If you frequent our blog (firstly, thank you), you already know that Faraday loves data, transparency, and [explainability](https://faraday.ai/blog/faraday-score-explainability). These ideas are at the center of our modeling and predictive agents—but it doesn't stop there. That emphasis permeates every aspect of our business.

So when we talk about return on investment (ROI), we’re not just talking about showing a nice number at the end of your campaign. We’re talking about helping you actually understand how Faraday is generating value for your business—not just the number, but where it came from, what influenced it, and how it can best inform your next move.

That’s actually why Faraday hired me.

Hi—I’m [Chris Waite](https://www.linkedin.com/in/christopher-waite-b3548015/), and I’m a data nerd who specializes in evaluating ROI for all our clients. My role is to make sure the value Faraday drives for your business is accurate, understandable, and, most importantly, guiding you towards further insights.

At Faraday, we don’t think of ROI as a final number you get at the end of a campaign. We treat it as a living, evolving data point—a signal that tells a story over time. And just like with any good story, you should be able to follow the plot. My job is to make sure you can.

ROI isn’t only about justifying past spend—it’s about helping you make better, faster, and more confident decisions moving forward. Knowing how things went is good… But knowing where to go next? Even better.

## ROI isn’t a score—it’s a strategy

A lot of companies say they help you track ROI—but what that actually looks like in practice can vary *a lot*.

Some treat ROI like a final grade. You get a slide at the end of a campaign with a nice round number and a chart pointing up and to the right. Others hand you a projection during onboarding that’s... let’s just say, aspirational. It’s a best-case scenario, not a reflection of what’s actually happening in your campaign.

At Faraday, we think that misses the point.

We treat ROI as a process, not a product. The real value of ROI isn’t just in a number at the end—it’s in what you learn along the way.

Done right, ROI helps you:

* Identify what’s working and what’s not  
* Catch issues and optimize while the campaign is live  
* Challenge assumptions you didn’t even realize were there  
* Build confidence in your strategy—not just in hindsight, but in real time

Because in a landscape where budgets are under pressure and every dollar has to count, ROI isn’t just about proving value. It’s about unlocking more of it.

## What to expect from Faraday’s ROI assessment process

Faraday takes ROI personally. Not just because it proves our value, but because—done right—it helps you uncover where your growth really comes from. And that starts long before the first campaign goes live.

When you work with Faraday, ROI isn’t an afterthought or a once-a-quarter deliverable. It’s built into the process from day one. Here’s what that looks like in practice:

#### We start measuring early

ROI modeling is part of onboarding—not something we tack on at the end. From the beginning, we’re thinking about what success looks like and how we’ll measure it.

#### We define assumptions together

ROI analysis is only as good as the logic behind it. We work closely with clients to make sure the “what if” scenarios we’re comparing against are grounded in data, reasonable, and transparent.

#### We track and adjust in real time

Our goal isn’t just to see if something worked—it’s to learn what’s working *while* it’s happening, so we can adapt. Think of ROI as a feedback loop, not a final grade.

#### We keep you involved

You won’t just get a chart in a deck and a polite “any questions?” We walk through the analysis with you, answer your questions, and collaborate on what the numbers actually mean for your business.

We know not every client comes in with a fully built-out internal analytics team. That’s okay. Whether you’ve got a room full of data analysts or none at all, we meet you where you are. Our job is to make ROI not just accurate, but clear and actionable—so you can make smarter, faster decisions with confidence.

## So what’s next

You might’ve noticed this post is the first in a new series covering all aspects of how Faraday approaches ROI—not just as a reporting tool, but as a driver of real, measurable value.

So what can you expect?

We’ll be diving into real client examples where ROI analysis didn’t just prove our impact—it helped uncover new opportunities and improve campaign performance. We’ll also get into the weeds with technical posts that break down the actual methods I use to assess ROI, from modeling assumptions to attribution logic and more.

In our next post, we’ll share a case study that shows how one client’s ROI review sparked new strategic decisions and unexpected insight.

Because if you’re investing in growth, you deserve more than a black-box metric. You deserve to know what’s working, why it’s working, and how to keep improving.

Let’s make that the standard. We can’t wait to see you here for the next one.

Faraday treats ROI not as a final score but as an ongoing strategy—helping clients understand, optimize, and expand on the value generating strategies we're driving throughout the entire campaign lifecycle.

ROI isn’t a scorecard—it’s a strategy

In every Faraday [Outcome](https://faraday.ai/docs/abstractions/outcomes) or [Recommender](https://faraday.ai/docs/abstractions/recommenders), you’ll find a list of traits—things like age range, buying habits, or location—that influenced the model’s predictions. This is called **feature importance**.

Each trait is given a percentage that represents how much it contributed to the model’s decisions. The list is sorted by level of impact, so the features at the top are the ones the model relied on most.

That percentage might seem small at first—especially if you’re seeing numbers like 2% or 3%. But those numbers can carry more weight than you’d expect. Let’s take a closer look.

## Imagine you're using our dashboard for the first time

You scroll to the Data section of your Outcome and see a list of features with importance percentages—maybe something like this:

[![Image of features of importance in a Faraday outcome](/images/blog/product/feature-importance.png)](/images/blog/product/feature-importance.png)

If you're new to predictive modeling, this might raise a few questions:

* Is 3% good, that seems low?  
* Should I be paying attention to this number?  
* Why are all the numbers so small?  
* How do I know what’s really driving the model?

These are totally valid questions. Let’s break it all down, step by step.

## Why 3% can be a big deal

To really understand why 3% can be a big deal, it helps to zoom out and understand how feature importance works in the first place.

When we train a predictive model, it starts by looking across a huge number of traits—over [1,500 possible features](https://faraday.ai/features/faraday-identity-graph) in our [Faraday Identity Graph](https://faraday.ai/docs/abstractions/traits)—to find patterns that relate to the outcome you're trying to predict (like conversion, churn, etc.). But not every feature is useful. Some are noisy, redundant, or irrelevant for your specific business case. So the model filters that list down to only the features that carry a useful predictive signal—on average, that’s around 200 features. 

