In the dynamic world of pricing, businesses are constantly searching for ways to optimize their strategies, leveraging advanced technologies to stay competitive. Among the various tools available, neural networks and decision trees are two prominent approaches that offer distinct advantages and challenges. Both methodologies are rooted in machine learning and artificial intelligence (AI), yet they diverge in how they process data and make decisions. As the demand for data-driven pricing continues to rise, it's crucial to understand these technologies' potential, limitations, and implications for your business, so join me for the lowdown on neural networks vs decision trees.

As providers of AI in our price optimization solutions for more than a decade now as part of our native cloud pricing software and increasing adding the powers of Generative AI (GenAI) into our pricing platform, Pricefx are perfectly positioned to discuss exactly how AI works in pricing software and explain what it can and cannot do, and why.

So, let’s first dive into each of neural networks and decision trees and what they are and how they work.

Later, we will move on to analyze their pros and cons for use in a pricing environment before we address the ‘elephants in their respective rooms’ on their suitability to use in your enterprise organization’s pricing regime, and how (or indeed ‘if’) to choose between the two.

What Are Neural Networks?

Neural networks are a type of machine learning algorithm inspired by the human brain's structure. They consist of layers of interconnected nodes, or "neurons," that process data and learn from patterns. The network's structure allows it to model complex relationships between inputs (such as customer behavior, market trends, and product features) and outputs (such as optimal prices).

Neural networks excel at recognizing intricate patterns in data, making them particularly useful for tasks like image recognition, natural language processing, and, increasingly, pricing optimization.

The diagram below illustrates a neural network’s basic structure:

As you can see, there are many different parts that work together to generate a price recommendation. Each part is easy to understand on its own, but when they are combined in a larger network, it is hard to tell how they reach their conclusions and how they fit into a pricing environment.

In other words, the real mystery of a neural network is not its structure but its reasoning. And with several hidden layers of activity between input and output, that question is very complex to answer in a definitive way.

The Pros of Neural Networks in Pricing

Complex Pattern Recognition: Neural networks are very good at finding subtle, non-linear relationships in data. This is very important in pricing, where factors influencing optimal prices are often complex and interdependent.

Adaptability: Neural networks can adapt to changing market conditions by continuously learning from new data. This makes them ideal for dynamic pricing environments where prices need to adjust quickly in response to changes in demand, competition, and other variables.

Automation and Scalability: Once trained, neural networks can automate the pricing process, making it possible to scale across large product portfolios with little human intervention. This automation can lead to significant efficiency gains.

Potential for Higher Accuracy: Because of their ability to process and learn from large volumes of data, neural networks have the potential to provide more accurate pricing recommendations than traditional rule-based systems.

The Cons of Neural Networks in Pricing

Lack of Transparency: One of the biggest drawbacks of neural networks is their "black box" nature. The complex layers of neurons make it difficult to understand how the network arrived at a particular pricing recommendation. This lack of transparency can be a big problem, especially when pricing decisions need to be explained to stakeholders or customers.

Instead of using a neural network, you could use a simpler and more transparent structure like a segmentation model. This way, you can identify the prices in a specific segment or cluster that you know work well, and then use them as a benchmark to spot and adjust the prices that are too high or low compared to that group.

From a practical standpoint, the first step in any efficient pricing process is to strategically identify the good prices from the bad and set up an efficient, transparent process to address this discrepancy. After all, you don't want to rely on a neural network to produce something logical, because as any pricing practitioner will tell you, hope is not a strategy.

Data Dependency: Neural networks require a lot of data to train effectively. If your business lacks enough historical pricing data, customer behavior data, or market data, the network's predictions may be unreliable or biased.

Risk of Overfitting: Neural networks are prone to overfitting, a phenomenon where the model becomes too specialized to the training data and fails to generalize to new, unseen data. This can lead to pricing recommendations that perform well in simulations but fall short in real-world scenarios.

High Computational Cost: Training and maintaining neural networks require a lot of computational resources. This can translate into higher operational costs, especially for smaller businesses with limited budgets.

To learn more specifically on neural networks and their suitability for pricing, check out this informative article below:

Decision Trees: A Structured Path to Pricing

What Are Decision Trees?

Decision trees are another type of machine learning algorithm that splits data into branches based on specific decision rules. Each branch represents a decision point, leading to an outcome or prediction.  See this deliberately oversimplified diagram below based on making the decision whether to take an umbrella with you on an outing or not.

Decision trees are often favored for their simplicity and interpretability, making them a popular choice for businesses looking for a more transparent AI-driven pricing solution.

The Pros of Decision Trees in Pricing

Transparency and Interpretability: One of the most significant advantages of decision trees is their transparency. Each decision point is clearly defined, making it easy to trace how the algorithm arrived at a particular pricing recommendation. This interpretability is crucial when pricing decisions need to be explained to stakeholders or customers.

