From data to prediction: How Faraday works under the hood

When brands partner with Faraday, they aren’t just looking for a score; they need to understand the mechanics behind that score. A common question we receive is: "How exactly is the system acting upon our data?"

While the underlying math is complex, the process is straightforward: identity resolution and enrichment, model training and validation, and deployment. Here's how each step works.

Identity resolution and enrichment

The process begins when your first-party data, whether it’s historical orders, lead lists, or churn records,is securely ingested into the Faraday system. But we don’t just store these rows; we resolve them.

We match your PII identifiers (emails, phone numbers, addresses) against the Faraday Identity Graph (FIG), a proprietary consumer dataset that contains over 1,500 attributes on more than 240 million U.S. adults and their households. This step is critical because your raw data is often fragmented or incomplete. By resolving identity first, we link your scattered records to rich, privacy-safe consumer profiles. This ensures our models are analyzing a complete person rather than a disconnected data point such as their address.

Once identity is resolved, the system enriches your data with relevant attributes from FIG, ranging from demographics to lifestyle interests. This enriched dataset becomes the foundation for modeling.

Model training and validation

Rather than relying on manual guesswork or generative AI such as ChatGPT, our models identify complex patterns in behavior that a human analyst might miss.

We first create a training set that includes your examples (customers who converted) and counter-examples (those who didn't). We then test various algorithms, such as Random Forests and Logistic Regression, on this enriched dataset. Each algorithm evaluates thousands of potential attribute combinations to find which approach best fits your data and business goals. We often combine multiple techniques to improve performance.

For instance, the model might discover that home ownership combined with recent purchase frequency and regional trends strongly predicts conversion for your business. This automated testing finds hidden signals that may take a human analyst months to find.

Before deployment, we validate the model on fresh data (holdout) to confirm the patterns hold up using methods, such as the 5-fold-cross-validation, to ensure model performance.

Deployment and transparency

Finally, the validated model generates actionable outputs that answer your business questions:, “Who is most likely to convert,” and “Which products will be most appealing to them.” The system then assigns a Propensity Score (percentiles from 0–100) to each subject for which one has been requested.

This process is transparent by design. Propensity scores are pushed directly back alongside explainability reports that details the factors driving each prediction, and how valid the predictions are. We don’t just deliver the prediction; we detail exactly why the prediction was made, giving your team full visibility into the model’s decision-making

Conclusion

In short, Faraday works by turning incomplete customer records into enriched profiles, then trains validated models that predict behavior. By combining secure identity resolution with the latest in machine learning, we ensure that the insights you get are not only accurate but fully explainable and ready to drive revenue.