Imagine you’re launching a new kitchen product—a sleek, ergonomic veggie chopper. How could it not fly off the shelves? You know your current customers: suburban moms in their 30s and 40s. So you decide to build a lookalike audience of... suburban moms in their 30s and 40s.

Seems logical, right? 

Well no. The campaign falls flat. But why did this happen?

Demographics alone don’t tell the whole story. What you really needed was a way to reach people who actually enjoy cooking—people with an affinity for trying new recipes, watching food content, or buying cookware online—not just the ones who check some of the same basic boxes as those who do.

Traditional lookalike audiences tend to mirror superficial traits. Faraday’s lookalikes go deeper—focusing on behaviors, affinities, and actions that actually drive engagement.

## What is a lookalike audience?

But let’s take a step back to define some terms. In marketing, a [lookalike audience](https://en.wikipedia.org/wiki/Lookalike_audience) is a group of people who share characteristics with an existing audience—usually one that’s performed well in a campaign. The goal is to find more people likely to convert based on their similarity to your known winners.

Meta (you know, the artist formerly known as Facebook) popularized the term with their [Lookalike Audiences tool](https://www.facebook.com/business/help/164749007013531), which matches new users based on shared traits with a seed audience. As they define it:

> “A Lookalike Audience is a way to reach new people who are likely to be interested in your business because they share similar characteristics with your existing customers.”  
> — [Meta Business Help Center](https://www.facebook.com/business/help/164749007013531)

But what exactly counts as a “similar characteristic”? 

That’s where the quality of your data—and your modeling—really starts to matter, and that is exactly where Faraday can make a critical difference.

## Out with the old, in with the smart

The old way of building lookalikes is all about surface-level demographics: age, gender, location, interests. That works... until it doesn’t. It can lead to broad audiences that seem right but don’t perform, or hyper-narrow audiences that limit scale.

Like in our veggie chopper example: targeting suburban moms might seem like a safe bet—but it only works if those moms actually cook. Without behavioral context, you end up advertising to people who might look like your best customers but have no interest in what you’re selling.

Smart lookalikes—like the ones Faraday builds—go deeper. Instead of focusing only on who people are, we focus on what they do. That means modeling on behavior, not just fixed traits.

We might start with a seed audience of loyal cookware buyers, and then build a lookalike based on behavioral signals like:

* Affinity for cooking content  
* Purchase patterns across category types  
* Life events like moving to a new home (hello, new kitchen)  
* Household makeup (like number of people they cook for)

This kind of modeling doesn’t exclude demographic data—it uses it for context, not constraint. The result is smarter, scalable targeting that finds the people most likely to engage because of what they do, not just what boxes they check.

## Powered by the Faraday Identity Graph

Faraday’s smarter lookalikes are powered by the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) (FIG)—our massive, privacy-safe third-party data asset that covers over 300 million U.S. adults. But it’s not just about having a lot of data. It’s about the kind of data we have—and what we do with it.

Most traditional lookalike tools lean heavily on deterministic data: fixed traits like age, gender, or location, and basic interest categories. That can give you surface-level matches—but it misses the deeper signals that actually drive intent.

FIG goes beyond that by integrating thousands of behavioral, demographic, and psychographic attributes. That includes things like:

* How often someone shops online, and in which categories  
* What life events they may be experiencing (like moving, getting married, or having kids)  
* Media consumption patterns, brand affinities, and spending behaviors  
* Numerous other traits, [visit this page for a full list](https://faraday.ai/features/faraday-identity-graph)

Using advanced modeling, we analyze these signals to surface [*probabilistic*](https://www.collinsdictionary.com/us/dictionary/english/probabilistic) *traits*—things that aren’t explicitly declared, but strongly inferred from real-world behavior. For example, we don’t need someone to tell us they love cooking; their actions (recipe site visits, cookware purchases, grocery frequency) say it for them.

So instead of just identifying people who look like your best customers, we can find the ones most likely to engage in the same purchasing behaviors.