Ease of Use: Decision trees are relatively simple to implement and understand, even for non-experts in AI. This ease of use makes them an attractive option for businesses that may not have extensive AI expertise in-house.

Flexibility: Decision trees can handle a mix of categorical and numerical data, making them versatile for various pricing scenarios. They can also be easily adjusted or pruned to improve performance and prevent overfitting.

Low Computational Cost: Compared to neural networks, decision trees require less computational power and are faster to train. This makes them a cost-effective solution for businesses with limited resources.

The Use of Categorical Data: Decision Trees are natively dealing with Categorical Data. In the business world, a lot of data is categorical rather than numerical, i.e. geography, segmentations, product hierarchies, etc., so being able to handle them without relying on numerics or obscure encoding can be beneficial.

The Cons of Decision Trees in Pricing

Limited Complexity: While decision trees excel at handling straightforward pricing scenarios, they may struggle with more complex relationships in the data. For example, decision trees might oversimplify pricing decisions that involve numerous interacting factors, leading to suboptimal outcomes.

Risk of Overfitting: Like neural networks, decision trees are also prone to overfitting, especially when the tree becomes too deep with many branches. Overfitting can result in a model that performs well on training data but poorly on new data.

Sensitivity to Data Changes: Decision trees can be highly sensitive to changes in the input data. Small variations in the data can lead to entirely different tree structures, resulting in inconsistent pricing recommendations.

Difficulty in Handling Large Datasets: While decision trees are generally faster to train than neural networks, they can become unwieldy and less effective when dealing with very large datasets. In such cases, ensemble methods which combine multiple decision trees, are often used to improve performance.

Choosing Between Neural Networks and Decision Trees: Key Considerations

When deciding whether to implement neural networks or decision trees in your pricing strategy, several factors should be considered:

1. Data Availability and Quality

The quality and quantity of your data play a crucial role in determining the effectiveness of both neural networks and decision trees. Neural networks require large datasets to perform well, while decision trees can work with smaller datasets but may not capture complex patterns as effectively. If your data is sparse or of low quality, a decision tree might be a better choice due to its simplicity and transparency.

2. Complexity of Pricing Strategy

Consider the complexity of your pricing strategy. If your pricing decisions involve multiple interacting factors, non-linear relationships, or require high adaptability to changing market conditions, a neural network may offer a more sophisticated solution. However, if your pricing strategy is relatively straightforward and you need clear, interpretable recommendations, a decision tree may be more appropriate.

3. Need for Transparency and Explainability

Transparency is a significant factor in pricing, especially when recommendations need to be justified to stakeholders or customers. Decision trees provide clear decision paths, making it easy to explain how a price was determined. In contrast, neural networks offer less transparency, which can be a drawback if you need to defend your pricing decisions.

4. Resource Constraints

Consider your computational resources and budget. Neural networks require more computational power and are more expensive to implement and maintain. If you have limited resources, a decision tree might be a more cost-effective solution.

5. Scalability

If you need a solution that can scale across a large product portfolio with minimal human intervention, neural networks may offer better scalability due to their automation capabilities. Decision trees, while simpler, may require more manual adjustments as your business grows.

Hybrid Approaches: Combining the Strengths of Both

While we've explored the pros and cons of Neural Networks and Decision Trees in pricing, it's worth noting that these approaches aren't mutually exclusive. Many successful pricing strategies employed by pricing software vendors use a hybrid approach, leveraging the strengths of both methods.

For example, you might use a Neural Network to generate initial price recommendations based on complex market dynamics, and then pass these through a Decision Tree to apply business rules and ensure explainability. Or you could use Decision Trees for broad pricing categories and Neural Networks for fine-tuning within those categories.

Ultimately, the key to success ies not in choosing one method over the other, but in understanding the strengths and limitations of each approach and applying them judiciously to your specific pricing challenges. By doing so, you can create a pricing strategy that is both sophisticated in its analysis and transparent in its application - a powerful combination in today's competitive business landscape.

However, without a price optimization approach, both neural networks and decision trees are limited to only recreating the past. For full price optimization transparency, a multi-agent optimization as used in Pricefx’s own AI Optimizationwill be necessary.

Remember, whether you opt for pricing software powered by the brain-inspired complexity of Neural Networks or the logical clarity of Decision Trees, the goal remains the same: to develop a pricing strategy that maximizes value for both your business and your customers. With careful consideration and implementation, either approach - or even a combination of both given your organization’s unique requirements and technology stack mix - can help you achieve that goal.

To put all you’ve learned in this article into context, check out my article below that supplies a comprehensive round-up of how AI works in a pricing software solution:

Meanwhile Happy AI-informed Pricing!