“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

[![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

**“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.

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

**TL;DR:** This guide shows you how to plug Faraday's predictive lead scoring directly into your LeadConduit flow. It takes about 10–15 minutes if your [Lookup API](https://faraday.ai/docs/features/lookup-api) pipeline is already set up, and you’ll get real-time intelligence on every incoming lead.

— — — 

Already an [ActiveProspect](https://activeprospect.com/) user? Great call 👏

But did you know you could be getting even *more* value out of [LeadConduit](https://activeprospect.com/leadconduit/)?

By integrating Faraday with ActiveProspect, you can automatically score incoming leads in real time, allowing LeadConduit to reject low-quality ones before they hit your CRM. That means more time for your team to focus on the right people—and less budget wasted on the wrong ones.

In this post, we’ll walk you through exactly how to connect Faraday to your LeadConduit flow using a custom step. It’s fast, flexible, and a whole lot easier than building an internal model from scratch.

## Why integrate Faraday into LeadConduit?

This integration gives you real-time access to Faraday’s AI-powered lead insights—including conversion propensities, persona classifications, and custom attributes. That means you can stop low-propensity leads at the source, route high-quality prospects based on persona-fit or probability thresholds, and enrich the rest for downstream personalization.

It's fast (millisecond response time), flexible, and fully customizable to your lead acquisition goals.

## Step 1: Get your Faraday setup ready

Before anything can flow through LeadConduit, you’ll want to make sure your Faraday pipeline is in place and ready to respond. This is your starting point: a deployed Lookup API endpoint that’s ready to score leads in real time.

To make the connection work, you’ll need a few pieces of info from your Faraday account:

* Your completed Lookup API deployment—this is where all your scores are stored before retrieval  
* Your target ID  
* Your endpoint URL: `https://api.faraday.ai/v1/targets/<YOUR_TARGET_ID>/lookup`  
* Your production API key (a.k.a. your token)  
* Optional: A list of payload response fields you want to retrieve (e.g. \- probability scores, persona set clusters, attributes, etc.)

If you’ve got those five pieces, you’re ready to build. And if you're not sure where to find some of these, don't stress—your Faraday point of contact can walk you through it. 

## Step 2: Set up your custom step in LeadConduit

Now that Faraday is ready to respond, let’s wire it into your LeadConduit flow. This step is where identity data flows in, predictions come out, and your routing logic can make smarter decisions in real time.

Inside LeadConduit, go to the flow you want to edit and add a custom integration step. Here’s how to configure it:

* Format: JSON  
* Method: `POST`  
* URL: `https://api.faraday.ai/v1/targets/<YOUR_TARGET_ID>/lookup`  
* Headers:  
  * `Content-Type: application/json`  
  * `Authorization: Bearer <YOUR_API_KEY>`

[![New Integration](/images/blog/how-to-set-up-activeprospect/new-integration.png)](/images/blog/how-to-set-up-activeprospect/new-integration.png)

[![Add Faraday Integration](/images/blog/how-to-set-up-activeprospect/select-fdy-integration.png)](/images/blog/how-to-set-up-activeprospect/select-fdy-integration.png)

### Field mappings

Next, you’ll need to map your LeadConduit identity fields (all as JSON properties) to Faraday’s inputs. Here’s what you’ll want to map.

* `person_first_name`  
* `person_last_name`  
* `house_number_and_street`  
* `city`  
* `state`  
* `postcode`  
* `email`  
* `phone`

The more identifiers you include, the better the match rate. A good rule of thumb: aim to send at least email or phone, plus name and address when possible. 

Once that’s set up, Faraday will respond with predictive scores in real time. But here’s the part that matters: what you do with that score.

[![Add Faraday Integration](/images/blog/how-to-set-up-activeprospect/field-mapping.png)](/images/blog/how-to-set-up-activeprospect/field-mapping.png)

## What do you do with the score?

Once Faraday returns a predictive score (and any other fields you've configured), you can use LeadConduit’s flexible logic tools—like filters or branching steps—to take action based on that information.

