Key Takeaways
Predictive AI scores who is likely to convert so you stop guessing on every send
It differs from generative AI: it forecasts outcomes instead of creating new content
Models learn from historical behavior, then output per-user likelihood scores
Marketers turn those scores into segments and journeys that prioritize high-intent users
Redfin drove a 72% lift reactivating inactive sellers with scored audiences
Predictive Audiences, powered by Nova Intelligence, puts those scores into daily workflow
Many marketers hear “AI” and think content generation. But focusing there alone risks missing some of AI’s highest-value applications.
Predictive AI helps marketers make the decisions that determine performance: who to engage, when to reach them, what to prioritize, and where intervention is needed before a customer churns.
As customer expectations rise and signals multiply, those decisions are becoming too complex and too fast-moving to manage manually. Generative AI helps marketers create. Predictive AI helps ensure they’re creating and acting for the right customer, at the right moment.
What Is Predictive AI?
Predictive AI uses statistical analysis and machine learning to identify patterns, anticipate behaviors, and forecast upcoming events. According to IBM’s predictive AI explainer, teams forecast outcomes, risk, and behavior from past data.
In marketing, that forecast shows up as audience priority. Models read past engagement and first-party events, then surface who is most likely to complete a goal you define, such as a purchase, click, or reactivation.
Core traits marketers should recognize:
Pattern detection: Models learn from historical engagement and events
Forward score: Each user gets a likelihood for a goal you define
Actionable output: Scores feed segments, journeys, and send priority
Marketers have used related techniques for years. The difference today is scale: models can score entire customer bases continuously, not only after a one-off analysis project.
Predictive AI rarely works alone in a live program. Optimization and automation carry scored priority into the next message, and generative tools help shape the creative once the audience is clear.
Predictive AI vs. Generative AI
Teams often mix the two because both sit under the AI umbrella. They solve different jobs, and the contrast is easiest to see side by side.
IBM’s comparison is direct. Generative AI creates original content from a prompt. Predictive AI forecasts likely outcomes from historical patterns. ChatGPT is generative. It is not a system that ranks conversion likelihood.
Keep the labels separate so teams pick the right tool for prioritization versus creative production.
Dimension | Predictive AI | Generative AI |
|---|---|---|
Function | Forecasts what is likely to happen next | Creates new content from prompts |
Output | Scores, rankings, risk or conversion estimates | Text, images, code, or other media |
Typical marketing use | Who may convert, churn, or reactivate | Subject lines, copy drafts, creative variants |
Use predictive models when the decision is prioritization. Use generative tools when the decision is what to say. Most modern programs need both, sequenced: score the audience first, then produce the message.
How Predictive AI Works
At a high level, predictive systems follow a clear path from data to action. Use the sequence below when you evaluate whether your data and goals are ready for scoring.
Ingest historical data. Pull first-party events and user properties with enough history to learn patterns.
Train on a defined goal. Tell the model what “convert” means for your business (purchase, cart add, or outcomes to minimize such as unsubscribes).
Score each user. Output a likelihood score for the window you care about.
Act on the score. Build segments, route people into journeys, and allocate send volume to higher-likelihood users.
Quality depends on volume and freshness. Thin history produces weak forecasts. In practice, teams aim for several months of relevant events and a large enough user base before treating scores as campaign-ready.
Explainability matters as much as the score. Glassbox-style views show predictive strength (for example, weak to strong), so you can see how reliable a forecast is before you bet a full send on it. For more on that transparency layer, read our guide on explainable AI in marketing.
How Marketers Put Predictive AI to Work
Once scores exist, the value is in how you operationalize them. Apply these patterns when you need the model to change who gets budget, offers, or journey paths:
Conversion and churn segments: Group users likely to purchase or click, or likely to return or unsubscribe
Offer and frequency focus: Increase offers for users likely to buy repeatedly, and ease pressure on low-likelihood cohorts
Journey branching: Use probability scores as journey criteria, such as a loyalty path for strong scores
Inside Iterable, Predictive Audiences, part of Nova Intelligence, analyzes your first-party history. It scores how likely each user is to convert on a goal you define. You can start from a template or set custom criteria, then apply those scores in static or dynamic segments and journeys.