Once the model is trained, we calculate **feature importance** to give you a sense of which of those \~200 features made the biggest impact. Each feature gets assigned a percentage that represents its contribution to the model’s predictions. All of those contributions are added up and scaled so they sum to 100%—that way, you can compare them directly.

Now here’s the key part: if every feature contributed equally in a 200-feature model, each one would have about 0.5% importance. But that’s not how models work. In practice, some features matter way more than others. So when one feature stands out with 3% or more, it’s doing a lot of heavy lifting.

It’s kind of like being the lead vocalist in a 200-piece choir. Sure, everyone’s contributing—but a few voices are probably carrying the melodies that most of the audience will notice. Those top features are the ones your model is leaning on to make decisions.

## What should I look for in the list?

Instead of just reading the numbers one by one, look for patterns:

* If your **top 20 features** are all clustered between 2–3%, it means the model is pulling from a wide range of signals. It’s a balanced prediction strategy, where no single feature dominates.  
* But if your **top 2 features** are 5% and everything else drops off quickly—say, under 1%—then those top traits are carrying a lot more weight in the model’s decision-making.

In other words, the **r**elative spread tells you more than any single number. The top features can help you understand what kinds of traits are making people more (or less) likely to take the action you're predicting.

## What about features working together?

This is where things get a little more subtle. Some features don’t just matter on their own—they’re powerful in combination with others.

For example, maybe *age* by itself is helpful, and *income level* is too—but together, they might form a much stronger signal. These kinds of relationships are called [interactions](https://christophm.github.io/interpretable-ml-book/interaction.html?), and our models are built to detect them.

That said, interactions don’t always show up clearly in a list of feature importances. A feature might look low on the list, but still be playing a key supporting role behind the scenes. 

## So what if a feature is too important?

In most models, a well-distributed mix of features is ideal. So if you see a single one standing out dramatically with a notably high importance score—we’re not talking about 6% as much as 40-50% here—that is likely a signal worth digging into.

What’s happening? Here are a few possibilities:

- The model may be over-relying on a single feature, which means it's leaning heavily on one signal while ignoring others. That can make the model less flexible and more vulnerable to changes in that one trait. If the feature drops out of future data or its relationship to the outcome shifts, the model may perform poorly.  
- It might be a sign of [data leakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning))—where the feature is unintentionally revealing information about the outcome it’s supposed to predict. For example, if a feature like "last purchase amount" correlates almost perfectly with whether someone converted, the model may appear highly accurate during training but fail in the real world. This is sometimes referred to as "cheating," and it’s one of the biggest pitfalls in predictive modeling.

This doesn’t mean there is a problem, but it certainly is worth a closer look. If you see a feature with importance that looks *too* high, reach out to us—we’ll take a closer look and either help you interpret the results, or make a change if something is happening in your model that shouldn’t.  

## Directionality

Each feature also includes [directionality](https://faraday.ai/blog/faraday-model-reports), which shows whether specific traits had a *positive* or *negative* influence on predictions.

This means you’re not just seeing how much something mattered—you’re also seeing *how* it mattered. For example, you might find that high-income households were more likely to convert (positive direction), while certain lifestyle types were less likely (negative direction). That context can be incredibly useful when refining messaging or targeting strategies.

You can read more about this aspect of feature importance in our previous [blog](https://faraday.ai/blog/faraday-model-reports).

## So what can you do with this info?

Once you understand what’s influencing your model, you can start using that insight in your day-to-day work:

* **Messaging**: If homeownership is one of your top features, your messaging might speak more directly to homeowners.  
* **Targeting**: If younger age ranges rank high, that could shift who you prioritize in your outreach.  
* **QA**: Seeing a feature rank high that *shouldn’t* be relevant? That’s a good moment to raise a flag and chat with us—we want your model to reflect reality, not noise.

Importantly, feature importance isn’t a checklist of what to act on—it’s a lens into what the model “sees” as predictive. Use it to guide your thinking, not to replace your judgment.

## Still have questions about what you’re seeing in your Outcome?

 We’ve got you covered. Just reach out to support—we’re always happy to walk through it with you\!

Feature importance in Faraday Outcomes and Recommenders shows which traits—like age or buying habits—had the biggest impact on the model’s predictions, helping you understand not just what drove decisions, but how and why.

Making sense of feature importance: Why 3% matters more than it might seem

We are excited to announce a significant improvement to how Faraday handles real-time identity resolution with **payload fallbacks**—a new strategy for producing consistent, reliable, and useful results for Lookup API users, even when an identity is only partially recognized.

## A challenge: partial matches

Faraday maintains a comprehensive, industry-leading database of U.S. consumer identities and insights, which we use to enrich identity data provided to us by clients. Even then, we sometimes are given identities that we only partially recognize—most commonly, we recognize an address, but not a household, or an address and a last name (household), but not a specific person. In these cases, Faraday would attempt to choose the closest or best match from our system, but would still resolve the request to a single identity.

## Our solution: payload fallbacks

To address this challenge, we've implemented a hierarchical approach to identity resolution that ensures meaningful results even for partially recognized identities.

Now, when our real-time API is queried with unrecognized individuals who have a known address or postal code, we aggregate data from other known individuals at that location to provide a meaningful composite score and set of attributes. Each API response includes a `match_type` field that transparently indicates which resolution level was used:

| Match Type | Resolution Level | Description |
| :---- | :---- | :---- |
| `address_full_name` | Person | Exact match to a specific individual |
| `address_last_name` | Household | Data aggregated from individuals with the same last name at the given address |
| `address_only` | Address | Data aggregated from all individuals at the address |
| `postcode_only` | Postcode | Zipcode-level aggregated data |

When the `address_last_name` or `address_only` match types are used, we additionally provide a `cohabitants` array (limited to 10 entries) that gives visibility into the individuals used for aggregation. This array helps you understand exactly how the aggregated values were calculated. Match types which do not consider address components (e.g. `email_last_name`) are not subject to aggregation.

