You’re likely here because you want to get more out of your lead acquisition—and if you’re using [ActiveProspect’s](https://activeprospect.com/) [LeadConduit](https://activeprospect.com/leadconduit/), you’re already ahead of the game. But that is only the first step. Imagine combining that real-time lead filtering power with the [predictive intelligence and lead scoring](https://faraday.ai/templates) that Faraday has on offer. That’s where things get interesting.

This integration gives you the best of both worlds: Faraday’s ability to assess how likely a lead is to convert, and LeadConduit’s ability to make an instant decision on whether that lead is worth bidding on through their ping-post functionality. The result? Smarter decisions, faster processing, higher conversion rates, and optimal use of your marketing budget.

## What is LeadConduit?

ActiveProspect is a leader in optimizing lead acquisition, offering tools that help businesses validate and qualify leads in real-time. Their product, LeadConduit automates the decision-making process of bidding on affiliate leads, including rejecting leads that don’t meet your criteria. If you're buying or routing a lot of leads, this kind of automation is critical.

## So why integrate with Faraday?

While LeadConduit handles the immediate pass/fail/bid criteria, Faraday adds predictive depth to your decision-making through one of our [AI agents](https://faraday.ai/templates/lead-rejection). Our models assess how likely a lead is to convert based on how closely they resemble your customers. Instead of working from a fixed rule set, you’re working from the probability of a lead converting.

By connecting the two platforms, you can automatically filter out low-potential leads before you even bid on them. That means your conversion rates improve, your budget goes further, your sales team spends less time chasing dead ends, and your funnel stays clean and efficient.

## How this works for Faraday customers

When a new lead enters your system, LeadConduit sends that lead’s basic identifying information—things like name, email, phone number, and address—to Faraday for evaluation. From there, the [Lookup API](https://faraday.ai/docs/features/lookup-api) handles two core tasks: [identity matching](https://faraday.ai/blog/identity-resolution) and [predictive scoring](https://faraday.ai/blog/predictive-lead-scoring-b2c).

First, we match the lead to a real individual in our consumer data universe. The more identifiers you include, the more likely we are to find a high-confidence match. Even partial submissions—like just an email and zipcode—can often yield results, though additional fields like full address or phone help improve match accuracy and reduce false positives.

Once a match is made, we instantly return a payload of predictive insights. This can include a [conversion likelihood score](https://faraday.ai/templates/lead-prioritization), [persona classification](https://faraday.ai/templates/thematic-personalization), and any other configured outcomes you’ve built in your Faraday target. These models are customized to your business goals—whether that’s subscription signups, purchases, loan applications, or any other high-value conversion.

All of this happens in milliseconds, a requirement for operating within ping-post. As soon as the lead hits LeadConduit, the Lookup API evaluates it, and LeadConduit uses that response to decide whether to accept or reject the lead in real time. That means you’re not waiting for enrichment or reviewing leads after the fact—you’re getting a go/no-go signal before any budget is committed.

It’s like embedding a predictive intelligence checkpoint directly into your acquisition pipeline. Instead of just filtering on static criteria like age or location, you’re making decisions based on real behavioral and demographic alignment with your highest-value customers.

For additional details on inputs, outputs, and setup options, check out our [Lookup API documentation](https://faraday.ai/docs/features/lookup-api).

## Why this integration matters for your broader strategy

When Faraday and LeadConduit work together, you're not just filtering more effectively—you're building a smarter, faster, and more resilient lead acquisition system that adapts as you grow.

This integration powers:

* **More automation** – Less manual lead review, more consistency at scale  
* **Smarter prioritization** – High-potential leads move forward, low-value leads don’t  
* **Scalable efficiency** – Whether you process hundreds or thousands of leads, the system just works  
* **More efficient use of your marketing budget** – Stops wasted spend on bad leads.

Because it runs on live data and your custom models, it evolves alongside your business. As your audience evolves, your predictions evolve with them. It also adapts in real time to new lead sources, new personas, or entirely new markets.

Perhaps most importantly, this setup takes the grunt work off your team’s plate. With automated evaluation and rejection, your team can focus on what really matters: building relationships, closing deals, and driving growth.

## Ready to put your lead acquisition on autopilot?

Faraday \+ LeadConduit makes it easy to scale smarter, spend better, and stay focused on what actually drives revenue. If you're ready to see what predictive, real-time lead filtering can do for your funnel, let’s talk.

LeadConduit is an extremely powerful tool on its own, but when combined with Faraday’s predictive AI agents, it becomes a real-time decision engine that helps you prioritize high-value leads before you ever place a bid.

Supercharge your lead acquisition with Faraday + LeadConduit

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

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

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.