Create a Predictive Audiences goal from scratch or start from a template in Iterable.
For example, criteria might look for users likely to add an item to cart or redeem a promo code. You can also require a Premium account type within a given month.
Set goal criteria that match the outcomes your business cares about.
After you build the goal, the dashboard shows how likely users are to convert from the last refresh through a rolling 30-day window. Predictive strength refreshes on a regular cadence so recent data shifts show up in the reliability signal.
Review predictive strength so you understand how reliable each forecast is.
For a step-by-step walkthrough, see how to forecast marketing success with Predictive Audiences. The user interface (UI) may still label the workflow Predictive Goals; product naming is Predictive Audiences.
How Redfin Used Predictive AI to Reactivate Sellers
For example, Redfin drove a 72% lift reactivating inactive sellers after Predictive Audiences scored who was ready to convert instead of relying on broad blasts.
Consider how Redfin also saw a 15% lift moving inactive buyers to active with the same score-then-send pattern on high-likelihood homebuyers.
Redfin reaches more than 50 million average monthly users across 100-plus markets. Only a small share is active: engaged with email or visited the site in the last 30 days. In real estate, those sparse signals are early signs someone may need an agent.
Guessing who would re-engage meant manual work and missed timing. With clearer priority from Predictive Audiences, Redfin sent conversion-oriented email to high-likelihood homebuyers and sellers, and used holdouts to measure incremental lift.
Redfin sent conversion-oriented emails to buyers and sellers with the highest likelihood to convert.
Holdout groups confirmed the incremental impact of those scored sends, including directionally positive movement in seller consultations and buyer tours. With those scores in hand, Redfin’s marketing team could act quickly with little engineering support, and marketers kept experimenting on the brand’s own customer data without waiting on engineering tickets for every audience cut.
Predictive Goals has been an absolute game-changer for our team, maximizing our efficiencies and accelerating time to value. We’ve seen the tangible benefits of AI on our business, and look forward to seeing the impact of Iterable’s new AI innovations.”
Lisa Tulloch, Email Marketing Channel Manager, Redfin
You do not need Redfin’s scale to benefit. Any team with enough history to train on a clear goal can score who deserves the next dollar of attention. A mid-market retailer with a few months of purchase and browse events can run the same score-then-send pattern on a smaller catalog.
Frequently Asked Questions
Quick jumps:
Definition: What predictive AI is for marketers
Contrast: Predictive vs generative (and ChatGPT)
Workflow: How scores enter campaigns
Product: Predictive Audiences in Iterable
1. What Is Predictive AI?
Predictive AI turns historical patterns into a forward-looking priority list. For marketers, the outcome is a clear call on who deserves the next send, suppress decision, or journey path before budget is spent.
2. How Is Predictive AI Different From Generative AI?
Predictive AI estimates future outcomes such as likelihood to purchase or churn. Generative AI creates new assets such as copy or images. ChatGPT is generative. Ranking who will convert is predictive. Strong programs often score the audience first, then draft the message.
3. How Does Predictive AI Work in Marketing?
You define a business goal, train on first-party history, and receive per-user scores. Marketers turn those scores into segments and journey branches. Budget and creative follow high-likelihood people instead of a flat blast list.
4. What Is Predictive Audiences in Iterable?
Predictive Audiences, part of Nova Intelligence, scores conversion likelihood on a goal you set. You build criteria, review predictive strength, and apply scores in segments and journeys without leaving your program workflow.
Put Predictive AI to Work
Predictive AI replaces guesswork with scored priority: who is ready, who is drifting, and where your next send should go. When those scores sit inside your journeys, every campaign starts from evidence instead of habit.
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