Send the message most likely to end in a purchase

Predictive marketing uses AI models to anticipate what each customer is likely to do next. Instead of sending the same campaign to everyone, a predictive system like Cordial AI™ determines the right audience, products, channel, and send time for every individual, sending the message most likely to end in a purchase (millions of times per day). 

Predictive marketing works by turning customer data into forward-looking scores, then using those scores to automate decisions across your messaging program. AI models analyze each customer's attributes and behavior to predict things like purchase propensity, unsubscribe risk, category affinity, and preferred channel — generating more than 20 predictive attributes per person.

Those predictions drive four connected functions:

• Audiences: build segments from predicted behavior, prioritizing likely buyers and suppressing customers who shouldn't receive a message.
• Journeys: trigger messages and branch paths based on predicted next actions — for example, launching a re-engagement sequence the moment someone's AI Engagement Score drops.
• Personalization: choose the products, offers, and content most likely to move each person, including dynamic discounts tuned to their likelihood to buy with one.
• Optimization: decide when, where, and how often to reach each customer so messages arrive when they matter.

For example, when a customer's purchase propensity increases, the system can automatically trigger a cross-sell email. Rather than relying on manual rules, predictive marketing continuously re-scores customers and adjusts. The result is marketing that adapts to each individual in real time.

The core benefit of predictive marketing is more revenue from the same audience, because every message is aimed at the person most likely to act on it. By predicting who will buy, unsubscribe, or respond to a discount, brands stop wasting sends and start prioritizing high-value moments. Levi's, for example, increased revenue per message (RPM) by 47% using Cordial's AI predictive models, alongside an 11% increase in click-through rate and a 6% increase in open rate.

Beyond revenue, predictive marketing delivers several practical advantages:

• Higher conversion: it sends the products, offers, and timing most likely to drive each customer to purchase.
• Healthier lists: it suppresses customers likely to unsubscribe, protecting long-term deliverability and engagement.
• Smarter discounting: it avoids discounting shoppers who would have bought at full price, protecting margin.
• Better experience: messages arrive on the right channel, at the right time, featuring the products and categories each person cares about.

Predictive marketing improves segmentation by grouping customers based on what they're likely to do next, not just what they've done before. Traditional segments rely on past attributes and actions; predictive audiences add forward-looking signals so you can prioritize likely buyers and suppress people who shouldn't receive a given message.

Cordial builds these audiences from predictive attributes such as:

• Discount Affinity: suppress anyone likely to purchase without a discount, so sale communications don't erode margin.
• Unsubscribe Risk: suppress anyone likely to unsubscribe, protecting your list and sender reputation.
• Purchase Propensity: always include individuals who are close to making a purchase, focusing spend on the highest-intent shoppers.

These are just three of more than 20 AI predictive attributes available to segment on. In practice, a flash-sale audience might combine several signals at once — segmenting by category affinity, including buyers likely to convert, and suppressing both discount-insensitive shoppers and those at risk of unsubscribing. 

The result is a smaller, sharper audience where nearly everyone who receives the message is a good fit for it.

Purchase propensity is a predictive score that estimates how likely an individual customer is to make a purchase in the near future. In predictive marketing, it's one of the most actionable signals because it tells you where intent is highest — so you can concentrate messaging and offers on the people closest to converting.

Cordial uses AI Purchase Propensity in two main ways. In audience building, it lets you always include individuals who are close to making a purchase, ensuring high-intent shoppers never get missed. In journeys, a rising purchase-propensity score can automatically trigger a cross-sell email, reaching the customer at the exact moment they're most receptive.

Predictive marketing personalizes product recommendations by using AI models that learn each customer's preferences, behavior, and likely next purchase — then choosing the products, offers, and content most likely to drive that person to buy. Rather than showing the same best-sellers to everyone, the system predicts what an individual is close to purchasing and features it.

Cordial powers this personalization with several predictive attributes:

• AI Product Recommendations: include the products an individual is most likely to purchase next.
• AI Discount Score: apply dynamic discounts based on how likely someone is to buy with a discount, so incentives go only where they change behavior.
• AI Category Affinity: replace a static banner image with the customer's favorite category, making the whole message feel tailored.

These sit among more than dozens of AI predictive attributes that can shape a single email. For example, one shopper might see their favorite category up top with a recommended product and no discount, while another receives a modest, personalized discount calibrated to their likelihood to convert.

Send time optimization is a predictive technique that delivers each message at the individual time a customer is most likely to engage, rather than blasting everyone at once. It's part of how predictive marketing optimizes a program — choosing when, where, and how often to contact each person so messages arrive when they're most likely to matter.

In Cordial, Send Time Optimization sends to each customer's preferred send time, learned from their past engagement patterns. It works alongside two related optimization levers: AI Channel Affinity, which picks each customer's preferred channel across email, SMS/RCS, push, and WhatsApp, and Frequency Optimization, which sends at each customer's preferred weekly send frequency so you neither over- nor under-message them.

For example, one subscriber might reliably open in the early morning while another engages at night — send time optimization reaches each on their own schedule.

Predictive marketing reduces unsubscribes and churn by spotting at-risk customers before they leave and adjusting how (or whether) you contact them. 

Three examples of how Cordial customers are using predictive models to reduce unsubscribes and churn:

1. You can use an Unsubscribe Risk signal to suppress anyone likely to unsubscribe from a given send
2. Trigger re-engagement sequence when someone’s AI Engagement Score drops
3. Trigger a win-back sequence when a customer stops making purchases in their favorite category, detected through AI Category Affinity 

AI Frequency Optimization further protects the relationship by capping sends to each customer's preferred weekly volume, reducing the fatigue that drives opt-outs.

Yes, when applied across audiences, personalization, and timing, predictive marketing is designed to increase revenue by making each message more likely to tend in a purchase. For example, Levi’s increased revenue per message by more than 40% after implementing Cordial’s AI predictive models.