Smarter Pricing Models: How AI Can Learn from Buyer Behavior

For B2B brands, pricing is a strategic lever for competitive advantage. Traditional pricing methods, relying on periodic reviews, competitor benchmarking, or static profit analyses, are increasingly inadequate in the face of rapidly evolving buyer behaviors and market dynamics.

Despite the recognized potential of artificial intelligence (AI) to revolutionize pricing strategies, a recent global study on pricing reveals that only 27% of companies currently employ AI-based pricing solutions, even though 76% consider them highly relevant for managing prices and boosting profitability. This gap underscores a significant opportunity for businesses to harness AI’s capabilities to transform pricing into a dynamic, responsive, and strategic asset.

This article explores how AI learns from buyer behavior to shape smarter, adaptive pricing strategies, and how this shift is redefining the role of pricing and CPQ systems in go-to-market (GTM) execution.

The evolution of pricing strategy

As digital transformation accelerates, so does the need for pricing strategies that move beyond static models. Historically, pricing in B2B settings was governed by tiered discounting rules and seasonal adjustments. The introduction of behavioral pricing marked a pivotal shift: understanding what buyers do, not just who they are.

Layered with dynamic pricing (real-time adaptation to demand, supply, and market forces), organizations now have the ability to continuously refine prices with precision.

AI takes this a step further by fusing behavioral insights and dynamic inputs into a proactive system of revenue optimization. Instead of responding to the market, businesses can predict it.

Behavioral pricing: learning from buyer intent

To make pricing more intelligent, AI must first understand behavior. Behavioral pricing leverages data points such as time spent on a page, cart abandonment rates, click paths, prior purchases, and engagement frequency to interpret intent.

For example, AI may identify a returning buyer who lingers on a premium product page and has a history of engaging with promotions. This could trigger a time-sensitive incentive, tailored to their likely response window.

By turning raw behavioral data into signals of purchase readiness or price sensitivity, AI enables sales teams to align price with context, boosting both conversions and margin.

AI-driven dynamic discounting

Once behavior is understood, the next step is adaptation. AI-powered dynamic discounting uses algorithms to adjust pricing based on a variety of conditions: deal stage, customer profile, buying history, competitive benchmarks, and real-time engagement.

This is especially powerful in B2B scenarios where pricing flexibility must balance customer value and margin protection. A high-volume customer nearing contract renewal might receive volume-based incentives, while a new prospect showing high digital engagement could be offered an introductory rate optimized for conversion.

Dynamic discounting, powered by machine learning, minimizes reliance on manual overrides and helps revenue teams move faster without sacrificing control.

Agentic AI in pricing systems

As AI becomes more embedded in business systems, a new class of agentic AI assistants is emerging to automate complex decision flows. Platforms like Salesforce AgentForce, Microsoft Copilot, and ServiceNow CRM AI Agents are redefining how pricing decisions are surfaced and executed.

These intelligent agents can propose optimized pricing, flag approval risks, and even auto-suggest next-best offers. By embedding intelligent automation directly into the pricing process, organizations empower their teams to act on insights faster, with less friction and greater consistency.

Deep insights for predictive revenue optimization

What separates reactive pricing from intelligent pricing is foresight. AI-enhanced analytics deliver predictive insights by modeling likely outcomes based on historical data and current trends.

This means pricing decisions are no longer just about the present moment. AI can forecast how a discount affects customer lifetime value, identify price elasticity thresholds by segment, and simulate different pricing scenarios to optimize outcomes before offers are made.

These deep insights turn pricing into a source of continuous optimization across revenue operations.

Integration and governance: making AI pricing work at scale

Data-driven pricing cannot exist in silos. For AI to function effectively, it must be connected across systems: CRM, ERP, digital commerce, and customer data platforms. This integration ensures consistency and context across the customer lifecycle.

Equally important is governance. AI-powered pricing systems must include structured approval workflows, policy enforcement, and audit trails. These safeguards ensure compliance while enabling speed and scale.

Agility to adapt faster than the market

The competitive advantage of AI-driven pricing lies in its agility. Revenue teams can test, refine, and deploy new pricing strategies in real time, whether for new product launches, shifting market dynamics, or evolving buyer behavior.

This level of responsiveness supports faster monetization of innovations and shortens the feedback loop between pricing strategy and revenue impact. Agility is no longer a differentiator; it’s a necessity.

From strategy to execution: the value of CPQ

While AI guides pricing strategy, CPQ (Configure, Price, Quote) systems operationalize it. CPQ platforms embedded and integrated with AI functionality can deliver real-time pricing recommendations, behavioral discounts, and optimized quotes directly to the sales team.

This is where the theory of intelligent pricing becomes practice. With modern CPQ solutions, organizations can close the loop between AI-driven insights and frontline execution, ensuring that pricing decisions reflect both business strategy and buyer behavior.

These systems leverage artificial intelligence to automate complex pricing structures, streamline approval workflows, and provide predictive insights, enabling organizations to respond swiftly to market changes and customer needs.

Leading AI-enabled CPQ platforms to consider

1. DealHub

DealHub stands out as a comprehensive, AI-driven revenue platform that seamlessly integrates CPQ with contract management, subscription billing, and digital sales rooms. Its adaptive pricing models support various sales scenarios, including usage-based, tiered, and fixed pricing structures. The platform’s advanced predictive analytics are specifically designed for revenue optimization, offering real-time insights into buyer behavior and deal progression. DealHub’s robust integration capabilities with leading CRM and ERP systems ensure a unified sales process, while its comprehensive governance features provide structured approval workflows and audit trails, enhancing business agility and compliance.

2. Oracle NetSuite

Oracle’s NetSuite has incorporated AI tools to expedite routine business tasks, including the generation of price quotes for complex purchases. Utilizing a chatbot interface, users can swiftly compile quotes, streamlining the sales process. Oracle’s collaboration with AI partners like Cohere enhances these capabilities, and future integrations with platforms like OpenAI are anticipated to further augment its AI functionalities.

3. Vendavo

Vendavo’s CPQ platform leverages AI and machine learning to analyze transactional data, providing insights into pricing, volume, and product mix fluctuations. This approach enables dynamic price optimization tailored to local market dynamics and competitive landscapes. Companies utilize Vendavo’s AI-powered Profit Analyzer to identify opportunities for margin enhancements, resulting in significant revenue gains.

Reimagine your pricing potential

AI is redefining the role of pricing from tactical response to strategic accelerator. By learning from buyer behavior and enabling continuous adaptation, AI-powered pricing unlocks new dimensions of growth.

For Sales and Revenue Operations leaders, the path forward involves embracing this intelligence and enabling it with the right technology. CPQ platforms that integrate seamlessly with AI and your broader revenue tech stack are foundational to GTM agility and success.