What do the best lifecycle marketing programs all have in common?
In our experience, they test deliberately, learn continuously, and measure success through incrementality. As channels multiply and customer expectations rise, these are the factors that will separate excellent programs from merely adequate ones.
That's why AI Decisioning is so compelling to us. It turns every send into a learning opportunity and helps teams move from routine production to intentional, data-informed decision-making.
These tools don’t replace strong strategy or experienced practitioners, they amplify both, making testing faster, learning continuous, and progress measurably incremental.
- Phi and Carlos, Co-founders
The best AI decisioning tools enable smarter testing,
stronger hypotheses, and first-principles thinking at scale.
That’s why we’ve chosen JustAI as our exclusive AI decisioning partner. This brings together Verbose’s lifecycle strategy expertise and Just AI’s reinforcement-learning technology to help teams move beyond manual A/B testing and one-size-fits-all campaigns toward continuous, data-informed decisioning.
Together, we offer a blueprint for more strategic experimentation. You get powerful AI with the guardrails, governance, and clarity required to stay true to your brand and strategy while generating compounding impact over time.
Companies we’ve supported stand up AI Decisioning for:
How Outschool Scaled Personalization with AI Decisioning
Outschool needed to deliver the right message to very different families across age groups, buyer states, topics, and weekly sends without multiplying campaigns or manual work. By treating lifecycle as a decisioning problem, not a content problem, they built a system that could learn and optimize at the scale they needed.
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Problem: The Limits of Personalization
Outschool’s lifecycle program had grown into dozens of parallel tests across buyer state, age, topic, and cadence. They suddenly had too many testing ideas and not enough bandwidth to implement and manage them.
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Insight: Lifecycle as Decisioning
To address this, they realized they needed to move beyond a series of one-off A/B tests to a system that optimized content, audience, and cadence together — automatically Lifecycle needed to become living decision engine, not a series of static experiments.
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Approach: Multi-Agent Optimization
By partnering with Verbose and JustAI, Outschool rethought their lifecycle program. Specialized AI agents dynamically made decisions about who to message, what to send, and when, all within a single unified system.
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Results: Scaled Learning, Real Lift
Outschool compressed years of testing into weeks, running 307 variants across 4M messages. The result was a +33% lift in membership purchases, +24% lift in purchases, and a dramatically simpler lifecycle setup.