At Faraday, we love when our tools help a mission-driven brand grow in tangible ways, and when that growth could be described as “massive,” we’ll take that too. That’s exactly what happened with [Bee’s Wrap](https://www.beeswrap.com/)—a sustainable alternative to plastic wrap—who built a pitch deck with help from Faraday’s [Personas](https://faraday.ai/docs/how-to/predictions/customer-personas) features which landed them a “massive” (there’s that lovely description in action) placement in over 550 Target locations around the country. 

[![Add Faraday Integration](/images/blog/bees-wrap.jpg)](/images/blog/bees-wrap.jpg)

## What Bee’s Wrap needed

Bee’s Wrap was preparing for a high-stakes sales pitch to Target, a major national retailer—perhaps you’ve heard of them ;). The team needed to prove that their customer base was the right kind of shopper that Target wanted to be ‘Target-ing”. For this they needed to show real data that would resonate with a national buyer.

Bee’s Wrap had a major data asset to leverage: their first-party DTC sales data from [Shopify](https://www.shopify.com/). However, this data only tells part of the story. Order data is not going to tell you why someone buys, who they are demographically, or how many more people like them exist across the country. That’s where Faraday came in.

## How Faraday helped

Bee’s Wrap connected their order data to the Faraday platform, and in just one afternoon, they had:

* Detailed demographic and psychographic segments  
* Purchase propensities broken down by geography  
* A clear, visual representation of who their customers really are—and where to find more of them  
* A ZIP-code-level view of where potential was highest—helping them identify which store locations to prioritize in their pitch

This gave Bee’s Wrap a concrete, compelling story to bring into their pitch—one that resonated with Target’s team.

## The results

That pitch didn’t just land—it turned into a deal.

Bee’s Wrap is now rolling out nationwide in over 550 Target stores, starting with an initial wholesale shipment worth well into six figures. It's a massive milestone for the brand—and one they credit, in part, to having the right data in hand at the right moment.

## Why this matters

As [Brendan Taylor](https://www.linkedin.com/in/bttaylor13/), Bee’s Wrap’s CEO, put it:

> “We approached Faraday with a simple but elusive set of questions about our consumers and their demographics. Armed with only our DTC sales data – in one afternoon – Faraday identified crisp consumer segments with actionable demographic and psychographic details. Their easy-to-use interface also provided powerful predictions about propensity to purchase based on geography and other demographic filters. 

The Faraday suite can synthesize incredibly powerful insights and predictions out of disparate data sets that previously left marketers and sales leaders scratching their heads. If you have lots of data and want to convert it to knowledge and insights, you’ve come to the right place.”

But here’s the thing, this story isn’t just about one brand—it’s about what happens when companies make their data work for them. With Faraday Personas, even a single afternoon of analysis can unlock game-changing insights.

Whether you’re pitching a retailer, planning a campaign, or just trying to understand your customers better, we’re here to help you tell a data-backed story that lands.

Ready to learn more? [Let’s talk](https://www.linkedin.com/in/davidtoddsmall/).

Bee’s Wrap used Faraday Personas to turn their DTC data into a compelling retail pitch—landing a nationwide deal with over 550 Target stores.

How Bee’s Wrap landed a nationwide retail deal with help from Faraday personas

**“If I have seen further, it is by standing on the shoulders of giants.”**

Recognize that quote? It’s from Newton—a generally accepted “smart guy” (and, admittedly, a generally accepted weirdo—but that’s a blog for another day).

So why start a post about AI and direct mail with a quote from a 17th-century physicist? Because in tech, Newton’s point holds up better than ever.

What Newton was saying is that even his most groundbreaking discoveries weren’t created in a vacuum—they were built on the work of those who came before him. Great ideas, and for our purposes, great products, happen when we collaborate and build on solid foundations. While some might think to reinvent the wheel, others want to take the excellent wheel that’s available to them and ride it to the finish line.

That’s exactly the kind of story we’re telling today.