**Example:** If you submit an address with no name information, and Faraday knows five individuals at that address, we'll aggregate their scores and traits to provide a representative result.

## Benefits for your business

These enhancements to identity resolution deliver several important benefits:

* More complete data with fewer gaps or null values  
* Higher-quality predictions by leveraging household and geographic patterns  
* Better handling of address-only or partial identity information  
* Transparent methodology that clearly indicates the resolution level used  
* Consistent results across all your Faraday deliverables

### Rollout details note

Some payload fallback features will be rolling out gradually by default, such as the `postcode_only` match type. For existing Lookup API users, this match type will only be available after your targets have been updated.

If you’d like access today or want to confirm your status, just reach out to your SAM and we’ll get you squared away!


## In conclusion

Faraday’s enhanced identity resolution ensures you get the most reliable insights, even from partial matches. By aggregating traits at the household, address, and postcode levels, we provide more complete data, higher-quality predictions, and greater transparency.

Want to see how we turn partial matches into actual, reliable predictions? Learn how score aggregation enhances modeling accuracy in this [blog](https://faraday.ai/blog/score-aggregation). Or already convinced to put better identity resolution to work for your business? Let’s [talk](https://faraday.ai/talk-to-sales).

Faraday has introduced payload fallbacks, a new identity resolution strategy that ensures more reliable results for Lookup API users, even when an identity is only partially recognized.

Smarter identity resolution through payload fallbacks with Faraday

A leading marketing, technology, and data company that specializes in direct mail campaigns came to Faraday with a problem: their targeting was working, but it wasn’t scalable.

They were helping their clients send mailers based on narrow behavioral triggers from their websites, like cart abandonment or time spent on a product page. 
That kind of targeting can be effective—sometimes. But often, the real world is messier.

Maybe someone hops on an apparel site, intent on making a purchase only to get pulled away by their toddler. Maybe a renter clicks through a solar quote tool, strictly because they’re curious, even though they can’t make the final decision to install solar on their roof. 

Not all signals mean intent. And more importantly: not every potential converter sends a clear, discernible signal every time they are about to make a purchase. 

**Reactive targeting wasn’t enough**

This behavioral-only strategy limited the number of people eligible for mailers—capping both reach and return. By relying solely on website interactions, the company’s campaigns were targeting a very specific kind of customer—one who was already halfway down the funnel and showing overt signals of intent. While this worked for a subset of visitors, it left out a much larger group of people who looked like good fits but didn’t quite meet the behavioral criteria.

The result: capped growth and missed opportunity.

## The solution: smarter lead scoring powered by Faraday

That’s where Faraday came in. By shifting from reactive targeting to predictive lead scoring, the company could stop waiting for the perfect website behavior and start identifying the right people to mail—before they ever clicked “add to cart.”

At the heart of this was the [Faraday Identity Graph](https://faraday.ai/docs/abstractions/traits)—a rich third-party dataset covering nearly all U.S. consumers and their households. By connecting their first-party behavioral data to this built-in body of insights and attributes, the company was suddenly able to understand far more about each identified website browser and score them accordingly. But that wasn’t all.

With Faraday’s AI-powered lead scoring, they could now evaluate:

* **Current prospects** not just by their actions—like browsing or abandoning a cart—but by their full profile. Was this someone who matched the traits of past converters? Did their income, lifestyle, or purchasing habits suggest they were ready to buy, even if their behavior didn’t scream it yet?  
* And, critically, **potential customers** who had never interacted with the brand at all—opening up entirely new audiences for outreach, no website visit or form fill required.

This opened up a whole new world of targeting. Instead of just reacting to a cart abandonment or a quote request, the company could proactively find high-potential leads who hadn’t yet taken any action—but still looked like strong matches.

No more waiting for someone to raise their hand. Now they could find and prioritize the right people, at the right time—and reach them with personalized mailers before the competition did.

## The result: 3x more mailers, same high conversion rate

Thanks to this predictive scoring approach, the company was able to triple its mailing volume—without compromising performance. That’s because every additional mailer was backed by a validated AI agent that accurately predicted likelihood to convert.

Their clients benefited too: more prospects reached, more conversions captured, and better ROI on every campaign.

## Why it worked

Faraday’s AI didn’t replace the company’s behavioral targeting-it enhanced it. By layering in predictive signals based on real people’s demographics and past purchasing behavior, the model was able to widen the targeting net without watering the quality of the leads down.

Now, the company helps its clients:

* Expand their reach beyond narrow behavioral triggers  
* Identify new high-potential leads using consented, privacy-safe data  
* Maintain high conversion rates even as mailing volumes increase

It’s not just smarter targeting—it’s scalable performance.

## The impact

By enriching their behavioral data with Faraday’s Identity Graph and predictive AI agents, this direct mail platform was able to triple their reach—without compromising on performance. More qualified leads. More efficient targeting. More revenue for everyone involved.

**Ready to scale your outreach without sacrificing ROI?**

Let’s talk about how Faraday can help.

How a direct mail platform used Faraday’s AI to triple their reach with smarter, scalable lead targeting—without losing conversion performance.

Smarter prospecting at scale: How a direct mail provider grew reach without sacrificing performance

So, you’ve signed up for Faraday’s lead rejection. Great choice—your bottom line will thank you.

Our [lead rejection](https://faraday.ai/templates/lead-rejection) helps customers buy smarter, not necessarily buy less. With [AI agent](https://faraday.ai/templates)\-powered filtering based on real conversion data, you're now able to pass on leads that aren't likely to perform—and reinvest in the ones that are. It’s a win for your marketing efficiency, your conversion rate, and your budget. But if you're working with third-party lead sellers, they might have questions when they see the change go into effect. That’s totally fair—and totally manageable. 

