# Using Predictive Segmentation to Target the Right Audience That Drives Results

Join our Sr. Solutions Architect, Stan Szeto for an introduction and demonstration of Blueshift and learn how impactful AI-driven predictive scores can be for creating segments so showing how those scores are created and then how they can be used in both syndications and campaigns.

### [View Transcript](/content/events/using-predictive-segmentation-to-target-the-right-audience-that-drives-results/#/index.html)

## Demo Series Transcript: Using Predictive Segmentation to Target the Right Audience That Drives Results

**Speaker:**
- Stan Szeto, Senior Solution Architect, Blueshift

### How Blueshift's AI Helps Predict Customer Behavior and Personalize Campaigns

**Stan Szeto:** Hi, I’m Stan Szeto, Senior Solution Architect at Blueshift. Thanks for joining our bi-monthly demo series. Today, I’ll walk you through how Blueshift’s AI and machine learning help predict customer intent, improve targeting, and drive better conversions.

We'll start with a quick five-minute overview of Blueshift for those new to the platform, followed by a deep dive demo into our predictive modeling and activation features. Finally, we’ll close with Q&A.

### What Is Blueshift and What Makes It a Smart Customer Engagement Platform

Blueshift is a customer engagement platform built around a SmartHub CDP, enabling marketers to unify, analyze, and activate data from across all their customer touchpoints.
- It connects data from websites, mobile apps, backend systems, and point-of-sale systems to create a unified customer profile.
- It uses AI to enrich that data—powering predictions like purchase intent or churn risk.
- And it enables omnichannel orchestration across email, SMS, push, direct mail, paid media, and more.

We support enterprise-grade compliance including GDPR, CCPA, HIPAA, and SOC2.

### How Predictive Scoring Works in Blueshift

Blueshift’s Predictive Studio helps marketers create AI-powered models tailored to their business needs—no data science team required. These scores can:
- Predict likelihood to purchase
- Detect churn risk
- Identify the best marketing channel for each user

Unlike black-box systems, Blueshift’s models are transparent, allowing marketers to:
- See top signals driving each score
- Analyze conversion likelihood by score band
- Continuously refine inputs based on new data

### Real-World Example: A Financial Brand Predicts Product Interest

A finance client used Blueshift to build scores across multiple product lines—credit cards, auto loans, and refinancing:
- Each customer was scored on their likelihood to convert for each product
- Segments were created based on score thresholds
- Audiences were synced to paid media platforms (e.g., Pinterest, Google Ads)
- Personalized ads were served based on the highest intent score

Once users returned to the site and became known, the same scores powered consistent engagement across email, mobile, and web.

### Live Demo: Personalization in Action on BluBluLemon.com

Stan walked through a live example on BluBluLemon.com:
- Viewed and added products to cart
- Removed an item

In Blueshift, the customer profile for "Stan" was instantly updated with:
- Viewed items
- Carted items
- Engagement scores across channels (email, push, SMS)
- Predictive scores (purchase intent, retention likelihood)

### How to Build and Customize Predictive Scores in Blueshift

In Predictive Studio, marketers can:
- Select the goal event (e.g., purchase)
- Define funnel events leading to that goal
- Choose time windows and attribute inputs
- Launch the model with 3 steps

Models auto-train using behavioral and demographic data, and update daily. Feature importance shows top signals, like recency or frequency of site visits.

### Using Predictive Scores to Build Smart Segments

Stan showed two segment examples:

**High Intent Shoppers:**
- Lifetime revenue > $300
- Lifetime orders > 3
- Purchase score between 75 and 100

These users were synced to paid channels like Facebook, Google, and Criteo for immediate remarketing.

**At-Risk Customers:**
- No site visits in 7 days
- No purchases in 30 days
- No email engagement in 2 weeks
- Retention score below 50

This segment entered a win-back flow.

### How to Orchestrate Omnichannel Journeys with Predictive Scores

Stan demoed a win-back campaign using:
- A/B testing for offers (e.g., 10% vs. 15% discount)
- Conditional logic (e.g., only email users who didn’t open last message)
- Channel scoring to route users to SMS, push, or in-app
- Purchase intent scores to tailor discounts (higher for low-intent users)
- Direct mail fallback if digital channels failed

### Customer Results Using Predictive Intelligence
- **CarParts:** Used category-level predictive scores, resulting in a 400% lift in engagement
- **PayPal:** Improved gross sales per session by 125% through mobile engagement scoring
- **LendingTree:** Used intent scores to segment users by product line, increasing revenue 35% through paid media
- **Groupon:** Drove higher revenue by targeting users based on product category predictions

### Final Thoughts and Next Steps

Blueshift’s predictive modeling is:
- Customizable by marketers
- Continuously refined
- Ready to use across segmentation, personalization, and campaigns

For a personalized walkthrough, visit [blueshift.com](/content/site-root.html) or reach out to your CSM.