Noticed a change in your score reporting? Here’s how score aggregation is improving your results

When you’re ready to use Faraday to build [AI Agents](https://faraday.ai/features/customer-behavior-predictions), the first step is getting your data into our system. There are two primary methods to make that happen—using a data warehouse or uploading individual `.csv` files. Your best option depends on your team’s setup and resources. This guide covers how to ingest your data and best practices to make it usable for modeling as quickly as possible.

## Part 1: Locating your data

### Data warehouse vs. file upload

**Data warehouse:**  
If your data resides in a warehouse (e.g., [Snowflake](https://www.snowflake.com/en/) or [BigQuery](https://cloud.google.com/bigquery?hl=en)), integration is straightforward. Your team can provision a small, dedicated space within the data warehouse for Faraday, providing secure, efficient, and timely transfers without the hassle of repeated manual uploads. Our [connections](https://faraday.ai/docs/how-to/connections) console provides the necessary credential details for integration.

**File uploads:**  
If your data is managed as individual files, you can upload them directly via our datasets console. Although this method is simple, it is time-consuming to keep data fresh over time because of the level of effort required each time.
- `note:` Faraday is looking for `.csv` (comma-separated values) files - they can be exported from databases, various business platforms and spreadsheets alike.

  **Automation considerations:**  
  For brands using cloud storage (e.g., [Amazon S3](https://aws.amazon.com/s3/) or [Google Cloud Storage](https://cloud.google.com/storage?hl=en)), setting up automated data transfers is simple and efficient. These services can be connected to Faraday with minimal effort and can be nearly as effective as connecting directly to a data warehouse.

  An SFTP transfer option is equally suitable, but may require slightly more knowledge and access to resources within your organization.

---

## `.csv` file formatting requirements

To ensure smooth processing, follow these guidelines when preparing your CSV files:

- **Format:**  
  - ***Files:*** Most of these files are comma-separated and UTF-8 encoded, ideally with a header row listing the column names. If headers are missing, you can add them in the dataset configuration's 'Advanced options'.
  - ***Data warehouse*:** Sticking as closely to simple and common types of fields such as decimals, integers, boolean (true/false) and text (string/varchar) are most effective. Tables or views are both acceptable.

- **Size:**  
  - ***Files:*** Individual files can be up to 5GB in size, and generally should be fewer than 50 columns. Consider splitting larger files into smaller parts and-or removing any fields not related to *behavior*, *time*, *quality* or *identity*.
  - ***Data warehouse*:** Generally, there is no size limit for data warehouse tables, but large tables should be handled with care because the larger the dataset, the longer the processing time.

- **Structure:**  
  - ***Files:*** If providing updates to data, ensure consistent structure across all files in a dataset (cloud bucket/SFTP: "*prefix*") - column names, orders and the data types they are set to (if applicable).
  - ***Data warehouse*:** If using a table, ensure it remains up to date through a regular refresh process. Flattened tables (not nested) are preferred in most cases. Plan on establishing a set of fields to include and avoid changing them over time (such as adding or removing fields).

- **Delimiter consistency:**  
  - ***Files:*** Prefer commas as standard delimiters, but this setting is customizable at the dataset level (tabs, semicolons). If necessary, ensure all columns are quoted.

- **Additional flexibility:**
  - ***Files:*** We accept zip-compressed files such as .gz or .zip if you are batching and saving on storage.

---

## Part 2: Data essentials for accurate predictions

Once your data is uploaded, it must include the necessary elements for precise predictions. Faraday supports various data types, but certain formats yield better results.

### Types of data

- **Event stream data:**  
  This type of data is preferred for its detailed insights into customer behavior, where each row represents an event (e.g., account creation or purchase) - with a *timestamp*. This granularity is key for understanding customer journeys. (See our [Datasets documentation](https://faraday.ai/docs/abstractions/datasets) for more details.)
    
    Flavors:
    - email engagement data
    - website engagement data
    - app usage data
    - order data
    - sales funnel data
    - customer journey mapping
    - direct mail response tracking

- **Customer-level (summary) data:**  
  This type of data captures one row per customer with summary information such as: purchase history metrics, customer tier, original source or lifetime value. With a joining key (e.g., customer_id), it works best when combined with event stream data.
  - `note:` Summary type data may hold valuable insights on your customers' [first-party traits](https://faraday.ai/blog/first-party-features). It *can* also include event timestamps as additional columns when relevant and is frequently seen in CRM exports.

### Essential fields

To ensure accurate modeling, include these key fields:

- **Personally identifiable information (PII):**  
  First name, last name, address (street, city, state, postal code), email, and phone number. At least name + address or email are required at a minimum. More PII enables enhanced data enrichment through the [Faraday Identity Graph](https://faraday.ai/docs/abstractions/traits), and can unlock a world of value.

