Not all leads are created equal. When call center capacity is limited, knowing who to call first can be the difference between a good month and a great one. That’s where lead scoring comes in—helping you focus on the opportunities most likely to convert, while still working the rest of your pipeline with intention.

In this joint session, Faraday’s Dave Small and CallTools COO Chaz Tedesco unpack how predictive scoring and data enrichment work together to supercharge your outbound strategy. From ranking hot leads to surfacing insights you can use in your scripts, they share how to put your data to work in real time—whether you’re working fresh leads, reactivating older ones, or responding to inbound calls.

**From this session, you’ll learn:**

- What goes into a predictive lead score and how to tailor it to your goals
- Ways to enrich leads with demographic, property, and behavioral data
- How to prioritize leads during high-volume periods to protect conversion rates
- Real-world examples of outbound teams boosting results with smarter dialing
- How to integrate scores into your call strategy, cadences, and follow-ups

By combining Faraday’s predictive insights with CallTools’ dialing capabilities, you can turn raw data into actionable intelligence—and make sure every call moves you closer to a win.

<iframe width="560" height="315" src="https://www.youtube.com/embed/CBqqAR35A0g?si=VVFoDqRyFVkcNiN5" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

Join Faraday’s Dave Small and CallTools COO Chaz Tedesco as they explore how predictive lead scoring and enrichment can help you prioritize the right prospects, improve conversion rates, and make every dial count.

Smarter dials: How lead scoring transforms your outbound strategy

The [Faraday Identity Graph (FIG)](/features/faraday-identity-graph) is the powerful, privacy-safe dataset that fuels everything we do, from predictive modeling and enrichment to identity resolution. It brings together hundreds of millions of U.S. consumer records from trusted partners, cleansed, enriched, and organized into over [1,500 actionable consumer data points](/leaderboard), which are ready to use [from day one](/stories/dtc-retailer). So whether you’re filling in your dataset or trying to figure out what your leads will do next, FIG delivers the complete, privacy-safe intelligence you need to power smarter outreach and better customer experiences.

We update FIG regularly to keep it fresh and reliable, but our latest release includes some pretty exciting new features in addition to our standard update, so we wanted to tell you about what’s going on in there. Here’s what’s new:

## 📈 Updated data from leading providers

We’ve incorporated the latest available data from the country’s best providers. This happens each time we update FIG.

## 👶 Better coverage on young consumers

This update significantly expands our coverage of younger adult populations—adding millions of new records for individuals aged 18–34, a critical segment for many brands seeking early loyalty and long-term value.

It also includes improved age sourcing and fewer inferred records overall, leading to sharper demographic and behavioral insights across the board. Whether you're targeting young renters, streaming enthusiasts, or first-time buyers, this release gives your models a more accurate picture of today’s consumers.

## 🧠 Smarter match boosting

[Match Boost](/blog/match-boost-improves-enrichment) is our identity resolution enhancement that improves your match rates by checking additional data sources when your records are sparse or messy. Instead of relying on a single source, it uses a fallback (or "waterfall") approach to find the best possible match.

With this update, Match Boost now works across both **batch** and **real-time** workflows—meaning better performance whether you're enriching large files or resolving identities on the fly. The upgraded pipeline is fully automated, so you’ll see stronger resolution and fewer missed matches.

This upgrade is included by default after Match Boost has been added to your account.

## 🧬 Multi-identity return

FIG now supports **multi-identity return**, meaning it can surface multiple validated contact points—like emails or mailing addresses—for a single individual when available. That gives you more flexibility in matching, targeting, and outreach across fragmented systems.

Contact your account manager to add this feature to your account if you’re interested\!

## All of this is already available at your fingertips

With improved demographic coverage and better underlying data, FIG now reflects real-world consumer complexity more accurately than ever—capturing a broader range of ages, life stages, and behavioral traits that matter when you're trying to reach the right people, in the right way.

If you’re already using Faraday, there’s nothing you need to do. These improvements are rolling out now and will begin powering your workflows automatically.

Curious how FIG fits into your customer data strategy? Let’s talk.

The latest Faraday Identity Graph update delivers sharper demographic insights, expanded coverage of young adults, improved real-time identity resolution, and multi-contact matching.

The Faraday Identity Graph just got even smarter

Whether you're scoring leads, personalizing outreach, or building omnichannel campaigns with predictive modeling, it all starts with identity. And if you can't confidently link a record to a real person, everything downstream—predictions, engagement, outcomes—starts to break down.

Faraday already offers powerful [identity resolution](/blog/payload-fallbacks) tools through the [Faraday Identity Graph](/features/faraday-identity-graph), which can do a lot with a little. Even sparse records like email-only or phone-only leads can often be matched and scored out of the box. But in high-stakes campaigns, every additional match counts and boosting that match rate, even slightly, can significantly impact your outcomes. 

So to make sure that match rate is not just excellent, but as good as possible, we’ve introduced a new feature, called **Match Boost**. It taps into additional third-party identity sources to improve match rates even further, especially on partial or hard-to-match records. Whether you're trying to expand reach, improve prediction coverage, or support downstream workflows that rely on complete identity data, Match Boost helps you go beyond “good enough.”