Because we’re all about making this process as smooth as possible, we’ve also thought about the vendor side of the equation. So to keep things streamlined from end to end, we’ve pulled together a few talking points you can use if (or when) they ask what’s changed. Think of it as your lead vendor cheat sheet: no awkward conversations, just clear communication.

[![Parks and Rec meme about awkward conversation](/images/blog/parksandrec-annperkins.gif)](/images/blog/parksandrec-annperkins.gif)

## Talking points to use with lead sellers

1. **We’re using AI agents to improve our lead buying.** Machine learning models help us predict which leads are most likely to convert, based on real conversion data.  
2. **This helps us buy more effectively, not necessarily buy less.** We’re reallocating the budget from lower-probability leads to higher-probability ones so we’re still investing in leads, just more strategically.  
3. **Stronger conversion rates mean a stronger partnership.** By improving our success rate, we can continue to grow and buy more leads from you over time.  
4. **If we pass on certain leads, it’s about efficiency, not cutting spend.** Our goal is to optimize performance, which ultimately benefits both of us.  
5. **Better results for us mean more long-term business for you.** The more effective our lead buying is, the more we can reinvest in your leads and scale our partnership.   
6. [**We can share insights on which attributes make a good lead.**](https://faraday.ai/blog/faraday-score-explainability) If you are looking to improve your sourcing strategies, we can share details on the top attributes we see as having positive or negative correlation with a lead converting.

## In conclusion

Making the switch to smarter lead buying doesn’t have to mean friction with your partners. In fact, it’s an opportunity to build stronger, more aligned relationships based on performance and transparency.

If you ever need help communicating with vendors—or want to dig into the data behind what’s working—we’ve got your back. The goal is simple: better leads, better results, better outcomes for everyone involved.

Please reach out if you have any other questions. We’d love to hear from you\!

After signing up for Faraday, your lead vendors might notice a change in your bidding habits. Here are some talking points to facilitate the conversation.

Talking to your lead vendors about AI-driven lead optimization

At Faraday, we talk a lot about [Forward-Deployed Data Science (FDS)](https://faraday.ai/blog/FDS-changes-the-game)—our team of data scientists who work directly with clients to build predictive models, validate results, and ensure you get the most out of AI agents. This service (although new) has already proven invaluable to our clients. It means that they get not only accurate predictions, but comprehensive strategic plans to ensure that our agents actually drive business value.

But data science is only half of the equation. The best predictive AI agents also require another type of data specialist to operate at maximum efficiency: **Data Engineers.** 

And just like our data science team, Faraday’s data engineers don’t disappear behind the curtain—as is often the case at our competitors. Instead they work directly with clients, in a “forward-deployed” capacity. They’re the ones helping you format and structure your data correctly, making sure your onboarding process is as smooth as possible, and reducing or resolving errors  to ensure your pipeline stays live and healthy. If you’ve ever submitted a dataset and gotten feedback on how to improve it, or had someone help troubleshoot an ingestion issue, you’ve already worked with our data engineering team.

Take [Chris Thomas](https://www.linkedin.com/in/krys-tomis/), one of our incredible data engineers, [who coincidentally just wrote a whole blog about how to best avoid pitfalls while importing your data into Faraday](https://faraday.ai/blog/ingesting-data-pt2). His job—and the job of all our data engineers—is to make sure your data is clean, structured, and ready for predictive modeling. And in just the last weeks he’s helped numerous clients avoid pitfalls that could have set their timelines back by months—thanks Chris\!

So what’s the difference between data science and data engineering, and how do both help you get the most out of Faraday? Let’s break it down like Raygun\!

[![Raygun break dancing](/images/blog/raygun.gif)](/images/blog/raygun.gif)

### Data science: turning data into predictions

Forward-Deployed Data Scientists (FDS) focus on understanding your business goals and applying AI to help you achieve them. They understand the nuances and behaviors of the predictive AI agents that power our business, but also can provide strategic advice on the implementation of agents that ensures that your predictions drive value. 

Their job is to help you:

* **Define and refine your use case**—what do you want to predict? How can AI drive better decisions for your team?
* **Data discovery and solicitation**–what data is needed for the use case.
* **Build predictive models** that identify key audiences, behaviors, and trends.
* **Validate model performance** to ensure accuracy and impact before deployment.
* **Interpret and apply insights** so you can confidently take action based on data-driven predictions.

For example, if you’re a solar company looking to expand into new markets, an FDS won’t just give you a model—they’ll help you determine the right questions to ask in the first place. How do we ensure that the data used for training isn’t biased by work in existing markets? What is the logical cut off for differentiating promising new markets from a likely low performing market? Which of our [powerful AI agents](https://faraday.ai/templates) can increase your conversion rates the fastest? By framing the problem strategically, they ensure that your predictions are aligned with real business objectives—helping you make informed decisions that drive sales, optimize marketing spend, and accelerate growth.

### Data engineering: optimizing your data and models

Data Engineers on the other hand focus on the infrastructure that makes data science possible, while working directly with you to ensure your data is in top shape. 

Their job is to help you:

* **Structure your data correctly** before ingestion so it matches well against the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) (FIG).  

* **Troubleshoot data issues** so you don’t lose time on formatting errors.  

* **Automate through properly configured integration** so your data flows seamlessly into and out of Faraday without constant manual updates.

Where data science can be viewed as more strategic and high-level—focusing on where you want to go, why, and how it will affect your business—data engineering is tactical, focused on how to get there efficiently. Engineers ask questions like, “How can we best implement this idea?” or “What changes will make this process more efficient?” 

At Faraday, a Forward-Deployed Engineer (FDE) like Chris undertakes tasks like supporting integrations with your data warehouse, reviewing your data during ingress, flagging potential issues, and suggesting optimizations to improve matching and model performance. 

### At Faraday, you benefit from both types of data experts

We don’t offer both Forward-Deployed Data Science and Forward-Deployed Data Engineering just because it sounds nice (although doesn’t it?)—we do it because it’s the fastest way to get actionable insights into our clients’ hands.

