Iterable AI capabilities sit inside Nova Intelligence, our native AI intelligence layer. Built natively into the Iterable platform, Nova Intelligence acts as a reasoning partner, embedding AI across execution, insights, and decisioning and turning real-time customer signals into immediate, personalized actions across every channel and interaction.

Iterable’s AI and Predictive Capabilities in 2026

Each capability reads specific data and produces a prediction or decision you can act on. The first six are part of Nova Intelligence; Catalog is part of Campaigns (and available in Journeys through Catalog Integration).

Capability (current name)

Data it reads

What it predicts or decides

Where it’s used (segments, journeys, sends)

What changes for the marketer

Predictive Audiences

Historical user profile fields plus purchase, system, and custom events, measured against a goal you define

Each user’s likelihood to convert on that goal over the next 30 days, saved as a score on their profile

Segments and journeys

You focus on the people most likely to reach your goal, without building a model.

Brand Affinity

Engagement depth, recency, and consistency

A sentiment label for each user’s connection to your brand

Segments and journeys

You separate disengaged users from loyal ones who only need a timely nudge.

Campaign Re-engagement

Engagement with your campaigns

Which users have disengaged, plus recommended next steps

Campaigns

You turn underperforming campaigns into re-engagement opportunities.

Send Time Decisioning

When each user actually opens messages

The send time for each individual

Email and mobile push sends

For example, GreenFi saw a 27% increase in open rates vs. a control group in a re-engagement journey.

Frequency Decisioning

Engagement and stated preferences

How many messages each person receives

Campaigns and journeys

You prevent over-messaging while keeping the sends that drive impact.

Channel Decisioning

User behavior and channel preferences

The channel each person is most likely to engage, with fallback options

Journeys

Each journey step reaches people on the channel they actually use.

Catalog

Product or content data such as inventory, pricing, and availability, plus browse and purchase history

Which products populate each message

Segments, Campaigns, and journeys through Catalog

Recommendations reflect what’s in stock and what each shopper has viewed or bought.

How glassbox AI fits in: Every Nova Intelligence outcome is explainable, brand-governed, and verifiable, so you can show stakeholders why a score or decision happened before acting on it.

What Is Predictive Marketing (and Why Does It Matter Now)?

Predictive marketing flips the script. Instead of analyzing what did happen, it answers what will happen.

What Is Predictive Marketing? Predictive marketing refers to the use of historical, contextual, and behavioral data, paired with machine learning, to anticipate customer actions and automate relevant messaging before those actions occur.

What Is Predictive Marketing? Predictive marketing refers to the use of historical, contextual, and behavioral data, paired with machine learning, to anticipate customer actions and automate relevant messaging before those actions occur.

Customers expect relevant interactions that keep pace with what they do. This demand for immediacy and personalization has only intensified as digital behaviors evolve faster than most legacy systems can track.

With vast amounts of behavioral, contextual, and historical data now accessible, marketers are equipped to shift from intuition-led strategies to data-driven foresight. In McKinsey’s 2026 State of AI survey, respondents most often attribute revenue gains from AI to marketing and sales.

As a clear performance multiplier, prediction drives value in more than one area:

Reduce churn by identifying signals of disengagement before customers drop off.

Reduce churn by identifying signals of disengagement before customers drop off.

Boost conversions by engaging the people most likely to act and responding the moment they do.

Boost conversions by engaging the people most likely to act and responding the moment they do.

Improve efficiency by prioritizing high-intent leads and automating lower-value tasks, allowing marketers to focus on strategic growth.

Improve efficiency by prioritizing high-intent leads and automating lower-value tasks, allowing marketers to focus on strategic growth.

Iterable’s AI Capabilities in Practice: Turning Data Into Decisions

We built Nova Intelligence to turn complex data into clear next steps. It helps you move from reactive campaigns to proactive, personalized engagement, so you shape outcomes instead of only forecasting them.

