How an established marketing platform delivered AI-powered audiences across 1,000+ accounts

At a glance

• The core challenge: An established auto dealer marketing platform was building AI-powered audience features but had no visibility into who car buyers actually were — only what happened inside dealerships.
• The integration: Embedded Faraday's API across 1,000+ dealer accounts, provisioning a dedicated prediction environment for each dealer with custom propensity models trained on their own transaction history.
• The business impact: Audience generation now runs programmatically across every connected account with automated monthly retraining — no data science team or manual intervention required.

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Every platform today knows it needs better data and smarter AI tools. But the question is how to get it.

For newer companies, the answer is often baked in from day one — consumer intelligence built into the architecture before the first customer signed on. But for established platforms — ones that have been delivering real value for years, with hundreds or thousands of customers already depending on them — the path looks different. You can't rebuild from scratch. You need to add intelligence to what already works.

That tension is at the center of what Faraday calls the context gap: brands know how their customers behave inside their product, but they don't know who those customers actually are. Their financial picture. Their life stage. Whether they're likely to be in-market right now. That kind of consumer context doesn't live in a SaaS platform's database. It doesn't come from a CRM feed. And building the infrastructure to generate it — the data sourcing, the modeling, the ongoing maintenance — is simply off the table for most established teams.

That's exactly where a leading automotive software and marketing technology company found itself. Years of real customers, real trust, and real product depth — a system that worked. But as they built out their AI-powered audience features, they kept running into the same wall: their product knew everything about what happened inside a dealership, and almost nothing about the real people interested in buying cars — beyond their clicks and views.

Why do established SaaS platforms face a "Consumer context gap"?

The instinct for many platforms at this point is to go get the data themselves. But sourcing production-ready consumer data isn't a weekend project. It means negotiating with data vendors, normalizing and unifying records across sources, building the modeling infrastructure to turn raw data points into useful predictions, and then maintaining all of it on an ongoing basis as data ages and customers change. Most established platforms don't have a data science team sitting idle waiting for that project. And even if they did, it would take years to reach the quality and coverage that purpose-built consumer data infrastructure can deliver from day one.

Building it in-house wasn't the answer. Embedding it was.

The solution: Faraday as the embedded context layer

Rather than rebuilding from scratch, the platform embedded Faraday directly into its product architecture via API.

Faraday's role was to supply what the platform was missing: real consumer context. That means access to the Faraday Identity Graph (FIG), which contains 1,400+ data points on 240M+ U.S. adults — wealth indicators, life stage signals, behavioral data points, geographic context. But consumer profiles alone aren't enough to build smart audiences. What dealers actually need to know isn't just who their customers are — it's who among the thousands of consumers in their market is most likely to be ready to buy right now. So on top of that consumer context layer, Faraday builds custom predictive models trained on each dealership's own transaction history — turning static profiles into forward-looking propensity scores that power every audience the platform generates.

Critically, this wasn't a one-size-fits-all enrichment layer. The platform operates at the individual dealer level, and Faraday mirrors that structure. Using Faraday's multi-account API architecture, the platform provisions a dedicated prediction environment for each dealer — its own data connections, its own cohorts, its own trained outcomes. Audiences are built on models that understand what a buyer looks like for that dealer, in that market — not a generic consumer profile applied uniformly across accounts. And because franchise businesses can only sell to customers they can actually reach, each dealer's audiences are geofenced to their specific area, so targeting never spills outside the radius they can serve.

When an account's data updates — new transactions, new leads, new service visits — Faraday retrains. The consumer context layer stays current automatically, with no manual intervention required.

What changed

Before Faraday, the platform's AI audiences were built from the inside out: start with what the dealer knows, and try to find patterns. After Faraday, audiences are built from both directions — the dealer's first-party signals enriched and extended by real-world consumer context that no CRM can generate on its own.

For the platform, the operational effects were meaningful:

• Audience generation, which previously required manual rules-based configuration, now runs programmatically across every connected account
• Monthly data refreshes trigger automated model retraining — no human intervention required to keep predictions current
• Every audience the platform generates now reflects real consumer behavior — not just dealership CRM activity.

None of this required the platform to hire data scientists or build modeling infrastructure. Faraday handled that complexity behind the API.

Key results

• Audience generation automated across 1,000+ dealer accounts
• Monthly model retraining runs with no human intervention
• Every audience now reflects real consumer behavior, not just CRM activity

The bigger pattern: why established platforms are embedding consumer intelligence

This case reflects something happening across B2B software right now. Established platforms are under real pressure to make their AI claims real — not just in the product copy, but in the actual outputs. And the honest answer is that most AI features, left to first-party data alone, produce mediocre results. The data simply isn't there.

What Faraday offers platforms in this position isn't a dataset to license. It's a consumer intelligence infrastructure they can embed without rebuilding their product. The AI story stays theirs. The context that makes it credible comes from Faraday.

For a platform serving hundreds of accounts, each with their own customers and markets, that's the difference between AI as a feature name and AI as a real capability.

Ready to optimize your platform?

If you want to talk through how customer context and embedded AI could work for your platform, talk to a Context Consultant or explore our developer documentation to see how easily we could integrate into your existing stack. Or if you'd rather get started on your own, try it out yourself on buy.faraday.ai.