If you read our last blog, [What’s in a deployment? How to read your Faraday outputs](how-to-interpret-your-outputs), you’ve already figured out what comes out of Faraday, now it’s time to understand how to shape it. While that post broke down the different types of payloads you can get from Faraday, this time, we’re going one level deeper: into the deployment settings themselves. 

Think of this as a user-friendly guide to all the options you might see when setting up a deployment—from filters to formats, geography to structure, and beyond. These settings help ensure that your predictions don’t just look good in a spreadsheet—they’re ready to plug into your actual tools, from CRMs to ad platforms.

If you've ever wondered what settings like "representation format," "add geometry," or “deployment analysis” actually do—or how they impact your final output—this blog is for you.

## But first, what’s a deployment again?

[Deployments](../docs/abstractions/deployments) (called “targets” in the API) are how you tell Faraday where to send your predictions and what those predictions should look like when they arrive. They’re created within a pipeline and allow for detailed customization to make sure the output fits your intended use case—whether you’re running a digital ad campaign, syncing data back into a CRM, or creating a list for direct mail.

### 1\. Where your predictions go

The first decision you make when creating a deployment is choosing its destination. This could be a Faraday-managed connection (like your CRM, ESP, or ad platform), a location in your own infrastructure (like a cloud bucket or database), or just a downloadable CSV. This is the foundation—everything else shapes what gets delivered. If you’d like to see a CSV broken down, check out that [last blog](how-to-interpret-your-outputs) we mentioned\!

### 2\. What your predictions look like

#### Representation format

In the app, we define this as the following: “Choose how you want the pipeline's output to be formatted for this target."

Think of representation format as the style or privacy level of your output—whether it’s raw, anonymized, or summarized. Deployments can be configured in a few different formats depending on how you plan to use the data:

* **Hashed**: This is the default format, typically used for ad platforms. PII is hashed to protect identity while allowing for matching.  
* **Referenced**: This format lets you use your own internal identifier to merge the data back into your system.  
* **Aggregated**: Useful for geotargeted ads or planning, canvassing, direct mail and more,  this format summarizes how many individuals fall into key groups (like a persona or outcome) per geographic area. You’ll get a row per geographic area, with counts of people per persona or outcome. In this row, data can be aggregated into the following categories: ZIP code, census tract, carrier route, county, metro area, state, etc.  
* **Identified:** This format includes direct personal identifiers like name and address, allowing you to activate immediately through mailers, call centers, or CRM integrations. Because this format exposes PII, it’s only available in secure workflows with appropriate safeguards and permissions in place.

#### Column headers

You also get to choose how the column headers are labeled:

* **Machine-friendly** headers are concise and consistent, ideal for automated ingestion. Useful especially for our API users.   
* **Human-friendly** headers spell things out a bit more, which can make it easier for marketers and other stakeholders to work with the data directly, especially in tools like CRMs or email platforms. For example, “`fdy_persona_set_{UUID}_persona_name`” could appear as “`fdy_persona_set_customers_persona_name`” in human-readable mode.

### 3\. Filters, limits, and structure tweaks

#### Filters

Filters allow you to narrow your deployment to just the segments of interest. You can filter by personas, outcomes (via percentile or probability), or even recommender results (such as top product matches). This is how you target only the individuals who matter most for a given campaign. For example, give me the top 10% of scorers for my outcome. 

#### Limits

Need to limit the number of people you're deploying to? You can set a top or bottom row limit—useful for campaign budgeting,  prioritization, or suppression. 

**📘Note:** in preview mode, deployments are capped at 1,000 rows. We break down the difference between preview mode and a deployment later in this blog\!

#### Structure

You can also rename or reorder columns to better match the formatting requirements of your destination platform. For example, if you're sending a list to LinkedIn or Facebook, you can use one of our pre-formatted options for easy compatibility.

### 4\. Geography & “Add geometry”

When building a geotargeted campaign, geographic options and the “add geometry” setting come into play. You can define the geographic granularity—ZIP code, metro area, census tract, etc.—and include shape data for use in map-based planning or delivery platforms. This setting only applies to aggregated deployments. 

If you’d like more information about how to read these outputs, just look at [our recent blog](how-to-interpret-your-outputs) which broke down how to read the labels\!

[![Example of what a add geometry output might look like](/images/blog/add-geometry-output.png)](/images/blog/add-geometry-output.png)

### 5\. Deployment analysis

Deployment analysis gives you a snapshot of what’s inside your deployment before you send it off. It includes summary stats on key traits and geographic distributions, delivered as a downloadable PDF or accessible via the API. This can help you validate that the list aligns with your goals—for example, checking that you’re really hitting the right income bands or urban areas before launching a campaign.

