“So what do we actually get from Faraday? Is there some kind of Excel file I can open, with all the data points laid out in plain English?”  \- Your CEO in your 1 on 1\.

Sound familiar? In this post we want to help you communicate to your leadership what they can expect the Faraday system to provide them, enabling the marketing and product workflows of their dreams.  

Faraday is often used to optimize outreach (through use cases like [lead scoring](https://faraday.ai/templates/lead-rejection) and [personalization](https://faraday.ai/templates/thematic-personalization)), helping teams identify which people are most likely to take meaningful action and tailor their communication to them. But the output you get includes much more than just a score. It’s a full snapshot of each person or household in your audience: scores, traits, behavioral segments, and cohort tags.

This post breaks down exactly what you'll find in a Faraday [deployment](https://faraday.ai/docs/abstractions/deployments), and how to use each piece to drive smarter strategy and communication across the funnel.

## Outcome scores: the two numbers that tell the story

This section refers specifically to [outcome models](https://faraday.ai/docs/abstractions/outcomes)—predictions like “which of my leads are most likely to purchase,” “who will respond to outreach,” or “who’s at risk of churn.” These are the most common prediction types in Faraday, and they return two scores per person.

Every outcome prediction in Faraday includes:

* [Propensity percentile](https://faraday.ai/blog/propensity-vs-probability-predictions): how this person ranks compared to everyone else in your prediction population. For example, a 95th percentile means they’re more likely to convert than 95% of people in the group.  
* [Propensity probability](https://faraday.ai/blog/propensity-vs-probability-predictions): the actual chance this person will take the action you care about. For instance, a 0.72 means a 72% likelihood of conversion.

If the percentile helps you prioritize, the probability helps you forecast. Together, they give you both a ranked list and a sense of real-world likelihood.

In this post, we’re using a CSV file as the example format for clarity, but keep in mind, the structure and content of a Faraday deployment is the same no matter how you receive it—whether via data warehouse, API, or direct integrations. 

But regardless of where you get this information, we know that all this information can feel a bit overwhelming at first. While our support team is always here to help you make sense of your results, we’ve broken things down below to give you a head start.

Below is an example of an output that you could expect to see when you request a probability or propensity outcome. These returns can look a little intimidating if you haven’t read one before so lets break down what you’re looking at.

[![Sample CSV output showing outcome score](/images/blog/how-to-interpret-outputs/prob-vs-prop-returncsv.png)](/images/blog/how-to-interpret-outputs/prob-vs-prop-returncsv.png)

A much more detailed breakdown of propensity vs. probability is explained in our previous blog, [Propensity vs probability: Understanding the difference between raw scores and probabilities](https://faraday.ai/blog/propensity-vs-probability-predictions), written by one of our incredible data scientists, [Thibault Dody](https://www.linkedin.com/in/thibault-dody/). If you'd like a more detailed explanation of how these scores are calculated and when to use them, check that one out\!

## Recommender outputs: ranked predictions across many options

While outcome models predict whether someone will take a specific action, [recommender models](https://faraday.ai/docs/abstractions/recommenders) answer a different kind of question: “Which of these options is best for this person?” Rather than producing percentiles or probabilities, they focus on relative ranking, helping you surface the best match for each person from a set of options.

For example, a recommender might tell you which product a customer is most likely to purchase, or which offer will resonate most. Instead of returning a single score, these models return a ranked list of options for each person, based on predicted likelihood.

In a typical recommender output, you’ll see:

* A **list of recommendations** (e.g., products, offers, messages)  
* A **rank** indicating preference order  
* A **relevance score** (optional), showing how strong the model’s confidence is

You can use these outputs to power personalized product recommendations in email or on your website, route individuals into different messaging tracks based on their attributes or behavior, and dynamically customize the content or offers they see to create a more relevant and engaging experience. This kind of real-time personalization can significantly boost conversions and improve lifetime value\!

Here, we get to see an example of a recommender model return:

[![Sample CSV output showing recommenders](/images/blog/how-to-interpret-outputs/rec-returncsv.png)](/images/blog/how-to-interpret-outputs/rec-returncsv.png)

## Personas: behavioral context you can act on

Faraday [personas](https://faraday.ai/docs/abstractions/persona_sets) are behavioral segments created by clustering your customers based on hundreds of data points. They help you go beyond just knowing who’s likely to act—you get a sense of why they might act, and how to engage them. We build these personas using a k-means clustering process, which groups similar individuals together based on shared traits and behaviors, you can learn more about how it works [here](https://faraday.ai/blog/k-means-clustering-customer-personas).

You can think of a persona as archetypes. An “Eco Enthusiast” might respond well to sustainability-focused messaging, while a “Status Seeker” may prefer premium positioning.

Here’s how you might expect a persona return to look.

[![Sample CSV output showing personas](/images/blog/how-to-interpret-outputs/persona-returncsv.png)](/images/blog/how-to-interpret-outputs/persona-returncsv.png)

Personas also help you:

* **Tailor messaging and offers** to match motivations  
* **Pick the right channels**, whether that’s social, email, or direct mail  
* **Inform product and UX decisions** based on lifestyle or priorities (e.g. “Busy Parents” value speed and simplicity)

Want to go deeper? Our CEO, [Andy](https://www.linkedin.com/in/rossmeissl/), just wrote a whole [blog](https://faraday.ai/blog/when-and-why-to-update-personas) about how personas work, and why they’re the foundation of various other predictive tools.

## Traits: the signals behind the scenes

[Traits](https://faraday.ai/docs/abstractions/traits) are the demographic, behavioral, and lifestyle characteristics that describe each individual in your prediction results—attributes like age range, income bracket, homeownership status, household size, and more. At Faraday, these traits are powered by our proprietary [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) (FIG)—a comprehensive, privacy-conscious dataset that includes rich, consent-based attributes on over 240 million U.S. adults and their households. FIG combines data from hundreds of public and commercial sources into a unified identity framework, allowing us to surface actionable insights without requiring you to collect or manage sensitive personal data yourself.

[![Sample CSV output showing traits](/images/blog/how-to-interpret-outputs/trait-returncsv.png)](/images/blog/how-to-interpret-outputs/trait-returncsv.png)

These are the features the model uses to make its predictions. They also help you:

* Filter or sort your results  
* Build secondary audiences  
* Spot unusual patterns (e.g., "why are so many top scorers renters?")

In short, traits let you peek under the hood. They can explain why someone scored highly—or give you clues about where to dig deeper. 

If you’d like to dig a little deeper into the 1500+ rich consumer traits included by default with every Faraday  agent, take a look [here](https://faraday.ai/features/faraday-identity-graph)\!

## Cohort membership: groups you define

Faraday also returns [cohort](http://i) indicators that show whether someone belongs to one of the custom segments you’ve defined—like "Past purchasers," "Subscribers," or "Churn risks."

You can also create cohorts using the data already available within Faraday. For example, you could define a cohort for men between the ages of 21–45 who live in California, Utah, or Nevada. Once created, each person in your population is tagged with a simple true/false value—do they match this cohort or not? That makes these cohorts incredibly useful as [boolean filters](https://www.scribbr.com/working-with-sources/boolean-operators/): you can use them to include or exclude groups from predictions, targeting, or reporting.