- **Event timestamps:**  
  Accurate timestamps are critical for tracking when events occur. "YYYY-MM-DD" is the preferred format, but other common selections are available via the app and API. 
  - `note:` Ensure all time-based fields follow the *same* format, whichever option you choose.

  - **Event properties:**
    Other values which are captured at the time of the event that may exist in your records. (Cost of goods, method of payment, unique products or services, result of event)
    
    In these cases, the additional data should answer the question: `How much value was generated for your business by this event?`

- **Unique identifiers:**  
  Each set of data should have a *primary key* (e.g., an order number) and a *reference key* to link said event to an individual. These datapoints deliver maximum flexibility to both you and Faraday through the process of updates.

### Identity resolution and matching

After your first-party data is connected, Faraday’s identity resolution software links individuals to additional third-party attributes with over 1,500 available traits and counting. Providing as much quality PII as possible improves matching accuracy and minimizes reliance on aggregated traits.

At a minimum, you must provide an email or a combination of first and last name with street address and postal code.

---

## Note on data hygiene

Organizing and labeling your data correctly is vital — poor quality input yields poor quality output every time. Clean, well-curated data leads to smoother processing and more accurate insights.

**Best practices include:**

- Maintaining consistent formatting: no type mixing across fields, names and addresses follow a similar structure, categories have fixed sets of standard values, and avoiding filler values for missingness (e.g., "None", "N/A")
- Verifying that phone numbers are a full 10 digits, accurate emails are provided, and names come with *full* physical addresses
- Deduplication
- Ensuring you do not have large gaps in field coverage or in time between groups of events
- Not providing more than you intend to use (aside from future use cases)

Investing time in even minimal data hygiene upfront saves time and reduces the likelihood of missing a problem that was lying in wait.

---

## What’s next?

Getting your data into Faraday is just the beginning. By following these best practices, your data will be structured and ready for predictive modeling—whether you’re predicting lead conversion or conducting market opportunity analyses. If you have trouble checking all the boxes above, don't worry - Faraday will help find a solution for your needs.

Let us know if you have additional questions as you prepare your data. You can connect with support at support@faraday.ai - happy modeling!

To get started with Faraday, proper data ingestion is essential. This article offers a technical overview of the processes and best practices for preparing and ingesting your data into Faraday's system, ensuring fast and accurate predictions.

Preparing your data for seamless ingestion - a technical guide

When brands partner with [Faraday](http://faraday.ai), the goal is clear: deploy predictive models as quickly and effectively as possible to [drive business ROI](https://faraday.ai/stories/financial_services_client). But before any predictions can happen, the foundation has to be set, and that means getting the right data into the platform.

For marketers without a dedicated data scientist on their team, this step can feel like a hurdle. What data does Faraday need? In what format? How can you ensure a smooth process that avoids issues with matching and model performance?

These are big questions, and answering them correctly could be the difference between actionable predictions in a week or two versus a much slower and less efficient process.

## Start with the big picture

Before diving into spreadsheets and data exports, take a step back. The first question we ask any new client is: ***What problem are you trying to solve?*** Your goal determines the data you’ll need. For example:

* Are you looking to improve conversion rates? You’ll need historical data on past conversions.  
* Want to identify high-value customers? Transactional data will be key.  
* Need to optimize outreach? Engagement metrics and response data should be included.

Knowing your objective up front ensures that the data you provide aligns with your business needs. 

So what data do we need? The best answer is probably to give us access to *everything—*or at least, as much as is feasible for your company. While some data points are necessary to make the right predictions, all of them can help us to build a better and more holistic understanding of your existing customer base and the best potential customers you could reach within the US population.

![Meme about how much we love data](/images/blog/welovedata-meme.png) 

## So more IS better, but formatting matters

*So we’ll just give Faraday all our data as is and let them figure it out?*

**So, the answer is both yes and no.** While more data is generally better, it’s not just about volume—it’s about data quality. Brands without an established system to ensure [data hygiene](https://www.indeed.com/career-advice/career-development/data-hygiene) may encounter issues like malformed addresses (e.g., missing state or zip code), missing customer details (e.g. no address at all), or improperly formatted columns (e.g., first and last names in one field). While Faraday’s team can spot these issues early on, working with clean, well-structured data from the start will drastically speed up the process, leading to faster, more accurate predictive models.

If you're facing challenges with data hygiene, don’t hesitate to reach out. In addition to our standard data cleanup, we offer more robust professional services upon request, with a variety of packages designed to help you wrangle your datasets. Even if your data isn’t perfect, we’ll help you identify and fix any issues, setting you up for quicker results.

