Horizon Media is doubling down on predictive intelligence.

The world’s largest independent media agency has inked an enterprise partnership with ZeroToOne.AI to integrate real-time predictive behavioral intelligence into HorizonOS, its open operating system, and Blu, its AI-native marketing intelligence platform.

The goal isn’t incremental automation. It’s anticipation.

Moving Beyond “Rearview Mirror” AI

Most marketing AI today is built on large language models that automate workflows—summarizing reports, generating copy, optimizing bids based on historical data. Useful, yes. Predictive in a meaningful sense? Not always.

ZeroToOne is pitching something different.

Its proprietary Large Behavioral Model (LBM) is designed to predict real-world human actions before they happen, not simply analyze what consumers did last week. The company claims its predictive audiences operate at 85%+ accuracy, refreshed daily and built to anticipate behaviors across categories like QSR, retail, travel, CPG, and hospitality.

Instead of optimizing after a campaign underperforms, the model aims to identify who is likely to convert, visit, or churn—before budgets are deployed.

That’s a meaningful distinction in an era when signal loss, privacy changes, and fragmented IDs have made historical targeting less reliable.

From Pilot to Platform Integration

The partnership follows a series of proofs of concept conducted through HorizonOS Labs, the agency’s innovation sandbox. According to the companies, those pilots delivered measurable gains in efficiency, visitation, and conversion—while reducing media waste.

Now, ZeroToOne’s predictive audiences will be integrated directly into Blu, embedding forward-looking decisioning into:

• Media planning

• Activation

• Audience suppression

• Measurement

By making predictive intelligence native to the workflow rather than a bolt-on data feed, Horizon is aiming to operationalize AI at the system level.

That matters. Agencies have long struggled with AI pilots that show promise but stall at scale. Embedding ZeroToOne’s outputs directly into HorizonOS lowers friction and increases the odds that predictive data actually influences buying decisions.

A Shift in Audience Strategy

The integration reinforces Horizon’s broader strategy: turning HorizonOS into an AI-native operating environment where partners plug into a shared intelligence layer.

In practical terms, that means predictive audiences become available across the agency’s client portfolio without requiring custom integrations for each brand.

For marketers, this could shift audience strategy from reactive optimization to proactive targeting. Rather than modeling lookalike segments based on past converters, brands can prioritize consumers likely to take specific real-world actions—visiting a store, ordering takeout, booking travel—within a defined timeframe.

That’s especially valuable in verticals where timing matters. In QSR and retail, for example, predictive modeling tied to short decision windows can materially impact foot traffic. In travel and hospitality, anticipating intent before booking searches spike could unlock earlier engagement.

Competitive Context: The Race to Predict

The move also reflects a broader industry pivot.

As third-party cookies fade and deterministic IDs become scarcer, agencies and ad tech platforms are investing heavily in probabilistic modeling and predictive analytics. Major holding companies have rolled out proprietary AI stacks, while platforms like Google and Meta push automated performance tools built on internal signals.

Horizon’s partnership with ZeroToOne suggests a desire to control predictive intelligence within its own ecosystem rather than rely exclusively on walled gardens.

If ZeroToOne’s accuracy claims hold up at scale, it could strengthen Horizon’s position as agencies compete on proprietary data and AI differentiation—not just media buying power.

What Comes Next

The collaboration isn’t stopping at audience deployment.

The companies say they are exploring deeper AI integrations, including enhancements to:

• Bid optimization

• Identity resolution

• Potential deployment of ZeroToOne’s modeling engine directly within HorizonOS

That last piece is particularly notable. Embedding the modeling engine itself—not just output segments—would signal a tighter coupling between predictive AI and execution mechanics.

For Horizon, the bet is clear: AI shouldn’t just accelerate workflows. It should reshape how decisions are made.

And in a market where efficiency pressures are rising and media waste is under scrutiny, acting ahead of consumer behavior may prove more valuable than simply analyzing it after the fact.

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