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 datapoints. 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 datapoints 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 datapoints.

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 attributes 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 datapoints. 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 datapoints, [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) _datapoints_—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 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)](/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](/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](/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 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

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 datapoints—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 datapoints 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 datapoints, 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 datapoints, 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.

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

How Faraday ensures secure and responsible customer data handling

Creating better predictive models with the Faraday Identity Graph