CRM, Lifecycle & Personalization Practical insights
Use AI to adjust message frequency without fatigue
Test AI message frequency with customer limits, fatigue indicators and a persistent control group to judge retention and complaints alongside response.
A customer can respond to a message and still feel overwhelmed by the total contact volume. Frequency personalization should therefore optimize a relationship over time, not simply find the maximum number of messages that produces another click. Begin with a clear ceiling and let experiments operate below it.
Count the whole experience
Inventory promotional email, push, SMS and overlapping lifecycle campaigns. Decide which necessary service messages are handled separately and prevent promotional content from inheriting their exception. Braze distinguishes delivery rate limits from per-user frequency caps; these controls solve different problems. [2] A slow bulk send can still contact one person too often.
Make restraint an available choice
Allow the decision system to reduce frequency or skip an eligible promotional send. Start with a small number of tested cadence options rather than unrestricted message counts. Preserve explicit customer preferences as constraints. Do not interpret silence as unlimited tolerance, and avoid increasing pressure solely because a model predicts a conversion is close.
Run a customer-level experiment
Assign eligible customers to stable groups so the same person does not alternate between experimental and control policies every week. Use a fixed evaluation window suitable for the purchase cycle. Monitor purchases or activation together with unsubscribes, complaints and longer-term inactivity. Look at exposure per person, because an acceptable average can conceal a heavily contacted minority.
Inspect the stopping behavior
For example, a customer who has already completed the target purchase should stop receiving that promotion even if a scheduled send remains in the queue. Test this case, preference changes and concurrent campaigns before expansion. Report the difference between groups with uncertainty and sample size. A lower send count can be a successful outcome when it preserves customer value with fewer interruptions.
Sources and evidence
Sources checked on 4 October 2026. Proposed workflows and hypothetical examples are editorial analysis.