## Smarter lookalikes aren’t just for digital ads—they’re transforming offline outreach too.

This smarter approach to lookalikes isn’t just changing digital campaigns—it’s reshaping how brands think about offline channels like direct mail too.

In fact, one of our favorite direct mail partners has been seeing some massive benefits by integrating behavioral modeling into their systems to bring precision and scale to their print outreach campaigns. Traditionally, direct mail strategies have focused on small, high-intent segments—like cart abandoners or loyalty members. These audiences convert well, but they’re also inherently limited.

When it’s time to expand reach, brands often assume they’re stuck building generic lists: same ZIP code, similar age range, same general profile. But with Faraday, they can go far beyond demographics—finding new households that act like their best customers, even if they’ve never visited the site or engaged with the brand before.

That means:

* Reaching new, high-propensity households—not just the usual suspects  
* Scaling volume without sacrificing conversion rates  
* Delivering campaigns to people whose real-world behavior signals true interest

It’s a smarter way to tap into the strengths of direct mail—tangibility, trust, high response rates—without relying on guesswork or outdated list strategies.

Because let’s face it: even the most beautiful mailer won’t work if it ends up in the wrong hands.

If you’d like to learn more about how our predictive lead targeting works, check out this recent [customer story](https://faraday.ai/blog/cs-smarter-prospecting-at-scale) about our work with a different direct mail provider.

## Smarter audiences. Smarter campaigns.

Lookalikes shouldn’t just look the part—they should work. Faraday makes that happen with behavior-based modeling, best-in-class data, and predictions that get smarter over time.

Whether you’re mailing coupons for kitchen gadgets or marketing mortgages, the smartest lookalike is the one that converts.

So ready to see the difference? Reach out and say hi\!

Faraday builds smarter lookalike audiences by modeling real-world behaviors—not just demographics—so you can find the people most likely to convert, not just the ones who look the part.

Not your average lookalike: How Faraday makes finding your next best customers easier

You know your customers—but do you know how to quantify them? Have you ever considered that something as seemingly random as **"roof type"** or **"interest in boating"** might influence whether someone wants to buy your product? The reality is, we’re all made up of a range of traits, and we don’t always know which ones will most impact purchasing behaviors.

That’s why the [Faraday Identity Graph, a gigantic set of 1,500+ customer attributes](https://faraday.ai/features/faraday-identity-graph) is so valuable. It captures both the obvious and the unexpected, giving businesses an edge in understanding what truly drives conversions. Still not convinced? Let’s take a look at some of the weirdest, most surprising traits that can help businesses make smarter decisions.

*Now, please note that these are hypothetical examples—after all, we wouldn’t want to disclose any of our current customers’ “secret sauce.” However, these vignettes illustrate the unexpected ways our FIG has informed value-driving outcomes.*

## Vignettes: expected problems with unexpected solutions

### Predicting insurance customers who need premium coverage

* **Challenge:** An insurance provider wants to upsell premium policies to the right customers but relying solely on income data hasn't been cutting it.

* **Unexpected solution:** "Spending habits percentile" — Individuals who consistently spend at higher levels tend to value protection for their assets and financial well-being. By identifying high-spending individuals, the insurer can target them with personalized offers for premium life, home, and auto insurance policies, ensuring they have the right coverage for their lifestyle.

### Finding the best locations for a new coffee chain

* **Challenge:** A growing coffee brand needs to find locations with high morning foot traffic.

* **Unexpected solution:** "Propensity to own a dog" — Dog owners tend to follow a routine and take their pets for walks in the morning, making them prime candidates for coffee shop visits. By targeting neighborhoods with a high concentration of dog owners, the coffee brand can identify areas with a ready customer base for morning foot traffic.

### Predicting high-end jewelry buyers

* **Challenge:** A trendy jeweler is aiming to branch into new markets. They know some indicators like income level and location are relevant, but what else might help?

* **Unexpected solution:** "Garage parking spaces" — People who have garage parking are often homeowners with higher incomes, typically living in more affluent neighborhoods. This demographic is more likely to invest in luxury goods, including high-end jewelry. By targeting individuals with garage parking, jewelry brands can identify customers who are more inclined to make premium jewelry purchases, such as fine watches, diamond rings, or luxury accessories.

