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.*
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.*
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 more and less on the transition to the new steady state. _
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.*
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.*
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.
