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

— — — 

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

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

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

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

## Why integrate Faraday into LeadConduit?

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

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

## Step 1: Get your Faraday setup ready

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

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

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

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

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

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

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

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

### Field mappings

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

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

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

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

## What do you do with the score?

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

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

Your team might choose to:

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

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

## Understanding the data exchange

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

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

### Input requirements

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

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

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

### Output fields you can expect

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

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

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

## Example request & response

An example call:

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

An example response:

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

## How this supports your lead acquisition strategy

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

That means:

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

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

## Final thoughts

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

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

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

Integrating Faraday and LeadConduit - a technical guide

You’ve got data—most likely lots of it. And if you’re the type of person who ends up on a Faraday blog, you’ve probably already considered the value predictive modeling could add to your brand. But not all predictions are created equally.

## When does your data require enrichment?

For some tasks, modeling on first-party data alone—like transactions or web events tied to known users—is enough. This works well for optimizing things like cart abandonment campaigns, existing customer retention, or upsells within your CRM. If you determine that’s all you need at your company, Faraday can absolutely model on just your first-party data.

However, first-party-only modeling comes with a few major limitations:

* It can’t help you reach new customers who haven’t engaged with you yet  
* It struggles with cold start problems when launching new products or campaigns  
* It’s limited by the scale and diversity of your existing audience  
* It can miss broader behavioral patterns visible only in third-party data

If your goals include reaching new audiences or launching into unknown territory, you’ll quickly find the cracks in a first-party-only strategy. That’s where Faraday’s data advantage comes into play.

We don’t just model on your existing data—we help you *expand* it. Through identity resolution, we enrich your records with insights from the Faraday Identity Graph (FIG), a privacy-safe dataset covering over 240 million U.S. adults. This allows us to bridge the gap between what you already know and the wider world of high-propensity customers you haven’t met yet.

Enrichment lets us go beyond who a person is, and start modeling what they’re likely to do.

But attentive readers will have noticed a word that might stand out: “**enriched**.” No, we’re not talking about flour, uranium, or even beer. In this context, enrichment refers to our ability to match the identities in your dataset to real individuals or households in FIG—so we can enhance your records with high-value predictive traits.

[![Image of "That's definitely enriched beer," meme](/images/blog/enriched-beer.gif)](/images/blog/enriched-beer.gif)


## So what is identity matching, anyway?

Identity matching is the process of taking the first party records you give us—names, emails, phone numbers, addresses—and matching them to real people and places in FIG. It’s how we go from “some person” to “this person, at this residence, with these behavioral signals.”

Faraday supports two levels of matches:

* **Person-level match**: We recognize the individual based on identifiers like name \+ address, email, or phone, and enrich with person-level traits (e.g. age, gender, purchase propensity) and property-level traits (e.g. home value, square footage).  
* **Residence-level match**: We recognize the household address, but not the individual. In this case, we can still provide property-level traits and aggregate averages for that address.

## How the Faraday Identity Graph fits in

As noted, at the core of this process is FIG—our proprietary dataset, which we’ve been building over the last 12 years. This set covers thousands of behavioral, demographic, and psychographic attributes on over 240 million U.S. adults and their households. When we get a match, FIG is what powers the predictive features we feed into your model.

The inclusion of this set—and third-party modeling more broadly—solves some critical problems that first-party data alone can’t address:

* **Cold start**: FIG helps predict outcomes even when you have little to no historical data on a segment  
* **Audience expansion**: You’re not limited to your CRM—you can model and target high-propensity lookalikes across the U.S.  
* **Signal density**: FIG adds new, behaviorally-relevant features that enrich your model’s inputs  
* **Long-term accuracy**: More diverse and representative data leads to more generalizable predictions

No match? No enrichment. And that means fewer signals to work with.

