Manufacturers and distributors have relied on cost-plus pricing as a standard strategy for years. The concept of cost-plus pricing is simple: cover costs and secure a target profit margin. Standard cost-plus and similar formula-driven strategies overlook the complexities of changes in market demand and consumer preferences, as well as competitive pressures.
Pricing AI, deep use of data, machine-optimization models or machine learning, and similar advancements have the potential to revolutionize B2B sales and commercial strategy. The result is a greater focus on customer value, or in pricing terms, value-based pricing. B2B industrial and distribution organizations seek to transition to value-based pricing because it aligns with market realities, customer expectations, and is most responsive to current market conditions with significant volatility.
Perceived value is at the heart of value-based pricing. More than just covering costs, the approach uses factors that focus on customer willingness-to-pay. Competitive pricing, product differentiation, special services, and even intangible attributes like brand or country of origin can be factors that drive willingness-to-pay.
The difficulty or complexity is in determining the right attributes that are significant or “turn the dial” for a given target customer. AI data-driven optimization models will be responsible for driving the transformation and adoption of value-pricing because it offers the advanced insights, analytics, and guidance needed to price based on value most effectively.
AI identifies patterns and works to predict customer willingness-to-pay with a high level of accuracy. Various sources of data include historical sales data, market trends, customer behavior, and competitive pricing. Even weather data, port and shipping data, or traffic patterns data can be useful for use cases where speed or reliability of supply are critical. B2B companies use this data to set prices that maximize the probability of an increase in margin, revenue, volume, or combinations of a mix of optimization goals, to remain ahead of competition and market performance.
Volatility is a key feature of the business environment of today, which requires sales, commercial, and pricing teams to be agile and flexible. Part of this agility is a focus on AI and the ability to facilities dynamic pricing strategies. Where traditional models are static, pricing AI is flexible and dynamic, allowing manufacturers and distributors to adjust prices in real or near-real time as new data comes in and market conditions change.
Demand can fluctuate, customer behavior change, and so called “black swan” events are becoming a feature of life in business. AI empowers non-expert users to anticipate and quickly react to volatility. During a period of high demand prices can increase in a reasonable and ethical way to capture margin. When low-demand occurs, pricing can be reduced in a controlled and targeted way to minimize margin compression and focus on customers and markets in a way that simulates one-on-one decision making. Flexibility for pricing AI means that pricing remains competitive and aligned with markets while still executing on business and margin goals.
Adding one more complexity to the mix, B2B manufacturing and distribution sectors are also increasingly adding services and subscription-based models based on customer demand and market needs. These require a different approach because they represent unique pricing challenges.
AI can address the challenges by optimizing pricing for both product as well as services or alternatively product-as-a-service. Bundle pricing and other complex configured product categories can similarly be considered as well. In all cases, the goal remains. Optimize pricing to reflect the value delivered to the customer based on willingness-to-pay and any other critical business factors.
The transformation to value-based pricing underscores the importance of embracing AI to stay ahead in a highly volatile and highly competitive landscape. AI-driven solutions that leverage analytics and machine learning help manufacturers unlock new revenue streams, optimize pricing, and delivery a customer-specific solution.
Analyzing customer data to identify willingness to pay, manufacturers can segment their customer base and tailor pricing strategies accordingly. For instance, premium customers who perceive higher value in a product or service can be charged a higher price, while price-sensitive customers can be offered discounts or promotions to drive sales. This segmentation ensures that pricing strategies are aligned with customer preferences and market realities.
Moreover, AI-powered pricing solutions provide enhanced transparency and collaboration within pricing teams. By delivering clear and understandable pricing recommendations, AI fosters trust and confidence in pricing decisions. This transparency is crucial for gaining buy-in from stakeholders and ensuring the successful implementation of AI-driven pricing strategies.
In addition to optimizing prices for existing products and services, AI can also support the development of new pricing models. For example, manufacturers can use AI to explore innovative pricing strategies such as pay-per-use, or outcome-based pricing. These models align pricing with the value delivered to customers, providing a competitive advantage in the market.
As manufacturers continue to navigate an increasingly complex and competitive landscape, leveraging AI for pricing becomes a critical advantage. The shift from cost-cutting to value-based pricing not only enhances profitability but also ensures that pricing strategies are responsive to changing market dynamics and customer demands.