### Finding the right audience for debt consolidation services

* **Challenge:** A debt consolidation provider wants to reach consumers who are likely to need their services but may not be actively searching for help.

* **Unexpected solution:** "Catalog Showroom credit card usage" — People who frequently use catalog showroom credit cards may have higher levels of revolving debt, often at steep interest rates. By identifying these consumers, the provider can target individuals who are likely to benefit from consolidating their debt into a lower-interest option, offering relief before they fall into deeper financial strain.

### Identifying high-value clients for a financial services firm

* **Challenge:** A financial services company wants to identify potential high-value clients for premium investment products.

* **Unexpected solution:** "Vehicle category \- luxury cars" — People who own luxury cars tend to have higher disposable income and an interest in premium goods and services. By identifying this segment, the firm can target prospects who are more likely to invest in high-end financial products, such as wealth management services, private banking, or exclusive investment opportunities.

## Why AI sees what humans miss

Does all this sound a little far-fetched? Well, we don’t mean to offend you, but that might be because you’re a lowly human. The whole reason predictive AI agents are so valuable is their ability to recognize patterns we might never think to look for.

We rely on AI because it takes care of the busy work—handling repetitive tasks far more efficiently than we ever could—but more importantly, AI spots the connections we don’t. If 90% of people with Dutch gabled roofs buy BMWs, our models will detect that pattern, even if no human ever thought to check.

Faraday’s FIG does exactly this across 1,500+ attributes, helping businesses uncover the surprising, data-backed insights that fuel real growth.

## Breaking FIG down

Faraday’s Identity Graph (FIG) is what makes all of these insights possible. By synthesizing these unique attributes across hundreds of millions of U.S. adults, FIG gives businesses an unparalleled ability to understand and predict customer behavior.

But let’s be honest—1,500 is a lot of data points to consider. To help make sense of it all, we’ve broken FIG down in the following graphic to show the scale of the 1,500+ attributes and also breaking them out by category. A more detailed listing of all FIG attributes can be found and downloaded as CSV [here](https://faraday.ai/features/faraday-identity-graph).

![](/images/blog/fig-visual-diagram.png)

FIG doesn’t just provide more data—it provides better, structured, and predictive-ready data that businesses can actually use to identify new connections that they'd likely never uncover on their own.

If you’d like to learn more about this powerful resource, take a look at the other blogs in this series\!

## Closing thought

Sometimes, the smallest, most unexpected data points unlock the biggest opportunities. FIG delivers both the obvious and the hidden predictors—helping businesses make smarter, data-driven decisions.

Want to see how these insights can transform your business? [Let’s talk](https://magic-pr-5668-faraday-ai.vercel.app/talk-to-sales)\!

Faraday's Identity Graph (FIG) includes unexpected customer traits—like "roof type" or "interest in boating"—and detects patterns amongst them that humans might miss, helping businesses make smarter, data-driven decisions.

The unexpected data that powers smarter predictions

You know how valuable predictive analytics could be to your business, but you haven’t signed up yet. So, what’s holding you back?

For many businesses, and especially those in notably security-conscious industries like healthcare and finance, data security is the biggest concern. You want the insights that AI and predictive analytics can provide, but you need to know your customer data is safe. At Faraday, we understand that concern. That's why we’ve built our entire platform around safeguarding your sensitive data, from collection to analysis.

In this blog, we’ll walk you through the steps we take to ensure your data remains secure, private, and ethically handled, so you can trust us with the insights that power your business growth. But first, we want to talk about our Faraday Identity Graph and why we take our security so seriously.

### The Faraday Identity Graph, and why we take privacy so seriously

The [Faraday Identity Graph (FIG)](https://faraday.ai/docs/abstractions/traits) is at the core of everything we do. It’s a powerful tool that [allows us to generate accurate, actionable insights from U.S. consumer data](https://faraday.ai/blog/how-faraday-enriches-first-party-data-with-fig), helping brands understand their customers at a deeper level (we also sell this data with [data appends](https://faraday.ai/blog/data-appends-blog)). But with great power comes great responsibility. We recognize that the data we work with is highly sensitive, and we take every precaution to handle it with the utmost care.