## Why match rate matters (more than ever) in 2025

[3rd party predictive models like Faraday’s](/blog/why-first-party-data-and-rfm-is-not-enough) provide deeper insight than their first-party-only alternatives, but they still run on identity matching, and live or die by their ability to effectively match customer records to their external datasets. When individuals in your customer data are loaded into Faraday and recognized—that is, successfully matched to the [Faraday Identity Graph](features/faraday-identity-graph), they’re enhanced with hundreds of rich traits. These new traits add fuel to your predictive models, allowing them to train on more information. Higher match rates mean:

* **More records scored** – fewer “unknown” rows languishing in the void.  
* **Sharper predictions** – more signals per person, which means better lift curves.  
* **Omnichannel reach** – physical addresses unlock direct mail, phone numbers power SMS, you get the idea.  
* [**Cracking the cold start problem**](/stories/dtc-retailer) – enriches brand‑new or sparse lists so models have enough context to make accurate predictions from day one.

The trouble? Many datasets are hard to match: a newsletter signup with only an email, a warranty card with just a phone, a loyalty file missing addresses—those single‑identifier records can still slip through the cracks and if that happens, your baseline match step stalls and your models lose context.

## Enter Match Boost

To solve this problem, we’re excited to share the latest feature added to the Faraday platform: **Match Boost**.

The Match Boost feature includes two distinct capabilities:

### 1\. Recognition enhancement for better modeling

Match Boost increases the portion of your customer data that Faraday can recognize and enhance with traits from the Identity Graph. That means:

* More records matched  
* More accurate and complete predictive models  
* Better prediction coverage—no more “no-score” results

Behind the scenes, Faraday uses a network of data providers, submitting *unmatched* records from your datasets to retrieve additional identity data. With additional/updated identifiers, we’ll be able to match even more of your data to FIG.

### 2\. Expanded identity enrichment for outbound engagement

Match Boost customers also gain access to identity data (like phone numbers and physical addresses) for deployment, enabling:

* Better channel coverage  
* Omnichannel activation from day one

[![Match boost diagram](/images/blog/match-boost-blog/persona-with-information.png)](/images/blog/match-boost-blog/persona-with-information.png)

## When would you use Match Boost?

So we get that Match Boost is powerful, but when, exactly is it the most useful? You’d reach for Match Boost when:

### 1\. Your input data is extra sparse

Think single identifiers—like just an email or phone number. Faraday can often match these, but Match Boost increases the odds by tapping into third-party identity sources.

### 2\. You need to engage on more channels

If you’re trying to run omnichannel campaigns but only have partial contact info, Match Boost helps fill in the gaps—like turning an email into a mailable household.

### 3\. You’re trying to maximize model coverage

Predictive models work best when more people are matchable. Boosting match rates means fewer “unknowns” and stronger lift curves.

### 4\. You want full enrichment—even for already-matched records

For clients with access to advanced features, Match Boost can run even on records already matched to Faraday’s graph, layering on additional identity details you wouldn’t get otherwise.

## Getting set up

If you're interested in adding the Match Boost feature to your Faraday account, contact your account manager to get set up.

### How it works in dashboard

1. If your account has Match Boost enabled, you’ll see a checkbox labeled ‘enrich this dataset with additional identity data where possible’ when creating or editing a dataset.

[![Match boost check box in dashboard](/images/blog/match-boost-blog/matchboost-enrich-button.png)](/images/blog/match-boost-blog/matchboost-enrich-button.png)

2. Faraday runs its normal graph match process.  
3. Any unmatched records automatically flow through Match Boost.  
4. Additional identity data is resolved and returned.  
5. These new identifiers inform model training and improve prediction coverage.  
6. You can view the impact directly in the dashboard—hover over the enrichment percentage in the dataset index view to see the lift.  

[![Match boost results pop-up](/images/blog/match-boost-blog/match-boost-display.png)](/images/blog/match-boost-blog/match-boost-display.png)

7. Whenever you make a new Deployment in Identified or Referenced mode, the output will automatically include identity data that was missing in your original dataset.

### How it works in API

Faraday’s API users can enable Match Boost for a dataset by including it in the `identity_providers` array when making `POST` or `PATCH` [dataset requests](/docs/reference/createdataset). This allows additional identity data to be resolved during the ingestion process, improving match rates and model performance.

```
{
  ...,
  "identity_providers": [
    { "provider": "fig" },
    { "provider": "match_boost" }
  ]
}
```

We’ll be publishing a full API how-to next week, so keep an eye out if you’re looking to integrate this into your workflows programmatically.

## Improve your predictive modeling, today

With Match Boost, your datasets stop freeloading and start pulling their weight. Contact your account manager to learn more, or if you’re not yet a customer, welcome\! [Come say hi to sales](/talk-to-sales) (they’re very friendly). 

Let’s make every record count, together.

Faraday’s new Match Boost feature improves identity match rates—especially for sparse or single-identifier records—by tapping into additional data sources, enabling better predictions and broader omnichannel engagement.

Match Boost improves enrichment coverage on sparse data

What if you could save over $100,000 every month, just by avoiding outreach to the wrong leads?

That’s exactly what [one debt consolidation firm achieved](/blog/debt-consolidation-company-saves-100k) with Faraday. Facing skyrocketing direct mail costs, they came to us looking for a smarter way to reach the right people without blowing their budget on low-intent leads.

To solve their problem, we helped them enrich their lead list with consumer data from the [Faraday Identity Graph](/features/faraday-identity-graph) and built a predictive AI agent to score each lead on likelihood to convert. With this in place, they started suppressing just the bottom 10% of their mailing—a segment they could confidently predict wouldn’t take action.

That small change made a massive impact: 
  * **Over 600,000 fewer mailers** sent each month
  * **\$100,000+ in monthly savings**
  * And **an extra \$20,000 in revenue** thanks to their reps focusing on better leads.

