AI News, BOOK REVIEW: Application of Machine Learning in Pricing and Revenue Management

Application of Machine Learning in Pricing and Revenue Management

Prescriptive Analytics and B2B Pricing Science Pricing science is the application of analytical techniques and methods to solve the problem of setting prices.

This discipline had its origins in the development of yield management in the airline industry in the 1980s, and has since spread to many other sectors and pricing contexts, including media, retail, manufacturing, distribution, etc.

The first step in creating a pricing strategy, developing a robust and reliable prediction model, is crucially important because failing to understand historical behavior and failing to capture market dynamics leads to irrelevant price recommendations.

 In B2B companies, pricing behavior depends on many factors, such as product type, industry, location, annual customer spends, order size, seasonality, and many more.

Machine learning (ML) algorithms are categorized into three main groups based on their interactions with the environment: supervised learning, unsupervised learning and reinforcement learning.

In this post we are going to review the application of first two categories for B2B pricing.   There two foremast application of supervised ML algorithms in pricing science: Market Price Prediction: Accurate market price prediction is extremely important in the price optimization process. In Figure 3, the actual market price is P0 and the optimum market price is P*.

Discovering hidden patterns and structures from data often leads to actionable insights that enable various trends to be predicted and helps businesses gain a competitive advantage.

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