Making data engineering a client-facing function is a huge step that moves the process forward, ensuring that costly holdups and mistakes don’t happen before your predictive models even have a chance to work and that they keep working at peak efficiency after deployment. 

Here’s what that means for you:

🤔 **No more guessing if your data is good enough.** FDEs provide hands-on support before and during ingestion, catching issues early so they don’t slow you down later. After deployment they’re the ones making sure your models stay healthy.

⏳ **Faster time to value.** Instead of back-and-forth troubleshooting that delays model deployment, our FDEs work with you upfront to get data right the first time.

📊 **Stronger, more accurate predictions.** The best AI models in the world can’t fix bad data. By optimizing your data at the start, we ensure your models perform at their peak.

💰 **Fewer costly mistakes.** If your data isn’t structured correctly, you could end up with mismatched records, poor model performance, or wasted budget. FDEs help prevent that before it happens.

🤝 **A seamless experience.** Whether you’re working with an FDS to interpret predictions or an FDE to streamline integrations, you have direct support from an expert who understands your business.

In short, data science helps you make smarter decisions, and data engineering ensures your data is ready to support them. While forward-deploying both roles isn’t yet standard practice in the industry, we believe transparency, explainability, and speed to deployment aren’t just nice to have—they’re essential. And we’re not in the habit of waiting for the industry to catch up.

Our clients see the impact firsthand. Numerous businesses have leveraged our forward-deployed approach to streamline their data, improve model performance, and drive real results. You can explore a few of their [success stories here](https://faraday.ai/stories).

If you’re already working with Faraday, you know what this means for your business and bottom line. And if you’re not—shouldn’t you be?

Let’s chat about how we can help you put your data to work.

Faraday’s forward-deployed data scientists and engineers work directly with clients to ensure clean, structured data and precise predictive models—maximizing accuracy, efficiency, and business impact.

Data science + data engineering: How Faraday’s forward-deployed team fulfills all your data needs

It's 2025, and direct mail isn’t just alive—it’s thriving. 

Despite the rise of digital channels, businesses continue to invest heavily in direct mail because it works. Consumers engage with physical mail in ways that digital ads simply can’t replicate. Seniors rank it as their second-favorite method of business communication, and even Gen Z—raised in the digital age—often find it a welcome reminder of the tangible world. In fact, over [one in ten](https://www.pymnts.com/credit-cards/2024/boomers-prefer-email-gen-zs-opt-for-apps-as-top-choice-for-reward-delivery/) Gen Zers say direct mail is their favorite way to hear about banking rewards, proving that a well-executed campaign can capture attention across demographics. So it’s no surprise, then, that companies are doubling down, with  a [staggering 82% of businesses](https://go.lob.com/rs/900-QJF-050/images/2024-Lob-State-of-Direct-Mail-Marketing.pdf) having increased their direct mail budgets in 2024, a sharp jump from 58% in 2023\. 

And the reason? Direct mail delivers. As our partner, [Lob](https://www.lob.com/state-of-direct-mail/2025-report)—one of the best direct mail providers in the business—[reports it,](https://www.lob.com/state-of-direct-mail/2025-report) nearly 4/5 of executives they surveyed last year said that direct mail was their best outreach channel. That’s how much value they see it driving for their brands. And as AI and automation tools continue to improve execution—now used by 56% of marketers, up from 40% in 2023—direct mail is becoming even more effective, making it an essential part of modern marketing strategies.

But here’s the catch: it’s not always easy to implement.

Firstly, it can come with a serious price tag. Marketers are finding themselves paying more and more each month to reach high-intent prospects via mail. The United States Postal Service (USPS) plans to raise stamp prices five times by 2027, with a significant increase expected in July 2025\. This follows a 5-cent hike in July 2024 and a series of price increases in recent years, [raising stamp costs by 36% since 2019\.](https://www.axios.com/2024/09/26/usps-stamp-price-increase-2025) Additionally, USPS has outlined plans for twice-yearly increases in 2026 and 2027, subject to approval. With rising costs adding up quickly, maximizing the efficiency of direct mail campaigns has never been more important. Secondly, even the best campaigns need to be smart to get good response rates. Even if your messaging is on point, your product is best-in-class, and your brand is well known, it’s very possible that many of those sends won’t get a glance if your campaign isn’t properly targeted

So you’re likely starting to get the picture. That despite the cost going up, direct mail isn’t going anywhere. 

But where does that leave the rest of us marketers? Those without unlimited budgets. Most of us need to carefully manage each and every dollar of marketing spend to ensure the greatest value for our brand, and that means direct mail is a fantastic but risky tool that needs to be used correctly.

## So what should we do about it?

One of the best solutions for managing the low response rate problem is comprehensive direct mail targeting. Highly targeted campaigns allow brands to send mailers to only the most promising prospects, improving conversion rates. However, this approach also has its challenges. First, fully customized direct mail campaigns can still be expensive, as they require detailed audience segmentation and personalized messaging. Second, they can be too complex for many brands, requiring significant time and resources to manage effectively. Third, they depend on high-quality data to ensure accuracy. Although Faraday customers don’t have to worry about many of these issues thanks to the [Faraday Identity Graph (FIG),](https://faraday.ai/features/faraday-identity-graph) which provides rich consumer insights on nearly every US adult and their households, for better targeting, [as well as our powerful AI agents](https://faraday.ai/templates), which handle personalization and optimization, automatically, this tool is still effortful and overkill for some campaigns. Finally, postage for first class mail is three to four times the expense of bulk mailing options.

On the other end of the spectrum, the USPS offers a more budget-friendly option: [Every Door Direct Mail (EDDM)](https://www.usps.com/business/every-door-direct-mail.htm). EDDM allows marketers to send mailers to every address along a specific mail carrier’s route, without the need for custom prospect lists or addressing individual recipients. This can significantly reduce costs, sometimes as low as 20 cents per mailer, making it an attractive option for marketers looking to cut down on expenses.

