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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Introduction
In today's competitive SaaS landscape, finding the optimal pricing structure can be the difference between thriving and merely surviving. Yet many SaaS executives still rely on gut feelings, competitor benchmarking, or simplistic surveys when making critical pricing decisions. Choice-Based Conjoint (CBC) analysis represents a more sophisticated, data-driven approach to pricing optimization that can dramatically improve revenue and customer acquisition. This methodology helps SaaS companies understand exactly what customers value and how much they're willing to pay for specific features—information that's essential for maximizing both adoption and revenue.
What is Choice-Based Conjoint (CBC)?
Choice-Based Conjoint is a research methodology that reveals how customers make trade-offs between different product features and price points. Unlike traditional surveys that ask customers directly about willingness to pay (which often yields unreliable data), CBC presents respondents with a series of realistic choice scenarios.
In each scenario, respondents choose between product packages with varying features and prices. By analyzing these choices across many respondents and scenarios, CBC reveals:
- The relative importance of different features
- Price sensitivity for specific market segments
- Optimal feature bundling strategies
- Revenue-maximizing price points
According to a study by the Pricing Society, companies using advanced pricing methodologies like CBC see an average 3-7% improvement in margins compared to those using more basic approaches.
Why CBC Works Better Than Traditional Pricing Research
Traditional pricing research often falls short for several reasons:
Direct questioning bias: When asked directly about price, customers typically understate their willingness to pay.
Feature value isolation: Simple surveys struggle to determine how features interact with each other in purchase decisions.
Unrealistic scenarios: Many research methods don't mirror actual purchase situations.
CBC addresses these shortcomings by:
Observing choices rather than asking directly: By analyzing what people choose rather than what they say, CBC reduces social desirability bias.
Creating realistic trade-offs: CBC forces respondents to make choices similar to those they would face in real purchasing situations.
Enabling sophisticated statistical analysis: Modern CBC tools can run simulations to predict market share and revenue at different price points.
Implementing CBC for SaaS Pricing Optimization
Step 1: Define Features and Price Levels
First, identify the key features that might influence purchase decisions. For a SaaS product, these might include:
- Usage limits (users, storage, API calls)
- Support options
- Integration capabilities
- Advanced features
- Contract terms
For each feature and for price itself, you'll need to define different levels to test. For example:
- Price: $29/mo, $49/mo, $79/mo, $129/mo
- Users: 5, 10, 25, Unlimited
- Storage: 10GB, 50GB, 250GB, 1TB
- Support: Email only, Email+Chat, 24/7 Priority Support
Step 2: Design the CBC Study
A well-designed CBC study carefully balances:
- Survey length (typically 8-12 choice scenarios)
- Number of packages per scenario (usually 3-4 plus a "none" option)
- Feature combinations that isolate key trade-offs
Modern CBC software platforms can help optimize these designs to maximize statistical power while minimizing respondent fatigue.
Step 3: Field the Study with the Right Audience
For SaaS pricing research, it's critical to survey people who actually make or influence purchasing decisions. This might include:
- Current customers (for upgrades or retention)
- Prospects in your sales pipeline
- Customers of competing products
- Decision-makers in your target market segments
Research firm Sawtooth Software recommends a minimum of 200 respondents for basic CBC studies, with larger samples needed for more complex analyses or segment-specific insights.
Step 4: Analyze Results and Simulate Pricing Scenarios
CBC analysis generates utility values for each feature level, revealing their relative importance in purchase decisions. These utilities can then be used to:
- Identify price sensitivity by segment
- Determine the optimal price points for different packages
- Estimate market share at different price points
- Test how customers might react to new pricing structures
According to PriceIntelligently, companies that used advanced pricing methodologies including CBC saw an average 30% improvement in monetization compared to their previous pricing strategies.
Real-World SaaS CBC Application
When project management software company Asana redesigned their pricing tiers, they reportedly used conjoint analysis to determine which features should be included in each tier and how to price them. The result was a clearer differentiation between their Basic, Premium, and Business offerings, with features allocated to maximize both conversion and upgrade rates.
Similarly, HubSpot has leveraged sophisticated pricing research to develop their modular pricing approach, where customers can select different combinations of Marketing Hub, Sales Hub, Service Hub, and CMS Hub products at varying price points.
Common Pitfalls in CBC for SaaS Pricing
While powerful, CBC analysis isn't without challenges:
Feature overload: Testing too many features makes the survey unwieldy and the analysis less reliable.
Unrealistic price ranges: If test prices are too far from market expectations, respondents may disengage.
Ignoring segmentation: Different customer segments often have dramatically different price sensitivities and feature preferences.
Implementation complexity: Translating CBC insights into actual pricing structures requires careful thought about communication, grandfathering, and competitive positioning.
When to Use CBC for Subscription Pricing
CBC is particularly valuable for SaaS companies in these situations:
- Launching a new product where price points are uncertain
- Adding significant new features that might justify price increases
- Restructuring pricing tiers or packaging
- Facing new competitive threats
- Expanding into new market segments with unknown price sensitivity
According to pricing consultancy Simon-Kucher & Partners, conducting proper pricing research before a launch or major change can improve profits by 20-50% compared to pricing based on intuition alone.
Conclusion: CBC as a Strategic Advantage
In the competitive SaaS marketplace, pricing optimization represents one of the highest-ROI activities available to executive teams. Choice-Based Conjoint analysis stands out as the most sophisticated and reliable methodology for making data-driven pricing decisions that align with customer value perceptions.
By implementing CBC as part of your pricing research process, you gain critical insights into how different customer segments value your features, what price points maximize revenue, and how to structure offerings to encourage upgrades. These insights allow you to move beyond mere competitive benchmarking to truly value-based pricing.
For SaaS executives looking to improve growth metrics, reduce churn, and maximize customer lifetime value, mastering the basics of Choice-Based Conjoint analysis is no longer optional—it's an essential component of strategic pricing optimization.