Faraday recently partnered with a leading direct mail automation platform known for helping brands modernize and automate their direct mail operations. Their mission? To empower businesses to deliver more personalized, effective marketing—and to do it with speed, precision, and reliability.

So when they saw an opportunity to build a new product that would deliver even greater value to their customers by enhancing audiences with predictive intelligence, they wanted a solution yesterday. And they turned to partners who’ve been building validated, value-driving AI since well before yesterday—enter Faraday. 

Our best-in-class predictive AI agents and well-documented, developer-friendly API were exactly what they needed to build this new tool. Here’s how it went:

## The story

#### The problem: customers needed better audience targeting

While this direct mail platform already excelled at automating delivery, address verification, and personalization, many of their customers still struggled with a fundamental challenge: finding the right people to reach.

Bad lead lists were a persistent pain point. Marketers were forced to rely on outdated, purchased data; incomplete CRM exports; or generic demographic targeting that missed the nuances of real buyer behavior. Instead of reaching high-potential customers, campaigns often landed in the wrong hands—wasting precious budget and eroding confidence in direct mail as a channel.

Their clients needed a built-in, intelligent way to target audiences in their platform that didn’t rely on guesswork, generic segmentation, or expensive third-party lists. This direct mail company needed a solution fast, and was hoping to get it without the financial burden and hassle of hiring an entire AI team to build predictive models or identity matching infrastructure, from scratch.

#### The solution: new AI-powered audience targeting, built with Faraday

To address this situation, the platform introduced a new AI-powered solution for creating high quality, targeted audiences—powered entirely by Faraday’s proven predictive AI agents.

Using the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph), our robust [API](https://faraday.ai/docs/reference) and developer docs, and the resource of our forward deployed data team (not to mention our powerful predictive AI itself), the company created a new audiences tool in just over a month that enabled its users to:

* Predict ideal audiences automatically  
* Build high-conversion lookalike lists  
* Improve targeting without the need for perfect starting data

The best part? This new feature is embedded directly into their existing SaaS platform. 

And as a bonus, while their engineering team built the tool, the capabilities were already offered in a white-glove fashion because their account management team could use the Faraday app in the background as soon as the partnership agreement was signed. Execs were happy because their clients could see immediate value without pesky roadmaps and engineering sprints. 

#### The results: smarter campaigns, faster

This brand is already seeing better results and happier customers using their new audiences feature. Thanks to Faraday, the direct mail platform was able to:

* Rapidly roll out AI-powered audience targeting to all customers  
* Deliver immediate value without requiring heavy client-side setup (even before the tool was completed)  
* See early adoption and positive feedback from users benefiting from improved targeting and higher engagement rates

The collaboration didn’t just add a new feature—it raised the bar for what’s possible in data-driven direct mail.

## Predictive power, without the pain

Choosing Faraday wasn’t just about adding predictive technology—it was about finding a partner that could deliver real results immediately, without a massive effort. Two key factors in their choice to use our services were:

The [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph), which offers unparalleled out-of-the-box predictive accuracy, leveraging a robust network of third-party data on over 240m US consumers and their household, to identify high-propensity targets with minimal setup. That meant no months of waiting for models to "learn" and no dependence on customer-provided data to drive insights.

Our flexible, [well-documented API](https://faraday.ai/docs), and accessible engineering team allowed the direct mail platform’s developers to integrate powerful AI functionality rapidly into their existing product—without needing to reinvent their stack or slow down their product roadmap.

The combination of turnkey predictive power and developer-friendly design made Faraday the obvious choice to bring this new feature to market quickly, confidently, and at scale.

And as you can probably tell, this outcome was a major win for this client. Faraday’s predictive AI agents made it possible to:

* Identify ideal customers even without a perfect starting list  
* Build intelligent lookalike audiences  
* Append missing data points for richer targeting  
* Target high-conversion prospects with greater accuracy
* And most importantly, it allowed them to roll all of these new value-adding features out immediately, before the tool itself was even completed.  