## Why match rate matters

Match rate—the percentage of records we’re able to enrich—is a direct lever on how well Faraday can perform. It governs:

* The **accuracy** of your predictive model  
* The **reach** of your campaigns  
* The **value** of our insights to your team

Here’s what typical match rates look like, depending on the identifiers in your dataset:

| Input type | Typical match rate |
| ----- | ----- |
| Name \+ address | 60–80% |
| Email only | 30–50% |
| Phone only | 5–10% |

If your list is heavy on phones or anonymized emails, that’s likely dragging things down. And if you’re seeing \<50% match overall, it’s time to investigate.

## Troubleshooting a low match rate?

One of the most common issues we encounter, and accordingly one of the biggest impediments we face when it comes to a good model is a low match rate. But sometimes diagnosing an issue isn’t as easy as you might expect. So here are a few common culprits to look for:

* Sparse or malformed identity data   
* Hard-to-match populations (e.g. younger adults, recent immigrants, underbanked)  
* Anonymized or junk emails (e.g. `email@email.com`, Apple relays, Doordash+ aliases)  
* Business names and commercial address (rather than personal records)  
* Missing names, flipped fields, or swapped first/last names  
* PO Boxes and apartment units with formatting issues

Sometimes it’s just formatting. Sometimes it’s about who’s in your list. But if your list looks like this, you probably have a guess where to start.

| First name | Last name | Email  | Phone number | Last outreach date |
| :---- | :---- | :---- | :---- | :---- |
| Rose, Benjamin | ben.rose@… | Faraday.ai  | 434 54🥕4 | Went great 👍 |

## What we do when match rate is low

When match rates dip below expectations, we kick off a diagnostic process. We look at the identifiers you provided, check for issues like junk emails or missing fields, and investigate how much of the dataset contains the high-value combo of name and physical address.

Depending on what we find, we might:

* Fix dataset mapping or formatting issues  
* Ask for additional identifiers (e.g. billing vs. shipping address)  
* Run a Match Boost through upstream vendors to attach new emails, phones, or addresses to help improve match

**Heads-up:** Match Boost isn’t always included in contracts, so we’ll confirm that before we proceed.

And if we’re getting solid residence-level matches but not person-level ones, it may just be that this population isn’t present in FIG. That’s a data coverage limitation, not a system failure.

## Why identity resolution is crucial for predictive modeling

Your predictive model learns from the signals in your data. The richer the signals, the more precise the model. If we can’t resolve identities, we can’t generate signals. And that means the model is essentially working with blind spots.

Match rate isn’t just a technical stat—it’s the foundation for how much value you can drive with AI. So if your match rate looks low, let’s dig in together. Because the better we know your audience, the better your predictions will be.

## Wrapping it all up

At the end of the day, your predictive model is only as strong as the signals it’s trained on. And assuming we’re not talking about limited, first-party only modeling, those signals depend entirely on whether we can recognize the people in your data—and enrich those records with meaningful traits.

That’s why identity resolution isn’t just a behind-the-scenes technical step. It’s the bridge between your raw data and the actionable insights that drive real outcomes. From expanding your audience and overcoming cold start challenges to increasing model precision and campaign ROI, enrichment through the Faraday Identity Graph is what unlocks the full power of predictive AI.

So if you’re seeing lower-than-expected match rates, or if you’re unsure whether your data is giving you the best possible results, let’s take a look together. Fixing a match rate issue might be as simple as a formatting tweak—or it might point to a strategic opportunity to boost your reach and improve your outcomes.

Because predictive modeling shouldn’t just be about what has worked before—it should be about what’s going to work next. And that starts with knowing who you’re modeling.

Your predictive model is only as strong as your data’s match rate—because if you can’t resolve who’s in your list, you can’t predict what they’ll do.

Identity matching makes or breaks your predictive strategy

You might’ve heard—the economy’s a mess. The stock market is up and down more than a toddler on a trampoline, tariffs are flying, inflation’s still hanging around like an uninvited guest, and budgets are tightening across the board. When that happens, marketing is often the first thing on the chopping block—especially experimental or “new” tools. You know the ones. The tools the old guard still side-eye.