Cohort membership helps you:

* Exclude irrelevant groups (like existing customers)  
* Compare segments side-by-side  
* Layer in your own business logic  
* Understand whether a person fits within an important audience segment

If that sounds similar to Personas, there are some critical differences worth calling out:

* **Personas are discovered.** Faraday generates them by identifying patterns in behavior and demographics across your customer base.  
* **Cohorts are defined.** You create them based on your business rules and goals—like engagement history, purchase frequency, or lifecycle stage.

In other words, Personas reveal who people are; cohorts reflect what they’ve done, who they are, or how your business thinks about them.

## Prediction explanations: why someone scored the way they did

Sometimes, knowing what someone scored isn’t enough—[you want to understand why](https://faraday.ai/blog/what-is-explainable-ai-why-is-it-important). That’s where prediction explanations come in.

When enabled, explanations provide a breakdown of which traits had the biggest impact on a person’s prediction. You might see that homeownership, household size, or a specific behavioral indicator pushed a score up or down. 

[![Sample CSV output showing explainability](/images/blog/how-to-interpret-outputs/explainability-returncsv.png)](/images/blog/how-to-interpret-outputs/explainability-returncsv.png)

These explanations can help your team:

* Sense-check the model's logic  
* Find patterns across high or low scorers  
* Build trust and transparency into outreach decisions

In short, explanations turn the model from a black box into something you can interrogate and understand. For more detail on how to interpret this output, see the documentation on [deployments](https://faraday.ai/docs/abstractions/deployments#understanding-score-explainability). 

## Putting it all together

A raw score can only tell you so much.

Let’s say you know Fred is **40% likely to convert**. That’s useful. But it’s limited. Without context, you don’t know who Fred is, what kind of message might resonate with him, or whether he even fits your audience strategy.

Now imagine you have a full output:

* Fred is in the **40th percentile**, which means he ranks higher than 40% of your prediction population—and he has a **0.40 probability** of converting.  
* He belongs to your **Newsletter Signups** cohort  
* He’s assigned to the **"Value Shoppers"** persona  
* He lives in a **three-person household** earning **$50-75k**

Suddenly, you’re not just guessing—you’re seeing the full picture. Maybe he’s not your top priority, but his persona suggests he’s responsive to discounts, and he’s already shown interest by signing up for your emails. That’s enough to make a targeted, budget-conscious offer worth testing.

Understanding your outputs turns a single prediction into a story. And stories are what make strategy work. Want to see a real example? 

## In conclusion

Prediction results from Faraday aren’t meant to sit in a spreadsheet. They’re designed to drive action: better segmentation, sharper messaging, and smarter growth. When you understand what each piece means—scores, personas, traits, cohorts—you can put the full power of predictive AI to work.

Want help interpreting your outputs? Reach out to our team or explore more in the Faraday docs.

This guide walks through every component of a Faraday deployment—scores, personas, traits, cohorts, and more—so you can turn raw predictive outputs into strategic, actionable insights.

What’s in a deployment? How to read your Faraday outputs

We recently shared a blog about our new [AI-optimized Every Door Direct Mail (EDDM)](https://faraday.ai/blog/AI-optimized-EDDM) offering, which is a cost-effective way to reach a wide audience. As acquisition costs rise and ad targeting regulations tighten—especially in finance, energy, and healthcare—marketers need options that balance scale, precision, and compliance. One of those options is Saturation Mailing. But direct mail isn’t about finding one universal approach and tripling down. It’s about building a flexible toolkit and adapting your strategy based on your audience, geography, budget, and goals.

That’s where [Saturation Mailing](https://www.growmail.com/blog/eddm-vs-usps-saturation-mailing/) comes in. It’s another powerful tool in the direct mail playbook—especially when you’re trying to reach as many qualified households as possible, while still maintaining control and strategic nuance. Let’s dig into how it works and why we think it deserves a spot in your toolkit.

## What is Saturation Mailing?

Saturation Mailing is a type of bulk mailing that allows you to qualify for discounted postage rates by delivering to a high percentage of addresses within a postal route—at least 90% of all *residential* addresses or 75% of all *total* addresses within that route. It's designed for reaching entire neighborhoods or regions, without requiring address-level targeting for each individual recipient.

It’s an efficient middle ground: you don’t need to build a fully personalized list, but you also don’t need to send indiscriminately. Instead, you focus on a geographic area with broad appeal and meet the threshold for postage discounts by sending to most—but not necessarily all—households.

This approach is especially useful when your product or service has broad local appeal—like community solar, internet service, or home improvement offerings. It helps you scale up affordably while keeping some strategic nuance, especially when paired with smart suppression and geographic targeting tools.

Thanks to USPS rules around Saturation Mailing, you can get smarter by suppressing addresses that don’t make sense for your campaign—without losing your discount, as long as you stay above the required delivery threshold. This flexibility lets you skip over existing customers, low-propensity households, or any other segments that aren’t a fit—without resorting to fully targeted mail.

## The role of suppression—and how Faraday helps

One of the most powerful parts of Saturation Mailing is suppression. You don’t need to mail every single address in a route, you just need to hit the threshold. Faraday helps you suppress existing customers, low-scoring leads, and households that don’t match your ideal personas.

Our platform uses behavioral and demographic data to score every household in the U.S., so you can make informed decisions about where to send—and where to hold back.

This is also a natural extension of other Faraday services, like persona modeling to define your most valuable segments, predictive scoring to prioritize high-intent households, and deployment planning to map out the smartest execution path.

## How does it compare to EDDM and fully targeted mail?

Every Door Direct Mail (EDDM) is another bulk mailing program offered by USPS. And while it is similar to Saturation Mailing, it comes with some specific limitations. It’s limited to flat-sized mailpieces (like postcards or bifolds), and doesn’t support letter-size mail in envelopes. It also doesn’t allow you to suppress addresses or use named recipients within a route. Accordingly, targeting services like our [AI-optimized EDDM](https://faraday.ai/blog/AI-optimized-EDDM) help you choose which routes will perform best in their totality, not how to target within individual routes. 

It’s great for broad awareness plays where cost is the primary concern and response rate isn’t as critical. Although EDDM is actually between 10 and 15% more expensive than Saturation Mailing in terms of postage, it often results in lower overall campaign costs due to simplified prep and no list requirements. Accordingly, it remains a popular choice for small businesses because of its simplicity. With no need for a mailing or suppression lists or complex prep, EDDM makes it easy to get a campaign out the door quickly—especially for hyperlocal efforts like restaurant promotions, political flyers, or community event announcements. But if you’re aiming for higher ROI, more precise targeting, or the ability to suppress known non-converters, Saturation Mailing is almost always the smarter long-term investment.

Fully targeted mail, on the other hand, gives you total control. With Faraday, that means using predictive models to select individual households based on their likelihood to convert, lifetime value potential, or alignment with your brand’s key personas. You can personalize messaging, time campaigns to buying cycles, and avoid waste almost entirely. It’s the most precise option in your toolkit—but it also comes with higher postage costs and more logistical complexity, which is why it’s often used for high-value offers or audiences where every conversion counts.

Saturation Mailing lands in the middle. It offers cost benefits through USPS discounts and more flexibility than EDDM—especially when it comes to address suppression and mail formats. While EDDM typically restricts you to postcards or flats, Saturation Mailing allows a wider range of formats, including letters, as long as they meet USPS requirements. It’s a great option when you want efficiency without the overhead of fully targeted campaigns.