## So what data should I include?

At a minimum, the following fields should be included to ensure proper matching and predictive accuracy:

* **Customer identifiers:** Name, email, phone, and address (as complete as possible)  
* **Leads:** Name and the date that someone became a lead  
* **Conversions (transaction-level preferred):** Dates of individual transactions, along with product details and amounts (if applicable)  
* **Engagement metrics:** Website visits, email opens, and interactions

#### Customer-Level vs. Transaction-Level Data

While you can provide customer-level conversion data (e.g., first or last transaction date), we usually do not need it and cannot use it for a holdout test. Instead, transaction-level data—with a date for each transaction—is preferred, as it enables more accurate predictive modeling.

And if you're unsure where to start, focus on conversion rates. Defining how your brand measures conversion—whether through completed purchases or booked appointments—helps us create more accurate predictive models and simplifies the entire process.

## Where does your data live?

Once you’ve identified the right data, the next step is understanding where it lives. Is it spread across multiple systems like a CRM, e-commerce platform, and customer support database? Do you have easy access to it and permissions to share it externally, or will you need internal approvals? For enterprise brands, pulling data may require specific requests within your organization. Understanding these logistics early on can prevent delays.

In the best case, your data lives in an organized data warehouse like [Snowflake](https://www.snowflake.com/en/), [Bigquery](https://cloud.google.com/bigquery?hl=en), or [Redshift](https://aws.amazon.com/redshift/). This makes ingress easy, as Faraday is a fully composable platform that can integrate with existing data warehouses, functionally sitting on top of them as a decisioning layer, and then automatically routing predictions to channels like [meta ads](https://aws.amazon.com/redshift/), [Shopify](https://www.shopify.com/), or many others. To learn more about our available integrations, take a look at [our integrations landing page](https://faraday.ai/integrations). 

![Faraday adds a decisioning layer on top of your data warehouse](/images/blog/data-warehouse-first-strategy.png) 

If your data isn’t stored in a warehouse, you can upload individual CSV files through our dashboard, but if you do make sure to pay attention to your data hygiene\!

## Data hygiene, what’s that?

Data hygiene is the practice of keeping your data clean, structured, and accurate to ensure the best possible outcomes when using predictive modeling and AI tools like Faraday. Poorly formatted, incomplete, or inconsistent data can lead to mismatches, errors, and weaker insights. Clean data doesn’t just make processing smoother—it directly impacts the quality of predictions and business decisions, **and this is the single most important thing to pay attention to when importing your data into Faraday\!**

The first step to good data hygiene is ensuring consistency in formatting. Datapoints such as names, addresses, and dates should follow a structured format, ideally with first and last names in separate columns and dates consistently standardized in a format like YYYY-MM-DD (I[SO 8601](https://en.wikipedia.org/wiki/ISO_8601)). Additionally, emails and phone numbers should be complete (no 7 digit phone numbers or filler values like “noemail@none.com”) to enable seamless enrichment with third-party attributes in the Faraday Identity Graph. Clean PII helps ensure the best possible enrichment rates for model performance.  
Properly labeled and complete fields prevent confusion and make it easier to match records correctly whereas missing or incomplete records can significantly weaken predictive accuracy. 

Despite the fact that data gaps can be somewhat mitigated by aggregated scoring, better results are always seen when providing a sound dataset from the get-go. If certain data points are unavailable, we can still make predictions, but providing as much information as possible—especially time-bound event data—will dramatically improve our ability to analyze your data quickly and accurately.

So in conclusion, taking the time to check, clean and structure your data before ingesting it into Faraday will save time, prevent headaches, and lead to stronger, more reliable predictions. If you have questions about how to prepare your data, don’t hesitate to reach out to your Strategic Account Manager or Faraday support. 

And speaking of our supportive team…

## Leveraging forward-deployed data science (FDS) for a faster start

Getting your data into Faraday doesn’t have to be a challenge—we offer multiple options to help you onboard seamlessly. Enterprise clients can access Forward-Deployed Data Science (FDS) for hands-on guidance, while all clients benefit from professional services, dedicated support, and self-serve resources to streamline data ingestion.

Our team helps eliminate bottlenecks by structuring and preparing data, optimizing predictive models, and providing strategic guidance. Whether you need help standardizing fields, refining lead suppression strategies, or aligning predictions with business goals, we ensure your models are built on a strong foundation.

For enterprise clients, FDS embeds our experts directly into your workflow, continuously refining models and adapting strategies to market shifts. No matter your level of support, we’re here to help you get started quickly and drive measurable impact from day one.