But here’s the thing: in unpredictable times, the worst thing you can do is pull back and wait for calm seas. When the wind picks up, it might feel safer to slow down—to take a step back—but that’s when it’s most important to steer into the wind. Into the waves. The more chaotic the market, the more you need to grab the wheel and take control.

And if you’re thinking about cutting predictive support now, you might as well be throwing your oars overboard. It might feel like a short-term budget win—but we can predict (with high confidence) that it’s costing you future sales.

## Why predictive is more critical than ever

Let’s be clear: predictive modeling isn’t just about getting *more* customers. It’s about getting the *right* customers. The ones who don’t just buy once and disappear—but who come back, bring friends, and grow with your brand over time. That’s what makes predictive AI so powerful, especially when the market is unstable. You’re not just acquiring customers. You’re investing in relationships.

Here’s why cutting predictive tools now could come back to haunt you:

### 1️⃣ Markets are shifting fast—and so are consumer behaviors

Everything’s changing—fast. Economic swings, political shakeups, social trends—you name it. And when the world changes, so do your buyers. Relying on last year’s data (or even last quarter’s) isn’t going to cut it. You need modeling that adapts in real time, rooted in current behaviors and signals. That’s how you keep pace with who’s actually buying, and why, even as the landscape shifts around you.

### 2️⃣ Consumers are spending more selectively

In tighter economies, people don’t just spend less—they spend smarter. That means you should be spending smarter, too.

At two recent conferences, we kept hearing the same thing:

“Lead quality is going down and I’m spending a ton. I’d rather pay more for a few high-quality leads than waste money on cheap ones that don’t convert.”

That’s where predictive modeling stops being a nice-to-have—and becomes a necessity. Faraday helps you zero in on high-propensity buyers—the ones who convert *and* stick around. Less wasted spend, more long-term value.

### 3️⃣ Your competitors probably aren’t guessing

Let’s be real—your competitors aren’t sitting around waiting for the economy to stabilize. They’re adapting, and many are investing in predictive tools to stretch their budgets further and maintain growth. They’re using AI to find better leads, launch more efficient campaigns, and reduce churn. If you’re cutting predictive while they’re doubling down on it, you’re not just falling behind—you’re handing them market share.

### 4️⃣ When budgets tighten, every dollar needs to punch above its weight.

Predictive modeling helps you do more with less by targeting smarter and reducing wasted spend. Instead of blanketing your audience and hoping for a hit, you’re focusing on high-likelihood converters from the start. That’s how you protect your investment and keep performance high—even when you’re spending lean.

### 5️⃣ Goodbye gut instinct, hello actual foresight

In unpredictable times, instinct can only take you so far. The days of "let’s just try it and see what happens" are behind us. Predictive tools help you replace guesswork with clarity—using actual data to guide your decisions, not just vibes. With Faraday, you can see what’s likely to happen next, so you can respond proactively instead of scrambling to react.

## So what does Faraday actually do?

Faraday delivers AI-powered predictive models built on the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph)—our massive, privacy-safe dataset of 1500+ consented third-party attributes covering nearly every U.S. adult and household. This isn’t just data—it’s actionable intelligence. Rather than waiting around for someone to click on an ad or fill out a form, **Faraday AI agents help you understand what a consumer is likely to do before they do it.**

Here’s a few examples of what that looks like in practice:

### 🎯 Lead scoring

Your CRM is probably packed with leads—but not all leads are equal. Some are ready to buy tomorrow, and others might never convert, no matter how many emails you send. Faraday helps you tell the difference. Our predictive lead scoring ranks your leads based on real behavioral and demographic signals—not just engagement metrics—so you can focus your time and budget on the ones most likely to take action. That means more efficient sales cycles, stronger conversion rates, and fewer hours chasing dead ends.

### 👤 Personalization

Most brands personalize based on what they know after the fact—like someone clicked a product, or abandoned a cart. But with Faraday, you don’t have to wait for those signals. Our models can predict the kinds of messaging, offers, and channels most likely to resonate with each individual *before* they’ve ever interacted with your brand. That means more relevant campaigns from the start, better first impressions, and a smoother path to conversion.