Each of these options has a time and place. The smartest marketers know when to lean into each—and how to mix them when the moment calls for it.

## When to use it

Saturation Mailing is ideal when you’re targeting neighborhoods with broad appeal, want to blend efficiency with some degree of targeting, and need to use mail formats (like letters) that drive better response rates. It's also a great option when your audience is geographically clustered but not defined by narrow demographics.

Because you can suppress part of the list—like current customers or low-propensity leads—you get more relevance and less waste. That’s where Faraday shines.

## Faraday is more than a mail vendor—we’re a strategic partner

Faraday isn’t in the business of selling templates or one-size-fits-all strategies. We offer a range of predictive tools and mailing options—from EDDM to household-level targeting to Saturation Mailing—because we know that every campaign has unique needs.

What makes us different isn’t just what we offer. It’s how we help you use those tools together to maximize results. We don’t just help you get in the mailbox—we help you get there smarter.

## Conclusion

In direct mail, versatility is everything. EDDM, Saturation, and fully targeted delivery all have a role to play. The key is knowing when and how to use each.

With Faraday, you’re not just choosing a tool—you’re gaining a strategic partner who helps you adapt, refine, and win. Let’s build campaigns where every piece counts.

This blog breaks down how Saturation Mailing offers a strategic middle ground between EDDM and fully targeted mail—helping marketers reach the right households at scale while remaining cost-effective.

Saturation Mailing could save your direct mail budget, but is it the right choice for this campaign?

Imagine your customer “John Smith” opens a marketing email and makes a purchase in-store a week later. You’d expect those two interactions to connect in your data—but they often don’t. Instead, your system treats them like two different people. You end up offering a discount to someone who’s already bought, and your analytics call both interactions anonymous.

[![GIF featuring John Smith from Disney’s Pocahontas introducing himself](/images/blog/john-smith.gif)](/images/blog/john-smith.gif)

This isn’t a rare glitch. It’s what happens when you don’t have a unified view of your customers.

## The real problem

Most companies are stuck doing basic marketing, not because they want to—but because their data is disconnected.

[Shopify](https://www.shopify.com/) or [Hubspot](https://www.hubspot.com/) says one thing; [Snowflake](https://www.snowflake.com/en/) has something else; [Klaviyo](https://www.klaviyo.com/) might match. You’re not sure if the "Mary Green" in your customer database is the same person who just moved across town, changed her number, or signed up for a newsletter under a new email.

That uncertainty makes personalization unreliable. It tanks targeting. And it keeps your team stuck in the past, running campaigns based on guesswork instead of insight.

## What Universal ID solves

Sometimes a gap between online engagement and offline transactions isn’t just inconvenient—it’s a dealbreaker. Without a way to connect the dots, your team can’t prove attribution, demonstrate ROI, build data pipelines or create powerful models. That’s where Universal ID comes in.

Faraday’s Universal ID links your customer data together—across systems, formats, and time. It assigns a persistent ID to each person, even as they move, change emails, or update their contact info.  

This means:

* You don’t need names to match records (great for privacy-forward workflows)  
* You can trace a person across historical and current addresses  
* You can enrich IDs using reverse email or phone lookups

Universal ID is available via real-time API or direct data warehouse (like [Snowflake](https://www.snowflake.com/en/)) integration, and includes address parsing and standardization built in.

## What Universal ID doesn’t do

Some identity resolution platforms promise to link anonymous website visitors to real identities. That’s not what Faraday’s Universal ID is built for. We don’t track or fingerprint web traffic. Instead, Universal ID focuses on resolving known data points across time, systems, and channels—like CRM records, email lists, purchase histories, and in-store transactions.

## How it's different from standard identity resolution

If you’ve used Faraday before, you might be thinking, “Wait, doesn’t Faraday already resolve identities?” And you’d be right.

Anytime you bring data into the Faraday platform—whether for persona analysis, modeling, or activation—we automatically resolve identities behind the scenes using our own Universal ID system. That’s baked into the platform and included at no extra cost.

But the **Universal ID** product is something more: it’s what makes those resolved identities portable. It lets you deploy persistent IDs from Faraday into your own systems—like your data warehouse—so you can unify and act on customer records outside our platform. That’s what sets it apart from typical identity resolution tools.

Traditional solutions often rely on matching names, emails, or device IDs—data that’s easy to lose or siloed by channel. They struggle when people move, update contact info, or interact across disconnected systems.

Faraday’s Universal ID is built for continuity and complexity. It draws on 70+ months of historical address data (that’s 22 months longer than NCOA and growing) and uses reverse lookups via phone or email to stitch together records others miss. It doesn’t require a name to work, making it uniquely privacy-forward and effective in real-world scenarios.

## So how does it look in practice?

Although Universal ID does sound nice on paper (just look at this blog), its value doesn’t stop there.

One of our consulting clients—an agency working with a fast-growing chain of brick-and-mortar stores—recently used Universal ID to solve a common but frustrating attribution problem. A retailer they work with had in-store transactions that were tied to customer identities captured via government-issued IDs, while online marketing campaigns used email-based prospecting lists with no shared identifiers.

There was no direct way to know if a person who received an email ended up making a purchase in-store. That gap made it nearly impossible to prove that digital outreach was driving foot traffic—or to justify campaign investment.

By bringing both datasets into Faraday and exporting Universal IDs back into their data warehouse, the agency was able to resolve identities across systems and attribute revenue to specific campaigns. This helped them demonstrate ROI and secure future spend. Without Universal ID, none of that analysis would have been possible.

## Why not build it yourself?

So yes, you could. 

But you'd be signing up for more than just development work. Replicating Faraday’s Universal ID would require licensing and integrating data from multiple vendors, like address standardization from [Smarty.com](http://Smarty.com) and personal data from [Epsilon](https://www.epsilon.com/us) or [TransUnion](https://www.transunion.com/). Then you'd need to build and maintain the logic to reconcile records across different identifiers, and stand up both batch and real-time infrastructure capable of handling identity resolution at scale. On top of that, maintaining high match accuracy over time is its own ongoing challenge, especially as people move, change contact details, or drop off your radar entirely.

And even if you managed to build all that, you’d still be missing the strategic advantages. Faraday brings a 70-month address history and counting—substantially longer than the most expensive NCOA license—which boosts match accuracy and depth. Its matching process doesn’t require names, making it more privacy-conscious and flexible. Plus, it’s already tailored to marketing workflows like prospect attribution, list enrichment, and campaign activation.

It also plugs straight into your data warehouse or your real-time pipelines, so you’re not stuck waiting to see results. Faraday’s done the heavy lifting so you don’t have to. You don’t just get a functional solution—you get one that’s already delivering value in the real world.

## Transparent pricing

Universal ID delivers enterprise-grade identity resolution at a fraction of the cost it would take to build and maintain in-house. Whether you're an agency proving ROI for campaign work or a brand investing in better personalization, the value comes not just from the data, but from the speed and accuracy with which you can act on it.

At Faraday, Universal ID pricing is straightforward and based on monthly lookup volume:

* 50,000 lookups/month: $250  
* 150,000: $500  
* 1M: $2,000  
* 10M: $9,000

But in 2025, a better question than what it costs might be: what does it cost not to have this information?

