Managed optimization
Pricing that learns what your customers will pay.
We integrate Curious into your pricing surfaces and run continuous price experiments for you. You set the constraints. We find the prices that maximize revenue without crossing them.
The problem
Most pricing is set once and left alone.
Someone picks a price. Maybe they looked at competitors, maybe they surveyed a few customers, maybe they just picked a round number. It ships. Months later, the market has moved, your product has changed, and the price is still sitting where someone put it in a spreadsheet.
The companies that do revisit pricing usually do it as a project: a quarterly review, a consultant engagement, a pricing committee. They test a few options, pick a winner, and leave it until the next review. Between reviews, money is left on the table or customers are lost to a price that's too high for what they need.
How most tools approach this
Rules and competitor tracking.
Most pricing software watches your competitors and adjusts your prices according to rules you set: stay 5% below the market leader, don't go below a margin floor, raise prices when stock is low. That works when your pricing question is "what are others charging?" It doesn't help when the question is "what would this specific customer pay for this specific plan on this page right now?"
A/B testing gets closer, but the versions are manual, the sample sizes are large, and you're testing two or three options at a time. By the time you have a winner, the market may have shifted again.
How we do it
Continuous experimentation, managed for you.
We connect Curious to your pricing surfaces: your plans page, your checkout flow, your upgrade prompts, your renewal screens. We define the reward structure together (revenue, minus refunds, subject to constraints on churn and support load), and then Curious takes over. Revenue is modeled the way it actually behaves: how often people buy, times how much they spend, net of what comes back.
It generates price candidates using AI, each built on a different hypothesis about what your users will respond to. Then it tests them through a contextual bandit that learns which price works best for which type of customer. A user on your free plan seeing the upgrade page on mobile at month three gets a different price than an enterprise prospect evaluating your top tier on desktop.
We manage the full process: integration, experiment design, constraint tuning, and ongoing monitoring. You don't log into a dashboard or design experiments. You get a weekly summary of what's working, what changed, and how revenue moved.
What you get
Managed end to end.
Integration.
We work with your engineering team to connect Curious to whatever serves your prices: your billing system, your pricing page, your in-app upgrade flows. The integration is a single API call per pricing surface.
Experiment design.
We define the reward structure, the constraints, and the context together. You tell us what matters (revenue, margin, LTV, retention) and what can't slip (churn rate, support tickets, refund rate). We configure the system and review it with you before anything goes live.
Ongoing optimization.
Curious generates and tests new price points continuously. We review performance, adjust constraints, and surface findings to you on a weekly cadence. When something interesting happens (a particular segment responds to a pricing structure you hadn't considered), we flag it and discuss whether to lean in.
Guardrails.
The system operates within the boundaries you set. Prices can't go below your floor or above your ceiling. Churn can't rise past the threshold you define. If a price candidate hurts a metric you care about, the system pulls it back automatically. And algorithmic-pricing laws are part of the design: where a jurisdiction requires disclosure or restricts how prices can be set, we build to the requirement, not around it.