However, the trade-off is clear: while the cost savings are substantial, EDDM lacks the targeting precision that custom direct mail offers. Since it sends to all households in a given area, marketers can’t ensure that their mailers are reaching high-intent or relevant prospects. This lack of segmentation means that some of the mailers will inevitably go to recipients who may have little to no interest in the product or service being advertised. As a result, brands risk wasting valuable marketing budgets on less targeted customers, potentially diluting the effectiveness of the campaign. Additionally, without the ability to target specific demographics or behaviors, the impact of the message might not be as strong or relevant, leading to lower conversion rates.

So, marketers often find themselves stuck between two good but limited options. Both can be powerful, but in some cases, neither can meet their exact needs. Do they want:

* High-cost, highly targeted direct mail campaigns, or  
* Low-cost, more loosely targeted EDDM campaigns?

As is often the case, a middle ground option would often best serve the needs.

## Enter: AI optimized EDDM

That’s exactly what Faraday has provided with our new high propensity AI optimized EDDM service. With approximately 15 carrier routes in an average zip code, knowing which ones perform best could strike the perfect balance between cost and effectiveness. Instead of choosing between expensive, hyper-targeted direct mail and inefficient, low-cost EDDM, brands can optimize their spend by targeting the best routes available through EDDM striking a balance between the other options. 

Carrier route aggregation provides a new tool by which brands can identify the carrier routes most likely to contain high-converting prospects but this functionality isn’t entirely new. Instead it expands on other existing aggregations we offer, including those at the state and household level (among a range of other options). But this feature is uniquely designed to take advantage of the EDDM option provided by the USPS. For marketers looking to take advantage of this specific service is a more targeted and efficient way, carrier route aggregation can be the perfect answer. With it, Faraday empowers clients to:

* Identify high-propensity carrier routes  
* Deploy cost-effective EDDM campaigns with improved targeting  
* Reduce waste while maintaining strong performance

And it is important to note that we’re not suggesting that aggregating by carrier route level is some magic bullet that will solve all your direct mail problems. The reality is that knowing which targeting method is best suited to the situation in question is the most important question to answer for effective targeting. 

Luckily for our clients, we have a team of expert, forward deployed data scientists (as well as some pretty spiffy AI agents) who can help you answer those questions or just solve the problem for you. At Faraday, you don’t just get predictions, you get targeted implementation strategies that mean those predictions can actually move your business forward, quickly.

## Client example

Let’s unpack how this is already helping one of our home service clients. 

Before this new feature, this client was dropping \~10 million EDDM mailers a year relying on broad demographic filters like household income and age to choose carrier routes. After developing and deploying a [likely customer AI agent](https://faraday.ai/templates/lead-prioritization) across their service territory, they discovered that the top 10% of routes identified by the model delivered more than **3x** the profit per route compared to their previous average.

In practice, the client was able to maintain the benefit of EDDM’s lower cost while still ensuring that mailers reach the most likely converters, resulting in significantly improved marketing and sales KPIs. The bottom line? Smarter targeting makes direct mail more efficient, cost-effective, and impactful—helping brands win more customers while optimizing their marketing spend. 

## In conclusion

Direct mail isn’t going anywhere, but the way marketers use it needs to evolve. Rising USPS costs make efficiency more critical than ever, and blindly choosing between ultra-targeted campaigns or broad EDDM mailings isn’t always the smartest move. Instead, the key is flexibility—having the ability to adjust your strategy to get the best return on your marketing spend. Services like Faraday provide this flexibility and the expertise to utilize it effectively.

We’re not here to tell you that carrier route aggregation is the only answer—but we are saying that using the right tool for the job will make your direct mail campaigns more cost-effective and impactful. Sometimes, broad, untargeted EDDM campaigns make sense (like when you need to reach every homeowner on a street). Other times, hyper-targeted mailers are worth the investment. And now, with carrier route aggregation, you have a powerful middle-ground option to strike the perfect balance between reach and efficiency.

At Faraday, our goal is to equip you with the best possible tools for any situation. Whether you need pinpoint accuracy, cost-effective broad reach, or something in between, we’ve got you covered. So what’s stopping you? Hate making money? If not, let’s talk and see how Faraday can help you optimize your direct mail strategy today. [Sign up here](http://faraday.ai/signup) for a free trial (with a complementary predictive model validation).

Faraday’s AI-optimized EDDM strikes the perfect balance between high-cost, well-targeted direct mail campaigns and low-cost, low precision EDDM by identifying the most profitable carrier routes, maximizing impact while minimizing waste.

Finding the sweet spot in direct mail: Cost-effective targeting with AI optimized EDDM

The real estate industry is undergoing a major shift. In March 2024, the [National Association of Realtors (NAR)](https://www.nar.realtor/) reached a settlement that will change how real estate agent commissions are handled. [Under the new rules](https://www.bankrate.com/real-estate/real-estate-commission-changes/), sellers will no longer be required to offer compensation to buyers’ agents via the [MLS (Multiple Listing Service)](https://www.mls.com/). Instead, buyers and their agents must negotiate compensation directly, rather than relying on a commission included in the home’s sale price. While this change is intended to increase transparency, it also adds complexity for buyers, who must now determine how they will compensate their agent—potentially before viewing homes. This shift could lead to more negotiation hurdles and uncertainty, potentially slowing down transactions for both buyers and agents. This new landscape presents a significant challenge for real estate agents who have historically relied on the established commission structure.   

In this evolving landscape, one thing has become clear: data-driven decision-making, particularly through the strategic use of lead scoring, is now essential for real estate agents to thrive. With commissions becoming more negotiable and unpredictable, agents need to focus on the highest-potential clients. Faraday’s [predictive lead scoring](https://faraday.ai/templates) agents—leveraging advanced AI to provide unparalleled accuracy in identifying and prioritizing prospects in this shifting market—help real estate professionals prioritize the right prospects, streamline their pipeline, and navigate the new commission structure with confidence.