If you’d like to learn more about this available resource, take a look at our [recent blog post](https://faraday.ai/blog/FDS-changes-the-game) detailing our decision to forward deploy our data science team.

## Final checklist: Setting your team up for success

To make the handoff between your business and technical teams easier, follow this checklist: 

✅ Clearly define your business objective

✅ Identify where relevant data lives 

✅ Ensure core customer identifiers are included 

✅ Provide date fields for key events 

✅ Clarify how conversion rates are measured 

✅ Work with FDS to streamline the process

By following these steps, you’ll avoid common pitfalls, speed up deployment, and get to actionable predictions faster—maximizing the impact of AI-driven insights on your business.

## In conclusion

Getting your data into Faraday doesn’t have to be complex. With the right preparation and support, you can streamline the process, move quickly from data ingestion to predictive modeling, and unlock actionable insights faster.

If you’re ready to learn more or have any questions about preparing your data for Faraday, don't hesitate to reach out. Or if you're feeling prepared to go at it alone, get started for free today, and [sign up here](https://login.faraday.ai/u/signup?state=hKFo2SBTU2FQa0dIbmg4QzBfV0kwTDczbXdiVHc5Wm1mOVB5TqFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIHk0S3JUSGlUMExSSFVPejduNDdBQnpFeHBjZVVjN00xo2NpZNkgMDF6SWFodVNaV203SmRVMFNtWmVNUHMzZ2ZHcklqWTI)\!

To deploy predictive models effectively with Faraday, clients need to start with clean, well-structured data that aligns with their business objectives, ensuring smooth ingestion and accurate predictions. This blog serves as a high-level introduction to this process.

Preparing your data for seamless ingestion - a marketer's overview

You know your customers—but do you know how to quantify them? Have you ever considered that something as seemingly random as **"roof type"** or **"interest in boating"** might influence whether someone wants to buy your product? The reality is, we’re all made up of a range of traits, and we don’t always know which ones will most impact purchasing behaviors.

That’s why the [Faraday Identity Graph, a gigantic set of 1,500+ customer attributes](https://faraday.ai/features/faraday-identity-graph) is so valuable. It captures both the obvious and the unexpected, giving businesses an edge in understanding what truly drives conversions. Still not convinced? Let’s take a look at some of the weirdest, most surprising traits that can help businesses make smarter decisions.

*Now, please note that these are hypothetical examples—after all, we wouldn’t want to disclose any of our current customers’ “secret sauce.” However, these vignettes illustrate the unexpected ways our FIG has informed value-driving outcomes.*

## Vignettes: expected problems with unexpected solutions

### Predicting insurance customers who need premium coverage

* **Challenge:** An insurance provider wants to upsell premium policies to the right customers but relying solely on income data hasn't been cutting it.

* **Unexpected solution:** "Spending habits percentile" — Individuals who consistently spend at higher levels tend to value protection for their assets and financial well-being. By identifying high-spending individuals, the insurer can target them with personalized offers for premium life, home, and auto insurance policies, ensuring they have the right coverage for their lifestyle.

### Finding the best locations for a new coffee chain

* **Challenge:** A growing coffee brand needs to find locations with high morning foot traffic.

* **Unexpected solution:** "Propensity to own a dog" — Dog owners tend to follow a routine and take their pets for walks in the morning, making them prime candidates for coffee shop visits. By targeting neighborhoods with a high concentration of dog owners, the coffee brand can identify areas with a ready customer base for morning foot traffic.

### Predicting high-end jewelry buyers

* **Challenge:** A trendy jeweler is aiming to branch into new markets. They know some indicators like income level and location are relevant, but what else might help?

* **Unexpected solution:** "Garage parking spaces" — People who have garage parking are often homeowners with higher incomes, typically living in more affluent neighborhoods. This demographic is more likely to invest in luxury goods, including high-end jewelry. By targeting individuals with garage parking, jewelry brands can identify customers who are more inclined to make premium jewelry purchases, such as fine watches, diamond rings, or luxury accessories.

### Finding the right audience for debt consolidation services

* **Challenge:** A debt consolidation provider wants to reach consumers who are likely to need their services but may not be actively searching for help.

* **Unexpected solution:** "Catalog Showroom credit card usage" — People who frequently use catalog showroom credit cards may have higher levels of revolving debt, often at steep interest rates. By identifying these consumers, the provider can target individuals who are likely to benefit from consolidating their debt into a lower-interest option, offering relief before they fall into deeper financial strain.

### Identifying high-value clients for a financial services firm

* **Challenge:** A financial services company wants to identify potential high-value clients for premium investment products.