📘 **Note:** Access to traits in deployment analysis depends on your account permissions. If you’re not seeing the traits you need, reach out to your account manager to learn more about available options.

### 6\. Score explainability

Finally, if you've selected prediction explanations during pipeline setup, your deployment can also include score explainability. This details which traits had the greatest influence on an individual’s predicted score. It’s useful for internal transparency, model evaluation, or even surfacing to customer-facing teams.

If you're not sure whether you've enabled or don’t know how to read it, reach out to your Faraday team, we’ll confirm or help turn it on.

### 7\. Preview mode vs. full deployment

Before launching a full deployment, it’s helpful to run a preview—and it’s important to understand how previews behave differently. In preview mode, your deployment is capped at 1,000 rows, no matter what filters or limits you’ve set. This cap exists so you can sanity-check your configuration without triggering a full export. Once you’re ready to go live, the full deployment will reflect your entire population and apply all settings as specified—no artificial row limits included.

## Wrap-up

Deployments are more than just the final step in your predictive workflow—they’re the bridge between insight and action. With the right configuration, you ensure your models aren’t just accurate, but actually useful—delivering the right predictions, in the right format, to the right destination.

A practical guide to customizing Faraday deployments—from filters and formats to geography and output structure—so your predictions arrive ready for action.

How to shape your deployments: a guide to filters, formats, and more

If you’re a marketer asking how your team can qualify and prioritize leads more effectively, you’ve probably heard about predictive lead scoring. But what actually makes it different from traditional lead scoring, and is this ‘upgrade’ worth it?

Well, it’s 2025, and nobody’s got time for rhetorical suspense: yes, predictive lead scoring is a major upgrade, and yes, it’s worth it. Let’s break down why. 

## Before we dive in

Our CTO, Seamus Abshere, recently published a deeper technical breakdown of why simple scoring systems like RFM and first-party data alone aren’t enough to guide modern marketing strategies. That blog walks through the data science behind predictive scoring and why it outperforms old-school heuristics.

This blog aims to take that same content and break it down for non-technical readers, without the code samples or statistical deep dives. But if you’re feeling extra brainy today, or just want to try to understand the concepts like an expert data scientist would, [you can find Seamus's original post, “Why first party data (and RFM) is not enough”, here](/blog/why-first-party-data-and-rfm-is-not-enough).

## What is lead scoring?

Lead scoring is the process of ranking leads to determine how likely they are to convert into customers. This helps sales and marketing teams focus their time and resources on the people most likely to say “yes.”

There are two main approaches: traditional and predictive. Each aims to support conversion efforts, but they differ fundamentally in how they evaluate leads.

### Traditional vs. predictive lead scoring: A quick comparison

To understand the evolution from traditional to predictive lead scoring, it's helpful to see the differences laid out side by side:

| Feature | Traditional Lead Scoring | Predictive Lead Scoring |
| ----- | ----- | ----- |
| **Method** | Rule-based (manual point systems) | Machine learning trained on past outcomes |
| **Data used** | Basic firmographics, behaviors, form fills | Hundreds of behavioral, demographic, and contextual signals |
| **Scalability** | Manual tuning required per campaign | Fully automated, adaptable, and always learning |
| **Accuracy** | Assumptions-based, subjective | Outcome-driven, empirically validated |
| **Maintenance** | Static, often neglected | Continuously improved via retraining |
| **Transparency** | Clear but simplistic | Advanced but explainable (e.g., model cards) |

This comparison highlights how predictive lead scoring offers deeper insights, broader data coverage, and greater efficiency—without the manual overhead.

## Why smart CMOs are shifting towards predictive

With the complexity of today’s customer journeys and an explosion of marketing channels, relying on static, point-based scoring just doesn’t cut it anymore.

Traditional scoring is great—if it’s 2008\. But in 2025, teams are managing thousands of signals across channels, and the limitations of hand-crafted scoring systems are showing:

* **They can’t scale**—manually assigning points doesn’t work across product lines or geos.  
* **They’re biased**—they reflect gut instinct, not real data.  
* **They’re static**—the model that “worked” last quarter might be off-base today.

Predictive lead scoring flips that script. By analyzing your historical data—who actually converted, when, and why—machine learning models generate scores for new leads that reflect real patterns, not assumptions.

This shift enables marketing and sales teams to act faster and with greater confidence, knowing their lead rankings are grounded in evidence.

## How Faraday does predictive lead scoring differently

Of course, not all predictive models are created equal. At Faraday, predictive lead scoring isn’t just a model—it’s an integrated strategy designed for accuracy, usability, and transparency.

