In the early days, I used email to experiment with pricing by sending different offers to customers, which helped us find the best price for our market.

Successful startups frequently test their pricing, adjusting it as they learn more about their market.

Pricing should be iterative; continuously adapt and change it based on market feedback and data until you find the right price point.

We started with a freemium model, but realized it distracted us; we shifted to a paid model and consistently increased prices over time, which helped us focus on serious customers.

Experimentation is key in pricing strategy, especially for AI products. Testing different pricing models helps determine what resonates with customers and drives value.

Customers can run A/B testing side by side in different locations to compare pricing strategies effectively.

We've developed an AI dynamic pricing engine that sets optimal prices based on historical data, market trends, and various external factors.

When setting pricing, it's important to guess a number that seems reasonable, and if you can close that sale, try doubling it the next time. Iterating based on feedback is key to finding the right price.

Test your pricing to find out how much customers are willing to pay; as your product matures, your ideal customer profile may change, and you should adapt your pricing accordingly.