Spectare vs rules-based tools like Optimizely and AB Tasty

Rules-based personalisation tools like Optimizely and AB Tasty let you define audiences and write if-this-then-that rules for what each audience sees. That works well when you already know what each audience wants. The problem is that you usually start without that knowledge, and writing rules before you have evidence means your configuration reflects instinct rather than behaviour.

Spectare starts in learning mode by default. Rather than asking you to configure rules upfront, it selects components using eligibility, safety, and conversion history from day one. When the data reaches the same statistical threshold as a measured verdict, the platform surfaces a suggested rule on the What's Working page with the supporting evidence shown alongside it. You approve or ignore the suggestion. Nothing is applied without your decision.

This matters because the suggestions are claims, not guesses. Each one requires two hundred windowed impressions on both the with and without arms and a valid anytime-valid confidence interval, the same bar the platform uses to declare a winner. A rules-based tool asks you to be right before the data exists. Spectare waits until the data exists and then asks whether you agree.

You can switch to manual mode at any time and the full rule editors appear, already showing the currently effective settings. There is no blank form and no starting from scratch. The two modes share the same personalisation engine underneath, so moving between them does not change what visitors see, only how much of the configuration is yours to adjust directly.