Building a 360° Customer Profile With AI: How Opal + Optimizely Unlock Predictive Personalization

Creating truly relevant customer experiences requires more than collecting data—it requires understanding it. Most organizations already have rich behavioral, purchase, and engagement data spread across their websites, commerce systems, customer data platforms, and marketing tools. Yet turning that raw information into actionable insights remains a challenge.

This project demonstrates how Optimizely Data Platform (ODP) and commerce data can be combined with Opal AI to generate a real-time, AI-driven customer profile that predicts user interests, intent, and next-best actions. The result is a 360° view that empowers marketers, merchandisers, and digital teams to design more relevant experiences across channels.

The Problem: Data Everywhere, Insight Nowhere

Marketing teams often face the same obstacles:

• Behavioral data lives in one system
• Purchase history lives in another
• Content engagement sits elsewhere
• Segmentation has to be updated manually
• Personalization rules are often static or outdated

Even with excellent tools, teams rarely have time to analyze every customer’s journey or determine what actions to take next. 

This is where AI fills the gap.

The Solution: Opal-Powered 360° Customer Profile Maker

The 360° profile maker uses Opal AI to unify signals from ODP and commerce data and generate:

• A complete customer summary
• Behavioral insights
• Interest and affinity prediction
• Product category tendencies
• Content preferences
• Education or learning needs
• Recommended actions for marketing, content, and commerce teams

By connecting to ODP via a custom agent tool, the Profile Maker agent retrieves customer interactions and uses AI reasoning to infer what each customer is likely to engage with next.

Think of it as having a real-time analyst for every individual visitor.

How It Works

The architecture centers on three components:

1. ODP Customer & Event Data

ODP provides a unified customer record that includes:

• Browsing history
• Purchases
• Add-to-cart behavior
• Campaign engagement
• Content interactions
• Device and location signals

This is the factual backbone of the profile.

2. Profile Maker Agent (Opal AI)

The agent performs three core steps:

A. Retrieve customer profile & event history

Uses the ODP browser tool to fetch recent activity (e.g., product views, orders, interests).

B. Analyze patterns and infer predicted behavior

The agent identifies signals such as:

• Frequent browsing of a specific category
• High-value items in carts
• Repeated research behavior
• Interest in style, features, or brand families
• Drop-off points in past purchases

C. Generate an updated customer profile with predictive fields

These predictive attributes support:

• Product recommendation
• Content recommendation
• Customer interest tagging
• Education content suggestions

This profile becomes a living document—continuously enriched as behavior evolves.

3. Updated Customer Profile Stored in ODP

The agent writes updated attributes back to ODP.

This allows:

• Optimizely personalization
• Targeted email campaigns
• Triggered journeys
• Dynamic on-site experiences
• Commerce recommendations

Marketers can finally act on insights without needing a data scientist.

Real Marketing Use Cases

This solution supports many day-to-day marketing challenges: 

1. Predictive Product Recommendations

If a customer frequently browses outerwear, compares materials, and revisits similar items, the profile maker can classify them as “outerwear-intent.”

This triggers personalized homepages or email recommendations.

2. Content Personalization

If a user reads guides about sizing, care, or fit, the system can recommend:

• Buying guides
• Sizing tools
• Comparison content
• Feature explainers

This increases engagement and reduces bounce.

3. Customer Interest Tagging

Interests like “workwear,” “premium casual,” “eco-friendly materials,” etc. can be inferred and stored as profile tags.

These tags feed segmentation and journey triggers.

4. Education & Support Recommendations

If a customer repeatedly returns an item category or visits help documentation, the AI can suggest proactive education content.

5. Email & Journey Automation

With predictive attributes stored in ODP:

• Triggers become more accurate
• Journeys become more relevant
• Email recommendations match real behavior

This bridges the gap between analytics and marketing action.

Why This Matters

By combining ODP’s data foundation with Opal’s reasoning capabilities, businesses can:

• Scale personalization without manual analysis
• Better understand each customer’s motivation
• Predict what a user might need next
• Fuel Optimizely personalization and experimentation
• Increase engagement, conversion, and lifetime value

It moves teams from reactive to proactive personalization—at scale.

Final Thoughts

AI isn’t replacing marketers, merchandisers, or content creators—it’s empowering them with insights they’ve never had before. The 360° Profile Maker demonstrates how teams can convert existing customer data into meaningful predictions and turn those predictions into personalized experiences across channels.

This is the future of digital experience—and the tools are already here.