The AI Price Advantage: Unlocking Pricing Efficiency with AI

I co-wrote a book titled “The Price Advantage,” with Walter L. Baker and Michael V. Marn. In it, we stressed the significance of the price advantage as a core capability accessible to almost every company. While businesses often focus on various advantages like cost, distribution, technology, innovation, brand, and service, a price advantage enables a company to get fully compensated for the value it offers to customers and allows for investments in other core strengths. Without a price advantage, even superior companies risk losing their drive and ability to maintain their competitive edge.

Over the past decade, many companies have made considerable investments in pricing capabilities. They have established dedicated internal teams, hired experts, and adopted technology. All with the goal of creating a price advantage. However, we are now standing on the brink of an AI-driven revolution that will elevate the standard for achieving this advantage.

Take the airline industry, for example, where advanced AI and scientific pricing algorithms have become essential for dynamic pricing and revenue management. Companies attempting to enter this market without leveraging AI have faced failure. Beyond airlines, AI is set to transform the entire pricing discipline, and those companies that fail to invest in this capability may find themselves at a similar disadvantage.

Here are the primary advantages that AI brings to companies investing in pricing capabilities:

1. “Panning for gold”: AI possesses a remarkable ability to identify pricing opportunities often overlooked by traditional methods. For instance, our empirical data shows that over 2/3 of pricing upside comes from small adjustments in price, particularly for products and customers with decent margins. Traditional pricing analysis, however, typically focuses on obvious outliers – usually lower-than-average margin products and customers. AI efficiently discovers and allows companies to act upon these minute adjustments on these typically hard-to-find opportunities, which can lead to substantial benefits.
2. Enhancing the customer experience: AI enables market-relevant pricing without the need for lengthy and cumbersome price negotiation processes. This improvement enhances the customer buying experience while also reducing sales costs. In fact, research indicates that two-thirds of B2B buyers prefer to buy from companies whose prices are algorithmically determined, trusting it more than opaque negotiation processes. AI automates the pricing and discounting process, providing much greater pricing precision and efficiency.
3. Dynamic market adjustment: AI processes vast amounts of data, customer insights, and market trends with unprecedented speed and accuracy. This capability allows companies to optimize prices in real-time, adapting to changing market conditions and responding proactively to competitor actions. The days of time-consuming meetings and massive Excel spreadsheets for smartly adjusting prices are coming to an end.

As artificial intelligence continues to infiltrate various aspects of our lives, its transformative impact on business pricing models is becoming increasingly significant. A seismic shift is upon us; pricing strategies, traditionally relying on the human analysis of market trends and consumer behavior, are now becoming AI-driven business processes.

Embracing AI-powered pricing capabilities is essential for companies seeking to establish and maintain a price advantage in today’s fiercely competitive business environment. As neural network AI technology is now available and proven, businesses that harness its potential will be better equipped to navigate market challenges, enhance profit margins, and thrive in the dynamic pricing landscape. The price advantage achieved through AI-driven pricing strategies will undoubtedly become a vital component of sustainable business success in the years to come.

Unlocking Pricing Efficiency with AI

A key strength of AI lies in its capacity to eliminate inefficiencies that often plague manual and human judgment-driven pricing processes.

Enhancing productivity stands as a paramount objective for nearly every business—achieving more with less and doing it better. Despite this, the pricing function in many companies continues to grapple with considerable inefficiencies.

For instance, a high-tech company found itself losing sales due to delayed quoting, mistakenly attributing it to the complexity of its product portfolio. However, a detailed time and motion study revealed that 68% of the quoting time was spent navigating the pricing review and approval process.

Similarly, an electrical distributor suffered significant margin losses because its price list change process couldn’t keep up with dynamic cost and market changes. These challenges persist across industries – leading to unnecessary internal costs and often making it more difficult for customers to buy. So, if productivity improvements are highly valued, why do inefficiencies persist in pricing processes?

1 – Lack of a Coordinated Market Model:
Today’s companies possess abundant data—transaction history, customer attributes, market changes, etc. However, many struggle to integrate this data in an automated manner to inform pricing decisions. Without a unified approach, achieving precise and responsive pricing becomes challenging. Decision-makers end up spending valuable time piecing together and interpreting disconnected data sources. And they rely on judgment instead of an automatic and holistic model to determine precisely how each data point impacts the final price.

2 – Distributed Decision-Making:
Pricing decisions often involve numerous stakeholders, each with their own opinions, experience, and interests. Sales, finance, marketing—each contributes to the complex landscape of price-setting. Reconciling these diverse and often conflicting inputs can be time-consuming, leading to manual and burdensome interventions for pricing decisions.

3 – Legacy Systems and Processes:
Outdated pricing systems on top of manual processes further aggravate the inefficiencies. Companies relying on static data and models often struggle to adapt swiftly to changing market conditions. Many early generation pricing technologies still depend largely on backward-looking analytics, necessitating human judgment and interpretation to preempt and improve future pricing decisions. The problem is that when these prices are published and distributed, the market condition for such pricing has already changed.

4 – Perceived Risk:
Fear of losing customers (from pricing too high) and fear of losing margins (by pricing too low) can simultaneously hinder companies from being nimble, especially in today’s ever-changing marketplace. Indeed, many of the pricing processes that companies have instituted in the last 20 years have generated significant improvements in margin but also added significant manual complexity, which decreases pricing efficiency.

Free Yourself from Inefficiencies:

How do we break free from all of the inefficiencies in pricing and their root causes? The good news is that there are now advanced AI pricing technologies that can handle the complexities of making great pricing decisions. For example, it would be impossible for airlines to hire enough people to handle all the complexities in the dynamic airline market environment. Even if they did, it would never achieve the speed, accuracy, precision, and consistency of today’s revenue management systems. Although human judgement and interventions still exist even in airline revenue management, they have been able to automate the vast amount of pricing decisions that happen.

What is required for other industries to make this leap towards much more efficient, dynamic, and profitable pricing processes? While I could go into detail on the differences between poor, good, and great pricing technologies that can help make this leap, the bigger challenge is more of a company leadership issue than a technology challenge (the technology exists and is well proven to work).

The leadership challenge requires building trust and confidence internally (and sometimes with customers) in the capabilities of AI pricing technology. It requires leadership to bring all of the disparate decision-makers mentioned previously to build trust in the automation of pricing. The good news is that it is easier now more than ever given the advancements in SaaS-based AI pricing technology.

Advanced AI pricing systems provide transparency on how all of the elements in the market model affect the AI-generated prices—this allows pricing leaders to demonstrate and build internal understanding of how the technology works and build confidence in its recommendations. Additionally, leaders can easily conduct A-B tests with this technology to prove its accuracy and precision compared to existing processes. Lastly, as adoption of AI-based pricing technologies becomes more pervasive across industries (PROS, for example, started in airlines and is now being used in over 60 industries), competitive pressure will necessitate skeptics to reconsider better and more efficient approaches for pricing.

As one of the last bastions of inefficiency in many businesses today, the opportunity to automate and improve pricing processes is within reach and represents a significant untapped opportunity for many companies.