What is value-based pricing: Definition, strategies, benefits
Value-based pricing is an effective way for companies to set prices, but isn't easy. Learn strategies for effective price management.
Pricing is getting incredibly complex. Gone are the days where companies could manage pricing once a year. In a constantly changing market, it needs to be dynamic, agile, and surgical.
The good news is that technology is evolving quickly, especially artificial intelligence. AI has been maturing over the years to the point where its breadth and ease of adoption are forcing companies to implement the technology into their processes or risk falling behind.
Broadly speaking, AI price optimization is a way for businesses to streamline the complex process of pricing and make better data-driven decisions at scale.
How AI optimizes pricing
Pricing managers, product directors, and sales leaders make hundreds of decisions every day: which discount offer to a customer; where, how and how much to increase list prices; which promotion level to target for special event; or how to respond to a competitor price change.
For each of these decisions, AI can analyze large amounts of data, identify patterns, and recommend changes that follow a commercial strategy.
For example, customer-specific pricing may rely on AI to identify and compare against similar customers’ prices and their recent behavior, while competition monitoring may use AI to identify fast-changing substitute products more efficiently and rationally.
Over time, companies that modernize their processes with AI get incrementally closer to the pricing holy grail: waterfall optimization.
This refers to algorithms that are capable of understanding and jointly optimizing all levers for each customer to maximize profitability: list prices, local adjustments, discounts, rebates, etc.
However, there are three key steps businesses should take to get the best results from AI price optimization.
1. Make pricing AI work in concert with humans
First, every pricing task requires extreme transparency, no matter the technological sophistication employed.
Any AI-powered recommendation that cannot be understood by pricing managers, explained by the sales team, and ultimately conveyed to customers will ultimately be rejected.
All stakeholders must understand what the technology does, and be trained to address potential customer-facing challenges.
Also, relying on data is necessary, but rarely enough for price optimization. Pricing is fast-changing and not all future prices can be set by only looking at data, especially in many industries where data scarcity is a challenge. AI technology for pricing must be able to extend historical or current data by incorporating strategic direction from multiple users, with a great deal of flexibility and agility.
Business leaders should also come prepared with an AI price optimization strategy; you can’t automate what you haven’t defined.
2. Integrate AI price optimization into all channels
Siloed solutions are bound to disappoint. AI creates value not because it has the most advanced algorithm, but because it transforms business processes consistently.
The implication is that over time, AI must be integrated into all commercial channels, including CPQ, CRM e-commerce, and ERP. This requirement will sound familiar to companies with large omnichannel dynamics.
Through integration, AI delivers more than a price; it’s a way to consistently transform processes. Consider these examples:
- CPQ – AI recommendations can drive workflow approvals, ensuring that as many quotes as possible are delivered without systematic manual approvals from sales leaders, slashing turnaround times.
- E-commerce portal – AI can understand customers’ past transactions and current shopping experience, improving the accuracy and user experience of upsell/cross-sell recommendations.
- CRM – AI can highlight critical insights to customer-facing teams, such as risks of churn, underperformance, or growth opportunities.
- ERP – Bidirectional integration with an ERP shouldn’t be overlooked. Pricing is fundamentally a data-driven discipline; receiving frequent updates of rich and accurate datasets from the ERP is essential. Conversely, recommendations sent to customer systems must be incorporated into the ERP to ensure seamless downstream transaction execution. Again, tight integrations help deliver seamless CX.
Finally, since democratizing pricing and making it a top priority for consistent, sustainable, and agile execution of a company’s strategy, user experience-level integration is paramount.
Basics include the ability to navigate between systems and data sharing with the emergence of conversational user experiences powered by generative AI.
Omnichannel pricing: B2B e-commerce requires price consistency
Inconsistent pricing across channels is hurting B2B sales and damaging revenue. Learn the benefits of omnichannel pricing.
3. Recognize the complexity of AI, use it wisely
AI has become massively complex over the last 20 years. What started as extensions of statistical models has now expanded into a wide field comprised of many subdomains that can appear loosely defined or even overlapping.
Yet being an AI expert shouldn’t be a prerequisite to adopt AI price optimization. In fact, pragmatic automation that can be easily adopted is better than indigestible mathematical complexity.
That said, we should strive for gradual education and resist over-simplification. Gone are the days where AI could be easily sorted by simple metrics such as generations or model classes. Business value and the ability to deliver it should be the primary driver for pricing AI. Fortunately, the technology can be modular and integrated into broader business value-driven roadmaps.
Take generative AI as an example. Like every model or domain, it comes with strengths and weaknesses that make it a fit for certain applications. Those applications are typically low risk to the business and focus on creation or transformation of unstructured data and natural language. Gen AI can be an asset in pricing AI and should be prioritized depending on expected value.
However, decision making in pricing goes far beyond unstructured data. For comprehensive and sustainable solutions, companies should consider and integrate other AI initiatives and models capable of capturing value from pricing.
5% back to your bottom line and up to 10% added to your topline. RISE to meet the future of pricing HERE.
A pricing strategy built to last
Both AI and pricing are changing fast, and the technological and business landscape is likely to look tremendously different three years from now – and so will your pricing strategy.
From an IT perspective, modularity, flexibility, and maintainability are key to ensure sustainable success in pricing AI. This can only be delivered through a platform that can:
- Collect data seamlessly as it’s created across the organization and third parties
- Integrate seamlessly in systems of action
- Bring to life a variety of universal or specialized methodologies or data science models that companies can use and maintain as they grow and as technology evolves.
With AI-driven platforms and technologies, companies can lay the foundation for innovation and future-proof their business. They should have clear value-driven roadmaps that focus on human symbiosis, IT integration, and flexibility.
