Pricing
Run it yourself
Price Testing
Runs structured price tests on the pricing page, reads the results against how demand across the portfolio responds to price, and rolls the winning price into the billing catalog.
82 percent of company pricing sits suboptimally against similar-vertical peers
628,000 subscribers in the pricing analysis, with price response measured across the full portfolio
Consistent effect across multiple independent deployments.
How we grade evidence →Scheduled trigger · Edition 1 · June 2026
What is it?
Most teams set a price once and never test it again, and the data says it shows: roughly 82 percent of company pricing sits suboptimally against similar-vertical peers. This tactic makes price testing a running discipline rather than a one-off project. It proposes a small set of price variants for a plan, splits new pricing-page traffic across them, and reads the results against the portfolio’s measured price response until one variant clears the decision bar.
How demand responds to price is what makes the experiment worth running. Across the portfolio, the measured price elasticity is −0.82: for every 10 percent a price moves up, demand falls by roughly 8.2 percent. Demand falls less than proportionally to price, which means a well-sized increase usually nets more revenue than the volume it costs. Most companies are underpriced and have never run the experiment that would tell them so.
When it fires
Experiments run in scheduled cycles. A cycle opens when the previous one concludes, either because a variant reaches the traffic floor and clears the decision bar or because the cycle times out without a winner. The engine then proposes the next test from the plans with the largest gap between current price and the peer benchmark.
It does not fire on impulse or on a single bad revenue week. Price experiments need enough traffic to read honestly, so the engine paces them: one experiment per plan at a time, each cycle sized to the traffic the pricing page gets.
What the evidence shows
The case for testing is the gap: 82 percent of company pricing sits suboptimally against similar-vertical peers, measured in a pricing analysis spanning 628,000 subscribers. The case for testing upward is the price response: at −0.82 portfolio-wide, demand falls slower than price rises, so increases lose less in volume than they gain in revenue, within reasonable bounds.
The supporting structure findings shape where variants should not land: the $50–$100 price band carries 88 percent churn across the same analysis, so the engine treats that band as a hazard rather than a destination. The evidence is graded strong: a large, consistent portfolio pattern, though each company’s own price response is what the experiment measures.
How it runs
In production, the engine renders the assigned variant on the pricing page for new visitors only. Existing subscribers never see a test price and are never repriced by an experiment. Each variant writes through to the billing provider so that a signup at a test price is a real subscription at that price, not a discount dressed up as one.
When a cycle concludes, the winning price rolls into the catalog for new subscribers, with existing subscribers grandfathered at their current price. Guardrails hold the variant range within operator-set bounds, keep variants out of the dead zone, and stop any experiment the operator pauses, immediately and cleanly.
Run this for your business
Want to run Price Testing for your business? Connect the Churnkey MCP to your favorite AI agent. It reads your own usage and billing data and recommends the growth and retention plays most likely to move your LTV—starting with whether this one fits.
npm install -g @churnkey/mcpChurnkey's retention products run on the same dataset behind this tactic.
Schedule a demoRelated tools
More in Pricing
All tactics →
Right-Plan Recommendations
Reads each subscriber’s usage against vertical benchmarks and recommends the closest-fit plan at signup, upgrade, and save.
View tactic
Discount Guardian
Standardizes every discount on the structure with causal evidence behind it, substituting a forever-percent discount at the moment any discount is created.
View tactic
Monthly-to-Annual Conversion
Converts monthly subscribers to an annual plan at the moment they are most likely to commit, pairing the new term with a forever-percent discount.
View tactic
Put the evidence to work.
The same dataset behind these tactics powers Churnkey's retention products. See what it finds in your subscription data.