## Know your customers. For real.

Disconnected data leads to disconnected marketing. If you want to stop guessing and start connecting the dots—for targeting, personalization, and attribution—Universal ID makes it possible, and we’re here to make that process easy.

Ready to unify your data and get more out of your existing systems? We can help. 

Curious how it might fit your team? Reach out—we’d love to chat.

Faraday’s Universal ID unifies customer records across systems, channels, and time, enabling accurate attribution, personalization, and ROI measurement by resolving identities without relying on names or emails.

Why Universal ID might be the missing link in your customer data strategy

At Faraday, we automatically detect and correct for geographic bias in our training data through a process we call “geonormalization”.

## Why geonormalize?

It's easiest to explain through some examples, so let's walk through two.

**Example 1: to re-balance an unbalanced training population**

Say you have a business with two customer bases: California and Nebraska. The business started in California, so its customer base in California is 10x what it is in Nebraska. Without geornormalization, because the business has more customers in California and those customers look totally different to those in Nebraska (e.g. higher household income in California), everyone in California is scored higher than those in Nebraska.

**Example 2: to allow for a prediction to generalize over geographical areas not covered in the training data**

If your business started in New York, so all of your data–demographics, financials, and so on–is New York-based, and now you're trying to find _brand new_ business in California, the predictive model won't have been able to learn about what makes California unique in terms of that data, so the model will struggle to learn and its predictions will be unreliable.

Neither scenario is particularly helpful for growth!

Geonormalization adjusts both of these types of geographic bias with geography-based scaling instead of a raw value: Jane Doe is in the top 10% of scores in her state instead of the top 10% of scores for everyone.

Without geonormalization, you might define someone as "wealthy" if their net worth is $500k. Having a definition this static can present problems when comparing people from different demographic and financial backgrounds. With geonormalization, and keeping the above examples in mind, the definition of wealthy becomes more dynamic: "they have a net worth over twice as much as the average in their county."

Just because someone lives in a location that’s different from your customer data doesn’t necessarily mean that they should score lower than everyone that resembles your customer data.

## How does Faraday geonormalize?

Hence the need for a correction factor, to ensure that we are able to find the most likely customers in all of the regions our client wants to expand into. Our approach requires a detection algorithm and a correction algorithm

### Detection

We use a simple, but effective, algorithm. We compare the market penetration (customers / total possible customers) among each distinct geographic area at the desired level of analysis. If the largest market penetration is greater than 100x the smallest market penetration then we perform our correction algorithm during training.

For detections we start at the largest geographic area of interest, the state level, and work our way down to the smallest geographic area of interest, the postcode level. If at any point during the detection, the 100x threshold is met we perform the correction for that geographic level. Therefore, we are always applying the correction at the largest level possible.

### Correction

We use a two-pronged approach to correction: instance weighting for the training examples, and bias correction for the training features.

#### Instance weighting

We assign a weight to the positive examples calculated from the penetration rate of those positive examples in their respective geographic area. This in essence levels the playing field and gives a boost to the positive examples from areas where there are relatively fewer examples (unsaturated markets) and gives a penalty to the positive examples from saturated markets.

From a modeling perspective, we are tricking the model to believe that the good examples are just as likely to exist in any of the geographic segments. This leads to less bias amongst the features used to distinguish good examples from bad examples.

#### Feature correction

Our other method we employ in tandem with the instance weighting, is to transform features so that we level the playing field across the geographic segments. Instead of providing the model with raw features, we transform them so each person is represented by the percentile they occupy within their segment. Again, this one is probably easiest to explain via an example.

Instead of providing raw household income values of \$84k for CA resident A and \$66k for NV resident B, we provide the model with how they rank amongst their peers within their respective states. I’ve chosen the median income for each state, so the model would receive 50 for resident A and 50 for resident B.

By correcting the features, we provide the model with fewer discriminatory features with which to determine that everyone from the more saturated geographic segment is a better fit than everyone from the less saturated segments.

[![Diagram of faraday's geonormalization process](/images/blog/geonormalization-process-diagram.png)](/images/blog/geonormalization-process-diagram.png)

## Interpreting geonormalized traits in Faraday's dashboard

When a feature is geonormalized, it means the model is looking at how someone compares to others in their local area—not just at the raw value. 

In the Faraday dashboard, when you see a feature labeled as “geonormalized,” the x-axis will show percentile ranges, not raw values. This is because the model is evaluating where an individual falls within their local geographic distribution. So, a range like 80.0, 90.0 on the x-axis represents people whose property lot sizes are in the top 10–20% within their own county, not people with a specific number of acres.

[![Example of geonormalization in dashboard](/images/blog/geonormalization-process.png)](/images/blog/geonormalization-process.png)

That’s why the chart might show higher likelihoods of attainment in the top percentile buckets—because being in the top 10% locally often signals above-average wealth or property characteristics for that area, even if they have less than those in a different region. This approach lets the model surface meaningful patterns without over-weighting regions with more or denser data, and ensures you're interpreting traits relative to local norms, not national ones.



## Conclusion

Geographic bias correction, aka “geonormalization”, means that when using Faraday to expand your business into new markets, you can do so confidently knowing that our models will not just default to recommending people from markets in which you already have a significant presence. Grow your business confidently with predictions from Faraday.

At Faraday, we automatically detect and correct for geographic bias in our training data through a process we call “geonormalization”.

Geonormalization in Faraday

[Product recommendation](http://faraday.ai/templates/next-best-offer) engines are everywhere these days—from Netflix queues to Amazon homepages. But in subscription commerce, where customers commit to recurring deliveries, recommenders aren’t just nice to have—they’re essential. Sending the wrong product doesn’t just hurt engagement. It increases churn, reduces LTV, and erodes trust in the brand.

And that’s why a leading subscription box company set out to improve how they matched products to people, and to solve this problem, they of course came to Faraday first.

## The problem: Manual rules and missed opportunities

Before working with Faraday, this brand was manually assigning monthly boxes using basic written rules. While the logic was informed by customer traits and preferences, it was still deterministic, rigid, and prone to human bias. It also couldn’t scale effectively across an ever-growing catalog or subscriber base.

Most importantly, the team didn’t have a reliable way to test whether these rules were actually the best way to drive retention and increase revenue.

## The solution: AI-powered product recommendations

To modernize their process, the brand partnered with Faraday to implement a machine learning–based recommendation engine.
Unlike traditional scoring models that evaluate products in isolation, Faraday uses a two-sided matching framework—scoring both:

- Customer affinity: How likely a specific person is to engage with a product
- Product performance: How well that product has performed with similar audiences

This leads to smarter, high-confidence pairings optimized for both personalization and conversion—at scale. With Faraday, the brand was able to deliver more relevant recommendations, reduce bias, and adapt to new products and preferences as their catalog evolved.

## The results: Higher revenue and lower churn

The team ran a clean A/B test comparing Faraday’s algorithmic recommendations to their manual rules. 

The outcome:

* 📈 ~5% initial lift in revenue per customer
* 📉 ~3% reduction to churn rate
* 💰 Over 5x ROI generated monthly with Faraday
* 🗓️ Four consecutive months of our recommendations outperforming their rules-based alternatives

These weren’t marginal gains—they were statistically significant improvements that validated the shift toward AI. And what’s more, this is just the start. The more this brand uses our models, the more they’ll learn and the better they’ll get, meaning they can expect even better results over time.

