B2B SaaS pricing optimization
Willingness-to-pay analysis and price sensitivity modeling for a B2B SaaS product, resulting in a new tiered pricing structure.
Key result
Revenue per customer ↑31%
The challenge
A B2B SaaS company had a single pricing tier and suspected they were leaving money on the table with enterprise customers while pricing out smaller businesses. They needed data-driven pricing tiers.
The solution
I designed and analyzed a conjoint study with 200+ current and potential customers to measure willingness-to-pay for different feature combinations. This revealed distinct customer segments with different value perceptions, which informed a new 3-tier pricing structure.
Technical approach
- Conjoint analysis survey design and deployment to 200+ respondents
- Customer segmentation based on usage patterns and company characteristics
- Willingness-to-pay modeling for different feature bundles
- Price sensitivity testing across customer segments
- Revenue simulation for different pricing scenarios
- Competitive positioning analysis and benchmarking
Results
Revenue per Customer
+31%
From tiered pricing
Enterprise Conversion
+45%
With premium tier
SMB Retention
+22%
With affordable entry tier
Feature Adoption
+18%
Better product-tier fit
Overall impact
The new tiered pricing structure dramatically improved monetization. Enterprise customers who were previously undercharged now had a premium tier that better matched their needs and willingness-to-pay. SMB customers gained access to an entry-level tier that removed pricing barriers. The feature-tier alignment improved overall product adoption and customer satisfaction.
Key lessons
- 01Customer research beats intuition: The conjoint analysis revealed price sensitivities and feature values that surprised stakeholders.
- 02Segmentation enables value capture: Different customer segments have radically different willingness-to-pay—one price fits none.
- 03Feature bundling drives value: The right combination of features in each tier significantly influenced perceived value.
- 04Test before launch: Revenue simulations with historical data validated the new pricing before risking customer relationships.
Tech stack
Similar project?
Need help with a similar challenge? Let's discuss how I can help.
More projects
AI-guided recommendation engine for premium floral e-commerce
A production-oriented recommendation system that guides customers through emotionally loaded floral purchases — using a deterministic state machine with LLM components constrained to intent parsing and rationale generation only.
Travel retail forecasting system
Multi-SKU demand forecasting pipeline for 30+ products across 100+ duty-free locations with automated monthly updates.
Consumer healthcare marketing mix model & budget optimizer
Proprietary Marketing Mix Model with budget optimization replacing intuitive allocation with data-driven decision making across multiple countries and touchpoints.