Machine learning in dynamic pricing: CHI Software’s experience

One of the critical decisions a company makes about a product is its price. Businesses face two key challenges: optimizing prices and managing income in a constantly changing environment. Companies use dynamic pricing strategies to solve these issues simultaneously. The method’s efficiency can be noticeably increased with artificial intelligence (AI) and machine learning (ML) capabilities.

In this article, we’ll analyze the advantages of machine learning for dynamic pricing and examine the use of the technology in e-commerce, drawing on examples from CHI Software’s case studies.

What is dynamic pricing?

Dynamic pricing is a flexible strategy in which the product or service price is determined by the current (and continually changing) market conditions. It uses big data to understand and act upon changing terms. Companies set the optimal product prices, considering their costs, sales volumes, competitors’ rates, market trends, etc.

The advantages of the strategy are most evident in e-commerce and online/offline retail. The field looks to have strong prospects: according to eMarketer, the e-commerce sales sector has been exploding over the past several years and will hit US$4.479 trillion worldwide by 2021.

The main advantages of a dynamic model are:

• Fast adaptive pricing,
• Increased competitiveness, and
• Improved insights into customers’ focus.

Each of these strategies requires the processing of vast amounts of data. And machine learning helps with that.

Why ML is necessary for e-commerce dynamic pricing

E-commerce generates large amounts of data — so large that humans simply can’t handle it on their own. To build effective pricing solutions, merchants must take advantage of machine learning. ML-powered software solutions process data faster and never stop getting information to produce dynamic strategies.

AI/ML-based solutions help retailers to:

• Repeatedly search for and collect important information about competitors, consumers’ opinions, and the pricing history over the last period;
• Get quick access to broader data analysis, which results in better functionality;
• Quickly associate a new product with similar items to clarify a price segment;
• Predict demand for items that don’t have transaction data; and
• Anticipate early trends, aid product bundling, and create favorable discounts.

In terms of software architecture, there are two types of dynamic pricing solutions: rule-based and ML. Systems of the first type use a base containing the rules and rely solely on the “built-in” knowledge to respond to the environment’s current state.

In contrast, software powered by machine learning demonstrates much better performance. ML gains knowledge from data and finds ways to solve problems itself. The more data is in the system, the more it learns and improves its performance without detailed instructions.

Instead of rule-based solutions, the AI/ML-based pricing software has many exciting capabilities:

• Cluster analysis for granular customer segmentation,
• Consideration of a vast number of variables for different elements,
• KPI-driven pricing,
• Real-time market data analysis,
• Legacy data analysis, etc.

How to use ML in dynamic pricing: CHI Software’s experience

We will demonstrate the procedure for processing data and illustrate them with CHI Software’s case of developing and implementing a dynamic pricing system. We use a methodology based on CRISP-DM. It breaks the ML solution development process into six major phases:

• Business Understanding,
• Data Understanding,
• Data Preparation,
• Modeling,
• Evaluation, and
• Deployment.

Usually, except for deployment, they are applied several times in a cyclical way.

In the beginning, it is necessary to understand input variables and gather different types of structured or unstructured datasets to train ML models. You have to define the strategic goals and constraints to set unique, clear objectives (e.g., maximize profit or increase customer loyalty).

The next step is modeling. Previously gathered data is used to train ML models. They know how to find similar products and be effective. That’s why machine learning is useful in the case of new, rare, or exotic products.

When the model is trained, you can estimate and test the prices. The estimation may be an exact price or a range. The prices obtained can be subsequently adjusted and optimized regularly.

Conclusion

ML capabilities can be used for tasks related to pricing effectively. Machine learning is an excellent approach for understanding the relationship between sales of related products, forecasting demand, and determining customer sensitivity to ad campaigns.

For instance, we at CHI Software are expanding the scope of ML by offering solutions for automation of price-tag tracking and developing modules integrated into the clients’ CCTV systems to show the factors that lead to revenue losses.

We create smart solutions that help eliminate time-consuming manual processes and dramatically improve analytics and pricing quality. We prove in practice that AI and ML are some of the most promising tools for business, and the future belongs to them. Best of all, we’ll help you take advantage of all their benefits — today.

Want to know more about ML in dynamic pricing? Read the full article on our blog.