Tggl AB testing

Tggl's A/B testing product is a powerful experimentation platform designed to help businesses optimize user experiences and improve key performance metrics through data-driven decision-making. It allows organizations to test multiple variations of features, designs, or functionalities on real users, providing empirical insights into which version performs best. By leveraging randomized traffic splitting and consistent user experiences across devices, Tggl ensures that experiments yield statistically valid results while minimizing biases. This capability enables businesses to confidently implement changes that drive conversions, enhance engagement, and achieve their strategic goals faster. The platform supports advanced segmentation and multivariate testing, allowing businesses to write complex rules for user targeting based on attributes such as country, device type, browser, or custom traits. Tggl enables simultaneous testing of hundreds of combinations to identify the most impactful changes across diverse user groups. Furthermore, it integrates seamlessly with existing analytics tools like Amplitude, ensuring smooth workflows without requiring users to switch between platforms. This integration simplifies tracking flag evaluation events and analyzing conversion rates directly within familiar tools, making experimentation accessible for technical and non-technical teams.

Tggl prioritizes privacy and security by adhering to GDPR standards and storing data within the European Union. For added security, it offers features such as two-factor authentication (2FA), granular role-based permissions, and the use of an open-source proxy to ensure that sensitive data never leaves client servers. The platform also supports anonymous visitor testing, making it ideal for experimenting on landing pages or applications with significant logged-out traffic. With its robust infrastructure and user-friendly interface, Tggl empowers agile teams to iterate quickly, reduce risks associated with new feature rollouts, and make informed decisions based on reliable experimental outcomes.

• Small Businesses (1-99)
• Mid Market (100-500)
• Enterprise (500+)