Millions of Mini Experiments
Aampe performs millions of mini experiments every day. Agents learn by sharing content with users, and since there’s always some randomness in message assignment, every customer touchpoint is effectively a separate experiment. If you love experiments, this sounds wonderful. But is there a way to organize agents to run more traditional experiments? When is that a good idea instead of relying on agents to deliver custom experiences?A/B Testing in Aampe
A/B tests compare two or more experiences to determine which performs better. In addition to the infrastructure required to support millions of agents, Aampe’s audience builder, connected channels, and message composer provide everything needed to run traditional A/B tests. To successfully execute an A/B test, you need:- A user randomization strategy
- Distinct user experiences to compare
- Tools for tracking and measurement
A/B Testing vs Agentic Personalization
There are times when traditional A/B tests may be preferable:- Important experiences for new users: You may still allow agents to experiment at the individual level, but there’s value in A/B testing to find the best default experience since many agents will still be in a learning phase.
- Experiences users encounter one time: Agents learn by trying many things and establishing patterns over time. If you’re limited in the number of times you can interact with a user, an A/B test would drive more business impact than agentic personalization.
- System-level comparisons: When evaluating whether agentic personalization itself outperforms traditional approaches, you need controlled experiments that compare entire systems rather than individuals.
Running Experiments with Aampe
See what experimental methods work in the world of agentic personalization and which ones do not. A 4-part series on how to run experiments in the world of agentic personalization