Pricing has the potential to offer more financial value than the value gleaned from operational efficiencies associated with improved configuration and quoting. For example, a landmark study published in Harvard Business Review examined the unit economics of 2,463 companies and found that a 1% price improvement results in an 11.1% increase in operating profit. However, running a successful B2B pricing organization is arguably challenging.

Unlike business-to-consumer (B2C) pricing, B2B pricing can involve complicated negotiations, long sales cycles, and multiple decision-makers. Furthermore, B2B pricing is often based on the perceived value of the product or service and the specific needs of the customer, making it difficult to determine the optimal price.

Identifying a customer’s willingness-to-pay for a product and/or service in real-time requires numerous data sources, complex predictive models, and intelligent software that is easy enough for salespeople to eagerly adopt. Typical challenges include data that is not readily available, frequently changing market conditions and large distributed user communities to manage. This complexity magnifies when considering that optimal pricing can span a large set of products and services in every quote.
Artificial Intelligence (AI) has the potential to navigate these complexities and revolutionize B2B pricing providing companies with the insights and tools they need to maximize profitability

What Role Can Artificial Intelligence Play in B2B Pricing?

AI in pricing usually refers to the use of machine learning algorithms to analyze large quantities of data and make predictions about the optimal price for a given product or service. These algorithms can take into account a wide range of factors, such as competitor pricing, customer behavior, and market trends. By analyzing this data, AI can identify patterns and make recommendations for pricing that are accurate and effective.

The application of AI in pricing needs to be assessed in the context of pricing that is broad spectrum, from simple rule-based algorithms to complex machine learning models. Some common applications of AI in pricing include price optimization, dynamic pricing, and predictive pricing.


What Are the Benefits of Using Artificial Intelligence in B2B pricing?

Rapidly Adapt to Market Variability

One of the primary benefits of AI in B2B pricing is the ability to optimize prices in real-time based on changing market conditions. This is particularly important in B2B pricing, where prices can be highly variable and subject to negotiation. By using AI to analyze market data and customer behavior, companies can empower any sales force with optimized prices on the go that are customized for individual customers (not just markets).

Improved Visibility

With ability to process large amount of data volume, AI can help companies quickly identify emerging pricing trends and patterns. This can help companies to fine tune their pricing approach based on data-driven insights. By analyzing customer behavior and market trends, companies can identify which products or services are most profitable, which markets and products face higher competitive pressure and adjust their pricing accordingly.

Quickly Identify and Fix Pricing Exception

The ability to quickly analyze large amounts of real time data can also help companies to identify pricing anomalies and inconsistencies. By analyzing pricing data across different products, markets, and customers, AI can flag issues such as pricing errors or inconsistencies that may be impacting profitability. This allows companies to quickly identify and fix these exceptions, minimizing the impact on their bottom line.

How Can Machine Learning Help In Recommending Optimized Prices?

Price optimization using machine learning algorithms is one specific application of Artificial Intelligence in pricing that involves the use of algorithms to optimize pricing strategies. This typically involves the following steps:

1. Data Collection:

This step typically involves collecting large amounts of pricing and market data, including customer behavior, purchasing/sales history, and competitor pricing, identifying which data elements are relevant to making pricing decisions, and typically some form of data cleansing as well.

2. Data Analysis:

The next step involves analyzing the data to identify patterns and trends in customer behavior and market conditions. This information can then be used to develop more effective pricing strategies that take into account the specific needs and preferences of customers.

3. Algorithm Development:

Data and Pricing Scientists can use the outcome of data analysis to uncover the factors that influence pricing decisions and combine these with a company’s pricing strategy to develop a algorithm that works best for the company.  These algorithms can be turbo-charged by using machine learning techniques that  automate data analysis, make recommendations for optimal pricing strategies and course correct as market conditions and business landscape changes. The use of price optimization is particularly important in B2B pricing, where prices can be highly variable and subject to negotiation.

4. Implementation:

Once algorithms that delivers optimal pricing have been identified, they then need to be codified by implementing/integrating  these algorithms in a pricing software or other tools to ensure pricing and sales teams have access to optimized price recommendations.

B2B pricing software and its features

Usually all B2B pricing software provide a wide range of features and functionality. A good B2B pricing software must include at least the following feature/functionality:

1. Real-time pricing optimization based on market data and customer behavior.

2. Dynamic pricing models that adjust prices in real-time based on changing market conditions.

3. Predictive pricing models that use machine learning to identify pricing trends and patterns.

4. Pricing analytics that provide insights into pricing performance and profitability.

5. Pricing strategy development tools that help companies develop more effective pricing strategies.
A good B2B pricing software, specifically designed to fit a companies unique pricing processes can help them optimize their pricing strategies

Artificial Intelligence and the future of B2B pricing

Artificial Intelligence has the potential to revolutionize B2B pricing, by providing companies with the insights and tools they need to maximize profitability.
AI continues to evolve, we can expect to see even more sophisticated pricing strategies and tools emerge. From dynamic pricing models to predictive pricing algorithms, the future of B2B pricing is bright, and AI is at the forefront of this transformation.

To stay relevant, B2B companies must embrace AI and incorporate it into their price planning, implementation and execution. By implementing a pricing software that uses machine learning algorithms to analyze market data and customer behavior, companies can optimize their pricing strategies in real-time, adjust prices based on changing market conditions, and identify pricing anomalies and inconsistencies.

Rakesh Devnani

About Rakesh Devnani

Rakesh leads global pricing initiatives for some of Vistaar’s most strategic customers. He brings deep experience executing global pricing transformation projects across Consumer Goods, Commodities, Industrial Manufacturing and Retail industry verticals.

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