* **Unexpected solution:** "Vehicle category \- luxury cars" — People who own luxury cars tend to have higher disposable income and an interest in premium goods and services. By identifying this segment, the firm can target prospects who are more likely to invest in high-end financial products, such as wealth management services, private banking, or exclusive investment opportunities.

## Why AI sees what humans miss

Does all this sound a little far-fetched? Well, we don’t mean to offend you, but that might be because you’re a lowly human. The whole reason predictive AI agents are so valuable is their ability to recognize patterns we might never think to look for.

We rely on AI because it takes care of the busy work—handling repetitive tasks far more efficiently than we ever could—but more importantly, AI spots the connections we don’t. If 90% of people with Dutch gabled roofs buy BMWs, our models will detect that pattern, even if no human ever thought to check.

Faraday’s FIG does exactly this across 1,500+ attributes, helping businesses uncover the surprising, data-backed insights that fuel real growth.

## Breaking FIG down

Faraday’s Identity Graph (FIG) is what makes all of these insights possible. By synthesizing these unique attributes across hundreds of millions of U.S. adults, FIG gives businesses an unparalleled ability to understand and predict customer behavior.

But let’s be honest—1,500 is a lot of data points to consider. To help make sense of it all, we’ve broken FIG down in the following graphic to show the scale of the 1,500+ attributes and also breaking them out by category. A more detailed listing of all FIG attributes can be found and downloaded as CSV [here](https://faraday.ai/features/faraday-identity-graph).

![](/images/blog/fig-visual-diagram.png)

FIG doesn’t just provide more data—it provides better, structured, and predictive-ready data that businesses can actually use to identify new connections that they'd likely never uncover on their own.

If you’d like to learn more about this powerful resource, take a look at the other blogs in this series\!

## Closing thought

Sometimes, the smallest, most unexpected data points unlock the biggest opportunities. FIG delivers both the obvious and the hidden predictors—helping businesses make smarter, data-driven decisions.

Want to see how these insights can transform your business? [Let’s talk](https://magic-pr-5668-faraday-ai.vercel.app/talk-to-sales)\!

Faraday's Identity Graph (FIG) includes unexpected customer traits—like "roof type" or "interest in boating"—and detects patterns amongst them that humans might miss, helping businesses make smarter, data-driven decisions.

The unexpected data that powers smarter predictions

## How direct mail lead suppression works at Faraday

In the highly competitive financial services industry, direct mail remains a powerful tool for customer acquisition. However, without precise targeting, companies often waste significant resources sending mail to leads who are unlikely to convert. This inefficiency inflates marketing costs and dilutes campaign effectiveness.

Faraday’s predictive [lead suppression](https://faraday.ai/templates/lead-suppression/recharge) AI agent solves this challenge by leveraging data-driven insights to eliminate low-intent leads from direct mail campaigns. This process follows a structured approach:

### Step 1: Data ingestion & enrichment

Faraday begins by securely ingesting the client’s lead data. Using the  [Faraday Identity Graph](https://faraday.ai/docs/abstractions/traits) (FIG)—which includes over 1,500 attributes on 240 million U.S. consumers—our platform enriches this data with key demographic, behavioral, and property insights.

### Step 2: Predictive modeling & trend analysis

Our machine learning algorithms analyze historical conversion patterns to identify traits associated with high-intent buyers—specific to the brand. This predictive analysis allows us to assign a probability score to each lead, ranking them from most to least likely to convert.

### Step 3: Lead decile segmentation

Leads are grouped into ten deciles based on their likelihood of conversion. This segmentation provides a clear understanding of which leads offer the highest return on marketing investment and which are unlikely to engage.

### Step 4: Establishing a suppression threshold

We work with clients to establish a **cut-off threshold**, ensuring that leads below a certain probability score are removed from direct mail outreach. This helps reduce wasted marketing spend while increasing the efficiency of each campaign.

### Step 5: Optimized mailing list delivery

Once the suppression model has been applied, we provide the client with a refined, high-value lead list, ensuring that marketing efforts are directed toward the most promising prospects.

## How lead suppression adds value to financial services clients

For financial services companies—especially those in debt consolidation, lending, and wealth management—**precision targeting** is critical. Predictive lead suppression delivers multiple key benefits:

### Reduced marketing waste

By removing low-intent leads from direct mail campaigns, financial institutions significantly cut costs associated with printing and postage, maximizing their marketing budget.

### Higher conversion rates

Focusing outreach on high-intent consumers improves conversion rates, leading to greater customer acquisition efficiency.

### Enhanced customer insights

Predictive modeling not only identifies which leads are least likely to convert but also provides valuable insights into the characteristics of high-intent buyers, allowing for more refined targeting in future campaigns.