### 🧠 Customer intelligence

You probably have a sense of who your best customers are. But do you really know what sets them apart? Faraday digs deep to uncover the traits—behavioral, demographic, and psychographic—that distinguish your highest-value customers from the rest. Then, we help you go out and find more people with those same high-propensity traits. It’s like lookalike audiences, but smarter, and based on what *actually drives loyalty and LTV*, not just surface-level similarities.

## The bottom line: predictive modeling improves your bottom line

Cutting predictive AI to save budget is like taking the headlights off your car to go faster. Sure, it might seem lighter—until you hit a wall. With Faraday, you’re not just marketing smarter. You’re building future-proof growth.

Uncertainty doesn’t mean take a step back and wait—it means take a step forward and figure out how to solve it first. The current economic turmoil we're all experiencing might look like a setback, but in reality, it could just as easily be an opportunity in disguise. An opportunity to differentiate, to show your customers how you handle a crisis, maybe even to redefine how you generate value. We all get to shape our own futures, so don't let the uncertainty scare you. Use it to drive even better outcomes.

Because when the storm hits, you don’t drift and hope for the best. You steer into it.

In uncertain markets, cutting predictive modeling might seem like a quick budget win—but it risks losing the high-value, loyal customers that drive long-term growth.

The economy is in flux, but cutting predictive modeling could cost your next lifetime customer

Imagine you’re launching a new kitchen product—a sleek, ergonomic veggie chopper. How could it not fly off the shelves? You know your current customers: suburban moms in their 30s and 40s. So you decide to build a lookalike audience of... suburban moms in their 30s and 40s.

Seems logical, right? 

Well no. The campaign falls flat. But why did this happen?

Demographics alone don’t tell the whole story. What you really needed was a way to reach people who actually enjoy cooking—people with an affinity for trying new recipes, watching food content, or buying cookware online—not just the ones who check some of the same basic boxes as those who do.

Traditional lookalike audiences tend to mirror superficial traits. Faraday’s lookalikes go deeper—focusing on behaviors, affinities, and actions that actually drive engagement.

## What is a lookalike audience?

But let’s take a step back to define some terms. In marketing, a [lookalike audience](https://en.wikipedia.org/wiki/Lookalike_audience) is a group of people who share characteristics with an existing audience—usually one that’s performed well in a campaign. The goal is to find more people likely to convert based on their similarity to your known winners.

Meta (you know, the artist formerly known as Facebook) popularized the term with their [Lookalike Audiences tool](https://www.facebook.com/business/help/164749007013531), which matches new users based on shared traits with a seed audience. As they define it:

> “A Lookalike Audience is a way to reach new people who are likely to be interested in your business because they share similar characteristics with your existing customers.”  
> — [Meta Business Help Center](https://www.facebook.com/business/help/164749007013531)

But what exactly counts as a “similar characteristic”? 

That’s where the quality of your data—and your modeling—really starts to matter, and that is exactly where Faraday can make a critical difference.

## Out with the old, in with the smart

The old way of building lookalikes is all about surface-level demographics: age, gender, location, interests. That works... until it doesn’t. It can lead to broad audiences that seem right but don’t perform, or hyper-narrow audiences that limit scale.

Like in our veggie chopper example: targeting suburban moms might seem like a safe bet—but it only works if those moms actually cook. Without behavioral context, you end up advertising to people who might look like your best customers but have no interest in what you’re selling.

Smart lookalikes—like the ones Faraday builds—go deeper. Instead of focusing only on who people are, we focus on what they do. That means modeling on behavior, not just fixed traits.

We might start with a seed audience of loyal cookware buyers, and then build a lookalike based on behavioral signals like:

* Affinity for cooking content  
* Purchase patterns across category types  
* Life events like moving to a new home (hello, new kitchen)  
* Household makeup (like number of people they cook for)

This kind of modeling doesn’t exclude demographic data—it uses it for context, not constraint. The result is smarter, scalable targeting that finds the people most likely to engage because of what they do, not just what boxes they check.

