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Price optimization solutions allow businesses to predict how buyers will react to different price points and pricing models. These optimization models use machine learning and artificial intelligence tools to set initial pricing, promotional pricing, discount pricing and substitute pricing.

Price optimization helps B2B businesses set the right price for each unique situation – a process that would be extremely challenging manually. For example, there are many ways a company prices: list, matrices or tiers, customer agreements, spot negotiations, overrides, etc. Add multiple product lines and complex product configurations, and you have an interconnected web of complexity.

In particular, you'll need Machine Learning models and key components of price optimization. You'll also require different types of information (structured and unstructured data) to build accurate market simulations, including:

• Customer information – who they are, what they purchased, review results and feedback

• Descriptions of products and services – product details, photos, manufacturing and purchase costs

• Transaction data – who purchased what and for how much

• Event data – purchase paths

• Outside data – market trends, seasonality, weather, etc.

• Competition – prices on similar products if available

The main difference is that price optimization is used with many different pricing strategies to reach organizational goals, while dynamic pricing is one type of pricing strategy. When using a dynamic pricing strategy, businesses set flexible prices for their products or services based on current market demands or to match their competitor’s price.

To find the right price for your product, one that maximizes value for your customers and profit for you, you need to start with understanding your customers’ behaviour. There are advantages to using AI-powered pricing software. It can help you determine how much your customers are willing to pay. This data can come from customer reviews, churn rates, demographics, surveys, etc.

Once Willingness to Pay for each of your customer segments is calculated, pricing objectives and constraints are established. This helps create a framework that aligns to greater organizational goals. Using this data, optimal target prices are set and implemented. Pricing is an ongoing process, which means continuously monitoring your prices, reviewing results and making adjustments.

AI speaks to computer programs that mimic human intelligence, using logic, if-then rules, decision trees and machine learning. Machine learning is a subset of AI. It enables machines to improve at tasks with experience.

It comes down to profit. Optimization begins with segmenting your customer base. Without segmentation, you would have one price and one profit. If you segment and differentiate your pricing, you can potentially increase your bottom line. Machine learning can help you identify your segments and their willingness to pay.