・
Launch
・
Sep 10, 2024
・
1Price Team
Welcome to 1Price 🧪
All the top software companies have done multiple experiments to determine what to best price their products.
If you’re not constantly running pricing tests, you are missing out on potential revenue and limiting your company’s growth potential.
video walkthrough of the latest version of 1Price (September 2024)
Price Experiment Options
We’ve worked with tons of software companies to figure out the best experiments to run to boost revenue with pricing.
Here are the most popular experiments that you can currently run on 1Price:
🧪 Price Increase: boost Lifetime Value (LTV) yet possibly reducing conversion rates.
🧪 Geographic Pricing: identify regional price variations to maximize revenue per region.
🧪 Annual Discounts: determine what discount percentage (%) you should be giving potential customers to maximize revenue
🧪 Adding a New Tier: to price anchor against existing ones or share with a select customer group.
🧪 Renaming Plans: having unique naming for plans such as ‘growth’ or ‘enterprise’ to better resonate with your audience.
🧪 Changing Pricing Models: trying pricing options such as usage-based pricing or per-seat models
Why Other A/B Testing Software Doesn’t Works
Most A/B testing software is only good for testing conversion rates and fail to capture data such as LTV, CAC and basic customer behaviour after purchase. These tools are typically only good for testing UI and copywriting changes rather than deeper effects of price adjustments.
How 1Price Works
We help any software company setup their first pricing experiment in as little as 35 mins.
After setting up your two Offerings and adding them to an Experiment, 1Price will then randomly assign users to a cohort where they will only see one of the two Offerings. From here we can determine your users’ behaviors and analyze the full subscription lifecycle to understand which variant is producing more value for your business.
This completely removes the guesswork and ensures you are optimizing pricing based on data-driven evidence.