{"id":166870,"date":"2024-02-05T16:58:07","date_gmt":"2024-02-05T16:58:07","guid":{"rendered":"https:\/\/userpilot.com\/blog\/increase-trial-to-paid-conversion-rate\/"},"modified":"2026-04-07T19:12:28","modified_gmt":"2026-04-07T19:12:28","slug":"increase-trial-to-paid-conversion-rate","status":"publish","type":"post","link":"https:\/\/userpilot.com\/blog\/increase-trial-to-paid-conversion-rate\/","title":{"rendered":"How to Increase Trial to Paid Conversion Rate [+ Actionable Tactics]"},"content":{"rendered":"

How you increase trial-to-paid conversion rate determines how good your SaaS model is.<\/p>\n

But, how can you maximize this metric to attract more customers organically and cultivate product-led growth<\/a>?<\/p>\n

Let\u2019s explore what trial-to-paid conversion rate truly means, how to measure it, and go through multiple free trial models, each with its unique advantages and ideal use cases.<\/p>\n

What is the trial-to-paid conversion rate?<\/strong><\/h2>\n

The trial-to-paid conversion rate calculates how many free-trial users make the leap from free to paid subscribers. It serves as an indicator of your product’s value proposition and your ability to demonstrate that value to potential customers during the trial phase.<\/p>\n

Plus, this rate not only measures success but also highlights opportunities for optimization and growth within your product strategies<\/a>.<\/p>\n

How to calculate trial-to-paid conversion rate?<\/strong><\/h2>\n

Trial-to-paid conversion rate is calculated by dividing the number of users who upgrade from a free trial to a paid subscription by the total number of trial users.<\/p>\n

The result is a percentage that represents your conversion efficiency. The formula looks like this:<\/p>\n

\"increase<\/picture>
Free trial conversion rate<\/a> formula.<\/figcaption><\/figure>\n

<\/p>\n

<\/p>\n
<\/div>\n<\/div>\n

What is a good trial conversion rate for SaaS?<\/strong><\/h2>\n

Now, it\u2019s normal that a lot of your trial users won\u2019t convert into paid customers. But how much?<\/p>\n

The average conversion rate<\/a> depends on the industry and model. Roughly, a 15% conversion rate is good enough for opt-in free trials in B2C. This means your trial is effective in demonstrating value to a significant portion of users.<\/p>\n

However, this benchmark increases to 25% in B2B. This is because you often need to offer high value upfront with live demos and a robust onboarding process.<\/p>\n

A conversion rate that\u2019s higher than 30%, on the other hand, is more than average and it means you\u2019re already doing a great job.<\/p>\n

Types of free trial models in SaaS<\/strong><\/h2>\n

Now, your conversion rates will depend heavily on your free trial model, as not every mode is made equal.<\/p>\n

That said, let\u2019s explore multiple models to see what\u2019s right for you:<\/p>\n

Opt-in free trial<\/strong><\/h3>\n

The opt-in free trial model invites users to try a product for a limited period without requiring upfront payment information. And it can only involve filling out a registration form and providing contact details.<\/p>\n

This model is popular because it lowers the barrier to entry. Therefore, it\u2019s easier for potential customers to experience the product’s value firsthand without much commitment.<\/p>\n

That said, this model is better when you want to maximize the number of trial users and if your product can quickly demonstrate value during the trial period.<\/p>\n

\"opt<\/picture>
Customer.io has an opt-in trial model.<\/figcaption><\/figure>\n

Opt-out free trial<\/strong><\/h3>\n

On the other hand, the opt-out free trial model requires users to provide billing information upfront.<\/p>\n

This means users are automatically charged for the subscription if they don\u2019t manually cancel before the trial ends. Thus ensuring that only highly interested users sign up (potentially leading to higher conversion rates).<\/p>\n

That said, this model is not only beneficial for filtering out less serious users, but it works particularly well for products with a longer learning curve or those that demonstrate their true value over longer periods of usage.<\/p>\n

\"opt<\/picture>
Moz, for example, requires payment information to sign up.<\/figcaption><\/figure>\n

Usage-based free trial<\/strong><\/h3>\n

Usage-based free trials offer access to a product or service for free but limit access or features based on usage. The same way Loom, for example, only allows 5-min recordings in its free version.<\/p>\n

This model fits really well for products where the value increases with more extensive use\u2014 encouraging users to upgrade to unlock full capabilities.<\/p>\n

\"usage<\/picture>
Loom free plan has feature usage limit.<\/figcaption><\/figure>\n

Freemium<\/strong><\/h3>\n

The freemium model provides a basic version of the product for free indefinitely. Users can upgrade to access additional features or enhanced functionalities.<\/p>\n

Tools like Loom adopt this approach. It offers essential features (like recording 5-min videos) for free while reserving more advanced options for paying customers.<\/p>\n

This model is best when your product benefits from deeper adoption, and you can offer premium features that provide more value to users who have already adopted your product.<\/p>\n

\"freemium\"<\/picture>
Loom eventually prompts you to upgrade.<\/figcaption><\/figure>\n

Sandbox<\/strong><\/h3>\n

A sandbox trial gives users a controlled and isolated environment to explore and test the product’s features with dummy data or limited functionality.<\/p>\n

In practice, this model works for complex products requiring configuration before full implementation. Especially if they\u2019re highly customizable or require user education to demonstrate their full potential.<\/p>\n

Mixpanel\u2019s live demo is a great example, as it\u2019s a product that integrates deeply with the user\u2019s workflow:<\/p>\n

\"sandbox<\/picture>
Mixpanel\u2019s sandbox demo.<\/figcaption><\/figure>\n

How to identify conversion friction?<\/strong><\/h2>\n

The first step to optimize the trial experience is to identify and address conversion friction. That said, let’s go over some methods to identify friction:<\/p>\n

Analyze user behavior using feature tag<\/strong><\/h3>\n

If users aren’t engaging with essential features that showcase your product’s value, they won\u2019t convert.<\/p>\n

With feature tagging<\/a>, you can monitor how users interact with core features during their onboarding and trial period. This will allow you to identify which aspects of your product are being overlooked.<\/p>\n

For this, you can use an event tracking<\/a> tool like Userpilot<\/a> to tag and monitor feature engagement. And then, offer targeted onboarding steps or in-app guidance for underused features to boost activation rates.<\/p>\n

\"feature<\/picture>
Tracking custom events with Userpilot<\/a>.<\/figcaption><\/figure>\n

Determine drop-offs using funnel analysis<\/strong><\/h3>\n

Funnel analysis<\/a> helps you optimize your trial-to-paid conversion rates by pointing out exactly where users disengage.<\/p>\n

For this, set up a conversion funnel that tracks the user journey from sign-up to paid conversion. Then analyze each friction point<\/a> and find ways to get feedback on why it happens. This can involve reaching out to customers or looking at session recordings.<\/p>\n

As you collect data, you can work your way up by implementing solutions until the journey is as smooth as it can get.<\/p>\n

\"funnel<\/picture>
Performing funnel analysis with Userpilot<\/a>.<\/figcaption><\/figure>\n

Understand onboarding friction with path analysis<\/strong><\/h3>\n

Path analysis<\/a> provides a detailed view of the user navigation path after sign-up. This includes which onboarding steps are completed and where users steer off course.<\/p>\n

This analysis helps you understand whether your onboarding flow effectively guides users toward conversion and which areas may require iteration.<\/p>\n

To do this:<\/p>\n