💡 For example, many LeadConduit users set up filters based on returned fields from third-party integrations. According to ActiveProspect’s [API documentation](https://activeprospect.redoc.ly), it's common to create conditions that gate or route leads depending on values returned from an external service.

Your team might choose to:

* Pause or reject leads below a certain score threshold  
* Route high-scoring leads directly to sales or a priority CRM path  
* Nurture mid-tier leads via a different flow  
* Append persona data for downstream personalization

Just note: exactly how you act on the data depends on how your LeadConduit flow is configured. You’ll want to coordinate with your ops team or your ActiveProspect contact to ensure the field names and logic steps match your goals.

## Understanding the data exchange

👀 Visual learner? Here’s a simplified view of how data moves through this integration:

[![Diagram of leadflow through Faraday and LeadConduit](/images/blog/integrate-leadsconduit-blog/lead-conduit-diagram.png)](/images/blog/integrate-leadsconduit-blog/lead-conduit-diagram.png)

### Input requirements

You don’t need to send everything, but certain combinations are more effective:

* Email (or SHA256-hashed)  
* Full address (street \+ city \+ state)  
* Phone \+ last name

The more data you include, the more accurate your results.

### Output fields you can expect

When Faraday processes the identity, it returns structured output to your flow. These include:

* The identity information submitted  
* Outcomes like `percentile` and `probability`  
* Persona details like `cluster_id` and `cluster_name`  
* Traits based on your use case (custom model attributes)  
* Cohort memberships  
* Metadata like `match_type` and `error`

Note: `match_type` and `error` are static key names (they don’t change). The others depend on your pipeline config and what you’ve set up with your Faraday team.

## Example request & response

An example call:

[![An example call](/images/blog/integrate-leadsconduit-blog/example-call.png)](/images/blog/integrate-leadsconduit-blog/example-call.png)

An example response:

[![An example response](/images/blog/integrate-leadsconduit-blog/example-response.png)](/images/blog/integrate-leadsconduit-blog/example-response.png)

## How this supports your lead acquisition strategy

This isn’t just a tech integration—it’s a strategy upgrade. With Faraday connected to LeadConduit, you’re applying predictive intelligence the moment a lead enters your flow.

That means:

* Lower acquisition costs, since poor-fit leads are rejected earlier  
* Better sales efficiency, with lead prioritization based on conversion likelihood  
* More personalized outreach, using appended persona and trait data

You’re making smarter decisions earlier—and giving your team a cleaner, more focused pipeline to work with.

## Final thoughts

This setup gives your lead flow an intelligence upgrade. Whether you're filtering out poor-fit leads, scoring for immediate action, or enriching records for downstream personalization, Faraday’s integration with LeadConduit upgrades your entire lead flow—from first contact to final conversion.

Have questions or want help configuring your deployment? Reach out—we’re happy to walk through it together.

Integrate Faraday’s real-time predictive lead scoring into your LeadConduit flow in under 15 minutes—so you can filter, route, and enrich leads based on high-propensity signals before they hit your CRM.

Integrating Faraday and LeadConduit - a technical guide

“I don’t need even more predictions—I need to understand why they’re being made. I need to *know* they’re accurate.”

We hear this all the time from CMOs and Data Officers. And honestly? Fair enough.

AI is everywhere, but for too many companies, it’s still a black box. A model spits out a score, a list, a result—and you’re just supposed to *trust* it? That’s a liability. Not just for your brand, but for your team, your strategy, and your sanity.

At Faraday, we think you deserve better. That’s why we built our predictive AI to be fully transparent, auditable, and trustworthy—by design.

Here’s what that actually looks like:

## You’ll see exactly what’s driving predictions

Faraday doesn’t just say “here’s a good lead.” We show you *why* it’s a good lead.

When a model identifies a high-propensity contact, we don’t stop at the score. We give you a window into the reasoning. You’ll see which traits contributed most, whether that’s behavioral patterns like purchase frequency or demographic signals like household composition. And we don’t just show you which features mattered—we show you how. For example, not just that age played a role, but that older customers were significantly more likely to convert in this context.