### The impact of new commission rules on real estate agents

The most significant change in the new commission rules is the requirement for buyers' agents to have a written agreement with clients about fees before showing any properties. This means buyers will be more directly aware of how much they’re paying for representation, making it crucial for agents to clearly communicate their value.

At the same time, some predict a "price war" will erupt, with buyers' agents competing fiercely for clients in an attempt to offer lower fees. Others fear this change will create confusion and extend the home-buying process, especially for first-time buyers who are already navigating high prices and interest rates.

### How lead scoring helps

In this new environment, agents who can prioritize high-value leads will have a significant edge. Lead scoring can use data to assess which leads are most likely to convert, allowing agents to focus their time and resources on the clients who are most likely to result in a sale. Faraday’s agentic lead scoring models analyze a wide range of signals—from behavioral patterns to buying intent—to give agents a clear picture of which leads are worth pursuing.

With Faraday’s platform, agents can assess leads in real-time, adjusting their strategies quickly to align with changing market conditions. Instead of wasting time on leads that aren’t ready to engage, agents can focus on those who are most likely to convert, maximizing their chances of closing deals while keeping costs in check. Agents using predictive lead scoring have reported a significant increase in conversion rates by focusing their efforts on the most promising leads.   

### Addressing the new challenges for buyers and sellers

The new commission structure also increases transparency, but it has its drawbacks. Buyers will be more aware of the costs of hiring an agent, and many may feel the pressure to negotiate commissions themselves. Sellers, meanwhile, could face more complicated negotiations if buyers demand concessions or if they struggle to cover commission fees upfront.

Lead scoring helps agents respond to these challenges by providing a data-driven way to approach both buyers and sellers. For buyers, lead scores can indicate their level of urgency and financial readiness, helping agents tailor their approach to address potential commission concerns proactively. For sellers, understanding buyer agent interest and engagement through lead scoring can inform strategies for offering competitive compensation. Agents can use lead scores to identify clients who are ready to negotiate or those who may need additional support. This allows agents to tailor their approach to meet each client's specific needs, whether that’s offering strategic advice on negotiations or focusing on clients who are already financially prepared.

### Stay ahead in a shifting market

As commission structures continue to evolve, and the world around us changes every day, the real estate industry faces both challenges and opportunities. Agents who embrace data-driven tools like lead scoring will be better equipped to adapt to these changes and thrive in a competitive market. By focusing on high-value leads, real estate professionals can ensure that they’re making the most out of every opportunity—even in uncertain times.

Faraday’s predictive lead scoring tools provide real estate agents with the insights they need to make smarter, more strategic decisions. In a world where commissions are more negotiable than ever, having a clear understanding of lead potential is key to staying ahead.

### Ready to transform your real estate business?

Learn more about how Faraday can help you navigate the new real estate landscape and stay ahead of the competition. [Request a demo](https://faraday.ai/talk-to-sales) to see Faraday in action and discover how our AI-powered lead scoring can revolutionize your business.

Faraday’s predictive lead scoring helps real estate agents adapt to new commission structures by identifying high-value prospects, streamlining their pipeline, and maximizing conversions in an increasingly competitive market.

Navigating new commission structures in real estate: How lead scoring can help agents adapt

Before committing to any predictive modeling platform, you need to know if their services actually work for you. That’s where a [validation test](https://faraday.ai/blog/cross-validation-technical-blog) comes in. Here at Faraday, a validation test (such as a holdout or cross validation) lets you see how well our AI agents can predict real outcomes—before you even sign up. By using your historical data, we can prove the accuracy of our agents and models without ever seeing your future results. Essentially, if the model is predictive, that means we're a good fit for helping you decide who to send direct mail to, how to accept and organize leads with lead scoring, and more. It’s the best way to validate AI-driven predictions—before you invest, and without any risk.

## How does it work?

A holdout test works by splitting your historical data into two sets: **one we see** (pulled from your recent conversion data, this is known as the test set) and **one we don’t** (pulled from the **most** recent conversion data, this is known as the holdout). First, we use only the training set to build a predictive agent. Once it is built, we generate predictions and compare them against the conversions that were ‘held out.’ If our agent accurately predicts those hidden outcomes, you can be confident in its ability to drive real results.

Cross-validation works similarly, but instead of holding out a single set for testing, it splits the data multiple times into different training and validation sets. Each subset takes a turn as the validation set while the rest are used for training, ensuring a more comprehensive assessment of model performance.

If you’d like a more detailed description of how this process works, take a look at this [blog](https://faraday.ai/blog/holdout-testing-with-faraday) from our COO, Robin Spencer, or for more detailed setup instructions, see this [blog](https://faraday.ai/blog/holdout-testing-evaluate-prediction-accuracy) from our CEO, Andy Rossmeissl.

Validation testing gives you a real-world view of the model’s accuracy, providing the reassurance that our predictions will stand the test of time—by making sure the AI isn't just memorizing past data, but identifying trends that matter going forward. But for the test to work, we need the right data—uploaded securely. Here’s what you need to know to get results as quickly as possible.

## What data do you need?

To set up a validation test, you must provide **names and dates** for both **leads and conversions**.

### Leads

We need to know when someone became a lead. Use the [Leads template](https://faraday.ai/data_solicitation/leads_template.xlsx) to provide this information, making sure to include the date for each lead. A leads file should ideally include the following fields:

* **First name**  
* **Last name**  
* **Address** (Street, City, State, Zip)  
* **Optional:** Email, Email Hash, Phone  
* **Date** (e.g., the date a lead was added to your system or when they received a mailer)

These personally identifiable details (PII) allow us to match leads to our demographic dataset. Without PII, we won’t be able to validate predictions, and the validation test cannot be run.

🚧 **Note on date formatting:** You can format the date in any standard way (e.g., YYYYMMDD, MM/DD/YYYY) and our system will automatically handle it.

### Conversion event data (transactions)

We also need the dates of individual transactions—along with optional details like product type and amount. Use the [Orders/Purchases/Transactions template](https://faraday.ai/data_solicitation/orders_purchases_transactions_template.xlsx) to submit this data. The conversion file should have the same structure as the leads file (first name, last name, address, etc.), but instead of lead dates, it should contain conversion dates.