FIG isn’t only concerned with unlocking insights; it’s also designed with privacy as a top priority. [From the very beginning, we’ve built it with robust security features to ensure that consumer data is kept safe and secure.](https://faraday.ai/security) Whether it’s protecting personal information or ensuring compliance with privacy laws, our mission is to uphold the trust you place in us by following the strictest data handling practices.

So let’s start at the top and take a look at some of the foundational security practices we follow to safeguard your data.

### SOC 2 compliance

[SOC 2 (System and Organization Controls 2\)](https://secureframe.com/hub/soc-2/what-is-soc-2) is a security framework designed to ensure that organizations protect customer data from unauthorized access and security incidents, and is often viewed as the gold standard for this purpose. It evaluates five key areas: security, availability, processing integrity, confidentiality, and privacy.

At Faraday our compliance with SOC 2 compliance standards means we undergo rigorous audits of our security practices every year, with an independent third party verifying that our data protection measures meet the highest industry standards.

Our security policy is built around important principles like the "principle of least privilege" (where users only have access to the data they absolutely need), prevention of supply chain attacks, and strict access controls. We also participate in  [SOC 2 Type II](https://secureframe.com/hub/soc-2/type-1-vs-type-2) audits, which not only check that security controls are in place but also assess their effectiveness over time. This ongoing process ensures that we continuously meet top-tier security protocols and maintain a secure environment for your data.

### Compliance with privacy regulations

To protect your consumer data, we comply with key privacy regulations, including [HIPAA](https://www.cdc.gov/phlp/php/resources/health-insurance-portability-and-accountability-act-of-1996-hipaa.html), [CCPA](https://oag.ca.gov/privacy/ccpa), and [GDPR](https://gdpr-info.eu/), to ensure your data is secure and handled responsibly.

For CCPA and other state privacy laws, we offer an easy opt-out system, allowing individuals to remove themselves from our datasets. What’s more, we extend this opt-out through a reciprocal process to our vendors, so when either Faraday or a vendor receives an opt-out request, the data is removed from both systems—ensuring full compliance across our ecosystem.

As for GDPR, since our Faraday Identity Graph focuses on U.S. data, we proactively filter out non-U.S. data at the "perimeter," ensuring we only process data we are authorized to use.

When handling Client data, Faraday operates as a Data Processor/Service Provider, meaning we will only use client data for the specific contracted purpose and will support our client’s own compliance with state data privacy laws. 

### Data vendor standards: permissioned and ethically distributed 

Ensuring responsible data handling goes beyond secure storage and processing—it also means sourcing data from reputable vendors. At Faraday, we take extra care in selecting the data providers we work with.

We only collaborate with vendors who explicitly affirm in their terms of service that their data is properly permissioned, legally obtained, and authorized for use and distribution. We are committed to maintaining the highest standards of ethical data usage, ensuring that every piece of data we use aligns with the proper permissions and legal requirements so you can confidently leverage accurate, responsible insights.

### Addressing bias in consumer data

As a data provider, Faraday acknowledges the reality that consumer data is inherently shaped by [historical and systemic biases](https://developers.google.com/machine-learning/crash-course/fairness/types-of-bias). We acknowledge these biases but take active steps to mitigate their impact on our models and predictions.

For example, mortgage lending and credit scoring have historically exhibited bias, with certain demographic groups, particularly minorities and women, facing higher rejection rates or higher interest rates due to systemic biases embedded in financial models. These models often relied on historical data that reflected past discriminatory lending practices rather than objective risk. 

To avoid perpetuating these systemic biases, Faraday takes a range of actions. For instance, we normalize data to reduce geographic disparities and offer bias detection and mitigation strategies to address disparities along sensitive dimensions such as age and gender. 

Responsible AI and ethical data practices are at the forefront of our methodology, ensuring that our models and insights promote fairness and inclusivity.