That’s a 10x ROI, without changing the offer or creative. Just smarter targeting.

But here’s the thing, this experience isn’t unique to this client. The debt settlement space is evolving fast. Rising lead costs, stricter compliance requirements, and shifting consumer expectations are pushing companies everywhere to find smarter ways to grow, target, and personalize. That’s where data and AI make all the difference.

Faraday helps companies identify and integrate the data their business needs to anticipate how their customers will behave. Whether you’re trying to maximize the value of your call center, prioritize leads, personalize outreach, or just reduce wasted spend, Faraday’s platform enables you to understand your audience more holistically and to act on accurate, validated predictions rather than just past performance. For you, that means more money, and less hassle.

## Core use cases for debt settlement and financial services clients

### Comprehensive data services

Nearly every Faraday project begins with data. Our platform [enriches](/blog/identity-matching-makes-or-breaks-predictions) your customer or lead records using the [Faraday Identity Graph](/features/faraday-identity-graph) (FIG)—a comprehensive, consented, and privacy-safe dataset covering over 1,500+ attributes on 240 million U.S. adults and their households. These data points include a range of industry relevant indicators like debt load, household composition, mortgage status, and even shopping habits. 

Whether you're working with new leads or existing customers, this enrichment process not only fills in the gaps but it also adds new actionable data that ensures you’re ready for AI modeling. This data layer is the foundation for everything that follows. Without it, predictive scoring, segmentation, and personalization simply wouldn’t be possible.

Learn more about our [comprehensive data services](/docs/abstractions/data-appends).

### Predict who’s ready to engage

Faraday’s lead prioritization tools help firms move beyond instinct. Using advanced AI, we help brands optimize their lead conversion process by scoring based on their likelihood of converting. This starts the moment a lead identifies themselves—when they first share their contact information—and continues throughout the entire lead-to-customer journey.  By enriching each new record with FIG attributes, Faraday also avoids the classic [cold-start problem](/stories/dtc-retailer), delivering confident predictions from day one.

Learn more about how we [predict who’s ready to engage](/templates/lead-prioritization).

### Suppress unqualified or risky leads

Faraday helps teams reduce acquisition costs and improve ROI by identifying leads that are unlikely to convert—or may raise compliance concerns—before any outreach occurs. Suppression models ensure that reps and budgets stay focused on higher-quality prospects. This approach is especially powerful in direct mail, predicting which households are most likely to respond, cuts costs without sacrificing performance. 

Learn more about how we [suppress unqualified or risky leads](/templates/lead-suppression).

### Segment smarter with behavioral Personas

Faraday’s AI-driven Personas group individuals based on lifestyle, values, and behavior patterns—not just age or income. These insights help with personalization and allow you to tailor messaging, tone, and timing to improve campaign performance across email, phone, SMS, and direct mail.

Learn more about how we [segment smarter with behavioral Personas](/templates/thematic-personalization).

### Recommend the right product, at the right time

Whether promoting a debt relief offer, a loan product, or financial counseling, Faraday can predict what product someone is most likely to engage with next. This allows teams to match offers to individual needs and improve adoption rates.

Learn more about how we [recommend the right product, at the right time](/templates/next-best-offer).

## Get in touch

Whether you’re a large national brand or a smaller operation, Faraday helps make every customer interaction count. [Reach out](https://www.faraday.ai/talk-to-sales) for a personalized walkthrough.

Faraday uses predictive AI and enriched consumer data to help debt settlement companies cut acquisition costs, prioritize high-intent leads, and drive smarter, more profitable outreach.

Save $100K/month with smarter targeting - how Faraday’s predictive AI agents are reshaping debt settlement

In our last blog in this series, we tackled a common question: [*When should I update my personas*](/blog/when-and-why-to-update-personas)*?* The answer, in short: almost never. Personas are a foundation—not a real-time dashboard—and you don’t pour new concrete every week.

But if that post answered the *when*, this one’s about the *what*. As in: How many [persona sets](/docs/abstractions/persona_sets) do I actually need? 

But it’s 2025 and we’re all very busy, so here’s the TLDR: **You almost certainly only want ONE persona set.** 

But for those of us who want a deeper dive, read on. 👇

## What is a persona set?

A [persona set](/docs/abstractions/persona_sets) is a small group of customer personas—each one representing a distinct cluster of your real customers. These personas are archetypes based on shared traits and behaviors, built from the people who have actually converted. They're the foundation for structured personalization across your brand.

So let’s clarify terms:

* A **Persona** is a single, representative profile within a set. Think "Urban Millennial Max" or "Suburban Saver Susan."  
* A **Persona set** is the full group of those profiles—usually 3 to 7 personas total—that collectively describe your customer base.

Personas aren't based on assumptions or hypotheticals. And they’re definitely not built around leads or website visitors. The reason is that personas need to be grounded in actual converted customers, you know the ones who said *yes*, not the ones who ghosted you. These people have the traits you want to seek out in new leads who you also want to convert.

The purpose of a persona set is to give your brand a coherent, actionable structure for engaging your audience. You use them to vary messaging, align creative, and personalize campaigns across the funnel.

## How do we build them?

At Faraday, we build persona sets using [enriched data](/docs/abstractions/datasets) and [k-means clustering](/blog/k-means-clustering-customer-personas). We start with your customer file—ideally recent customers—and enhance it with third-party traits from the [Faraday Identity Graph](/features/faraday-identity-graph), which includes demographic signals (like age, household size, income range), geographic markers, lifestyle and behavior data, and more. This rich, consented data allows our AI to surface patterns that wouldn’t be obvious from first-party data alone.