Now, instead of their clients having to manually assemble lists, this direct mail provider can trust Faraday’s AI agents to find them the right audience—saving time, improving ROI, and delivering better campaign outcomes for their customers.

## Why it matters—today and tomorrow

The marketing landscape is shifting fast. With rising acquisition costs and tightening privacy regulations, brands need smarter, faster ways to reach the right audiences—and make every dollar count.

Embedding predictive intelligence directly into operational workflows like direct mail isn’t just a "nice to have" anymore—it’s a critical strategic advantage.

By partnering with Faraday, this platform didn’t just add a feature; they future-proofed their product. Now they’re delivering better results for their customers today, while building a foundation for even more sophisticated, data-driven campaigns tomorrow.

## Conclusion

Partnerships drive progress. By joining forces, Faraday and this direct mail provider have created a smarter, faster, more precise way to deliver direct mail audiences.

Customers now get:

* More personalized, high-conversion campaigns  
* Higher ROI with less manual effort  
* Streamlined marketing operations from targeting to execution

Ready to see how predictive targeting can transform your marketing?

Let's build something smarter together.

A leading direct mail platform rapidly built and launched a powerful new predictive audience feature—fully embedded in their SaaS product—by building on Faraday’s developer-friendly API and proven AI agents.

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

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

— — — 

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

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

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

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

## Why integrate Faraday into LeadConduit?

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

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

## Step 1: Get your Faraday setup ready

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

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

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

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

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

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

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

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

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

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

### Field mappings

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

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

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

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

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

## What do you do with the score?

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

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

Your team might choose to:

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

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

## Understanding the data exchange

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

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

### Input requirements

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

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

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

### Output fields you can expect

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

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

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

## Example request & response

An example call:

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

An example response:

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

## How this supports your lead acquisition strategy

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

That means:

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

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

## Final thoughts

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

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

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

Integrating Faraday and LeadConduit - a technical guide

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

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

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

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

Here’s what that actually looks like:

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

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

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

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

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

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

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

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

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

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

## You’re protected—because your data matters

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

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

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

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

## The bottom line

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

With *Powered by Faraday*, you can:

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

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

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

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

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

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

It’s the whole toolbox.

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

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

If you’re evaluating Faraday and wondering what goes into our pricing, we want to be transparent—not just about what you’re paying for, but why it’s structured the way it is.

Because here’s the thing: Faraday isn’t traditional software. We’re not handing over a login and disappearing. You’re getting a tightly integrated system of predictive AI agents, rich data infrastructure, and top-level, human data expertise—all geared towards one common goal: making your marketing smarter and your business more efficient.

## It’s not just software—it’s strategy

We don’t believe in "plug-and-play" AI. Faraday’s pricing reflects an ongoing partnership: predictive strategy, data enrichment, model refinement, and real human support. When you work with us, you're getting a team that collaborates closely to ensure your campaigns are driven by data science that actually delivers results. But we don’t stop at generating predictions—we help you apply them effectively, align them to your KPIs, and optimize them for maximum business impact. Our team isn’t just technical—they’re strategic partners focused on making your marketing and sales smarter at every step.

And it is worth noting that this caliber of expertise never comes cheap. High-quality predictive modeling takes serious time, talent, and infrastructure. If you've ever seen a data science job listing, you know the level of skill it demands—like this [Gusto listing](https://job-boards.greenhouse.io/gusto/jobs/6375140) for a Staff Data Scientist at around \$250,000 per year. At Faraday, our forward-deployed data scientists and engineers (FDS and FDE) bring that level of experience directly to your business. Even companies with minimal internal data teams can leverage predictive tools without needing to make a $250K hire. That’s a huge differentiator—we’re not just offering AI, we’re offering embedded expertise to power smarter, faster decisions.