## How Faraday recommendations work

Faraday’s recommender system doesn’t just surface “popular” products. It predicts the mutual affinity between a person and a product, using thousands of behavioral signals—purchase history, tenure, geography, psychographics, and more. It also incorporates product-level attributes and past performance to ensure the best-fit items rise to the top.

What’s more, clients can tune the system with constraints like inventory availability, product diversity, or subscription cadence—ensuring not just personalization, but operational feasibility.

## In conclusion

Want to see how our recommenders could work for your business? Just reach out and talk to sales, they’ll tell you exactly how this use case, as well as a range of [other AI agents](https://faraday.ai/templates) could help your business thrive\!

A leading subscription box company replaced manual product rules with Faraday’s AI-powered recommendations—boosting revenue and retention through smarter, scalable personalization.

How one subscription box brand leveled up their product recommendations with Faraday’s AI agents

We recently shared a blog about our new [AI-optimized Every Door Direct Mail (EDDM)](https://faraday.ai/blog/AI-optimized-EDDM) offering—a cost-effective way to get smarter about reaching a wide audience. It’s a powerful service that helps our clients move fast and efficiently. But like we said in that post, direct mail isn’t about finding one universal approach and tripling down. It’s about building a flexible toolkit and adapting your strategy based on your audience, geography, budget, goals, and a range of other factors.

That’s where [Saturation Mailing](https://www.growmail.com/blog/eddm-vs-usps-saturation-mailing/) comes in. It’s another powerful tool in the direct mail playbook—especially when you’re trying to reach as many qualified households as possible, while still maintaining control and strategic nuance. Let’s dig into how it works and why we think it deserves a spot in your toolkit.

## What is Saturation Mailing?

Saturation Mailing is a type of bulk mailing that allows you to qualify for discounted postage rates by delivering to a high percentage of addresses within a postal route—at least 90% of all *residential* addresses or 75% of all *total* addresses within that route. It's designed for reaching entire neighborhoods or regions, without requiring address-level targeting for each individual recipient.

It’s an efficient middle ground: you don’t need to build a fully personalized list, but you also don’t need to send indiscriminately. Instead, you focus on a geographic area with broad appeal and meet the threshold for postage discounts by sending to most—but not necessarily all—households.

This approach is especially useful when your product or service has broad local appeal—like community solar, internet service, or home improvement offerings. It helps you scale up affordably while keeping some strategic nuance, especially when paired with smart suppression and geographic targeting tools.

Thanks to USPS rules around Saturation Mailing, you can get smarter by suppressing addresses that don’t make sense for your campaign—without losing your discount, as long as you stay above the required delivery threshold. Typically, that means mailing to at least 90% of residential addresses or 75% of all deliverable addresses (residential \+ business) on a given route. This flexibility lets you skip over existing customers, low-propensity households, or any other segments that aren’t a fit—without resorting to fully targeted mail.

## The role of suppression—and how Faraday helps

One of the most powerful parts of Saturation Mailing is suppression. You don’t need to mail every single address in a route, you just need to hit the threshold. Faraday helps you suppress existing customers, low-scoring leads, and households that don’t match your ideal personas.

Our platform uses behavioral and demographic data to score every household in the U.S., so you can make informed decisions about where to send—and where to hold back.

This is also a natural extension of other Faraday services, like persona modeling to define your most valuable segments, predictive scoring to prioritize high-intent households, and deployment planning to map out the smartest execution path.

## How does it compare to EDDM and fully targeted mail?

Every Door Direct Mail (EDDM) is another bulk mailing program offered by USPS. And while it is similar to Saturation Mailing, it comes with some specific limitations. It’s limited to flat-sized mailpieces (like postcards or bifolds), and doesn’t support letter-size mail in envelopes. It also doesn’t allow you to suppress addresses or use named recipients within a route. Accordingly, targeting services like our [AI-optimized EDDM](https://faraday.ai/blog/AI-optimized-EDDM) help you choose which routes will perform best in their totality, not how to target within individual routes. 

EDDM is great for broad awareness plays where cost is the primary concern and response rate isn’t as critical. Although it’s typically 10–15% more expensive than Saturation Mailing, it remains a popular choice for small businesses because of its simplicity. With no need for mailing lists, suppression logic, or complex prep, EDDM makes it easy to get a campaign out the door quickly—especially for hyperlocal efforts like restaurant promotions, political flyers, or community event announcements. But if you’re aiming for higher ROI, more precise targeting, or the ability to suppress known non-converters, Saturation Mailing is almost always the smarter long-term investment.

Fully targeted mail, on the other hand, gives you total control. With Faraday, that means using predictive models to select individual households based on their likelihood to convert, lifetime value potential, or alignment with your brand’s key personas. You can personalize messaging, time campaigns to buying cycles, and avoid waste almost entirely. It’s the most precise option in your toolkit—but it also comes with higher postage costs and more logistical complexity, which is why it’s often used for high-value offers or audiences where every conversion counts.

That’s why Saturation Mailing is such a valuable middle ground. It offers many of the benefits of targeting—like address suppression and flexible mail formats—without the full cost and complexity. Compared to EDDM, it gives you more strategic control. Compared to fully targeted mail, it’s faster and more affordable to scale.

## When to use it

Saturation Mailing is ideal when you’re targeting neighborhoods with broad appeal, want to blend efficiency with some degree of targeting, and need to use mail formats (like letters) that drive better response rates. It's also a great option when your audience is geographically clustered but not defined by narrow demographics.

Because you can suppress part of the list—like current customers or low-propensity leads—you get more relevance and less waste. That’s where Faraday shines.

## Faraday is more than a mail vendor—we’re a strategic partner

Faraday isn’t in the business of selling templates or one-size-fits-all strategies. We offer a range of predictive tools and mailing options—from EDDM to household-level targeting to Saturation Mailing—because we know that every campaign has unique needs.

What makes us different isn’t just what we offer. It’s how we help you use those tools together to maximize results. We don’t just help you get in the mailbox—we help you get there smarter.

## Conclusion

In direct mail, versatility is everything. EDDM, Saturation, and fully targeted delivery all have a role to play. The key is knowing when and how to use each.

With Faraday, you’re not just choosing a tool—you’re gaining a strategic partner who helps you adapt, refine, and win. Let’s build campaigns where every piece counts.

Saturation Mailing offers a smart middle ground between broad EDDM and fully targeted mail—giving you postage discounts, flexible suppression, and better ROI when paired with Faraday’s predictive data.

Is Saturation Mailing the missing piece in your direct mail strategy?

[![Faraday + ActiveProspect](/images/blog/activeprospectcollab/fdy-active-prospect-graphic.png)](/images/blog/activeprospectcollab/fdy-active-prospect-graphic.png)

In today's competitive landscape, acquiring high-quality leads isn't just about quantity—it's about precision, speed, and compliance. That's why [ActiveProspect](https://activeprospect.com/leadconduit/) and [Faraday](http://faraday.ai) have partnered to offer a seamless integration between [LeadConduit](https://activeprospect.com/leadconduit/) and Faraday's real-time lead scoring (via our [Lookup API](https://faraday.ai/docs/features/lookup-api)).