This level of transparency gives marketers and data teams the ability to defend their decisions internally. You’re not just saying “the model told us to.” You’re showing exactly why a given segment is worth prioritizing and what drives performance within that group. It also means you can spot opportunities earlier—if you notice, for instance, that a surprising feature is driving conversion, that might be a signal to adjust messaging, rethink a campaign, or even explore a new product fit.

You also get full access to segment profiles, so you can zoom out and understand the broader picture behind performance—what cohorts are responding, how they align with your goals, and what that means for future campaigns. It's not just data. It's insight you can act on.

## You’ll know it works before you use it

We validate every model on your actual historical data before it goes live.

That means we’re not asking you to take our word for it. Through holdout evaluation and cross-validation, we assess how the model performs against real customer outcomes. You’ll see metrics like top decile lift spelled out clearly, so you understand how the model performs compared to a random baseline. And if it doesn’t hit the bar? We don’t ship it.

Validation isn’t a one-time event either. We continue monitoring performance through score efficacy reports that track how predictions are holding up in the real world. These reports don’t just say “green checkmark” or “fail.” They help you understand where the model might be drifting, how segment behavior is shifting over time, and when it’s time to retrain or tweak. For many of our clients, this becomes a core part of their ongoing marketing operations strategy.

We make it easy to spot drift, diagnose performance shifts, and refine accordingly—all without having to hire a data science team. In that way, Faraday acts like a partner that grows with you, rather than a vendor delivering static tools.

## You’re protected—because your data matters

Trust doesn’t stop at the predictions. It extends to how we treat your data.

Faraday is fully compliant with privacy regulations like GDPR, CCPA, and HIPAA. We use encryption for data at rest and in transit, and we operate under clear roles as a Data Processor, never repurposing your data for anything beyond your own success.

That also means our data sourcing practices matter. We only work with vendors who provide ethically sourced, permissioned data. Our customers don’t have to wonder if their campaigns are built on shaky foundations. Every enrichment or identity match comes with confidence—confidence that it’s compliant, safe, and built to scale.

We’ve gone through enterprise-level procurement and security reviews at companies across finance, healthcare, insurance, and retail. We know what matters to those teams, and we’ve built our infrastructure accordingly. From audit trails to access controls, our platform is designed to meet the standards of even the most risk-averse organizations.

## The bottom line

Just don't just believe us, as one of our clients noted for our recent blogs,  [**"Faraday vs. Think Unlimited"**](https://faraday.ai/blog/faraday-vs-think-unlimited),

“The transparency of the data science behind the modeling was refreshing. There were no secrets. It was our data, and it was given back to us in a digestible fashion.”

That’s the goal. Not magic. Not mystery. Just reliable, explainable predictions you can act on with confidence.

And that’s not an edge case—it’s our standard.

AI should be an asset, not a risk. At Faraday, we’ve built a platform that gives you full visibility into the *how* and *why* behind every prediction—so your team can move faster, smarter, and with total confidence.

If you’re building AI into your growth strategy—or trying to figure out how to do it without losing control—let’s talk.

We’ll show you what transparent AI actually looks like.

AI shouldn't be a black box—Faraday makes every prediction transparent, explainable, and trustworthy, giving teams full visibility into how and why decisions are made.

The AI black box is a liability. Here’s how we make predictions transparent

When you think of Faraday, you probably think of AI agents—or maybe the powerful, unified data contained within the FIG. And sure, those are things we do. But honestly, that's just the tip of the iceberg.

The truth is, Faraday is built to flex around your needs. We work with brands at every stage of sophistication, and with a huge range of business goals. Some come to us with a relatively defined and straightforward need, like improving lead scoring. Others want to clean up and append their messy or incomplete customer data, or to assess an entirely new market. Still others plan to build our predictive agents directly into their own SaaS products \- we call this Powered-by-Faraday. Finally, some just need help connecting all the complicated pieces together.