If you’d like to learn more about how our team is here to help you build a strategy, not just get accurate predictions, check out this [blog](https://faraday.ai/blog/roi-isnt-a-scorecard-its-a-strategy) from our Head of Customer Analytics, Chris Waite, or this one from [one I wrote](https://faraday.ai/blog/FDS-changes-the-game) about why we chose to forward deploy our DS team.

## We prove our value before you ever sign

But that’s not all. Before a single contract is signed, we run predictive tests to ensure we’re a good fit. That includes techniques like [pre-signup holdout validation](https://faraday.ai/blog/are-you-ready-for-holdout) and [cross-validation](https://faraday.ai/blog/transparent-reporting), giving you a clear picture of how our models are likely to perform on your data. We’re not asking you to gamble on potential—we’re aligning your investment with real, measurable value. And the validation doesn’t stop there. Our team continues to monitor model efficacy over time, issuing regular performance reports that highlight what’s working and where we can improve. We won’t recommend a contract unless we’re confident it will generate a meaningful return—and we back that up with ongoing transparency.

Want to learn more about our pre-purchase model validation? Check out this [blog](https://faraday.ai/blog/holdout-testing-with-faraday) which details the holdout test process, or if you’re a real smarty-pants, take a look at this [technical deep dive](https://faraday.ai/blog/cross-validation-technical-blog) from two of our incredible Data Scientists.

## We get that big investments can feel risky

We know that investing in a new platform—especially one centered on predictive AI agents—can feel daunting. That’s why we prioritize regular ROI reporting throughout the engagement. We don’t just "set it and forget it"—we actively measure, report, and refine to make sure you’re seeing the returns you expect. From detailed model performance reports to campaign impact summaries, we make sure you have full visibility into how your investment is performing over time.

And if you clicked on [that blog from Chris](https://faraday.ai/blog/roi-isnt-a-scorecard-its-a-strategy) in the first section of this blog, you already understand that at Faraday, our ROI analysis doesn’t stop at a report card with a score to prove our value, it’s a whole additional element of building, testing, and refining our ongoing strategy. ROI is central to how we approach our strategy, and because we take it so seriously you can rest assured that you’ll never have to question just how much value you’re getting out of your investment.

## At Faraday, you're paying for outcomes

At the end of the day, our pricing is shaped by what it takes to drive your success: how much data you're working with, how complex your goals are, and how much guidance you need. The more value we help you extract, the more efficient your spend becomes. 

### A few things that shape the cost:

* **Lead volume:** How many leads per month need to be scored or prioritized?  
* **Data complexity:** Are we working with first-party data, third-party data, or both?  
* **Delivery destinations:** Where should predictions go—CRMs, CDPs, direct mail, paid media?  
* **Support needs:** How much ongoing guidance will make the engagement successful?

We have standard packages that fit most businesses, but we can customize engagements to make sure you're only paying for what you actually need.

## We don’t believe in vague pricing or fuzzy math

If it feels like Faraday includes a lot, that's because it does. But we don’t hide it—we explain it. We'd rather have an honest conversation about value than play pricing games that sound good on paper but disappoint in reality.

You’re not just getting a tool. You’re getting a team, a strategy, and a system designed to drive real results—starting from day one. And when the right systems, models, and minds work together in sync? That’s where the magic—and the ROI—happens.

Faraday’s pricing reflects not just software, but a full strategy, expert support, and proven ROI—designed to drive real business outcomes from day one.

Behind the price tag: What you’re actually paying for with Faraday

In our last blog, we explored why ROI analysis at Faraday isn’t just about proving value after the fact—it’s also about continually uncovering new opportunities. 

We shared what that looks like at a high level. But this isn’t just a philosophy expressed in our marketing; it’s how we work every day. So today, we’re sharing a story from the \~real world\~ that shows just how much insight you can unlock through our comprehensive ROI evaluation process.

It also drives home an important point: these evaluations can only achieve their full value if you actually engage with them. The top-line numbers might say “Yep, it’s going great,” but just because that’s good news doesn’t mean it reflects the whole story. 