### Secure data handling practices

Faraday accepts data exclusively through secure channels—no emailed files or unprotected transfers. We also support hashed data ingestion and egress, allowing brands to share encrypted email addresses and phone numbers for added security. While hashing is not a foolproof privacy solution, it remains a valuable safeguard, and we ensure it is an option for those who prioritize additional data protection.

### Customer data is never shared between clients

One of the most common questions we receive is whether we share customer data across different client accounts. The answer is unequivocally no. Our terms and data processing agreement (DPA) explicitly state that client data is used solely for delivering services to that client, and it is never shared, pooled, or combined with other clients' data in any way. We believe that maintaining clear, stringent data boundaries is essential for protecting client trust and upholding our ethical standards.

### Conclusion: a commitment to ethical data handling and security

At Faraday, we don’t just meet industry standards—we set them. From rigorous security audits to responsible data sourcing and compliance with privacy regulations, we prioritize the safety, privacy, and ethical handling of your data at every step. Our commitment to transparency and secure practices ensures that your customer data is protected, and our proactive efforts to address biases help foster fairness in every insight we generate.

By choosing Faraday, you’re not just getting advanced predictive analytics—you’re gaining a trusted partner dedicated to upholding the highest standards of data security and privacy. We believe that responsible data use is the foundation of meaningful insights and lasting business success.

If you have any questions about our data security policies or compliance measures, don’t hesitate to reach out. We're here to help you navigate the complexities of data protection, so you can focus on what truly matters—growing your business with confidence.

Safe and responsible handling of data is a primary priority at Faraday, but what does that really mean? In this blog, we'll unpack some of the ways we ensure your data remains secure, private, and ethically managed throughout our platform.

How Faraday ensures secure and responsible customer data handling

In today's data-driven world, businesses can amass a wealth of first-party information, but this presents only a partial picture of their current and potential customers. Gaps in their data can limit the effectiveness of their marketing strategies, market research, and predictive models. That’s where Faraday’s data appends come in. By enhancing their existing datasets with high-quality third-party attributes, brands can unlock deeper insights to make smarter, more informed decisions.

## What are data appends, and why do they matter?

Data appending is a process by which businesses can purchase and integrate external data into their own datasets to supplement their first-party information. These attributes might include demographic information, behavioral patterns, or even lifestyle indicators, data points that are generally not available through first-party channels alone. By enriching your data with these additional insights, you can gain a fuller picture of your customers and their behaviors. 

But Faraday’s data appends go beyond simple enrichment. Our data is rigorously vetted, privacy-compliant, and built for seamless integration into your existing workflows. Whether refining market research, improving segmentation, or even powering your own predictive models, these appends help businesses make smarter, data-driven decisions. With Faraday, you’re not just adding data—you’re adding intelligence.

## How businesses use data appends

Data appends address various business needs. Here are some common ways companies leverage them:

### Exploratory research

Understanding your market often requires more than just your existing data. Take, for example, a roofing company. They might want to analyze roof sizes across different regions to gauge demand. Instead of sourcing this data themselves (which would be slow and expensive) or purchasing a generic dataset that requires extensive processing, they can use Faraday to buy and append only the relevant geographic and property data, seamlessly. This allows them to conduct targeted research efficiently and make informed decisions—like leveraging predictive targeting with Faraday next.  

### Supplementing existing datasets

Businesses often lack key customer details. Take a retailer that has always relied on email marketing. They would have extensive email lists but likely never collected addresses or phone numbers. If they want to expand into direct mail or call center outreach, they’ll need that missing data. This is where Faraday’s data appends come in, providing the necessary information without the hassle, time commitment, or high costs of sourcing it themselves. 

### Building custom predictive models

 An advanced use case for our data appends is supporting businesses that prefer to develop their own machine learning models. Just as we use this data at Faraday, these companies leverage our high-quality, cleaned, and structured attributes to enhance their predictive outcomes. In this case, Faraday acts as a trusted data provider, delivering the precise insights needed to model a variety of customer attributes—such as likelihood to churn, lifetime value, or purchasing behavior—ready to be seamlessly integrated into customers’ [Python](https://www.python.org/) environments.

