B2B SaaS pricing optimization
Willingness-to-pay analysis and price sensitivity modeling for a B2B SaaS product, resulting in a new tiered pricing structure.
Résultat clé
Revenue per customer ↑31%
Le défi
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.
La 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.
Approche technique
- 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
Résultats
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
Impact global
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.
Enseignements clés
- 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.
Stack technique
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