Introduction
Traditional value-based pricing in B2B has long relied on conjoint analysis to simulate customer preferences and assign value to product features. While effective in the past, this hypothetical approach’s reliance on surveys, static reporting, and macro results is increasingly out of step with today’s dynamic, data-driven markets.
With the rise of AI and behavioral data modeling, companies can now leverage real-time insights from actual customer interactions, purchases, usage patterns, and external market factors. This shift enables faster, more accurate, and adaptive pricing strategies—grounded in what buyers do, not just what they say.
The Traditional Use of Conjoint in B2B
Conjoint analysis is a statistical technique used to determine how customers value different attributes of a product or service. In B2B, conjoint analysis has historically been used to conduct the baseline quantitative analysis for building a value-based pricing approach:
- Simulate how buyers weigh product features such as brand, delivery speed, service levels, and functionality
- Estimate price elasticity by segment or persona
- Design good-better-best product tiers
- Inform GTM launch pricing and bundling decisions
This is especially common when entering new markets, launching new products, and pricing a product portfolio. Conjoint’s appeal lies in its structure: it allows companies to create controlled trade-off scenarios and quantify preferences.
Comparison: Conjoint Analysis vs. AI-Driven Analysis
| Dimension | Data Source | Speed | Scalability | Accuracy | Use Case Fit |
|---|---|---|---|---|---|
| Conjoint Analysis | Survey-based | Slow (weeks) | Low (hundreds) | Moderate | Static Baseline Segmentation Analysis, New product launches, regulated markets |
| AI-Driven Analysis | Behavioral data (transactions, usage) | Real-time | High (millions) | High | Dynamic value pricing, renewals, new product launches, Continuous market analysis |
Limitations of Conjoint in the Modern Era
Despite its usefulness, conjoint analysis suffers from five key drawbacks in today’s environment:
1. Hypothetical Responses ≠ Real Behavior
People often say one thing in surveys and do another in practice. Conjoint captures stated preferences, while AI leverages revealed behavior. This is especially prevalent in B2B, where buyers are plagued by politics, budgets, and cross-functional influence. AI can instead learn directly from transaction history, competitive data, market data, win data, and discount behavior.
2. Slow & Static
Today’s environment requires an agile pricing strategy with a near real-time capability to account for competitor moves, commodity shifts, and customer churn rates. Conjoint studies take weeks or months to complete and represent a snapshot in time. Furthermore, static conjoint studies are expensive to execute. AI models ingest real-time data streams and update continuously in a feedback loop.
3. Limited to Defined Attributes & Scenarios
Conjoint requires predefined attributes and scenarios, essentially limiting exploration of value drivers. AI can dynamically discover drivers of value automatically from unstructured data on a transactional basis.
4. Low Scale and Granularity
B2B value-based pricing strategy often uses a tiered approach consisting of regionality, customer size, product line, and vertical. In conjoint, sample sizes are typically too small (hundreds) to assess this complexity, while AI can analyze millions of transactions, enabling granular price elasticity modeling and dynamic segmentation.
5. No Continuous / Real-Time Optimization
B2B value-based pricing is not a static calculation. It requires continuous learning across product launches, renewals, and custom deals. AI systems support live simulations, dynamic discount guardrails, and real-time rep guidance—conjoint does not.
The AI Alternative: Behavior-Driven, Real-Time Pricing Strategy
Modern pricing strategy is increasingly powered by machine learning, natural language processing, and dynamic simulation models. Here’s how neural network artificial intelligence has disrupted the conjoint approach for building value-based strategies:
1. Behavior-driven Price Elasticity Modeling
AI estimates price elasticity from actual behaviors (purchase history, engagement, churn, promotional sensitivity), not surveys. AI models such as neural networks can analyze massive amounts of unstructured data and display those value attributes for a given selling motion.
2. Unsupervised Attribute Valuation
Using natural language processing, AI models extract which attributes customers value, even those not explicitly tracked by pricing analysts. By triangulating transaction, market, purchasing, and product data (and any other data fed into the model). Furthermore, neural network models dynamically evaluate how much each attribute is valued relative to each transaction, customer, and group.
3. Scalable Real-Time Price Optimization
AI price engines’ continuous learning nature allows them to recommend product-customer based target, floor, and expert price recommendations based on real-time evaluation of all correlated attributes.
4. Continuous Learning
Conjoint studies are expensive, time-consuming, and often require an external partner to conduct on a timely basis. Rather than re-running an expensive conjoint every 6–12 months, AI models continuously adapt as new data is collected—enabling live pricing feedback loops, win-loss sensitivity adjustments, and attribute tuning.
Where Conjoint Still Has Value
While less dominant, conjoint is still useful in some contexts:
- New product launches in new markets with no transaction data.
- Heavily regulated industries (e.g., pharmaceuticals, medical devices)
- B2B organizations with limited purchase frequency or high deal complexity
In these cases, conjoint can still serve as a valuable first step—especially when paired with AI models later in the product lifecycle.
Conclusion
Traditional conjoint analysis helped generations of product and pricing teams understand how customers think about value. Though the limits have been made increasingly clear: it’s slow, artificial, and constrained by its need for survey design and hypothetical responses. The shift from benchmarking one’s value-based pricing strategy on conjoint analysis to AI-driven value pricing reflects a deeper transition to modernity with a behavior-driven, real-time, and adaptive approach.
Traditional conjoint has a place, but AI has taken the lead—enabling faster, more granular, and more profitable pricing decisions across the entire deal lifecycle. By adapting modernity in one’s pricing approach, it will enable a faster, increasingly granular, and margin optimal strategy across your sales channels.
Interested in learning more about the adoption path to AI-powered pricing? Check out From Segmentation to AI: How Neural Networks are Revolutionizing Price Optimization which includes the price optimization maturity curve.
Learn more about the AI-powered PROS Smart Price Optimization solution here.
Frequently Asked Questions
Value-based pricing assigns prices based on the perceived value of a product or service to the customer, traditionally using conjoint analysis.
AI leverages real-time behavioral data, such as purchase history and market trends, to create dynamic, adaptive pricing strategies that outperform static models.
Conjoint relies on hypothetical survey responses, is slow to execute, and lacks scalability, making it less effective in today’s fast-paced, data-driven markets.
AI models analyze aggregate customer behaviors, such as transaction history and discount sensitivity, to estimate price elasticity with greater accuracy.
While AI is more effective for dynamic pricing, conjoint analysis remains useful for new product launches, regulated industries, or markets with limited transaction data.
Industries with high transaction volumes, dynamic markets, or complex pricing needs, such as manufacturing, distribution, and technology, benefit significantly.
AI models continuously learn from new data, enabling real-time pricing adjustments, dynamic discounting, and live feedback loops for optimal pricing strategies.
