offers that improve their shopping experience and help build repeat business.
5. Aligns with business objectives
A good dynamic pricing algorithm ensures that price adjustments are always aligned with corporate goals. For instance, companies known for low prices should therefore define prices in their algorithm that are below the market average.
How to collect data to implement dynamic pricing?
The most important metric to a successful implementation of dynamic pricing is data. Businesses need to collect historical and current data about the market, competitors, customers, trends, etc. This data can be obtained by:
- Buying / Generating intent data: Intent data covers data concerning purchasing behavior of individuals or companies such as product reviews, whitepaper downloads, google searches, subscriptions and registrations, and website visits. Some companies generate intent data using buyer intent data tools, whereas others buy intent data from data providers.
- Scraping online data: Business can use web crawlers to automate the extraction of data from online resources about the market and competitors, such as product prices, reviews, customer comments.
Why is dynamic pricing relevant today?
It is difficult to achieve significant financial improvement in a large company. Most large organizations
- move slowly because of multiple levels of hierarchy
- are reluctant to make radical changes as they need to be conscious about the sensitivities of a broad customer base
- need hundreds of millions of additional sales to achieve a significant percentage improvement in profits
- need hundreds of millions of OPEX improvements to achieve a significant percentage improvement in profits. However, OPEX improvements are painful and slow. For example, headcount reductions are demoralizing and severance packages negatively impact financials in the short term.
However, dynamic pricing is one of the few approaches that can lead to quick results in large companies and make the responsible team heroes.
Different dynamic pricing modules & strategies
Because of the complexity of a successful dynamic pricing strategy, different modules are sometimes used for different product categories and market responses to manage complexity. In this section, we discuss those dynamic pricing methods or modules.
Module name | Purpose |
|---|---|
Long Tail | Price new or low-data products using data from similar items. |
Elasticity | Estimate how price affects demand, factoring seasonality and cannibalization. |
Key Value Items (KVI) | Focus on consumer perception by managing prices of popular products. |
1. Long tail module
This module is for new products or long-tail products with little or no historical data. Main challenge for this module is to use product attributes to match products with little purchase data with products that have rich purchase data so prices can be informed by rich data.
2. Elasticity module
Elasticity module calculates the impact of price on demand considering seasonality and cannibalization.
A leading Asian e-commerce player built an elasticity module based on a multi-factor algorithm that drew on ten terabytes of company’s transaction records. Data included product price, substitute price, promotions, inventory levels, seasonality, and competitors’ estimated sales volumes.
3. Key Value Items (KVI )module
Key value items are popular items whose prices consumers tend to remember more than other items. KVI module aims to manage consumer price perception by ensuring that items that strongly impact customer’s price perception are appropriately priced.
This is important for resellers like grocery companies. Because they are not selling their own products, they need to make sure that customers see them as the lowest cost option. A leading European nonfood retailer built a sophisticated KVI module statistically scoring each item’s importance to consumer price perception on a scale of 0 to 100.
This scale guided pricing decisions and company was willing to lose more on KVIs to retain and improve the customer price perception about their company.
4. Competitive-response module
This module leverages granular pricing data from competitors and impact of those prices on company’s customers to react to competitors’ prices in real-time.
5. Omnichannel module
Companies manage prices between channels both for price discrimination and also to encourage customers to visit less costly channels. Omnichannel modules ensure that prices in different channels are coordinated.
6. Time-based pricing module
Online retailers may charge customers more or less at the specific time of the day due to the following reasons:
- seasonality of products
- retailers charge more between 9 AM-5 PM since most online retail customers shop more during weekly office hours
- if customers want a same-day delivery or shopped right before end of the working hour, retailers are eager to charge more
- if the product has an expiration date, as time goes by, the price of the product decreases.
7. Conversion rate pricing module
If most leads (which are the views of the website in retail terminology) aren’t turning into sales for specific products, then retailers may lower the price to increase conversion rate.
How does a dynamic pricing algorithm work?
Dynamic pricing algorithms work by estimating the dependency of a price on-demand in the following manner:
- Processing historical sales and price data, pricing points, and current market demand (e.g., data about wrapping paper during Christmas)
- Identifying significant parameters that the price depends on. For example, “school opening” is a parameter that affects stationery sales.
- Generating a mathematical model based on significant parameters.
- Rerun the model using new data (when available)
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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This is a great article. Learning about dynamic pricing in business is one of the key methods of staying ahead of the competitor. customer satisfaction is always the number one priority since profits and customer loyalty is based on it and offering competitive prices using dynamic pricing strategy is a very good way to achieve it. Brilliant article.
Hello, Patrick! Thank you for your feedback. To learn more about dynamic pricing, click here: https://research.aimultiple.com/dynamic-pricing/