1. AI-Powered Predictions

You shouldn’t need a data science team to put predictions to work. Predictive Audiences builds the model for you from a goal you define. We translate complex algorithms into clear, actionable insights that any marketer can use.

With Predictive Audiences (formerly Predictive Goals), powered by Nova Intelligence, marketing teams can build dynamic audiences based on the likelihood of conversion, churn, or specific actions. Then automatically update those segments as behavior shifts. For example, a brand could use Predictive Audiences to identify customers with a +70% probability of purchasing within the next week and deliver limited-time offers to accelerate that conversion.

Brand Affinity, powered by Nova Intelligence, quantifies emotional connection by analyzing engagement depth, recency, and consistency, providing marketers with a measurable understanding of brand sentiment over time.

Predictive Use Case: Media & Entertainment

Imagine a global streaming service trying to curb rising subscriber churn. It could use:

Predictive Audiences to identify users whose engagement patterns indicate a high likelihood of cancellation, such as those who haven’t watched content in the past two weeks or who have recently skipped promotional emails.

Predictive Audiences to identify users whose engagement patterns indicate a high likelihood of cancellation, such as those who haven’t watched content in the past two weeks or who have recently skipped promotional emails.

Brand Affinity scoring to separate disengaged users from those who still feel loyal but need a timely nudge, then tailor outreach to each group. For instance, low-affinity users might receive concise messaging about new releases based on their past viewing habits, while high-affinity users could be offered a personalized discount or early access to premium content.

Brand Affinity scoring to separate disengaged users from those who still feel loyal but need a timely nudge, then tailor outreach to each group. For instance, low-affinity users might receive concise messaging about new releases based on their past viewing habits, while high-affinity users could be offered a personalized discount or early access to premium content.

2. Behavioral and Engagement Insights

Predictive power starts with data, and not just any data, but high-quality, current data. That’s where Smart Ingest comes in: our flexible data ingestion framework that connects to the systems you already use (CRMs, CDPs, analytics tools, commerce platforms, mobile apps, and more) and brings that data into Iterable in a clean, structured, marketer-ready format.

Once your data flows into Iterable, Segmentation lets you slice and activate it on the fly, using dozens of criteria such as inactivity, low affinity, or churn prediction scores. Because segmentation is real-time, audience membership updates as user behavior changes, with no batch processing or delayed targeting. This kind of agility is useful for time-sensitive journeys, such as re-engagement or upselling, where it’s important to trigger users’ entry or exit points based on behavioral data.

Predictive Use Case: Subscription Apps

Consider a subscription-based meal delivery brand seeking to boost customer retention:

Using Smart Ingest, it connects its order management system, website, and app to Iterable, bringing in customer order frequency, delivery preferences, and engagement data from email and mobile.

Using Smart Ingest, it connects its order management system, website, and app to Iterable, bringing in customer order frequency, delivery preferences, and engagement data from email and mobile.

With this data foundation, it builds a dynamic segment of users who have not placed an order in the past three weeks and whose Brand Affinity score is trending downward. It further enriches the segment by layering in churn prediction scores, identifying users with a 60% or higher likelihood of canceling within the next month.

With this data foundation, it builds a dynamic segment of users who have not placed an order in the past three weeks and whose Brand Affinity score is trending downward. It further enriches the segment by layering in churn prediction scores, identifying users with a 60% or higher likelihood of canceling within the next month.

The brand then launches a multichannel reactivation campaign. High-risk users receive personalized SMS messages with limited-time offers, while medium-risk users are prompted via push notifications to showcase new menu options.

The brand then launches a multichannel reactivation campaign. High-risk users receive personalized SMS messages with limited-time offers, while medium-risk users are prompted via push notifications to showcase new menu options.

