Smarter decisions, without the overload.

Every customer is different.
Manual personalization doesnโ€™t scale.

One size does not fit all

Audiences engage differently, yet most programs use the same route, timing, and cadence for everyone, limiting relevance and results.

Rules eventually become wrong

Static rules quickly become outdated. As customer behavior changes, campaign performance quietly declines.

Broad rules leave value behind

To keep campaigns manageable, teams simplify complex settings into broad audience rules, sacrificing relevance, engagement, and revenue.

Choose the right channel every time.

Stop defaulting to one channel. Channel Decisioning uses engagement behavior to select email, push, SMS, or in-app based on what is most likely to perform, strengthening outcomes without changing your strategy.

Reach customers at the moment theyโ€™re most likely to act.

Poor timing can turn a strong message into a missed opportunity. Send Time Decisioning adjusts each customerโ€™s ideal window as new data becomes available, making each interaction more effective.

Build stronger connections without over-messaging.

Prevent fatigue with intelligent guardrails. Frequency Decisioning adapts communication cadence as preferences evolve, helping maximize response without overwhelming recipients.

Decisioning keeps getting smarter.

Nova Decisioning learns from every interaction and automatically improves channel, timing, and frequency decisions, while you set the goals and guardrails.

Compounds over time

Nova Decisioning does more than automate individual choices. It learns from customer responses and applies that intelligence across future interactions.

Reduces optimization work

Nova Decisioning continuously adjusts channel, timing, and frequency without rebuilding rules or needing manual adjustments to campaign logic.

Transparent by design

Nova Decisioning works within your business rules and shows how recommendations are made, so you can explain outcomes confidently to stakeholders.

โ€œTherabodyโ€™s success proves that meaningful personalization requires a blend of asking for direct input and observing behavioral signals. By collaborating across departments and utilizing Iterable to its full potential, Therabody has transformed its lifecycle marketing into a powerful engine for both retention and new customer growth.โ€

Carlye Wycykal

Director of Lifecycle Marketing at Therabody

+45%

conversion transformation

Make trustworthy recommendations at scale.

Move Beyond Automation with Nova Decisioning

Discover how Nova Decisioning determines when, where, and how often to reach each customer with AI-guided personalization.

How Therabody Scaled Personalization Natively

Learn how Iterable helped Therabody turn first-party data into personalized experiences, increasing conversions by 45%.

AI Decision Intelligence in Marketing G2 Report 

See how marketers use AI decision intelligence to move from insight to action and strengthen program performance.

Nova Decisioning FAQs

Nova Decisioning is Iterableโ€™s AI-powered decisioning capability that learns from customer behavior and campaign outcomes to make and adapt decisions across the customer journey. It helps marketers move beyond static rules and manual optimization to deliver more individualized, effective experiences at scale.

Nova Decisioning uses customer responses, behavior, and engagement patterns to inform future channel, timing, and frequency choices. As new interactions occur, it adapts those choices to current conditions rather than relying only on static rules or fixed schedules. This allows campaigns to improve without marketers continually rebuilding logic or making individual adjustments by hand.

Nova Decisioning is designed to operate within the goals, rules, and guardrails marketers establish. Iterable positions its AI as explainable and transparent, helping teams understand and maintain control over AI-supported outcomes. Marketers remain responsible for strategy while the platform manages the individual channel, timing, and frequency choices needed to support it.