Skip to main content

​
Learning from Experience

Even well-designed experiments can fail due to implementation issues. Understanding these common pitfalls can help ensure your tests work out well.

​
Error 1: Incomplete System Integration

Groups defined in Aampe but not excluded from other tools. This means we’re testing Aampe in addition to the business-as-usual experience and there is no no-message group. Prevention*: Audit all messaging systems, implement exclusions universally, verify clean execution via message logs.*

​
Error 2: Premature Testing

It’s possible to test agentic personalization too soon. Testing with insufficient content or limited use cases is not representative of Aampe’s potential impact. Aampe provides the infrastructure to make this easy. Prevention*: Build a robust content library before testing. Ensure several relevant message types per user segment. Take the time to build out the Aampe experience before setting up a grand experiment.*

​
Error 3: Results Aren’t Immediate?

It can take time for an experiment group to adjust to a new system. While analyzing results, this transition period is ignored and biases the results. Prevention: Run tests long enough to observe cumulative patterns. Focus more on where the groups finish and less on the transition to the new steady state. _

​
Error 4: Asymmetric Capabilities

Business-as-usual includes promotions or other core offerings unavailable to Aampe. Prevention*: Assess all message types in both systems and ensure comparable offerings. Either add those use cases to Aampe, or pause them for the business-as-usual experience.*

​
Error 5: Excessive Broadcasts

Frequent “emergency” broadcasts to all users. Prevention*: This isn’t always preventable. At the very least, document all emergency broadcasts and be ready to exclude those days from the final analysis as appropriate.*

​
A Common Thread

These errors often stem from applying campaign-testing frameworks to a system evaluation. Agentic AI requires testing highly personalized, adaptive experiences over time, not comparing individual messages. Design tests that measure what the system actually does: learn, adapt, and personalize at scale.