Reverse trial was created by Elena Verna and has now become the standard for product-led growth (PLG) playbook within the product management community.

Basically, a reverse trial is a pricing model that gives users access to a product's paid features for a limited time. After the trial ends, users are downgraded to a free plan with more limited access

The 2 main types of reverse trials are optional and mandatory that are known are:

• Optional reverse trial: Users can choose whether to start with a free plan or a trial of the premium tier.

• Mandatory reverse trial: Every new user is automatically enrolled in a free trial. 

However, there is a third type of reverse trial that is less common and wasn't possible until AI came along - Triggered Reverse Trial.

The Emerging Third Type: Triggered Reverse Trial

Triggered reverse trial activates trial for paid features based on usage-based triggers. The trigger can either be based on rules or anomalies:

1. Rule-Based Trigger (Quantitative Activation)

Rule-based triggers are predefined conditions that automatically initiate a premium trial when specific, measurable thresholds are met. These triggers create a systematic approach to unlocking advanced features based on quantitative metrics that indicate potential value or growth.

Consider an A/B testing software using a rule-based trigger approach. In this scenario, the trial could be activated when:

• The number of website visitors reaches a specific milestone (Eg. 40,000 visitors)

• Total test runs exceed a predetermined limit (Eg. 10 concurrent A/B tests)

• Data collection volume approaches a critical threshold (Eg. 50,000 data points analyzed)

The rule-based trigger is predictable and scalable across user segments since it's objective and metrics-driven.

2. Pattern-Based Trigger (Qualitative Activation)

Pattern-based triggers represent a more sophisticated approach to initiating premium trials, which leverages advanced behavioral analytics and machine learning to identify nuanced user engagement signals.

Unlike rule-based triggers that rely on simple quantitative thresholds, pattern-based triggers analyze complex behavioral sequences, user interactions, and contextual usage - then autonomously determine optimal moments for trial activation.

In this scenario with the same A/B testing software, the trial is triggered after the user demonstrates a specific behavior pattern that indicates they're likely to benefit from premium features. For example:

• The user creates multiple A/B tests

• The user tests variations across different pages

• The user shows advanced segmentation needs

• The user frequently checks detailed statistical significance of test results

The pattern-based trigger relies heavily on tracking the user behavior at which stage of the journey, then using algorithm to make decisions on which premium features and/or paid plan to enable for the user.

Real-World Examples of Triggered Reverse Trial

Here are how some of known tech companies are applying triggered reverse trial:

• Slack triggers premium features when:

  • Message history limits are approached

  • Integration complexity increases

  • Communication volume expands

  
  

• Calendly activates advanced features upon:

  • Multiple event type creation

  • Complex scheduling rule requirements

  • Team collaboration needs

  
  

• Mailchimp unlocks premium capabilities when:

  • Email list size grows significantly

  • Advanced segmentation becomes necessary

  • Detailed reporting is repeatedly accessed

The Future

The future of product activation lies not in rigid, one-size-fits-all models, but in adaptive, context-aware experiences that anticipate and address user needs before they even articulate them. It wasn't practical until AI started to mature.

In line with building an AI-first operations, more and more companies will look to change their traditional upgrade paths into intelligent, adaptive experiences that feel intuitively aligned with user needs and product potential.