In my last blog post, I delved into the potential of AI to create a price advantage for companies. 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Outdated pricing systems on top of manual processes further aggravate the inefficiencies.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:
So 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.
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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.