This collaboration empowers marketers to make informed decisions instantly, ensuring that every lead is a step closer to conversion.

## The challenge: Navigating the complexities of lead acquisition

Modern lead acquisition is increasingly high-stakes. Marketers are asked to generate results faster, with less margin for error, all while adhering to complex privacy regulations and managing ballooning acquisition costs. For many brands, this means sourcing leads from third-party vendors or affiliate networks—scenarios where every inbound lead represents a cost, and every delay or misstep translates into wasted budget.

But not all leads are created equal. Without knowing who a lead is or how likely they are to convert, sales teams are often forced to make rapid-fire decisions with limited context. This creates a constant tension: respond quickly, or respond smartly. Worse still, the risk of buying low-quality or non-compliant leads adds further friction. It's easy to overspend on volume while underdelivering on results.

This is why the integration between Faraday and ActiveProspect is so valuable. By pairing Faraday’s real-time lead scoring with ActiveProspect’s powerful lead routing and filtering, brands gain the ability to instantly qualify, prioritize, and act on leads the moment they arrive—without sacrificing compliance, wasting budget, or slowing down sales.

## ActiveProspect's LeadConduit: Streamlining lead management

In the context of these challenges, it's clear that marketers need a system that can help them act fast, but with confidence. That's exactly where [LeadConduit](https://activeprospect.com/leadconduit/) comes in.

LeadConduit is ActiveProspect's real-time lead optimization platform. It gives marketers full control over their lead flow, from the moment a lead enters the system. Leads are captured in real time from trusted sources, instantly validated and enhanced with additional data, then routed based on configurable rules that reflect each brand’s quality standards and compliance requirements. The result is a system that helps ensure only high-quality, high-intent leads make it through to sales teams.

By implementing LeadConduit, businesses can reduce wasted spend, prioritize the most promising opportunities, and create more efficient acquisition funnels. But while LeadConduit enables smart decision-making based on known, validated data—it becomes even more powerful when enriched with predictive intelligence. 

And that’s where Faraday comes in!

[![Faraday in ActiveProspect dashboard ](/images/blog/activeprospectcollab/fdy-in-ap-dashboard.png)](/images/blog/activeprospectcollab/fdy-in-ap-dashboard.png)

## Faraday's Lookup API: Real-time predictive lead scoring

Once you've got a system like LeadConduit in place—one that can instantly act on incoming leads—the next big question becomes: what information should you act on? Knowing a lead came from a trusted vendor and passed validation is important, but it still doesn’t tell you whether that lead is likely to convert. That’s the missing puzzle piece Faraday delivers.

Faraday’s real-time lead scoring, powered by our Lookup API, brings predictive intelligence into the moment of decision. When a lead enters the system, Faraday analyzes it against the rich [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph), which features traits and attributes on over 240 million U.S. adults and their households. In just milliseconds, we deliver a score that predicts the likelihood of conversion—along with persona data and behavioral propensities that paint a fuller picture of who that person really is.

Instead of treating all validated leads equally, marketers can now prioritize outreach, set bidding rules, or trigger routing logic based on real predictive insight. The Lookup API integrates directly into existing LeadConduit flows, so there’s no disruption—just better decisions, made faster.

With Faraday in the loop, you're not just reacting to lead data. You're anticipating outcomes—and acting accordingly.

## The synergy: Combining LeadConduit and Faraday for optimal results

Integrating Faraday's predictive scores into LeadConduit's workflows offers:

* **Optimized lead flows**: Real-time scoring and filtering ensures teams focus only on high-propensity leads.  
* **Higher ROI**: Marketers can reduce acquisition costs and increase conversion rates.  
* **Data-backed agility**: Teams gain the ability to make fast, informed decisions based on predictive analytics.

This integration gives you the best of both worlds: Faraday's ability to assess conversion potential, and LeadConduit's power to act on it in real time—including through ping-post bidding decisions.

[![Faraday and ActiveProspect leadflow diagram](/images/blog/activeprospectcollab/lead-conduit-diagram2.png)](/images/blog/activeprospectcollab/lead-conduit-diagram2.png)

## Real-world impact: A home services brand cuts waste and lifts ROI with Faraday \+ LeadConduit

But this isn’t just some abstract idea. Real clients have already been generating significant value through this partnership.

For instance, one national home services company put the Faraday and ActiveProspect integration to the test—and the results were clear. By incorporating Faraday’s real-time scoring into their LeadConduit flow, they began automatically rejecting low-propensity leads (the bottom 10%). These leads were converting at just half the average rate, and cutting them from their pipeline meant marketing and sales teams could focus entirely on higher-value opportunities.

And the impact was real. In just one month, the company saved over **$36,000 in lead costs** and avoided pursuing nearly **1,200 low-fit leads**, while still maintaining strong sales volume. With Faraday scores powering rejection logic and LeadConduit automating the decision-making, the brand achieved a nearly **6x ROI** on their initial investment—proving that smarter, faster lead decisions aren’t just easily achievable, they’re tangibly profit-driving.

## Conclusion: Elevate your lead acquisition strategy

The best lead acquisition strategies aren't just fast—they're smart. They balance the urgency of real-time response with the discipline of data-backed decision making. That’s exactly what this partnership between ActiveProspect and Faraday delivers.

By combining ActiveProspect’s LeadConduit with Faraday’s real-time predictive scoring, brands can transform how they evaluate and act on every incoming lead. From the moment a lead hits your system, you can determine not just whether it’s compliant or complete, but whether it’s worth your time, budget, and attention. That means fewer wasted impressions, faster follow-ups, and more wins for your sales team.

This is more than an integration. It's a smarter, faster, and more strategic approach to lead acquisition.

**Ready to see what this looks like in action?** Contact us to learn how [ActiveProspect](https://activeprospect.com/contact-us/) and [Faraday](https://faraday.ai/talk-to-sales) can help you modernize your acquisition strategy and hit your growth goals.

Faraday and ActiveProspect combine predictive scoring with real-time decisioning, giving marketers the power to qualify and act on leads instantly—cutting waste and boosting ROI.

ActiveProspect + Faraday: Smarter lead acquisition starts here 

[![Faraday personas dashboard](/images/blog/when-to-update-personas/persona-set.png)](/images/blog/when-to-update-personas/persona-set.png)

Not long ago, a client asked us a simple question: *"When do we need to update our personas?"* They had just launched a new campaign, were seeing some shifts in engagement, and started wondering if maybe their whole persona set needed a refresh.

It’s a fair question—and a tempting one. When you’re working with data-driven segments, it’s easy to assume they should constantly change. But our answer surprised them.

**“Probably not.”**

Faraday Personas serve as the foundation a range of predictions can be built on. They help you organize your customer base in a way that’s strategic, insightful, and easy to act on. But like any good foundation, you want to get it right before you start building on top of it.

Sure, things can change. Maybe you want to add an extension, knock out a wall, or open up the kitchen. But those changes are a lot easier—and less disruptive—if the foundation is solid and built to last.

So when do you actually need to revisit your personas?

The short answer: rarely. Once you’ve landed on an architecture that works, stick with it.

Let’s unpack why—because the logic behind that “rarely” is more interesting than it sounds.