## Powered by the Faraday Identity Graph

Faraday’s smarter lookalikes are powered by the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) (FIG)—our massive, privacy-safe third-party data asset that covers over 300 million U.S. adults. But it’s not just about having a lot of data. It’s about the kind of data we have—and what we do with it.

Most traditional lookalike tools lean heavily on deterministic data: fixed traits like age, gender, or location, and basic interest categories. That can give you surface-level matches—but it misses the deeper signals that actually drive intent.

FIG goes beyond that by integrating thousands of behavioral, demographic, and psychographic attributes. That includes things like:

* How often someone shops online, and in which categories  
* What life events they may be experiencing (like moving, getting married, or having kids)  
* Media consumption patterns, brand affinities, and spending behaviors  
* Numerous other traits, [visit this page for a full list](https://faraday.ai/features/faraday-identity-graph)

Using advanced modeling, we analyze these signals to surface [*probabilistic*](https://www.collinsdictionary.com/us/dictionary/english/probabilistic) *traits*—things that aren’t explicitly declared, but strongly inferred from real-world behavior. For example, we don’t need someone to tell us they love cooking; their actions (recipe site visits, cookware purchases, grocery frequency) say it for them.

So instead of just identifying people who look like your best customers, we can find the ones most likely to engage in the same purchasing behaviors.

## Smarter lookalikes aren’t just for digital ads—they’re transforming offline outreach too.

This smarter approach to lookalikes isn’t just changing digital campaigns—it’s reshaping how brands think about offline channels like direct mail too.

In fact, one of our favorite direct mail partners has been seeing some massive benefits by integrating behavioral modeling into their systems to bring precision and scale to their print outreach campaigns. Traditionally, direct mail strategies have focused on small, high-intent segments—like cart abandoners or loyalty members. These audiences convert well, but they’re also inherently limited.

When it’s time to expand reach, brands often assume they’re stuck building generic lists: same ZIP code, similar age range, same general profile. But with Faraday, they can go far beyond demographics—finding new households that act like their best customers, even if they’ve never visited the site or engaged with the brand before.

That means:

* Reaching new, high-propensity households—not just the usual suspects  
* Scaling volume without sacrificing conversion rates  
* Delivering campaigns to people whose real-world behavior signals true interest

It’s a smarter way to tap into the strengths of direct mail—tangibility, trust, high response rates—without relying on guesswork or outdated list strategies.

Because let’s face it: even the most beautiful mailer won’t work if it ends up in the wrong hands.

If you’d like to learn more about how our predictive lead targeting works, check out this recent [customer story](https://faraday.ai/blog/cs-smarter-prospecting-at-scale) about our work with a different direct mail provider.

## Smarter audiences. Smarter campaigns.

Lookalikes shouldn’t just look the part—they should work. Faraday makes that happen with behavior-based modeling, best-in-class data, and predictions that get smarter over time.

Whether you’re mailing coupons for kitchen gadgets or marketing mortgages, the smartest lookalike is the one that converts.

So ready to see the difference? Reach out and say hi\!

Faraday builds smarter lookalike audiences by modeling real-world behaviors—not just demographics—so you can find the people most likely to convert, not just the ones who look the part.

Not your average lookalike: How Faraday makes finding your next best customers easier

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

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.

All posts

Integrating Faraday and LeadConduit - a technical guide

Identity matching makes or breaks your predictive strategy

The economy is in flux, but cutting predictive modeling could cost your next lifetime customer

Not your average lookalike: How Faraday makes finding your next best customers easier

ChangelogSee all

Smarter identity resolution with payload fallbacks

CSV dataset file improvements

Persona sets are now downloadable as CSV

Improved persona charts

Supercharge your lead acquisition with Faraday + LeadConduit

ROI isn’t a scorecard—it’s a strategy

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

Smarter identity resolution through payload fallbacks with Faraday

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

Talking to your lead vendors about AI-driven lead optimization