Sure, it’s going great, but how great, and where is there room for improvement? Which parts of the campaign are driving the most value? The least value? Do we need to revise any major assumptions to be more accurate? 

Indeed when it comes to ROI analyses like these, digging deeper is where the real opportunity lies.

## A deeper dive reveals something unexpected

Hi, ROI extraordinaire Chris Waite here. After I came on full-time at the start of the year as Faraday’s Head of Customer Analytics, I kicked off ROI initiatives with several of our existing customers. The goal was simple: move beyond one-time reports and start treating ROI as an ongoing conversation—one that actively drives value, not just records it.

With one financial services client, we began with my usual process: aligning on key assumptions—value per conversion, attribution logic, unit costs—and setting up monthly check-ins to track progress and dig into results over time.

And the returns looked great. Performance was strong, and the model was holding up. But during one of those check-ins a couple of months ago, someone on their team made an offhand remark that caught our attention: some of their hardest-to-convert prospects—sometimes rejected due to their low predicted conversion rates—actually had the potential to generate the *most* value if they did convert.

Our interest was piqued, so we ran the numbers.

We analyzed the relationship between Faraday’s prediction scores and the average conversion revenue. Sure enough, there was a clear pattern: while certain low-scoring groups converted less frequently, they delivered more value when they did.

The selection process didn’t need a full overhaul—but it did need a refinement. We brought the results back to the client, and together we incorporated these insights into their approach. By adding an additional variable into our selection process, we could proactively identify the majority of these high-value individuals and ensure their inclusion in the campaign. Once updated, the campaign began to improve—conversion rates dipped slightly (by design), but total revenue went up.

This is a perfect example of how our ROI reporting process uncovers new and unexpected insights that drive value. In this case, we used it to fine-tune suppression logic and reframe scoring thresholds—without a full process overhaul. The outcome was a smarter, more mission-aligned strategy that helped the client prioritize not just likely conversions, but the ones with the greatest potential overall value.

And through that process, we uncovered something more. These high-impact conversions weren’t just good for business—they were cultural wins. Inside the company, signing one of these clients—often individuals carrying significant debt—was seen as a major moment. It represented the core of their mission: to support people who need it most. The whole team felt it.

So yes, ROI is about financial returns. But sometimes, it's also about morale, alignment, and purpose. In this case, their investment in Faraday didn’t just lead to better outcomes on paper—it reinforced the values that drive their business forward.

## From revenue recap to campaign shift

This paradigm shift is exactly Faraday’s goal when we present our clients with our ROI reports. We can talk about them driving value as much as we want but they drive the most value with buy-in and funding from the teams that use them.

In this case, thanks to the deeper ROI dive, the client was able to:

* Identify underutilized customer types  
* Refine their lead suppression strategy  
* Shift budget allocations across customer segments  
* Drive more high-impact conversions

None of that was the original intent. But that’s the point—when ROI becomes an ongoing process, not just a retroactive check-in, it drives value in real time.

## Why Faraday treats ROI like strategy

We’re not sharing this story to pat ourselves on the back—we’re sharing it because it highlights something bigger: most companies treat ROI as a postmortem. We treat it as a feedback loop.

At Faraday, ROI modeling isn’t a final deliverable—it’s built into every engagement from day one. We co-develop success metrics with our clients, revisit assumptions as new information emerges, and treat every report as a living document meant to drive change in real time. Because when ROI is treated as a collaborative, evolving process—not a static snapshot—it doesn’t just validate past performance. It unlocks new value, sharpens ongoing strategy, and turns every insight into an opportunity for future impact.

## Coming up next

In our next post, we’ll pull back the curtain on how we build some of our ROI models at Faraday—what goes into them, what we measure (and why), and how we handle the gray areas most companies avoid.

Because the better you understand your own ROI, the greater your ability to power new growth.

A routine ROI check uncovered a surprising insight that helped a financial services client shift strategy, boost revenue, and realign with their core mission.

This financial services brand’s ROI assessment empowered a whole new strategy

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’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.

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