[![Image of foundation](/images/blog/when-to-update-personas/foundation.jpg)](/images/blog/when-to-update-personas/foundation.jpg)

## How personas are generated: a quick look at k-means

Just a second there—we are going to tell you when to update your personas, but first, in the spirit of explainability, we wanted to give you a little more context about how these personas are computed to begin with. Under the hood, Faraday uses a variant of a decades-old algorithm called “k-means clustering” to create personas.

Here’s how it works:

* **Fig 1:** The algorithm starts by placing random center points across your dataset—think of these as placeholders for where clusters might end up. It’s easiest to visualize this in two dimensions—like income and age (as shown below)—but in practice, our models actually cluster across seven dimensions at once.  
* **Fig 2:** Each customer record is assigned to the center point it’s closest to, using straight-line (Euclidean) distance. This step groups people based on proximity to those initial centers.  
* **Fig 3:** Once everyone is grouped, the center points “move” to the average location of their assigned members—this is the re-centering step.  
* **Fig 4:** After the centers update, each customer is reassessed and may be reassigned to a new group based on updated distances. The process repeats—reassign, recenter—until the centers stop moving.

[![K-means diagram](/images/blog/when-to-update-personas/k-means_diagram.png)](/images/blog/when-to-update-personas/k-means_diagram.png)

That final clustering defines your persona set.

The result is a set of personas that’s grounded in data, tuned to your strategy, and stable enough to support real decision-making.

## Personas are a framework, not a forecast

A [Persona set](https://faraday.ai/docs/abstractions/persona_sets) in Faraday’s platform is a structured way to segment your customer base. It’s not a supervised prediction model in itself—but it is still predictive. Built using unsupervised machine learning, Persona sets identify natural clusters in your customer data based on shared behaviors and traits. These clusters don’t predict a specific future outcome, but they do reveal the underlying structure in your audience—patterns you can act on.

In that way, Personas become a foundation for other predictions. By dividing your audience into meaningful, behavior-based clusters, you create a consistent lens through which you can view performance, target messaging, and even optimize creative strategies.

That’s why it’s important to get it right. And sometimes, getting there takes a few iterations:

* You may need to refine *who* you're segmenting (e.g. maybe you only want to include actual buyers, not people who just requested a sample).  
* You might experiment with *which traits* produce the most useful clusters (like dropping gender if it isn’t meaningful for your business).  
* You’ll need to choose how many Personas your team can support operationally—four might be optimal, but maybe three is more manageable.  
* You might decide to limit the data to only recent customers if your business has changed significantly over time.

These early decisions shape your architecture. But once you're confident in the structure—and it's aligned with your business goals—you shouldn't need to change it often.

In other words, once that foundation is poured and leveled, you’re ready to build. And just like with a house, it's much easier to make layout decisions or room additions early—before the drywall goes up and the furniture’s moved in. After that, you're not just shifting strategy; you're tearing up the floorboards. So it's worth taking the time to get it right.

## Stability matters

So now that we understand where the Personas come from and what they do, we can start to talk about why or why not to consider updating them. 

And here’s the upside of *not* tinkering: consistency gives you clarity.

If your Persona structure stays stable, you can actually see how your audience shifts over time. Maybe one segment’s getting smaller. Maybe another’s growing thanks to a campaign you launched six months ago. Maybe a new customer type is starting to show up in a meaningful way.

None of that is obvious if you keep changing the structure. You need that solid foundation if you want to track change.

Think of it like this: if you renovate your house every year, you’ll never really know whether the neighborhood’s changing—or if you’re just seeing different views through different windows.

## When you *should* consider a change

All that said, sometimes it’s the right move to rebuild.

Here are the situations where it makes sense:

* **Gradual obsolescence.** One of your Personas slowly fades out over time. They’re no longer showing up in your data, and that segment just isn’t relevant anymore. It might be time to re-cluster and rebalance the set.  
* **Big business changes.** Think mergers, acquisitions, or divestments. If your business fundamentally changes—like Hasbro acquiring Wizards of the Coast—you’re suddenly dealing with a whole new audience (hello, D\&D players). That’s not a tweak. That’s a structural shift.

These are rare, but when they happen, it’s worth stepping back and reassessing.

## Build it to last

At the end of the day, your Persona set isn’t just a report—it’s the foundation your customer strategy is built on. It supports everything: creative, targeting, product design, lifecycle messaging, and more.

That’s why it’s built to be stable, and consistent. You don’t want to tear up the foundation unless you have a very good reason.

And when that moment does come—whether it’s time for a renovation or a full rebuild—Faraday’s team is here to help you do it the right way.

But most of the time? You’re better off getting it right the first time and building on what’s already solid.

A solid persona set should be built to last—because stability, not constant revision, is what makes it most valuable for long-term strategy.

When to update your personas - and why you probably shouldn’t

At Faraday, we love when our tools help a mission-driven brand grow in tangible ways, and when that growth could be described as “massive,” we’ll take that too. That’s exactly what happened with [Bee’s Wrap](https://www.beeswrap.com/)—a sustainable alternative to plastic wrap—who built a pitch deck with help from Faraday’s [Personas](https://faraday.ai/docs/how-to/predictions/customer-personas) features which landed them a “massive” (there’s that lovely description in action) placement in over 550 Target locations around the country. 

[![Add Faraday Integration](/images/blog/bees-wrap.jpg)](/images/blog/bees-wrap.jpg)

## What Bee’s Wrap needed

Bee’s Wrap was preparing for a high-stakes sales pitch to Target, a major national retailer—perhaps you’ve heard of them ;). The team needed to prove that their customer base was the right kind of shopper that Target wanted to be ‘Target-ing”. For this they needed to show real data that would resonate with a national buyer.

Bee’s Wrap had a major data asset to leverage: their first-party DTC sales data from [Shopify](https://www.shopify.com/). However, this data only tells part of the story. Order data is not going to tell you why someone buys, who they are demographically, or how many more people like them exist across the country. That’s where Faraday came in.

## How Faraday helped

Bee’s Wrap connected their order data to the Faraday platform, and in just one afternoon, they had:

* Detailed demographic and psychographic segments  
* Purchase propensities broken down by geography  
* A clear, visual representation of who their customers really are—and where to find more of them  
* A ZIP-code-level view of where potential was highest—helping them identify which store locations to prioritize in their pitch

This gave Bee’s Wrap a concrete, compelling story to bring into their pitch—one that resonated with Target’s team.

## The results

That pitch didn’t just land—it turned into a deal.

Bee’s Wrap is now rolling out nationwide in over 550 Target stores, starting with an initial wholesale shipment worth well into six figures. It's a massive milestone for the brand—and one they credit, in part, to having the right data in hand at the right moment.

## Why this matters

As [Brendan Taylor](https://www.linkedin.com/in/bttaylor13/), Bee’s Wrap’s CEO, put it:

> “We approached Faraday with a simple but elusive set of questions about our consumers and their demographics. Armed with only our DTC sales data – in one afternoon – Faraday identified crisp consumer segments with actionable demographic and psychographic details. Their easy-to-use interface also provided powerful predictions about propensity to purchase based on geography and other demographic filters."

The Faraday suite can synthesize incredibly powerful insights and predictions out of disparate data sets that previously left marketers and sales leaders scratching their heads. If you have lots of data and want to convert it to knowledge and insights, you’ve come to the right place.”