We help brands:

* **Enrich leads with real-world context** using the Faraday Identity Graph (240M+ U.S. adults)  
* **Train custom conversion models** based on your actual outcomes—not someone else’s benchmarks  
* **Deploy scores in real time** into CRMs, ad platforms, and lead routing systems  
* **Understand what’s working** with model transparency and segment-level insights

The goal is not just to predict who is likely to convert, but to help your team understand why they’re likely to convert—and how you can act on that knowledge.

## Common questions (and misconceptions)

As predictive scoring becomes more popular, a few key questions tend to come up. Here’s how we typically address them—drawing from our own platform and our CTO Seamus Abshere’s deeper technical dive on the topic.

#### “Isn’t predictive AI just a black box?”

This is one of the most common concerns we hear, and it’s a fair one. Many predictive tools offer opaque outputs with no clear path to explain how a lead was scored. At Faraday, we’ve made transparency a core design principle. Every model we build includes:

* Model explainability breakouts that document the data used, what the model was trained to predict, and how well it performed  
* Feature importance scores that show which attributes are driving conversion likelihood  
* Segment insights that break down what’s working and why in human-readable terms

If you want to dig into the technical mechanics of explainability, [this post from Seamus](blog/why-first-party-data-and-rfm-is-not-enough) goes deep on how we make models transparent and actionable, not just accurate.

#### “What if I don’t have enough data?”

Another myth. The truth is, most brands have more data than they think, they just need help unlocking it. And when first-party data really is sparse, Faraday fills in the gaps with [enrichment](/blog/identity-matching-makes-or-breaks-predictions).

We tap into our proprietary [Faraday Identity Graph](/features/faraday-identity-graph) covering 240+ million U.S. adults, enabling your models to learn from behavioral and contextual signals beyond your CRM. This lets you:

* Model outcomes even with limited internal history  
* Boost accuracy by adding real-world context  
* Reach prospects with higher confidence

Predictive lead scoring doesn’t require perfection—it just needs patterns. And sometimes, those patterns are hiding outside your own CRM.

Take one client, for example: Bee’s Wrap.

They were able to take their targeting to the next level by tapping into third-party data through Faraday.

## How Bee’s Wrap went beyond demographics

[Bee’s Wrap](https://www.beeswrap.com/), a sustainable food storage brand, needed help identifying new retail partners—but traditional targeting methods like household income or zip code weren’t cutting it. They turned to Faraday to build predictive personas based on actual behavior, not just surface-level traits.

Using third-party behavioral data, Faraday identified nuanced lifestyle patterns—like eco-consciousness and repeat purchase behavior—that wouldn’t show up in first-party data alone. That insight helped Bee’s Wrap secure distribution in over 550 locations of a major national retailer - you might've heard of them, they're called Target.

It’s a perfect example of why modern scoring needs to go deeper than form fields or simple demographic filters. 👉 [Read the full story](/blog/bees-wrap-persona-analysis)

## Want to see how your leads would score?

If you’re curious how predictive lead scoring could work with your data, we’d love to show you. Reach out to see what it looks like in practice—and how it can plug directly into your workflow.

A non-technical guide to why predictive lead scoring outperforms traditional methods—and how Faraday makes it transparent, scalable, and ready to use.

Predictive vs. traditional lead scoring, for dummies

At Faraday, transparency and explainability aren’t just ideals, they’re a core and ever-present aspect of our product strategy. We believe that understanding how and why predictions are made is critical to building trust in AI. That’s why we’re continually expanding what you can see and do inside the platform.

In a [previous post,](https://faraday.ai/blog/what-is-explainable-ai-why-is-it-important) we outlined how we expose the various steps we undertake—through explainability reports and other transparency measures—to help you understand the what and the why of your predictions. But now, we’re going a step further.

## Predictive modeling isn't magic, it's strategy

For every [Outcome](https://faraday.ai/docs/abstractions/outcomes) you define in Faraday, we build hundreds of models using a variety of strategies. These strategies define how we create candidate models, and after rigorous cross-validation, the best performers are promoted to production. 

In Faraday’s API, a *strategy* is a specific configuration or approach for building a predictive model. It may define which data sources to use, how to segment the population, and what modeling technique to apply. Strategies are automatically applied and tested behind the scenes as part of our modeling pipeline.

While this information was always available, until now, it appeared only in the technical report, buried, and inaccessible to machines. But with our latest release, that changes.