After we enrich your data, we use a method called *k-means clustering*, which automatically groups all customers into 3–7 distinct clusters. That range is deliberate: too few, and you lose nuance; too many, and the personas stop being practical—after all, personas power personalization and you don’t really want to customize 37 emails each time you make a send\! Remember, our AI is always powerful but is only value-driving when tied to a concrete business function.

The result: a persona set that reflects your business as it is today—and that you can use to personalize everything from top-of-funnel ads to post-purchase journeys.

## So how many persona sets do I need?

**In a nutshell, one is enough (we promise).**

Here’s the temptation (and we’ve heard it many times): you start to see interesting subgroups, and you think, “Should I just make a whole new persona set for my high-value customers? Or for leads? Or for gift buyers?”

But this is important: resist the urge. One persona set, built on your customer base (ideally recent customers), can answer all of those questions *if* you use it well. And what’s more, every additional persona set you create adds complexity—fragmented messaging, duplicated creative, messy reporting—with very little return.

Think about it practically. Let’s say you’re using personas to personalize emails. If you have 35 personas, you need 35 distinct emails each time you make a send, tailored to each persona. The same would be true for a direct mail campaign, or any other use case built on personas. 

And if you really want to dig into those subgroups, that is still a great option with one persona set, you just use **secondary analysis** instead of additional personas.

## Use secondary analysis for deeper insights

Let’s say you’re curious about your high-LTV buyers. Or people who tend to buy during promotions. Or those who give gifts.

The instinct might be to build a new persona set just for those customers—but that’s the wrong move.

Instead, use **secondary analysis** on the persona set you already have. This means identifying which of your existing personas are most likely to exhibit those behaviors. Does persona B have more high-value buyers? Does persona D respond more often to promotions? 

This lets you tailor messaging, promotions, or next-best-action logic *within* your current framework—without rebuilding the architecture from scratch.

You’re analyzing, not segmenting. That distinction matters. If your persona set is the architectural structure, secondary analysis is like putting sensors in each room to see who uses what, when, and why. It adds intelligence without requiring you to redraw the floorplan.

That’s why secondary analysis is the right tool for exploring behavior. It lets you get more specific without fragmenting your strategy.

## So would I ever use a second persona set?

We get this question a lot—and it’s not a bad one. In theory, there *are* situations where a second persona set could make sense. But in practice, they’re vanishingly rare.

Let’s say your business has two truly distinct models—like Netflix in the DVD-by-mail era vs. its streaming service. Maybe the customer base for each product barely overlaps. In a case like that, sure, there’s a logic to considering separate persona sets.

But even then, we don’t recommend starting there. First, build a single persona set from your full customer base and run secondary analysis. You may find that your personas naturally reflect the split without needing to manufacture two totally separate structures.

The risk of jumping too soon into multiple sets is high: fragmented messaging, duplicate effort, and confusing strategy. You’ve gone from one coherent framework to parallel tracks that require twice the upkeep—and often deliver half the clarity. 

**As Andy put it in a recent meeting:** 

**“If you really, really, really, really think you need more than one… you still don’t.”**

## How to use personas

Once you have a persona set built from your customers, the value comes from how you apply it.

You don’t just file it away as “interesting.” You use it as a structure to guide how you engage people across the entire funnel.

### Start with leads

Even though your personas were built on customer data, you can (and should) apply them to leads and prospects. Every lead can be mapped to the closest-matching persona based on their traits.

That lets you do smart things early—like showing persona-aligned Facebook ads, driving people to persona-specific landing pages, or customizing messaging tone based on emotional drivers. 

**As Andy says:**

**“You want continuity between how you handle leads and how you handle customers. That means the architecture has to start early.”** 

### Personalize campaigns

This is personalization 101\. Instead of blasting one-size-fits-all emails or ads, you vary your content based on what you know about each persona. Not to the point of infinite complexity—but enough to make the message feel more relevant, more human.

And you don’t need 37 personas to get there. Three to seven is plenty.

### Operationalize and scale

Personas are a strategic lens. They help you organize your audience, inform creative, and structure internal decision-making. In a large org, you could even assign brand managers to own each persona—getting deep into what makes that group tick.

Just don’t mistake them for a silver bullet. You’re not using personas to predict exact behaviors (like who’ll buy Product X on Thursday). For that, you’d use specific outcomes or predictive models. But for building audience structure and making smarter content decisions, personas are the starting point.

## Conclusion

If you take nothing else from this, remember this: your persona set is not a one-off project. It’s the structural framework—the architectural blueprint—that supports your entire customer engagement strategy.

When you get it right, everything else becomes easier. Ads align. Messaging resonates. Creative connects. And the decisions you make, from acquisition to retention, rest on a solid foundation.

Don’t overcomplicate it. Build once. Use often. And always start with the people who said yes.

Nearly every brand needs just one carefully built persona set—use secondary analysis, not additional sets, when you want more detail.

So how many persona sets do you need anyway?

This spring, a team of interns from [Middlebury College](https://www.middlebury.edu/#) joined Faraday with a plan: take Faraday’s powerful [Persona](/docs/abstractions/persona_sets) data and make it even easier for marketers to leverage. And armed with skills in statistics, math, and understanding of client marketing challenges, they took on one of the most common hurdles our clients face—how to identify insights that drive results.

