Why every business should implement price management algorithms

For any company that is engaged in retail trade, it is important to set the right price. The company can have a lost profit without the correct price. Traditional methods of price management do not always allow you to set the optimal price.

Dynamic pricing algorithms are most effective in e-commerce due to frequent price changes and the collection of feedback data in real-time.

If we compare traditional and dynamic price management, then in the first case, demand is estimated based on historical sales data, that is, by conducting a regression analysis for the observed price pairs and the requirements.

Since the price-demand ratio changes over time, the traditional method usually regularly reviews the demand function. This is similar to the dynamic pricing algorithm, which can be described as follows:

1. Collection of historical data on various prices offered in the past, as well as on the observed needs at these points.
2. Estimation of the demand function.
3. Finding the optimal price at which the revenue or profit will be maximum and will meet the restrictions imposed by the pricing policy or inventory.
4. The application of this optimal price for a certain period.

The disadvantage of the approach is the passive study of the demand function without properly studying the relationship between price and demand. This can become a problem depending on the dynamism of the system.

To understand a particular pricing method in more detail, you should focus on the design goals:

• Optimization of exploration and operation, in which the seller does not know the demand function in advance.
• Providing an opportunity to limit the number of price changes during the product life cycle.
• Providing the ability to specify acceptable price levels and price combinations.
• Price optimization in conditions of inventory constraints or specified dependencies between products.

Following A Guide to Dynamic Pricing Algorithms on Grid Dynamics Blog let’s understand the methods of achieving these goals.

Experiments with limited prices

Here we use a set of hypotheses based on historical demand functions for similar products or categories. When creating a dense series of demand curves, we can assume that the true demand function will be in this range.

This method is effective when the demand function can be considered stationary.

Disadvantage: not suitable for dynamic systems.

Continuous experimentation by the pricing rules

It uses more versatile tools that can continuously investigate environmental changes, balancing between exploration and exploitation.

This method has the following drawback.

When the structure for estimating demand and optimizing prices for several products is expanded, the optimization task becomes more complicated when there is a dependence between products or time intervals.

Multiple products or time intervals

This method eliminates the disadvantage of the previous one.

If the products are fully or partially replaceable, evaluation and optimization can be complicated. One of the possible simplifications is the use of the demand function, which does not depend on individual prices for other goods, but on the average price in a group of interchangeable goods.

Disadvantage: Developing probabilistic models for Thompson sampling and other algorithms can be difficult.

Complex demand models

The disadvantage of the previous method can be circumvented by using the framework of probabilistic programming, which allows us to descriptively define models and abstract the inference procedure.

All these methods are comprehensive tools for creating dynamic pricing systems and configuring them by the requirements and needs of the business.

In practice, the use of these methods can have a significant impact on sales and revenue. Many market leaders, including Amazon and Walmart, achieve super-profits thanks to dynamic pricing.

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