## What’s new

### API

The API for `GET /outcomes/:id/analysis` now includes a `strategies` object that lists all strategies used in the Outcome. Each includes:

* A unique ID and slug  
* Strategy name and description  
* Providers (e.g. Faraday Identity Graph, self-provided)  
* Experimental flag 

If you're inspecting results for specific individuals, you can now trace exactly which strategy applied to them.

### UI

This information now also shows up in the Dashboard. Choose one of your Outcomes, then find a new “Strategies” tab in the performance reporting.

📘 *Note:* Strategy metadata will only appear for newly generated Outcomes. Older Outcomes may not include this information unless reprocessed.

## Here’s why it matters

This update makes model strategy metadata available to humans and automations alike, enabling more transparent audits, better debugging, and smarter automation for advanced users.

It also surfaces a key part of the Faraday platform that's typically invisible: the structured experimentation that goes into every prediction, and that’s so central to our work, it’s literally where we got our name (but enough about [Michael Faraday](https://en.wikipedia.org/wiki/Michael_Faraday) and his experiments)\!

If you're building automation on top of Faraday or just want to better understand what goes into your Outcomes, this update is for you.

And if you want to learn more, ask your account manager, or visit [the Outcome API documentation](https://faraday.ai/docs/reference/getoutcomeanalysis)\!

Faraday now surfaces the modeling strategies used to build Outcomes—via API and dashboard—bringing deeper transparency and auditability to how predictions are made.

More visibility into how your models are made: strategies now available in the API and Dashboard

Welcome to **Faraday Delivers**, a new blog series where we respond directly to insights shared on [USPSDelivers.com](https://www.uspsdelivers.com/). USPS has long been a trusted voice in marketing strategy, particularly around direct mail and customer engagement. In this series, we’ll explore their key themes and offer a Faraday perspective on how predictive AI and data science can take those strategies even further.

Today, we’re responding to ["Why Personalization May Help You Win with Millennials"](https://www.uspsdelivers.com/why-personalization-may-help-you-win-with-millennials/?).

## Why this article stood out to us

USPS makes a compelling case: Millennials value personalization. According to the article, 46% of millennials are willing to share personal data in exchange for more relevant brand experiences. When done well, personalization builds loyalty, drives purchases, and helps brands stand out in an increasingly noisy market.

But to us, that stat isn’t just interesting, it’s a launchpad for smarter strategy. At Faraday, we work with brands every day to help them reach millennial consumers through relevance—and one of the best ways we’ve found to do that is through predictive personalization.

## From personal to predictive

The USPS talks about making marketing feel personal. But here’s the thing: without prediction, is it really personal? Calling someone by name or referencing their last purchase isn’t true personalization. It’s decoration. To deliver real relevance, you need to anticipate what they actually want next.

That’s where Faraday comes in. Our AI agents don’t just analyze who your customers are, they forecast what they’re likely to do next. That lets you personalize with purpose, not just polish.

For example, our agents can:

* Identify which customers are most likely to convert soon  
* Forecast repeat purchases or churn risk  
* Recommend channels and messaging based on predicted behavior

And yes, that includes direct mail. Our predictive models help you decide:

* **Who** should receive a mailer  
* **Which** personas live at that address  
* **What** products are most likely to resonate

All based on real, measurable likelihood to act, not just surface-level traits.

## A (hypothetical but realistic) example

Let’s say a debt consolidator brand wants to re-engage lapsed millennial customers. USPS suggests a personalized postcard could do the trick.

Faraday would build on that approach:

* Use historical data to identify which customers are most likely to buy in the next 30 days  
* Segment that audience by behavioral persona (e.g. student debt holders vs. big credit-card spenders)  
* Optimize the mailer’s creative and timing for each segment

The result? Higher response rates, lower acquisition costs, and a better customer experience—because the message feels right and arrives at the right time.

## Why this matters more than ever

Millennials are tech-savvy, budget-conscious, and, most importantly, inundated with marketing. They expect brands to get them. Predictive marketing helps ensure you meet that expectation without wasting spend on low-likelihood segments.

Direct mail is powerful—when it’s targeted. And for good targeting, personalization is critical—but prediction is the key unlock.

## Stay tuned

This is the first in our "Faraday Delivers" series, where we dive into USPSDelivers.com blogs and share how predictive AI can elevate the strategies they spotlight. We’re big fans of USPS’s work, and we think there’s a lot of room to build on it with the tools we use every day.

Want to learn more about predictive personalization for your millennial audience? Let’s talk.

This blog argues that personalization alone isn’t enough—brands need predictive AI to truly engage millennials with timely, relevant outreach that anticipates their next move.

Faraday delivers: Using predictive AI to engage millennials more effectively

“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.

Product