Buynomics

Analytics

Virtual-shopper AI that simulates willingness to pay and tests pricing and packaging scenarios before launch.

Updated July 2026 buynomics.com

Overview

Buynomics is pricing simulation software that builds virtual customer models — populations of simulated buyers calibrated on real purchase and market data — and uses them to predict how demand responds to price, packaging, and promotion changes. Pricing, revenue management, and commercial strategy teams use it to test scenarios before putting them in front of real customers. Its roots are in consumer goods and it extends to other pricing-intensive industries. In the revenue stack it sits at the design stage, informing pricing models before CPQ and billing execute them.

Capabilities on the RevOps map

Which of the capability map's modules Buynomics covers — each links to the module's own page, with every tool that supports it.

Module Phase Depth Note
Define What You Sell
Willingness-to-Pay Research Design & Setup Core virtual-shopper simulation in place of one-off conjoint studies
Pricing Model Design Design & Setup Supported scenario testing informs model and packaging choices rather than authoring the price book itself

What makes it different

Where traditional willingness-to-pay research relies on periodic surveys and conjoint studies, Buynomics maintains a standing simulation you can query repeatedly as conditions change. That turns pricing research from a project you commission into an operating capability, and lets teams evaluate full portfolio and cross-product effects rather than one SKU at a time.

Frequently asked questions

How does Buynomics differ from survey-based tools like Conjointly?

Survey tools measure stated preferences at a point in time; Buynomics builds a persistent simulated market you can re-run against new scenarios without fielding a new study. The tradeoff is a heavier initial calibration in exchange for continuous scenario testing.

Is Buynomics useful for SaaS or usage-based pricing?

Its heartland is consumer and B2C-adjacent pricing with rich transaction data. SaaS teams exploring seat and usage pricing can apply the same simulation logic, but should validate that their data volume supports calibrating a credible virtual customer base.

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