_If you want actionable ideas to jumpstart your next campaign, check out our _Behavioral Triggers Cheat Sheet_ for more examples of relevant messaging powered by real-time insights._

_If you want actionable ideas to jumpstart your next campaign, check out our _Behavioral Triggers Cheat Sheet_ for more examples of relevant messaging powered by real-time insights._

3. Personalization at Scale

What good is a prediction if you don’t act on it? We make it easy to turn predictive insights into highly personalized experiences automatically.

Product and content data: Catalog, part of Campaigns (and available in Journeys through Catalog Integration), is a content and product data store that personalizes emails, messages, and in-app experiences. It tailors product recommendations, pricing tiers, loyalty status, or regional availability to each user’s context.

Product and content data: Catalog, part of Campaigns (and available in Journeys through Catalog Integration), is a content and product data store that personalizes emails, messages, and in-app experiences. It tailors product recommendations, pricing tiers, loyalty status, or regional availability to each user’s context.

Modular content: Templates and Snippets are modular content blocks that adapt based on user behavior, preferences, and predictive scores. For example, a promotional email might show different offers to a high-propensity buyer than to someone showing signs of churn.

Modular content: Templates and Snippets are modular content blocks that adapt based on user behavior, preferences, and predictive scores. For example, a promotional email might show different offers to a high-propensity buyer than to someone showing signs of churn.

Cross-channel delivery: Cross-channel orchestration delivers cohesive, relevant messaging across email, RCS, SMS, mobile push notifications, in-app messaging, and more. The result is a journey that feels intuitive, not intrusive.

Cross-channel delivery: Cross-channel orchestration delivers cohesive, relevant messaging across email, RCS, SMS, mobile push notifications, in-app messaging, and more. The result is a journey that feels intuitive, not intrusive.

Predictive Use Case: E-Commerce

Picture an e-commerce brand looking to improve conversion rates during its seasonal campaign.

By leveraging Predictive Audiences, it identifies users with a high likelihood of making a purchase within the next 7 days.

By leveraging Predictive Audiences, it identifies users with a high likelihood of making a purchase within the next 7 days.

Using Catalog, it dynamically populates each user’s email with top-recommended products based on browsing history and past purchases.

Using Catalog, it dynamically populates each user’s email with top-recommended products based on browsing history and past purchases.

Templates and Snippets personalize content even further by tailoring imagery and messaging to user location, preferred category, and engagement level.

Templates and Snippets personalize content even further by tailoring imagery and messaging to user location, preferred category, and engagement level.

These communications are deployed across email and push, with intelligent orchestration (more on this below) ensuring no user receives duplicate or misaligned content.

These communications are deployed across email and push, with intelligent orchestration (more on this below) ensuring no user receives duplicate or misaligned content.

_Learn how to build dynamic journeys in Step 3 of our Playbook for Moments-Based Marketing: _Scaling Contextual Personalization with Modular Content.

_Learn how to build dynamic journeys in Step 3 of our Playbook for Moments-Based Marketing: _Scaling Contextual Personalization with Modular Content.

4. Automation and Optimization

Great marketing is perfectly timed, paced, and delivered through the right channels. Nova Decisioning, powered by Nova Intelligence, chooses the timing, frequency, and channel each person is most likely to respond to, and acts when the signal fires.

Send Time Decisioning chooses each person’s send time from their real engagement patterns, then automatically schedules delivery for that moment.

Send Time Decisioning chooses each person’s send time from their real engagement patterns, then automatically schedules delivery for that moment.

Frequency Decisioning helps avoid burnout by dynamically adjusting message volume based on engagement and preferences.

Frequency Decisioning helps avoid burnout by dynamically adjusting message volume based on engagement and preferences.

Channel Decisioning determines which channel (email, SMS, push, or in-app) each user is most likely to respond to, then routes messages accordingly. It works in Journeys, not Campaigns.

Channel Decisioning determines which channel (email, SMS, push, or in-app) each user is most likely to respond to, then routes messages accordingly. It works in Journeys, not Campaigns.

Journey Agent builds, tests, and updates journeys without SQL.

Journey Agent builds, tests, and updates journeys without SQL.

