Note: You may still see "Optimization" referenced in parts of Iterable today. For consistency, with the March spotlight and related content, we'll use "Decisioning" throughout this post.
In March, we introduced Nova Decisioning, including Channel Decisioning, Send-Time Decisioning, and Frequency Decisioning, to help teams move from automated execution to intelligent engagement. Did you miss it? Check out the March Product Spotlight.
Over the past few weeks, we've been listening to how teams are evaluating these capabilities, testing them in real workflows, and asking some really thoughtful questions along the way.
One Theme Kept Emerging 👀
As we listened to customers over the past few weeks, one theme kept surfacing:
Teams are evaluating more than performance outcomes. They're also looking for visibility, confidence, and control as they adopt decisioning capabilities.
Whether the conversation was about channel selection, send timing, or communication frequency, teams consistently wanted to understand how decisions are made and how those decisions impact results.
That shift feels especially important as marketers move from manual optimization toward AI-powered decisioning.
Channel Decisioning 📱
Choosing a channel is easy. Choosing the best channel for every customer is where things get interesting.
What teams are learning
One recurring theme we're hearing is that teams want greater confidence and visibility as they evaluate Channel Decisioning.
- Some teams are evaluating how Channel Decisioning handles customer preferences, opt-outs, and fallback behavior before expanding usage more broadly.
- We're hearing teams explore how to interpret decisioning outcomes and measure the impact of channel selection.
- Several teams are exploring where Channel Decisioning can reduce manual routing logic while still maintaining control over the customer experience.
💡 Takeaway: Teams are looking for confidence and transparency before scaling usage across more journeys.
Where we're seeing it used
- Determining whether email, SMS, or push is the better channel for a given customer
- Reducing manual routing logic across journeys
- Creating fallback paths when customers are eligible for multiple channels
Questions to consider 🤔
- How much manual channel routing and fallback logic are you maintaining today?
- Are there journeys where customers are eligible for multiple channels, such as email, SMS, or push?
- Are there journeys where you're manually deciding whether a customer should receive an email, SMS, or push notification?
Send-Time Decisioning ⏰
If you've ever manually tested send times, watched performance, and adjusted schedules by hand... this one is for you.
What teams are learning
Several teams are evaluating how Send-Time Decisioning fits into existing optimization and campaign planning workflows.
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Several teams already have manual optimization processes in place and are evaluating where Send-Time Decisioning can reduce ongoing effort.
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We're hearing questions about how optimized send times fit alongside launches, recommendation programs, hold periods, and other business timing requirements.
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Teams are using reporting and insights to better understand optimization outcomes and measure impact over time.
💡 Takeaway: The biggest consideration isn't timing. It's balancing optimization with business timing requirements.
Where we're seeing it used
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Recurring recommendation and lifecycle programs
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Campaigns where send timing can influence engagement
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Replacing manual testing and optimization workflows
Questions to consider 🤔
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Which campaigns currently require manual send-time testing or ongoing optimization?
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Are there recurring programs where timing can meaningfully impact engagement or conversion?
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Are there campaigns where timing matters, but you're still relying on fixed send schedules?
Frequency Decisioning 🔁
The goal isn't to send fewer messages. It's to send the right amount.
What teams are learning
Teams are exploring how Frequency Decisioning can help balance engagement opportunities with customer experience.
- We're hearing teams think about Frequency Decisioning less as a volume reduction tool and more as a way to find the right communication threshold for each customer.
- Several teams are evaluating how different channels and message types should be managed independently rather than through a single communication cap.
- Teams are exploring how Frequency Decisioning can help reduce fatigue and unsubscribes while still preserving engagement opportunities.
💡 Takeaway: The goal isn't fewer messages. It's finding the right communication threshold for each customer.
Where we're seeing it used
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Reducing customer fatigue and unsubscribes
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Creating guardrails around communication volume
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Managing different message types like promotional, lifecycle, survey, and product communications
Questions to consider 🤔
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Are your current frequency caps applied broadly, or do they vary by channel and message type?
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Do different message types require different communication thresholds?
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Where are you seeing signs of fatigue, disengagement, or unsubscribes today?
Questions We Keep Hearing 🤔
- How much visibility do I get into decisioning?
One recurring theme across Channel, Send-Time, and Frequency Decisioning is that visibility matters as much as performance.
Teams want confidence in how decisions are made and how those decisions impact results.
Features like Send-Time Optimization Insights and Frequency Optimization Insights can help teams evaluate outcomes.
- Should I start with all three decisioning capabilities?
Most teams are beginning with a specific use case or journey before expanding more broadly. Starting with an existing optimization challenge often creates the clearest path to learning.
- Does decisioning replace existing strategy?
No.
Teams are primarily evaluating decisioning as a way to automate manual optimization and operational decision-making while maintaining control over the customer experience.
Marketers still define the guardrails, priorities, and business rules, while decisioning helps identify the best path within those boundaries.
What This Signals 🔎
If we zoom out, the conversation is about more than AI.
It's about helping marketers spend less time managing rules and more time creating great customer experiences.
Across all three capabilities, a few themes continue to emerge:
- Teams are looking to reduce manual optimization and rule management
- Visibility, trust, and measurable outcomes all play an important role in adoption.
- Personalization is expanding beyond content into channel, timing, and frequency decisions
- Decisioning is helping teams scale engagement strategies without adding operational complexity
As teams continue exploring these capabilities, we're seeing a broader shift toward more individualized customer experiences powered by smarter decision-making.
We’ll continue surfacing patterns and conversations in Plaza as teams implement Iterable features.