## How Faraday’s data appends work

The Faraday Identity Graph (FIG) is the backbone of our data appending services. This robust dataset combines multiple, meticulously cleansed, and feature-engineered data sources into one comprehensive dataset. [With over 1,500 data attributes covering nearly all U.S. adult consumers and their households](https://app.faraday.ai/traits), it offers a deep well of insights into things like demographic trends, property ownership, life events, and financial indicators, just to name a few.

The process to access this data as appends is simple and streamlined:

1. **Select attributes**: Choose the data attributes that align with your business goals (e.g., income levels, household size, location-specific behaviors).  
2. **Match & append**: Faraday will match these attributes to your existing customer records, providing a more complete profile of your audience.  
3. **Integration**: The enriched data is delivered in a clean, structured format that’s easy to integrate with your models and business processes.

[![Data-appends diagram](/images/blog/data-appends-diagram.png)](/images/blog/data-appends-diagram.png)

## Why not just find the data yourself?

Some companies attempt to enhance their marketing efforts by purchasing third-party data directly from various vendors themselves, rather than using packaged and processed services like Faraday’s data appends. However, this approach often presents significant challenges:

**Contracting complexity**: Sourcing offline data can involve lengthy negotiations and large upfront commitments.

**Security risks**: Handling sensitive data requires strict compliance frameworks, such as SOC-2 certification. Unlike many vendors, all Faraday data appends are privacy-compliant, ethically sourced, and adhere to high standards of data protection, ensuring your customers' personal information remains secure while providing actionable insights.

**Regulatory challenges**: Companies storing offline data must navigate consumer privacy laws, including opt-out and deletion requests. Faraday manages these complexities for you, so you can focus on what matters—leveraging new insights to drive revenue.

**Data science overhead**: Raw offline data often requires significant processing, feature selection, and bias mitigation before it can be effectively used. This can result in costly time and resource investments as companies try to integrate third-party data into their models.

Faraday eliminates these challenges by providing ready-to-integrate data that is pre-processed and structured for immediate use. Our data appends come with built-in privacy compliance, so you don’t have to worry about regulatory hurdles or the risk of handling sensitive information. Additionally, the data is feature-engineered and bias-mitigated, saving you the time and resources typically needed to prepare raw data. With Faraday, you can access high-quality, actionable insights without the complexities and overhead associated with managing third-party data directly. This allows your team to focus on refining your marketing strategies and driving better business outcomes.

## How to get started

Getting started with Faraday’s data appends is simple. Once you’ve identified the attributes you need, we’ll work with you to integrate them into your existing systems. Whether you’re looking for a one-time data boost or ongoing data enrichment, our flexible pricing and easy integration make it simple to scale.

To learn more or request a demo, **contact us today**.

Faraday’s data appends enrich your existing customer data with third-party attributes, helping you fill gaps, refine targeting, and make smarter decisions.

Leveraging the power of the Faraday Identity Graph through Data Appends

As businesses race to integrate AI into their software, many turn to first-party data modeling techniques like [RFM (recency, frequency, monetary value)](https://en.wikipedia.org/wiki/RFM_\(market_research\)) 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)](https://faraday.ai/blog/unlocking-the-power-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](https://www.sciencedirect.com/topics/computer-science/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](https://faraday.ai/blog/why-first-party-data-and-rfm-is-not-enough). 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. 

[![Data-appends diagram](/images/blog/first-party-merged-w-fig.png)](/images/blog/first-party-merged-w-fig.png)


## 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.**

The Faraday Identity Graph (FIG) allows users to create more accurate, stable, and predictive AI models within our platform with deeper insights into customer behavior and preferences.

Creating better predictive models with the Faraday Identity Graph

In the world of predictive analytics, data is everything. The more accurate, comprehensive, and up-to-date your data is, the better your models perform. That’s where the [**Faraday Identity Graph (FIG)**](https://faraday.ai/docs/abstractions/traits) comes in—a massive, carefully curated dataset designed to help businesses make smarter decisions with ease.