Together, these tools turn predictive intent into meaningful engagement.

Predictive Use Case: Travel Marketplace

For example, imagine a travel brand that wants to improve retention among recently active users.

Capability

What it does for the travel brand

Send Time Decisioning

Schedules trip inspiration emails when users are most likely to engage.

Channel Decisioning

Ensures high-value content appears on each user’s preferred channel: push notifications for mobile-first users, and email for desktop travelers.

Frequency Decisioning

Protects against over-messaging frequent flyers by capping outreach based on past response rates.

Journey Agent

Helps the team quickly adjust workflows mid-campaign to align with new predictive insights about user intent.

_Ready to put these capabilities to work? Start with _Your Checklist for Unlocking the Power of AI.

_Ready to put these capabilities to work? Start with _Your Checklist for Unlocking the Power of AI.

How Marketers Benefit from Iterable’s Predictive Capabilities

Predictive marketing is only powerful if it delivers real results. Here’s how we help marketers turn predictions into growth.

Smarter Segmentation

Predictive scoring and real-time segmentation empower marketers to identify high-converting audiences before they take action. Instead of static lists, you can dynamically group users based on intent, engagement level, and likelihood to convert, then tailor campaigns accordingly.

For example, Redfin saw a 72% lift in converting inactive sellers to an active state (and a 15% lift for buyers) after targeting high-intent users with Predictive Audiences.

“Predictive Audiences has been an absolute game-changer for our team, maximizing our efficiencies and accelerating time to value.” ~Lisa Tulloch, Email Marketing Channel Manager at Redfin

“Predictive Audiences has been an absolute game-changer for our team, maximizing our efficiencies and accelerating time to value.”

~Lisa Tulloch, Email Marketing Channel Manager at Redfin

Improved Customer Retention

Churn doesn’t have to be a surprise. Predictive Audiences flags who is most likely to churn on a goal you define, so you can act before they disengage. Marketers can then trigger personalized re-engagement journeys to win them back, before it’s too late.

Take BIG4 Holiday Parks: it increased conversion rates by 156–500% by using Predictive Audiences to find high-intent guests it historically would not have contacted.

Higher ROI

Why spend equally across all leads when some are far more likely to convert? Our predictive insights allow you to prioritize high-intent users and suppress low-engagement segments, helping you stretch your marketing dollars further. The result is more revenue and less waste.

Consider how Wolt approached a new retail category: within one month, it saw 97% more first-time supermarket users and a 60% increase in revenue in key markets compared to a control group, after focusing on the customers Predictive Audiences flagged as most likely to convert in that category.

“Previously, we required data scientists to build audiences based on a smart data model. Through Iterable’s predictive audiences, we discovered a scalable method to craft intelligent audience segments. This enabled us to target users more effectively, focusing solely on those likely to achieve specific goals that are key for our business.” ~Selen Kucukarslan, Senior CRM & Marketing Automation Manager at Wolt

“Previously, we required data scientists to build audiences based on a smart data model. Through Iterable’s predictive audiences, we discovered a scalable method to craft intelligent audience segments. This enabled us to target users more effectively, focusing solely on those likely to achieve specific goals that are key for our business.”

~Selen Kucukarslan, Senior CRM & Marketing Automation Manager at Wolt

Operational Efficiency

Our AI and automation features, including Journey Agent, Frequency Decisioning, and Send Time Decisioning, streamline the entire campaign lifecycle. Manual tasks shrink. And teams can shift their time from production to planning, strategy, and creative experimentation.

For example, Morning Brew drove nearly 20% of event registrations for its Marketing Brew newsletter while contacting only 10% of that audience by using AI to find high-intent readers. Separately, cross-promoting its professional newsletters using subscription and engagement data generated 15,000 new subscriptions in six months, saving over $100,000 in acquisition costs.

How Iterable Makes Predictive Marketing Accessible

Predictive marketing once demanded data scientists, complex tech stacks, and months of engineering support. Not anymore.