### Take a look at the interview: 

<iframe
  width="560"
  height="315"
  src="https://www.youtube-nocookie.com/embed/oJ650NC2cp0?rel=0"
  title="Faraday interns built an even smarter Persona pipeline"
  loading="lazy"
  frameborder="0"
  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
  allowfullscreen>
</iframe>

## The problem: too much data, not enough insight

[Faraday’s Identity Graph](/features/faraday-identity-graph) provides over [1,500 consumer traits](/leaderboard), and our [Persona](/docs/abstractions/persona_sets) feature organizes audiences into distinctive [cohorts](/docs/abstractions/cohorts) (like "Middle-aged Mom Moira"). But marketers often need help identifying which traits matter for each Persona and how to act on them.

Faraday already offers several ways to tackle this challenge, from [forward-deployed data scientists](/blog/FDS-changes-the-game) to [model explainability tools](/blog/faraday-score-explainability) built into the platform. It’s 2025 and our interns saw an opportunity to create a new, agentic workflow to automate the process: a tool that could not only automatically surface the most distinctive traits of each Persona, but also translate those traits into actionable messaging and product recommendations, without manual intervention.

## The solution: an automated pipeline

The interns designed a pipeline that pulls Persona data from the [Faraday API](/docs/reference), identifies distinctive traits, and feeds them into [Perplexity](http://perplexity.ai) AI. From there, Perplexity generates tailored messaging suggestions, product recommendations, and even market sizing insights—all mapped back to the Persona’s unique profile.

The final output? A clean, shareable marketing report that makes complex trait data immediately usable. No spreadsheets. No guesswork. No manual data sifting.

[Download sample report \#1](/images/downloads/sample-persona-report-1.pdf).

## How it works

To measure how distinct each trait is across Personas, the team used two core metrics:

* Energy Distance for continuous variables like income or age  
* Total Variation Distance for categorical traits like gender or household type

These tests compare distributions across every Persona pair, flagging the trait where a Persona stands out most. The process is statistically grounded, with hypothesis testing and p-values to ensure real, meaningful differences are surfaced.

Once the key traits are identified, they’re passed into Perplexity AI along with client-specific context (like a brand website). The result? A few paragraphs of tailored content that includes:

* A narrative summary of the Persona  
* Marketing language suggestions  
* Recommended products  
* Market size estimations

All compiled into a client-ready report with citations.

[Download sample report \#2](/images/downloads/sample-persona-report-2.pdf).

## Turning Personas into action

The real win here isn’t just technical—it’s strategic. Marketers don’t want a firehose of 1,500 traits washing over them at once, they want to know:

* What makes this Persona unique?  
* How should we talk to them? 
* What should we sell?

This project answers those questions automatically—bridging the gap between complex analytics and real-world execution.

#### For instance

Imagine you’re a DTC skincare brand, exploring expansion into a more mature market segment. You’re already using Faraday to generate Personas, and one of them—“Self-Care Susan”—represents health-conscious women in their 60s who regularly purchase wellness products online. With this new pipeline, your team can instantly uncover what makes Susan unique (say, high engagement with plant-based ingredients and interest in loyalty programs), along with AI-generated messaging tailored to her values and product suggestions rooted in actual purchase behavior.

Suddenly, instead of wading through hundreds of traits or manually brainstorming campaign copy, your marketing team is armed with a ready-to-use report: what to say, what to sell, and why it’ll resonate.

It’s a leap from data to decision—saving time, improving targeting, and increasing campaign impact.

## Special thanks

Big thanks to our incredible interns, [Kayley](https://www.linkedin.com/in/kayley-watson/), [Zach](https://www.linkedin.com/in/zach-fu/), and [Xander](https://www.linkedin.com/in/alexander-swann/)—as well as Middlebury College—for their fantastic work. This is only the beginning, but it's already showing what happens when you combine statistical rigor with real-world marketing needs.

## Time to get started

Want to learn more or hear how these powerful new Persona pipelines could benefit your company? [Reach out](https://faraday.ai/talk-to-sales)\!

Faraday’s interns built an automated pipeline that analyzes Persona traits and generates ready-to-use marketing reports—turning complex data into clear messaging, product recommendations, and strategic insights.

Faraday’s interns built an even smarter Persona pipeline, here's how it works

It’s tempting to think of ROI analysis as a simple math problem. Plug in the numbers, calculate the return, and call it a day.

But the truly specialized secret to great ROI isn’t in the arithmetic; it’s the methodology and strategy that underlie and inform the analysis. It’s knowing *where* to look, *what* to measure, and *how* to choose the right approach for the situation. 

At Faraday, we don’t start our ROI analyses by creating an equation. We start by picking the right tool from the toolbox. Different tools unlock different kinds of answers. In our last blog, we made the case that [ROI isn’t a scorecard—it’s a strategy](/blog/roi-isnt-a-scorecard-its-a-strategy). This post digs into the next layer: how we measure ROI in a way that actually reflects the individual strategic value of your campaigns. 

And that starts by understanding which is the right method for the job.

## ROI isn't just about math, it’s about methodology

The actual math behind ROI analysis is pretty simple. You’re mostly dealing with addition, subtraction, multiplication, and division. In this case, complexity comes not from the operations themselves, but how we choose to apply them.

[![Comedic GIF showing arithmetic operations](/images/blog/mathishard.gif)](/images/blog/mathishard.gif)

An ROI specialist knows which data points matter, how to structure the comparison, and what kind of baseline you’re using to judge performance. In other words, the real work isn’t in crunching the numbers; it’s in designing the analysis.

And importantly, different ROI methods exist for a reason. Each one comes with trade-offs, and the right choice depends on your goals, your timeline, and the level of precision you need.