But here’s the thing, this story isn’t just about one brand—it’s about what happens when companies make their data work for them. With Faraday Personas, even a single afternoon of analysis can unlock game-changing insights.

Whether you’re pitching a retailer, planning a campaign, or just trying to understand your customers better, we’re here to help you tell a data-backed story that lands.

Ready to learn more? [Let’s talk](https://www.linkedin.com/in/davidtoddsmall/).

Bee’s Wrap used Faraday Personas to turn their DTC data into a compelling retail pitch—landing a nationwide deal with over 550 Target stores.

How Bee’s Wrap landed a nationwide retail deal with help from Faraday personas

**“If I have seen further, it is by standing on the shoulders of giants.”**

Recognize that quote? It’s from Newton—a generally accepted “smart guy” (and, admittedly, a generally accepted weirdo—but that’s a blog for another day).

So why start a post about AI and direct mail with a quote from a 17th-century physicist? Because in tech, Newton’s point holds up better than ever.

What Newton was saying is that even his most groundbreaking discoveries weren’t created in a vacuum—they were built on the work of those who came before him. Great ideas, and for our purposes, great products, happen when we collaborate and build on solid foundations. While some might think to reinvent the wheel, others want to take the excellent wheel that’s available to them and ride it to the finish line.

That’s exactly the kind of story we’re telling today.

Faraday recently partnered with a leading direct mail automation platform known for helping brands modernize and automate their direct mail operations. Their mission? To empower businesses to deliver more personalized, effective marketing—and to do it with speed, precision, and reliability.

So when they saw an opportunity to build a new product that would deliver even greater value to their customers by enhancing audiences with predictive intelligence, they wanted a solution yesterday. And they turned to partners who’ve been building validated, value-driving AI since well before yesterday—enter Faraday. 

Our best-in-class predictive AI agents and well-documented, developer-friendly API were exactly what they needed to build this new tool. Here’s how it went:

## The story

#### The problem: customers needed better audience targeting

While this direct mail platform already excelled at automating delivery, address verification, and personalization, many of their customers still struggled with a fundamental challenge: finding the right people to reach.

Bad lead lists were a persistent pain point. Marketers were forced to rely on outdated, purchased data; incomplete CRM exports; or generic demographic targeting that missed the nuances of real buyer behavior. Instead of reaching high-potential customers, campaigns often landed in the wrong hands—wasting precious budget and eroding confidence in direct mail as a channel.

Their clients needed a built-in, intelligent way to target audiences in their platform that didn’t rely on guesswork, generic segmentation, or expensive third-party lists. This direct mail company needed a solution fast, and was hoping to get it without the financial burden and hassle of hiring an entire AI team to build predictive models or identity matching infrastructure, from scratch.

#### The solution: new AI-powered audience targeting, built with Faraday

To address this situation, the platform introduced a new AI-powered solution for creating high quality, targeted audiences—powered entirely by Faraday’s proven predictive AI agents.

Using the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph), our robust [API](https://faraday.ai/docs/reference) and developer docs, and the resource of our forward deployed data team (not to mention our powerful predictive AI itself), the company created a new audiences tool in just over a month that enabled its users to:

* Predict ideal audiences automatically  
* Build high-conversion lookalike lists  
* Improve targeting without the need for perfect starting data

The best part? This new feature is embedded directly into their existing SaaS platform. 

And as a bonus, while their engineering team built the tool, the capabilities were already offered in a white-glove fashion because their account management team could use the Faraday app in the background as soon as the partnership agreement was signed. Execs were happy because their clients could see immediate value without pesky roadmaps and engineering sprints. 

#### The results: smarter campaigns, faster

This brand is already seeing better results and happier customers using their new audiences feature. Thanks to Faraday, the direct mail platform was able to:

* Rapidly roll out AI-powered audience targeting to all customers  
* Deliver immediate value without requiring heavy client-side setup (even before the tool was completed)  
* See early adoption and positive feedback from users benefiting from improved targeting and higher engagement rates

The collaboration didn’t just add a new feature—it raised the bar for what’s possible in data-driven direct mail.

## Predictive power, without the pain

Choosing Faraday wasn’t just about adding predictive technology—it was about finding a partner that could deliver real results immediately, without a massive effort. Two key factors in their choice to use our services were:

The [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph), which offers unparalleled out-of-the-box predictive accuracy, leveraging a robust network of third-party data on over 240m US consumers and their household, to identify high-propensity targets with minimal setup. That meant no months of waiting for models to "learn" and no dependence on customer-provided data to drive insights.

Our flexible, [well-documented API](https://faraday.ai/docs), and accessible engineering team allowed the direct mail platform’s developers to integrate powerful AI functionality rapidly into their existing product—without needing to reinvent their stack or slow down their product roadmap.

The combination of turnkey predictive power and developer-friendly design made Faraday the obvious choice to bring this new feature to market quickly, confidently, and at scale.

And as you can probably tell, this outcome was a major win for this client. Faraday’s predictive AI agents made it possible to:

* Identify ideal customers even without a perfect starting list  
* Build intelligent lookalike audiences  
* Append missing data points for richer targeting  
* Target high-conversion prospects with greater accuracy
* And most importantly, it allowed them to roll all of these new value-adding features out immediately, before the tool itself was even completed.  

Now, instead of their clients having to manually assemble lists, this direct mail provider can trust Faraday’s AI agents to find them the right audience—saving time, improving ROI, and delivering better campaign outcomes for their customers.

## Why it matters—today and tomorrow

The marketing landscape is shifting fast. With rising acquisition costs and tightening privacy regulations, brands need smarter, faster ways to reach the right audiences—and make every dollar count.

Embedding predictive intelligence directly into operational workflows like direct mail isn’t just a "nice to have" anymore—it’s a critical strategic advantage.

By partnering with Faraday, this platform didn’t just add a feature; they future-proofed their product. Now they’re delivering better results for their customers today, while building a foundation for even more sophisticated, data-driven campaigns tomorrow.

## Conclusion

Partnerships drive progress. By joining forces, Faraday and this direct mail provider have created a smarter, faster, more precise way to deliver direct mail audiences.

Customers now get:

* More personalized, high-conversion campaigns  
* Higher ROI with less manual effort  
* Streamlined marketing operations from targeting to execution

Ready to see how predictive targeting can transform your marketing?

Let's build something smarter together.

A leading direct mail platform rapidly built and launched a powerful new predictive audience feature—fully embedded in their SaaS product—by building on Faraday’s developer-friendly API and proven AI agents.

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What’s in a deployment? How to read your Faraday outputs

Saturation Mailing could save your direct mail budget, but is it the right choice for this campaign?

Why Universal ID might be the missing link in your customer data strategy

Geonormalization in Faraday

ChangelogSee all

Recipes builder retired

Prediction scores preserved for outcome attainers in dynamic pipelines

Target-level analysis now available

Smarter identity resolution with payload fallbacks

How one subscription box brand leveled up their product recommendations with Faraday’s AI agents

Is Saturation Mailing the missing piece in your direct mail strategy?

ActiveProspect + Faraday: Smarter lead acquisition starts here

When to update your personas - and why you probably shouldn’t

How Bee’s Wrap landed a nationwide retail deal with help from Faraday personas

This direct mail provider built a new predictive audience feature with ease using Faraday’s powerful API