## What is the Faraday Identity Graph?

The Faraday Identity Graph is a powerful combination of multiple data sources, meticulously cleansed, de-duplicated, and feature-engineered to create one comprehensive dataset. It consists of [1,500 data attributes](https://faraday.ai/features/faraday-identity-graph) covering virtually all U.S. adult consumers and their households, offering a deep well of insights into demographic trends, property ownership, life events, financial indicators, and more.

And what’s even better is that it comes built-in with our services.

## So where does the data come from?

We partner with **best-in-breed data providers** like [Epsilon](https://www.epsilon.com/us) to compile a rich dataset that encompasses:

* **Demographic information** (age, education, income, etc.)  
* **Property details** (homeownership, property value, etc.)  
* **Life events** (moving, marriage, new child, etc.)  
* **Financial indicators** (credit attributes, equity, etc.)  
* **Buying patterns** (how they spend across dozens of categories)  
* **Shopping behavior** (e.g. in-store vs online)

By combining multiple premium data sources, we eliminate the need for businesses to juggle multiple vendors—saving them time, effort, and money.

## How frequently is the data updated?

The Faraday Identity Graph is updated regularly throughout the year as we receive new data from our vendors. The frequency of updates varies based on data type, but customers can rest assured that the data contained in our graph is as up to date and relevant as possible to ensure the best predictive outcomes.

## Can I buy the data directly?

Yes\! We offer direct data licensing, allowing businesses to purchase cleansed, consolidated, and competitively priced data. If you're looking for a variety of data sources, without the hassle of amalgamation and the time and expense of working with a range of vendors, we should talk—it’s likely we can save you all that time and money by bundling everything you need into one seamless package, and since we already made that package, you could be accessing that data tomorrow\!

## How can businesses use this data?

Our clients use the Faraday Identity Graph in a variety of ways but here are two core use cases:

* **Data appends:** Businesses can purchase specific data attributes to enhance their own internal models and processes.  
* **AI agents:** We can combine our data with your first-party data to build agents that answer key questions, such as:  
  * **Where are your best customers?** Read about our [market opportunity analysis service](https://faraday.ai/docs/abstractions/MOA).  
  * **Who is most likely to buy?** Check out our [lead scoring quick start guide](https://faraday.ai/docs/quickstarts/leads/lead-scoring).  
  * **What are they most likely to purchase?** Dive into our [product recommender](https://faraday.ai/docs/abstractions/recommenders) system, which predicts the best products or services for each individual.

Whether you're looking for raw data or full-service predictive modeling, Faraday makes it easy to harness the power of data-driven decision-making.

## The Faraday advantage

What makes this data even more valuable is access to our team of forward-deployed data science experts. Our team works alongside you to ensure that you are accessing not just the highest quality data, but the right data for your specific needs. With the addition of this service, whether you're looking for raw data or full-service predictive modeling, Faraday makes it easy to harness the power of data-driven decision-making. 

Additionally, Faraday delivers a broad suite of other services like real-time scoring, giving you instant access to dynamic, data-driven insights that enable rapid decision-making and model explainability, ensuring you can trace every prediction back to its data-driven roots—this transparency enhances model trust and guides smarter adjustments. 

What's more, with immediate access to this rich data from day one, you can avoid the cold start problem, quickly deploying predictive models without sacrificing accuracy. Lastly, Faraday’s flexible API integration allows you to seamlessly incorporate our data into your existing systems, providing a scalable solution that evolves alongside your needs and analytics workflows.

## Questions?

Sounds pretty great right? We think so too. If you want to learn more, reach out to talk to our sales reps or even our data team to learn how Faraday’s data can push your business to the next level\!

The Faraday Identity Graph (FIG) is a massive, meticulously curated dataset of 1,500 attributes covering virtually all U.S. adults, enabling businesses to enhance their models, streamline data sourcing, and make smarter decisions with ease.

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Unlocking the power of the Faraday Identity Graph (FIG)