Where you start depends on what you need. We've designed Faraday to meet you at exactly that point.

## 🧱 Start here if: You need a clearer picture of your customers

If you're trying to understand your audience, clean up your CRM, or prep for future modeling—this is your starting point.

Our data enrichment and identity resolution services help you fill in the blanks, standardize messy records, and generate a complete, unified view of your customer base:

* [**Data Appends**](https://faraday.ai/docs/abstractions/data-appends): Append lifestyle, demographic, and behavioral traits—standard or premium—directly to your records and sync with your core systems. [More here](https://faraday.ai/blog/data-appends-blog).  
* **Identity Match Boost**: Add names, emails, and addresses to incomplete profiles, helping expand the size and accuracy of your training sets.  
* **Universal ID**: Standardize addresses, resolve identities across systems, and enable reverse email and phone lookups using a persistent person ID.

Not everyone is ready for predictive modeling—and that’s okay. Strong foundations make everything else possible.

## 🔩 Start here if: You need to connect the dots across systems

If you already have data and strategy in place but struggle to make it all work together, our professional services are here to bridge the gap.

We offer white-glove support to operationalize the intelligence Faraday provides:

* [**Managed connections**](https://faraday.ai/integrations): We’ll integrate Faraday with Salesforce, Meta, your internal CRM—whatever your stack looks like.  
* [**Market Opportunity Analysis**](https://faraday.ai/docs/abstractions/MOA): We review your market penetration quarterly and show you where the biggest geographic opportunities lie.  
* **Custom data science & analytics**: Execute bespoke projects with our senior data science team.  
* **Custom tooling & automation**: Build internal tools or workflow automations tailored to your business.

You don’t have to do it alone—we’ll help you turn strategy into action.

## 🧠 Start here if: You’re ready to predict and personalize

This is where many of our customers begin—and what we’re best known for: AI that helps you understand what your customers are likely to do next.

If you're looking to score leads, reduce churn, or create more personalized outreach, Faraday’s predictive models and audiences are built to deliver:

* **Predictive AI agents**: Go beyond lead scoring with models for churn, lifetime value, upsell potential, and more.  
* **Audiences for digital channels**: Receive high-fit hashed emails, ready to activate across Meta, Google, TikTok, and others.  
* **Audiences for direct mail**: Get precise household addresses for targeted or EDDM-style campaigns.

You bring the data—we’ll bring the intelligence.

## 🚀 Start here if: You want to build predictive power into your own product

Some of our most forward-thinking partners aren’t just using Faraday internally—they’re embedding it into the products they deliver to their own customers.

[*Powered by Faraday*](https://faraday.ai/blog/AI-Powered-insights-for-SaaS-companies-the-fast-way) lets you integrate predictive intelligence directly into your SaaS platform, offering rich insights without having to build a data science team from scratch.

With *Powered by Faraday*, you can:

* Embed predictive scores into your product workflows  
* Offer enriched customer profiles based on Faraday’s Identity Graph  
* Provide market opportunity insights or customer segmentations as native features

Our flexible APIs and white-labeled services make it easy to extend your platform’s capabilities—and drive even more value for your users.

## 🧰 Wherever you’re starting—we’ve built it to work together

Faraday isn’t a product you grow out of. It’s one that grows with you.

We’re not just here to predict. We’re here to help you deploy, adapt, integrate, and scale. Whether you're just getting started or refining a sophisticated strategy, we’ve got the tools—and the team—to support your growth.

That’s what makes Faraday more than just a hammer.

It’s the whole toolbox.

Faraday is more than just AI agents or unified data—it’s a flexible platform built to meet brands exactly where they are, no matter their goals or level of sophistication.

Product

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

ActiveProspect + Faraday: Smarter lead acquisition starts here

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

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

Integrating Faraday and LeadConduit - a technical guide

The AI black box is a liability. Here’s how we make predictions transparent

Faraday isn’t just a powerful predictive hammer - it’s the whole data science toolbox