### Conversions (customer-level data)

Customer-level conversion data (e.g., just the first or last transaction date) **is not ideal** because we need each individual transaction date. Although you can provide customer-level data using the [Customers template](https://faraday.ai/data_solicitation/customers_template.xlsx), we typically do not use it for a validation test.

## Uploading your data securely

To keep your data safe, always upload it through the **Faraday dashboard** in the Datasets section. You can also connect your platform to your existing data warehouse, but as this integration is more involved, this step would likely come after your successful validation test has been completed and you’ve decided to sign up with Faraday\!

🚧 **Do not email us any data.** The Faraday dashboard is the only secure place to upload files.

### Steps to upload:

1. Go to [app.faraday.ai](https://app.faraday.ai) and sign in.  

[![Sign in page](/images/blog/are-you-ready-holdout/sign-up.png)](/images/blog/are-you-ready-holdout/sign-up.png)

2. If you see a test mode warning, disable it before uploading your data.  

[![Make sure to leave test mode](/images/blog/are-you-ready-holdout/leave-test-mode.png)](/images/blog/are-you-ready-holdout/leave-test-mode.png)

3. Navigate to the **Datasets** section. 

[![Navigate to the Datasets section](/images/blog/are-you-ready-holdout/datasets.png)](/images/blog/are-you-ready-holdout/datasets.png)

4. Click **New Dataset** and select **CSV**. 

[![Select new dataset](/images/blog/are-you-ready-holdout/new-dataset.png)](/images/blog/are-you-ready-holdout/new-dataset.png)

5. Upload your **leads.csv** file and your **conversions.csv** file.

[![Choose CSV upload](/images/blog/are-you-ready-holdout/upload-csv.png)](/images/blog/are-you-ready-holdout/upload-csv.png)

## Want a little more help?

If you're still unsure how to set up your validation test, we've got you covered. [Watch this video walkthrough](https://www.loom.com/share/ab3750fdb50d47c2b6a0fc05e96e10dd?sid=aefe17c6-ba5f-458f-986f-73db3cf43877) with Faraday CTO Seamus to see the process step by step.

And if you still have questions, reach out to us at **support@faraday.ai**—we're happy to help\!

And that’s it\! Once uploaded, we’ll take care of the rest. Ready to get started? Upload your data today and see how Faraday’s AI agents can drive real results for your business.

Validate Faraday’s predictive accuracy risk-free with a holdout test—just upload your data, and we’ll prove our models work before you commit.

Are you ready for your pre-purchase AI agent validation?

Score aggregation is a method we use to enhance the accuracy and stability of predictions when individual-level data is incomplete. Instead of relying solely on aggregated traits—an approach that works great when appending data but can introduce volatility in model outputs when scores are produced with these aggregated inputs—we take the next step and aggregate scores themselves. This ensures that models can still generate high-quality predictions, even when full individual identity resolution isn’t possible.

By leveraging aggregated scores, we can create smoother, more consistent predictions across varying levels of enrichment. This builds on our existing strategy of using aggregated traits for data appends but tailors it specifically for improving modeling outcomes.

## A simple example

Sounds a little complicated? It’s actually easier than you think\! Let’s break this down with a simple scenario that unpacks two hypothetical individuals: 

#### John Doe – Fully identified

John lives at 456 Oak Street, and we have all the necessary PII to match him confidently in our system. Because of this, we can generate a highly individualized score based on his specific traits and history no problem. This is how our identity resolution process ideally works.

#### Jane Doe – Partially identified

Jane, on the other hand, lives at 123 Main Street, and we don’t have enough personal information to fully match her. However, we do know a lot about her household. Previously, we would have used aggregated traits from the household (or street, postal code, depending on how far out we’d need to go to find accurate information) to generate her score, but this could result in fluctuations.

Now, instead of scoring her based on those aggregated traits directly, we leverage an **aggregated score**—we average the **scores** of people in similar groups (in this case her household) rather than creating an average person based on traits and then scoring that, itself. This provides a more stable and accurate prediction, reducing potential variability and improving reliability.

[![Score aggregation diagram](/images/blog/score-aggregation.png)](/images/blog/score-aggregation.png)

## So how does it affect you?

The key impact of score aggregation is that it makes every model better. However, you might also notice changes to individual scores, and here’s why:

* **More reliable predictions:** Instead of introducing potential spikes from scoring based on aggregated traits, we now rely on aggregated scores, which reflect a more stable signal.  
* **More inclusive modeling:** Even when we can’t fully identify an individual, we can still provide a meaningful prediction using data from their household or surrounding population.  
* **Fewer gaps in data:** If a person’s identity is incomplete, we don’t discard the opportunity to score them—instead, we use the best available aggregated score to fill in the blanks.

### Key considerations

When implementing score aggregation, there are a few key considerations to keep in mind. First, using aggregated scores helps prevent sudden shifts in predictions that might occur when only aggregated traits are used. This approach provides a more stable and reliable signal. Second, our methods adapt based on the available data: when full PII (Personally Identifiable Information) is available, we prioritize direct individual scoring. However, when only partial data is present, aggregated scores serve as a reliable fallback. Lastly, if only household-level data is available, rather than estimating an individual’s score based on household traits, we derive a score from the averaged predictions of others in similar households, ensuring that the prediction remains accurate and meaningful despite incomplete individual information.

### In conclusion

By adopting score aggregation, we ensure that models remain robust and useful even when some data points are incomplete. This helps businesses make better decisions with confidence, regardless of data availability. If you'd like to learn more about how this—and the numerous other services and [AI agents](https://faraday.ai/templates) that Faraday provides—could benefit your business, [reach out](https://faraday.ai/talk-to-sales)\!

Score aggregation improves prediction accuracy and stability by averaging scores from similar groups rather than estimating individual scores from aggregated traits.

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