## Common "tools" in the ROI toolkit

There’s no single “best” way to measure impact. It depends on the shape of the campaign, the outcome you care about, and how precise your measurement needs to be.

Different methods are used depending on what you're measuring, what kind of attribution you need, and what trade-offs you’re willing to accept.

#### 1\. Holdout testing (A/B testing)

**How it works:** A holdout test randomly splits your audience into two groups, one that receives the intervention, and one that doesn’t. Then simply compare the outcomes.

**Advantages:**

* Delivers highly reliable attribution. You can isolate the effect of the intervention with minimal noise.
* Especially useful when you need clear proof of impact—for example, before rolling out a new campaign strategy.

**Disadvantages:**

* You’re intentionally withholding the (potentially better) treatment from part of your audience, which can mean sacrificing short-term gains.
* Longer-term metrics like LTV can require months or years of tracking, which slows down decision-making.
* Can be difficult to set up for more integrated processes.

**How this might look in practice:** A home services company wants to evaluate the impact of a new predictive lead scoring model. To measure its effectiveness, they randomly split inbound leads into two groups. The test group receives scores from Faraday, and the sales team uses those scores to prioritize outreach. The control group is handled as usual, without scores. After 30 days, the test group shows a 15% higher conversion rate. Because the only variable that changed was the introduction of scoring, the team can confidently attribute the improvement to Faraday’s model—and deploy it more broadly.

#### 2\. Longitudinal studies

**How it works:** These studies measure a key performance indicator (KPI) before and after an intervention, and attribute any change to the strategy.

**Advantages:**

* Easier to implement when the change affects a system or workflow that can’t easily be split, like internal operations.   
* No need to create and manage control groups.  
* Can continue tracking performance indefinitely without missing out on performance benefits.

**Disadvantages:**

* Harder to separate the signal from the noise. External factors like seasonality, sales events, or even weather can cloud the results.

**How this might look in practice:** Picture a credit union using Faraday to launch a predictive direct mail campaign aimed at promoting HELOCs. Since they want to reach the entire audience, they don't create a holdout group. Instead, they measure HELOC applications for eight weeks following the campaign and compare that to the eight weeks prior. Applications increase by 25%, and with no other major marketing efforts running in that window, the credit union sees a strong directional signal that the campaign worked.

#### 3\. Relative performance improvement studies

**How it works:** This method builds on a longitudinal approach, but instead of simply comparing “before” and “after” for one group, you compare the *change* in your test group to the *change* in a baseline group that wasn’t affected by the intervention.

In other words: both groups experience whatever else is happening in the world (seasonality, ad spend changes, etc.), but only the test group is affected by the treatment. By comparing the test group’s performance to the baseline group, you subtract out the “background noise” and can isolate the true impact.

**Advantages:**

* More resilient to outside variables. Since both groups are exposed to the same external forces, the comparison helps filter out unrelated effects.  
* Doesn’t require randomization—useful when you can’t run a traditional holdout test.

**Disadvantages:**

* Can be harder to explain to stakeholders unfamiliar with the method.  
* Requires a valid control group that closely tracks with the test group’s normal performance. If your baseline group isn’t steady or comparable, the whole thing falls apart.

**How this might look in practice:** Imagine a regional furniture retailer running a new predictive direct mail campaign in their Midwest region, while using their Northeast region as a comparison. Both regions are similar in customer base and seasonality, but only the Midwest receives the new campaign. During the test window, Midwest sales rise by 12%, while Northeast sales only increase by 2%. The 10-point gap is attributed to the campaign, with the unaffected region helping control for external factors like seasonal shopping trends or macroeconomic changes.

## Choosing the right tool for the job

Not every situation calls for the same approach, sometimes a "perfect" test isn’t practical or cost-effective. The best ROI analysts know how to match the method to the moment, balancing rigor with speed, and clarity with real-world constraints.

Here’s a quick cheat sheet to guide your choice:

* Use holdout tests when you need the cleanest attribution possible and you have time to run a structured experiment—ideal for testing new acquisition channels or proving the value of a bold strategy shift.

* Use longitudinal studies when A/B testing isn’t practical—like when rolling out changes to workflows, routing logic, or systems that touch your entire organization.

* Use relative performance studies when you can’t randomize, but you *can* find a comparable group or region to act as a baseline—helping you filter out external noise and isolate the true impact.

Choosing the right approach isn’t about checking a box—it’s about working with what you’ve got and building an analysis that supports better decisions.

## ROI is an art of trade-offs

Every method comes with trade-offs: between speed and precision, between statistical rigor and operational feasibility, between what you *want* to know and what you *can* realistically measure.

That’s why ROI analysis isn’t a checklist, but a craft. It requires judgment. True expertise means knowing when “good enough” is actually better than “perfect,” because it lets you act faster, test more often, and make decisions that still move the needle.

At Faraday, we don’t believe in one-size-fits-all measurement. Instead, we tailor ROI analysis to fit the strategy, timeline, and success metrics of each client. Whether that means running a formal holdout test, analyzing past performance, or finding a creative workaround, we help our clients make smart decisions grounded in evidence—not just instincts.

## Conclusion

At the end of the day, calculating ROI isn’t just a mechanical process, it’s a strategic one. The numbers only matter if they’re telling you something useful. And that usefulness depends on choosing the right tool, the right comparison, and the right trade-offs for your business.

