Creating better predictive models with the Faraday Identity Graph

As businesses race to integrate AI into their software, many turn to first-party data modeling techniques like RFM (recency, frequency, monetary value) to build predictive models. These models typically rely on user interactions, including website navigation, email engagement, and purchase behavior to anticipate action. But first-party models alone have significant limitations, lacking the depth, speed-to-deployment, and predictive accuracy that businesses need to get ahead in today’s competitive market.

Faraday’s models solve these problems by integrating rich third-party data from the Faraday Identity Graph (FIG), out of the box. The FIG enhances our models by integrating high-quality, consented offline insights into your first-party data, creating a more complete and actionable dataset. By enriching your existing data with verified external attributes—such as demographics, lifestyle indicators, and purchasing behaviors—the FIG fills in gaps, improves predictive accuracy, and speeds up deployment to help businesses reach the right audiences at the right time with the right product.

Why first-party data alone falls short

First-party data has its advantages, but it also has critical limitations that can hinder AI-driven personalization and prediction.

First-party data is information that businesses collect directly from their customers and audience through owned channels. This includes website activity, purchase history, and email engagement. Because it comes straight from the source, first-party data is highly accurate, privacy-compliant, and essential for personalized marketing and predictive modeling. However, it can be limited in scope, which is why many businesses enhance it with external data for deeper insights.

One major challenge to overcome with first-party data alone is the cold start problem. When a new customer visits your business, there is no past activity to analyze, making it impossible to generate useful predictions without supplemental third-party data. A first-party data-based model will most likely only improve over time as the customer interacts with the brand, but this delay can mean lost opportunities, particularly in industries where real-time data is crucial like financial services.

Even as first-party data accumulates, it often remains noisy and incomplete. Activity data is unpredictable, with various factors influencing when a user engages with your site or makes a purchase, meaning first-party models must quickly adjust to these fluctuations. External factors like age or income level are also essential for understanding customer behavior, but they’re typically not captured in first-party data. Additionally, first-party data only reflects activity, not affinity. For instance, knowing that someone purchased a pillow tells you little about their broader lifestyle, financial capacity, or long-term interests.

How Faraday enriches customers’ datasets to power our models

Our data enrichment process uses identity resolution to securely match first-party identifiers—such as email addresses or hashed customer IDs—with the FIG’s extensive dataset before they are fed into our models. This process ensures a seamless connection between your existing data and additional insights, following three key steps:

1. Secure matching – Faraday uses identity resolution techniques to match first-party data with offline records, ensuring privacy and compliance along the way.
2. Data enrichment – Once linked, Faraday appends high-quality demographic, behavioral, and lifestyle attributes to each record.
3. Model integration – These enriched profiles are seamlessly fed into our AI models, enabling more accurate predictions and personalized customer experiences.

Powering better AI with enriched data

Our models’ ability to access the Faraday Identity Graph (FIG) solves numerous problems experienced by first-party-only models. Here are some examples:

1. Solving the cold start problem

With the FIG, even first-time visitors are no longer unknowns. By matching first-party identifiers to offline, third-party data, Faraday provides insights into likely interests, purchasing power, and household composition, allowing our models to make informed predictions from the very first interaction.

2. Boosting model stability and accuracy

AI models trained solely on first-party data must constantly adapt to new patterns in an ever-changing environment. The FIG introduces stable, high-quality attributes, reducing reliance on transient, high-variance signals like click-through rates. This results in more durable models that perform consistently over time.

3. Enhancing AI with affinity data

Instead of just looking at past transactions, the FIG integrates data that reflects who customers are, not just what they’ve done. This includes household income, education level, homeownership status, and even media consumption habits.

The best of both worlds

Faraday’s FIG takes you data to the next level. By seamlessly integrating high-quality, consented third-party insights with your existing first-party information, Faraday empowers you to access more accurate and predictive models through our platform. With this rich array of demographic, lifestyle, and behavioral attributes, you can unlock deeper insights into your customers and make smarter, data-driven decisions. The Faraday Identity Graph goes beyond the limitations of first-party data alone, giving you a clearer, more holistic view of your audience.

Want to see how the FIG can supercharge your AI models? Let’s talk.