### Increased return on investment (ROI)

By reallocating marketing spend to the most promising prospects, companies see a higher ROI on direct mail efforts, turning a traditionally broad-channel marketing approach into a high-precision acquisition strategy.

## Case study: Debt consolidation company saves over $100K per month

### Challenge: High direct mail costs with low returns

A leading [debt consolidation company](https://faraday.ai/stories/debt-consolidation-company) relied heavily on direct mail for customer acquisition but was struggling with inefficiencies. A large percentage of their outreach was going to low-intent consumers, inflating costs and reducing campaign effectiveness.

### Solution: Faraday’s predictive lead suppression

The company partnered with Faraday to implement a [custom lead suppression model](https://faraday.ai/templates/lead-suppression/recharge), which analyzed their historical customer data alongside external consumer attributes to pinpoint the least-likely-to-convert leads.

### Implementation: Multi-segment predictive modeling

While the initial suppression model provided immediate savings, Faraday’s data science team took optimization further by identifying four distinct customer segments within the company's audience. Instead of using a single predictive model, we developed four tailored models, each optimized for its respective segment. A rigorous validation process confirmed that this approach yielded significantly better results.

### Results: Immediate cost savings & higher revenue

By implementing Faraday’s predictive suppression approach, the company achieved:

* **600,000 fewer** pieces of wasted direct mail per month  
* **$100,000+ in monthly savings** on direct mail costs  
* **An additional $20,000 per month** in revenue due to higher targeting precision  
* **10x ROI** on their predictive modeling investment

This company didn’t just reduce waste—it transformed how they acquire customers. By leveraging AI-driven predictive suppression, they turned direct mail into a high-precision growth engine, ensuring that every marketing dollar was spent on reaching the right audience.

### In conclusion

For financial services firms, predictive lead suppression is a game-changer. By eliminating low-value leads and refocusing efforts on high-intent prospects, companies not only reduce costs but also increase customer acquisition efficiency and boost revenue. With Faraday’s AI-driven insights, financial institutions can turn their direct mail campaigns into powerful, data-driven marketing strategies that drive sustained growth.

Want to harness this predictive power for your brand? [Sign up today](https://login.faraday.ai/u/signup?state=hKFo2SBYd0tiZ1hRSmU4UlhCV052VFI4dzBzUWx5bU9pejM1a6Fur3VuaXZlcnNhbC1sb2dpbqN0aWTZIFhhbDZFLW9YSWxJd2NBWmNmYWFld0hXdUR5WGFJakNxo2NpZNkgMDF6SWFodVNaV203SmRVMFNtWmVNUHMzZ2ZHcklqWTI)!

Faraday’s predictive lead suppression leverages AI to remove low-intent leads from direct mail campaigns, helping financial services companies quickly maximize marketing efficiency and ROI by only targeting high-value prospects.

Direct mail lead suppression for financial services clients

At Faraday, we often hear from clients about their experiences working with our competitors. These conversations give us valuable insights into how our platform stands out in the market, and where we have room for improvement. Recently, one of our clients shared a specific experience that really resonated with us, and we wanted to take the opportunity to highlight it to our audience.

This client, who recently worked with [Think Unlimited](https://thinkunlimited.com/) before moving to Faraday, pointed out some key areas where we outshine our competition. As she put it:

*"The transparency of the data science behind the modeling \[at Faraday\] was refreshing. There were no secrets. It was our data, and it was given back to us in a digestible fashion. We could also easily access the experts behind the science to ask questions."*

We believe that transparency is crucial, and we’ve built our platform with the goal of making data science accessible and understandable for our users, so this was rewarding to hear.

This client also observed a significant difference in the quality of predictions between Faraday and Think Unlimited, noting that Think Unlimited's data science just didn’t seem as robust under the hood. This aligns with our commitment to providing high-quality, validated predictive models that you can trust.

But those observations are just the start. Let’s take a deeper dive into some of the key differentiators we've observed ourselves and how they contribute to Faraday’s unique value proposition.

## Faraday vs. Think Unlimited: Our core differentiators

### 1\. Real-time scoring with Faraday's Lookup API

One of Faraday's core strengths is our ability to score leads in real time. Thanks to our powerful [lookup API](https://faraday.ai/docs/features/lookup-api), users can get immediate feedback on lead quality, before bids are even placed. By receiving immediate feedback, businesses can optimize decisions in real-time, leading to better outcomes in high-pressure or fast-moving situations, such as the [ping post process](https://faraday.ai/blog/so-what-is-a-ping-post-anyway). Think Unlimited, on the other hand, lacks this level of real-time scoring capability, which can slow down the workflow, impact decision-making timelines, and in the case of the ping post process, result in lower quality leads being purchased. 