Faraday’s approach is built around that idea. We help our clients go beyond surface-level metrics to get a clearer, more actionable picture of what’s working, what’s not, and what to do next. Whether you’re trying to justify spending, test a new channel, or make smarter bets for the future, we’ll help you measure what matters—on your terms.

Want to see how predictive insights and tailored ROI reporting can work together? [Let’s talk](/talk-to-sales).

Faraday’s strategic approach to ROI uses tailored analysis methods—like holdout testing, longitudinal studies, and relative comparisons—to deliver insights that actually reflect each client’s unique goals, constraints, and campaign impact.

Behind the numbers: The methodologies Faraday uses to turn ROI into value

In the world of home renovation financing—whether it's a new roof, solar panels, or energy-efficient upgrades—understanding a customer’s financial picture early saves everyone time. It tells you who to target, what to offer, and when to engage. More importantly, it reveals critical second-order insights—like upsell potential, churn risk, or even a household’s likely financing preference. But too often, teams wait until deep in the sales process to get that clarity, and often, by then, they’ve already invested precious time and resources in the wrong prospects.

## A smarter first signal

What if you had a way to gauge financial readiness right from the start? As soon as a prospect shows interest—downloads a brochure, fills out a form, clicks an ad—you’d already have a sense of whether they’re in a position to move forward. No sensitive data. No guesswork. Just strong, early indicators of capacity to convert.

That’s exactly what Faraday provides.

## How Faraday helps

Using ethically sourced, privacy-safe data from the [Faraday Identity Graph (FIG)](/features/faraday-identity-graph), we help brands surface deep and nuanced financial readiness signals. Thousands of household-level and demographic and behavioral attributes—like financial capacity, payment patterns, home ownership details, and purchase activity—come together to create a rich, predictive profile of each contact.

This empowers you to:

* Focus outreach on households most likely to engage  
* Reduce wasted effort on unlikely buyers  
* Accelerate deal cycles by prioritizing strong leads early

And financial signals are just the start. The same FIG data can forecast purchase intent, churn risk, and product fit, so your outreach isn’t just accurate—it’s relevant.

## Results you can bank on

Teams that layer FIG into their acquisition funnel see fewer wasted dials, sharper targeting, and faster closes. 

In short: less waste, more wins, and a growth engine that scales without ballooning acquisition costs.

Curious which signals matter most for your product? [Let’s talk](/talk-to-sales)\!

Faraday’s AI helps you identify financially ready prospects from the very first touch—cutting waste, accelerating sales, and scaling growth responsibly.

Gauge homeowner financial readiness from day one—with Faraday

[Dharmesh Shah](https://www.linkedin.com/in/dharmesh/) ([HubSpot](https://www.hubspot.com/)'s CTO) just [announced](https://www.linkedin.com/posts/dharmesh_breaking-news-hubspot-is-the-first-crm-to-activity-7336128750297735169-3O1-/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAtKS-0BhomXJQm0PPBSMetJRw03HNXFZ2M) that HubSpot is now the first CRM natively connected to ChatGPT. 

This marks a major shift in how people will interact with their business data. Instead of clicking through dashboards or waiting on reports, users can now ask their CRM complex questions in plain English—and get instant answers. It’s a breakthrough in accessibility, speed, and strategy.

But while the interface is changing, the fundamentals aren’t. AI can only work with the data it’s given. So if your CRM is full of low-quality, outdated, or incomplete records, your shiny new chatbot will still give you shallow, outdated, incomplete answers.

## Your CRM can't predict the future if it's stuck with bad data

Imagine you're a CEO checking in on your sales pipeline. You fire up your shiny new CRM chatbot and ask, "Who are our warmest leads right now?" It spits out a list of accounts that sort of resemble last year’s ICP—based on a handful of clicks, a webinar signup, maybe a stale lead score from Q4. No context, no nuance, and no real signal. It’s not business intelligence, it’s data theater.

On the flip side, if your CRM is enriched with high-quality behavioral data and real-time propensity scores from a platform like Faraday, the response gets sharper: "These households show high intent for solar based on how similar they are to the last 5 deals you sold. They all have a high interest in environmental causes, they all drive EVs and they make a similar amount of money. Prioritize them via SMS, and afternoons work best." Now that’s useful.

## Good AI needs great data

Everyone’s racing to integrate ChatGPT into their stack. But very few are thinking beyond the UI. The big unlock isn’t just asking better questions, it’s giving AI better context.

That’s where platforms like Faraday come in. We bring:

* Thousands of predictive traits from the [Faraday Identity Graph (FIG)](https://faraday.ai/features/faraday-identity-graph), including behavioral signals like payment habits, shopping styles, and lifestyle indicators.  
* Campaign-ready propensity scores that predict conversion, churn, upsell potential, and more.  
* Powerful consumer segmentation and personas that help you tailor messaging, creative, and channel strategy to match how real people behave—not just who they are on paper.  
* Privacy-safe enrichment that keeps your customer data siloed and secure.

## Better inputs, better outcomes

So yes, talk to your CRM. Ask it for trends, next steps, and hot leads. But if you want answers that are actually actionable, make sure your data is ready to support them.

Better interfaces are exciting. But better inputs are what make them work.

Want to see how smarter data makes AI work harder? [Let’s talk.](https://faraday.ai)

Smarter CRMs need smarter data—because even the best AI can’t deliver useful insights if it’s working with outdated or incomplete records.

Why smarter CRM experiences need smarter data

[![predictor leaderboard](/images/blog/predictor-leaderboard.png)](/images/blog/predictor-leaderboard.png)

We just launched something we’re really excited about: the [Faraday Predictor Leaderboard](/leaderboard). It’s a live look at the most consistently predictive traits in our platform, based on thousands of AI models and billions of real-world events.