Nova Intelligence brings predictions directly into marketers’ hands, with no code or guesswork. By optimizing journeys and agentic workflows based on your pre-defined business goals, Nova Intelligence makes AI accessible and deeply usable.

What makes Nova Intelligence stand out is:

No-code predictive tools that fit into any workflow

No-code predictive tools that fit into any workflow

Plug-and-play integrations for faster onboarding

Plug-and-play integrations for faster onboarding

Activates data from your source of truth (data warehouse, CDP, or apps) across every channel

Activates data from your source of truth (data warehouse, CDP, or apps) across every channel

Glassbox AI, so every Nova Intelligence outcome is explainable and you can see why a prediction or decision was made

Glassbox AI, so every Nova Intelligence outcome is explainable and you can see why a prediction or decision was made

Unlike legacy tools that treat AI as a bolt-on feature, Nova Intelligence is AI-native by design. That means faster time to value, lower technical barriers, and more room for marketers to focus on what really drives growth.

How to Compare Predictive AI Across Platforms

When you evaluate predictive AI, ask how a platform builds, explains, and applies its predictions. These four criteria work for any vendor.

Built-in models vs. a data science team: Check whether the platform builds models from goals you define or expects your team to build and maintain them. A Gartner survey of martech leaders found half cite a shortage of technical talent for AI agent deployment.

Built-in models vs. a data science team: Check whether the platform builds models from goals you define or expects your team to build and maintain them. A Gartner survey of martech leaders found half cite a shortage of technical talent for AI agent deployment.

Explainability of each prediction: Ask whether you can evaluate transparent AI decisions and see why each person got a score. In McKinsey’s research on AI trust, 40% of respondents identified explainability as a key risk in adopting gen AI, yet only 17% were working to mitigate it.

Explainability of each prediction: Ask whether you can evaluate transparent AI decisions and see why each person got a score. In McKinsey’s research on AI trust, , yet only 17% were working to mitigate it.

Where predictions can be used: Confirm that scores and decisions flow directly into segments, journey entry and splits, and individual sends.

Where predictions can be used: Confirm that scores and decisions flow directly into segments, journey entry and splits, and individual sends.

Freshness of the underlying data: Ask how often models read new behavior and how quickly updated scores reach your audiences.

Freshness of the underlying data: Ask how often models read new behavior and how quickly updated scores reach your audiences.

If you’re weighing specific platforms, see how we compare with Bloomreach, Braze, and Klaviyo.

Best Practices for Getting Started with Predictive Marketing

Ready to put predictive marketing to work? Start here:

Integrate your data sources in Iterable with Smart Ingest.

Integrate your data sources in Iterable with Smart Ingest.

Start small with action-specific journeys, such as reactivation or upselling.

Start small with action-specific journeys, such as reactivation or upselling.

Use predictive scores to refine your segments based on desired actions.

Use predictive scores to refine your segments based on desired actions.

Test, measure, and iterate using messaging insights and automated experimentation.

Test, measure, and iterate using messaging insights and automated experimentation.

Tap into strategic expertise with our professional services, agency consultants, and community.

Tap into strategic expertise with our professional services, agency consultants, and community.

Predictive capabilities don’t just forecast behavior. They empower marketers to influence and shape it. We give you the tools for smarter targeting, retention, ROI, and personalization, so you can engage customers with proactive precision.

Frequently Asked Questions

1. What AI and Predictive Capabilities Does Iterable Have?

Iterable AI capabilities live in Nova Intelligence, our native AI intelligence layer, which spans Nova Agents, Nova Decisioning, and Nova Predictive Insights. Predictive Audiences forecasts who is likely to convert or churn on a goal you set, and Brand Affinity labels each person’s sentiment toward your brand. Send Time Decisioning, Frequency Decisioning, and Channel Decisioning (in Journeys) choose when, how often, and where to reach each person. All of it runs on explainable glassbox AI.