### 2\. A more robust data set for better predictive power

Our built-in Faraday Identity Graph is a rich third-party [data set](https://faraday.ai/features/faraday-identity-graph) that goes far beyond basic metrics by incorporating a wide range of insights that enhance the accuracy and depth of our predictive models. In addition to traditional data points, we include rich demographic, lifestyle, and behavioral data and this combination of static and dynamic data allows us to understand not just who a customer is, but how they engage with products, services, and brands. Using these third-party insights, Faraday’s models can create a dynamic, multi-dimensional picture of potential customers, leading to more accurate predictions of future actions, such as conversion or product interest.

Faraday’s robust data set also helps solve the [cold start problem](https://www.sciencedirect.com/topics/computer-science/cold-start-problem) by providing this rich data from day one, meaning that brands don’t have to wait for a build-up of their own first-party data before accurate scoring can commence. It also allows them to immediately target new customer segments or groups that they don’t have existing data for, facilitating expansions and allowing them to quickly roll out new campaigns and products.

The depth of our data enables Faraday to generate more accurate, quick, and actionable insights compared to our competitors, empowering businesses to make data-driven decisions with confidence. 

### 3\. Score explainability: know exactly why leads get their score

Faraday emphasizes clear [score explainability](https://faraday.ai/blog/faraday-score-explainability) with all of our models. This means that users can actually know exactly why each lead receives a particular score. This level of transparency is crucial for businesses that need to understand the "why" behind their results. Having this clear visibility into the model’s scoring also helps businesses gain confidence in their decisions and ensures they can optimize their approach based on a better understanding of the process.

That earlier client specifically highlighted this point as being one of the biggest advantages she appreciated with Faraday compared to Think Unlimited, where such transparency can be harder to come by.

### 4\. Flexible integration across use cases

Faraday isn’t just limited to lead scoring. Our platform is adaptable and can fill a range of other use cases, including [product recommendation](https://faraday.ai/docs/how-to/use-cases/customers/next-best-offer), [lead suppression](https://faraday.ai/templates/lead-suppression), and even [churn prediction](https://faraday.ai/templates/churn-scoring), [plus a bunch of other options](https://faraday.ai/templates). This versatility allows businesses to leverage Faraday’s insights across different departments and applications, and also sets them up for success when expanding their business or launching new types of campaigns they haven’t undertaken before. Think Unlimited’s capabilities are more narrowly focused, which can hinder adaptability and expandability.

### 5\. Access to experts

At Faraday, we take our data science seriously. But we also understand that predictive modeling is inherently complex and that just presenting where data comes from doesn't necessarily mean that the client will be able to understand it. 

Score explainability measures are a critical first step but sometimes, the best way to ensure a score is actually explainable is pretty straightforward: just have an expert explain it. That’s why we made the game-changing choice last year to forward deploy our entire data science team, making them directly accessible to our clients—our CTO even works with a range of client accounts to ensure that they’re getting the most possible value out of their investment. This level of open communication and transparency is something Think Unlimited lacks and is the ultimate failsafe to ensure users are able to understand their predictions, and the models behind them.

### 6\. Fully API-based for seamless integration

[Faraday is fully API-based](https://faraday.ai/docs/reference), meaning it can be integrated into any system programmatically, offering unmatched flexibility and customization. This structure not only simplifies integration but also provides the flexibility to adapt the models into any custom workflows, making it an ideal choice for companies looking for a solution tailored to their unique needs. Our API is also well-documented, which facilitates integration into clients’ existing tech stacks and ensures easier operation throughout the customer lifecycle. 

These features dramatically increase speed-to-deployment, with some clients seeing major cost savings or revenue growth [within their first month](https://faraday.ai/blog/optimized-direct-mail-outreach-in-one-month) after signing on. Think Unlimited, in contrast, lacks the API-based framework that makes Faraday so adaptable to a wide range of use cases and workflows.

## Quality predictions and transparency make the difference for our clients

At the end of the day, the quality of the predictions you rely on is what matters most. Our predictions are not only proven to be accurate and value driving, but they’re constantly monitored and revalidated to make sure we’re delivering as much value as possible to our clients. Additionally, Faraday’s data science, transparency, and flexibility make it the clear choice for businesses seeking high-quality predictive analytics that can scale and adapt to their needs, all while remaining understandable and accessible. Think Unlimited may have its place, but when it comes to powerful, explainable models and real-time scoring, Faraday leads the way.

Faraday outshines competitors like Think Unlimited with real-time scoring, a richer data set, transparent predictions, flexible integrations, expert support, and a fully API-based platform.