You can filter by industry and use case, so whether you're in solar or e-commerce, doing lead scoring or churn prevention, you see what relevant-to-you traits are driving results, right now. Things like “recent mortgage inquiry” or “owns a pool” aren’t just trivia—they’re signals that real models are using to generate lift.

In other words: the leaderboard gives you a peek at what’s driving value across the Faraday predictive universe, in real time.

And the takeaway? You don’t have to guess what traits matter anymore. The leaderboard shows you what’s working *right now*—so you can shortcut the discovery process and zero in on the signals that are already driving lift across industries like yours.

## How the Signal Index works

When you look at the leaderboard, the first thing you likely notice is the signal index. After all, it is the first column, but what exactly does that mean? 

Each trait on the leaderboard is assigned a signal index—a relative measure of how often it appears as an important predictor across thousands of live Faraday models. It’s not a raw number or percentage, but a way of showing which signals are standing out right now compared to others. This approach, sometimes called "indexing," is common in benchmarking and lets us highlight what’s rising to the top across industries and use cases.

## It all starts with FIG

Behind the scenes of the Predictor Leaderboard is one of the most powerful engines at Faraday: the [**Faraday Identity Graph (FIG)**](/features/faraday-identity-graph). FIG is our proprietary graph of nearly the entire U.S. adult population, connecting thousands of behavioral, demographic, and life event traits to real individuals. It’s how we detect stable, predictive signals that drive meaningful outcomes—like improving retention in financial services, increasing acquisition in solar, or boosting reactivation in e-commerce.

## So what exactly is a "trait"?

At Faraday, a trait is a specific detail about a person that helps explain their behavior—things like whether they’ve recently moved, have kids at home, or use a certain kind of credit card. When our models spot consistent patterns between these traits and real-world outcomes, they become powerful predictors of what someone might do next.

The traits on the leaderboard aren’t just interesting facts, they’re there because they’ve proven, across live campaigns, to consistently drive results. They help our models understand not just what someone did, but who they are and what matters to them.

So while the leaderboard gives you a snapshot of what’s working today, it also reveals the deeper, often surprising, behavioral signals powering our predictions.

## How to use the leaderboard

Think of the Predictor Leaderboard as a window into how Faraday models actually work.

It’s not a tool for tweaking models or choosing traits—in fact, all Faraday deployments already include built-in explainability, so users can see what’s driving predictions in their own campaigns. Instead, the leaderboard is a live demonstration of the kinds of traits that consistently drive predictive lift across industries and use cases.

If you're considering using Faraday, it gives you a real-world preview of the kinds of signals our models uncover—like how “dwelling type” shows up in solar acquisition, or how “shopping style” influences reactivation in e-commerce.

You’re not picking traits. But you are seeing what matters—and that insight can inform how you think about your audience, your positioning, and the outcomes you want to drive.

## So what’s trending this week?

Here are the top 5 traits currently driving predictions across Faraday:

1. **Shopping styles** 🛍 How a household prefers to shop (online, in-store, catalog, etc.). This trait is dominating campaigns that aim to personalize outreach based on behavior—especially in e-commerce and omni-channel retail.

2. **Age** 🎂 Simple but powerful, age is still one of the strongest predictors of conversion, churn, and reactivation, across almost every industry.

3. **Dwelling type** 🏡 Is it a single-family home? An apartment? A duplex? This trait is crucial for verticals like solar and insurance, where property type impacts eligibility and likelihood to act.

4. **Gender** ⚧ When used thoughtfully, this trait helps tailor messaging and product recommendations, especially in health, wellness, and fashion.

5. **Online household** 🌐 Does the household have a reliable internet connection? This trait is extremely predictive for digital product adoption and campaign engagement in rural and suburban markets.

Right now, these traits are being used to improve conversion rates, suppress churn, and drive smarter acquisition. That’s what the leaderboard shows: what’s working, right now, across real businesses and real campaigns.

## Iteration is the key

The leaderboard isn’t static—and that’s the point. Traits rise and fall, and we track those shifts obsessively. Not just because it’s interesting, but because it’s essential.

Getting prediction right isn’t about one great model—it’s about improving with every pass. That’s why we monitor trends over time, ask what’s changing and why, and use those insights to refine how our models learn.

This is core to how Faraday works: persistent observation, rigorous iteration, constant tuning. The result? Smarter campaigns, sharper targeting, and better results for our customers—week over week.

Want to see what traits are trending this week? [Check out the leaderboard](/leaderboard)\!

The Faraday Predictor Leaderboard offers a live, filterable view into the traits driving the most predictive lift across industries—giving you a real-time peek into what’s working in AI-powered marketing today.

Product

Smarter dials: How lead scoring transforms your outbound strategy

The Faraday Identity Graph just got even smarter

Match Boost improves enrichment coverage on sparse data

Save $100K/month with smarter targeting - how Faraday’s predictive AI agents are reshaping debt settlement

ChangelogSee all

FIG updated with powerful new features

Match Boost now available

Support for OR logic in datasets and cohorts

Faraday predictor leaderboard

So how many persona sets do you need anyway?

Faraday’s interns built an even smarter Persona pipeline, here's how it works

Behind the numbers: The methodologies Faraday uses to turn ROI into value

Gauge homeowner financial readiness from day one—with Faraday

Why smarter CRM experiences need smarter data

Just launched: the Faraday Predictor Leaderboard