Dynamic Pricing Algorithms in 2024: Top 3 Models

Dynamic pricing is a method used by business leaders, such as Amazon and Airbnb, to optimize their pricing strategy according to market and consumer data in order to attract more customers and increase profit. While traditional dynamic pricing algorithms use historical data to estimate the best prices, modern dynamic pricing algorithms leverage more data, as well as AI and machine learning capabilities to better predict market trends and achieve dynamic pricing optimization.

In this article, we explore the algorithms underlying dynamic pricing and how to choose the most suitable one for your business.

What is a dynamic pricing algorithm?

A dynamic pricing algorithm or sometimes called algorithmic pricing is the set of inputs and instructions underlying any dynamic pricing strategy. Dynamic pricing algorithms input data about a product/service and output what would be an optimal price for it within given circumstances in order to maximize the vendor’s profits while maintaining customers.

Dynamic pricing algorithms leverage historical data about:

• Product prices
• Production costs
• Market trends
• Customers’ purchase behavior

Modern algorithms may also include real-time data about competitors’ prices and stocks collected from online websites using:

• Web scrapers
• Web scraping APIs
• RPA bots

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The following video demonstrates how Bright Data’s data collector can be used to extract travel pricing data from travel agency websites:

To learn how to leverage scraped travel data, check out 5 Common Web Scraping Applications in the Travel Industry 2023

How does a dynamic pricing algorithm work?

Dynamic pricing algorithms work by estimating the dependency of a price on-demand in the following manner:

1. Processing historical sales and price data, pricing points, and current market demand (e.g., data about wrapping paper during Christmas)
2. Identifying significant parameters that the price depends on. For example, “school opening” is a parameter that affects stationery sales.
3. Generating a mathematical model based on significant parameters.
4. Rerun the model using new data (when available)

What are the models of dynamic pricing algorithms?

Depending on the mathematical model, businesses can create numerous algorithms that fit their dynamic pricing strategy. Here are a few approaches compiled from research articles:

1. Bayesian model

In a Bayesian model, the user picks a prior value indicating the 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 the most important feature to decide on the final price, like 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.

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.

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 and attract their customers

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 and maintains loyalty

Additionally, a dynamic pricing algorithm should be able to provide customers with personalized prices, discounts, and offers that improve their shopping experience and help build repeat business.

5. Improves customer experience and maintains loyalty

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.

6. 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.

To explore types of dynamic pricing and different industries’ uses, feel free to read our in-depth article about the 6 types of dynamic pricing and how AI can improve them.

Further reading

If you want to explore dynamic pricing implementation and case studies, feel free to read:

• Top 5 Price Monitoring Tools: Vendor Selection Guide
• Top 10 Competitive Intelligence Tools & Selection Guide
• eCommerce Price Benchmarking

If you believe your business will benefit from a dynamic pricing tool, feel free to check our guide Dynamic pricing: what it is, why it matters & top pricing tools. And if you’re looking for tools to collect data for your business, check out our data-driven lists of web crawlers, and pricing software.

And we can guide you find the right solution for your business: