**See our complete guide:** [Predictive AI for banks and credit unions](/blog/banks-credit-unions-complete-context-guide).

---

Credit unions exist to serve their members and strengthen their communities. That mission-driven focus is what sets them apart—but it also means they don’t always have access to the same marketing tools, resources, or budgets as megabanks and fintechs. The challenge today is clear: how can credit unions compete against larger institutions armed with endless resources and cutting-edge technology?

If you’re leading marketing or member experience at a credit union, you know this pressure firsthand. You’re competing with organizations that have bigger budgets, flashier tech, and armies of data scientists. Meanwhile, your team is lean, your resources are stretched, and the board wants clear proof that every marketing dollar is driving measurable growth.

The issue is not that you don’t have data — you’ve got mountains of member information. The challenge is turning it into something actionable. And too often, your campaigns default to generic blasts: everyone gets the same credit card offer, the same auto loan promo, the same mortgage refi pitch. Members tune out, conversions lag, and you’re left struggling to show ROI.

The truth is simple: **without predictive product recommendations, you’re leaving millions on the table every quarter.**

## The problem: your members don’t take your offers

If you’ve gotten this far into the blog, you probably already know some of this pain:

- **Spray-and-pray campaigns don’t resonate.** When every member gets the same offer, most of them see it as noise.
- **Lean teams can’t build predictive models in-house.** You don’t have the bandwidth to mine your data and run experiments the way megabanks do.
- **Boards expect measurable ROI.** It’s hard to prove marketing’s value when campaigns feel like guesswork.

This all results in missed opportunities to deepen relationships and grow wallet share. Every generic email is a missed chance to connect with the right member, at the right time, with the right product. That means money is left on the table and members don’t get the services and products they actually need and want.

## The solution: predictive data points to show customer intent

While this is a complicated problem, Faraday makes the answer simple.

By combining your first-party member data with the [Faraday Identity Graph](/features/faraday-identity-graph), which features 1,400+ consumer data points on 240M U.S. adults and their households, we can generate custom predictive signals like: likelihood-to-open-auto-loan, likelihood-to-refinance-mortgage, or likelihood-to-apply-for-credit-card.

Most importantly, we can generate a predictive data point [that ranks which of your products each member is most likely to choose](/templates/next-best-offer). That way, instead of blasting every member with the same offer, you’ll know exactly which product resonates. Imagine seeing: Jan is 90% likely to open an auto loan, but only 40% likely to refinance her mortgage. You’d probably send her the offer for the auto loan over the mortgage refi — and as a result, she’d be much more likely to convert.

Our data solves various core challenges:

- **More relevance, less noise.** No one likes ads that don’t resonate, this way members see offers that actually matter to them.
- **Smarter use of lean teams.** You don’t need an in-house data science department to compete with megabanks, Faraday adds a predictive data layer into your existing tech stack.
- **Clearer ROI.** Campaigns become more efficient and easier to measure, making it simple to show the board real growth impact.
- **Transparent and compliant.** Every model is also fully transparent and auditable, so you can stay aligned with fair lending requirements.

Once the recommenders are built, delivery is seamless. Predictive data points flow directly into your CRM or marketing automation platform via API — no heavy IT project required. And because every model is fully transparent, you can align campaigns with clarity and demonstrate to the board how marketing spend translates into measurable member growth.

## How this looks in practice

Take [Advia Credit Union as an example](/blog/advia-auto-loan-growth). Like many credit unions, Advia wanted to deepen relationships with existing members and grow wallet share, but generic campaigns weren’t moving the needle. After linking their first-party member data with the Faraday Identity Graph, they used predictive product recommendations to identify which members were most likely to open an auto loan. That meant they could target offers only to the people who were truly interested — no wasted touches, no guesswork.

The impact was immediate. Within 30 days, Advia had already generated \$1.1 million in new auto loans. After 90 days, the campaign had delivered \$2.7 million in auto loans with an application rate more than four times higher than their baseline. And because every model was transparent and bias-checked, Advia was able to stay aligned with fair lending requirements while still giving members the personalized offers they actually wanted.

## Ready to stop guessing and start predicting?

If you’re tired of generic campaigns that don’t move the needle \- and tired of the board breathing down your neck for ROI \- predictive data points from Faraday can make growth measurable, compliant, and easy.

Faraday is built for teams exactly like yours. Ready to see how predictive data can unlock your next $2.7M? [Reach out.](/contact)

In a competitive market, credit unions need to make sure to maximize the value of every account holder. Without predictive product recommendations, they’re leaving millions on the table every quarter.

Tired of credit union members not taking your offers? Fix it with predictive data

When marketers start looking into lead scoring and predictive data, they often hear about "real-time" delivery and get pretty excited. And we get it. It has that instant-gratification appeal: the idea that you can get a prediction in the blink of an eye and make a decision on the spot. But here’s the catch—real-time isn’t always the smartest path. In fact, for most businesses and use cases, **batch predictions actually deliver richer, more useful results.**

At Faraday, we offer both methods—batch and real-time—because they serve different needs. But understanding when to lean on one versus the other can save you money, improve campaign efficiency, and make sure you’re not chasing speed at the expense of insight.

So when is "real-time" actually what you're looking for? Let's find out 👇

## Why batch predictions are the default

After that setup, you might be not surprised to learn that real-time actually isn’t the default way Faraday delivers data. 😱

That’s because batch processing is the real workhorse behind most predictive data and modeling. Think of batch like a doctor running a full health checkup versus just taking a pulse. Sure, the pulse might tell you something critical in a hurry — like Charlie isn’t dead — but it doesn’t actually reveal much else about what’s going on. For the doctor, both the pulse and the full checkup are useful, but they don’t serve the same purpose and can’t replace each other.

Like the full checkup, Batch doesn’t have the immediacy of the pulse check. But what it gives you instead is significantly more nuance and detail.

At Faraday, batch is our default delivery method because most of the time, you need the nuance and the deep context more than the immediacy. And beyond just getting more info about an individual, batch allows you to score an entire audience (your CRM, acquisition list, or customer database) all at once.

Batch is our default because it gives you the _complete picture_ instead of just a quick, decisive signal. Here’s what that really means in practice:

- Score entire populations you already know (like your CRM or customer list)
- Incorporate first-party and third-party attributes into your predictions
- Add multiple data points (including predictive data points) at once, rather than one-off signals
- Deliver results in flexible formats—CSV, Snowflake, S3—that plug into your systems
- Run on a custom schedule (daily, weekly, monthly) so the data is always refreshed at a rate that matches your use case and needs
- Enrich identity data more deeply, returning additional emails, phones, and addresses

Together this means that you’re not just scoring people, you’re understanding them in context (and scoring them even more). Want to prioritize segments of your CRM contacts? Assign reps to the right accounts? Qualify appointments with [Credit Score Proxy](/blog/prioritize-with-credit-score-proxy)? Batch makes all of that possible, while giving you flexibility and scale.

> **TLDR**: Batch gives you depth, flexibility, and a much more complete picture of your customers and prospects. That’s why it’s the default choice for most of our clients.

## When real-time makes sense

So yeah, batch is pretty great.

But there are moments when speed really does matter. Imagine a call center that has seconds to decide whether to accept or reject an incoming lead instantly in a [ping/post](/blog/so-what-is-a-ping-post-anyway) environment. Or a sales rep standing on someone’s porch with a tablet, trying to decide whether to make the pitch. In those scenarios, waiting for batch processing isn’t an option, or necessary. You don’t need the deep context, like the pulse, you just need a clear signal in a hurry.

That’s where [Faraday’s Lookup API](https://faraday.ai/docs/features/lookup-api) comes in. By pulling from our 3rd party data and pre-scored national populations, we can give you an accurate prediction on the fly. It’s perfect for situations like:

- [Bidding in ping/post systems](https://faraday.ai/blog/what-happens-when-predictive-AI-meets-real-time-lead-filtering) like with [ActiveProspect's](https://activeprospect.com/) [LeadConduit](https://activeprospect.com/leadconduit/)
- [Scoring addresses during door-to-door sales](https://faraday.ai/blog/salesrabbit) with tools like [SalesRabbit](https://salesrabbit.com/)
- Assessing likelihood to sign up on a landing page in real time

**The key tradeoffs:**

- Data must be sent via POST request to a specific API endpoint
- Egress path is narrow: you hit us, and you get the data point back — no [Snowflake](https://www.snowflake.com/en/), [S3](https://aws.amazon.com/s3/), or warehouse delivery
- Requires careful configuration of scope populations in advance
- You can only get one data point at once — giving you a less complete picture of your target

> **TLDR**: Use the Lookup API when you need an instant signal about someone you just met — like a new lead, form fill, or sales visit. It’s fast, simple, and scoped — but limited to national data only.

## How theses look in the ✨real world✨

Both delivery methods generate real value for Faraday customers—but in very different ways.

Take real-time first: when a regional home-renovation company saw conversion rates slipping by \~21% during peak call‑center traffic, Faraday’s instant lead prioritization turned things around. By feeding a custom “likelihood‑to‑convert” data point straight into their CRM, reps focused on high‑quality leads right as they arrived. The impact? [A 13% lift in conversion during high‑traffic periods and a 22x monthly ROI](/stories/call-center).

Now contrast that with batch: [Bee’s Wrap](/stories/bees-wrap), a fast‑growing DTC brand, used Faraday to connect its [Shopify](https://www.shopify.com/) order data to the Faraday Identity Graph and run persona modeling at scale. The batch analysis surfaced detailed personas and geographic propensities that became the backbone of their Target retail pitch.

The outcome? Placement in 550+ stores and an initial six‑figure wholesale order.

Together, these examples show the complementary strengths: batch delivers the depth and context needed for strategic moves, while real‑time provides the immediacy required to win in the moment.

## So which is better?

Well here’s the thing, batch and real-time aren’t competitors — they’re complements. Two specialized tools to keep in your belt for different situations. Batch is about depth, flexibility, and context. Real-time is about speed and decisive action. Used together, they give you both the strategic insights to plan campaigns and the tactical edge to win in the moment.

At Faraday, we deliver both. Most of our clients rely on batch as their foundation, completing identities, enriching consumer data, and providing rich predictive data points for entire audiences on a schedule that keeps campaigns humming. Then, when a moment calls for immediacy, our Lookup API steps in to provide the instant signal. The combination ensures you’re never forced to choose between nuance and speed; you get both, working in concert.

Want to learn more about how these can work for you? [Come say hi](/talk-to-sales).

Faraday offers both batch and real-time predictions, helping brands balance the depth and context of large-scale scoring with the speed and decisiveness of instant lead insights to maximize ROI.

Real-time predictions vs. batch data enrichment: what you need to know

If you’ve ever sat through a pitch about data or identity resolution, you’ve probably heard at least one big shiny number: _“We can match 80% of your records\!”_

But here’s the catch: an 80% match rate is meaningless if those matches aren’t real people you can actually contact or use for modeling. False positives don’t just pad a report — they waste budget, erode trust with your customers, and poison downstream predictions and outreach. Worst of all, they mean you can’t plan effectively.

At Faraday, we’ve spent over 13 years refining a simple philosophy: every match should be a record you can confidently act on. And that philosophy isn’t just talk — it’s baked into how our system works day in and day out.

## Faraday delivers matches you can trust

Faraday’s identity resolution system is built to prioritize confidence over vanity stats that sound good on paper but fall apart in real campaigns. This results in a system designed not just to maximize match rates, but to ensure that when we say _“this is the person,”_ you can act with confidence.

Here are some steps we take to ensure this outcome:

**Deduplication and householding**  
We don’t just stitch data together — we actively dedupe records and recognize when multiple identifiers belong to the same household. That means one clean, accurate profile instead of duplicates inflating your numbers.

**Handling life changes**  
People move, change names, swap emails. Most systems struggle to keep up. But the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) (FIG) is built on longitudinal data, so we can track those shifts and keep matches accurate over time.

**Built-in scale and enrichment**  
Every match taps into our FIG, which contains 1,400+ data points on 240M U.S. adults and their households. That means when we confirm a match, you’re not just identifying someone — you’re enriching them with trustworthy, actionable context for campaigns and predictions.

**Multiple paths to identity**  
Matches aren’t only about exact email or address. We account for nicknames, partial addresses, and alternative identifiers — always with strict rules to avoid false positives. And thanks to our newest update, we’ve added yet another path to identity. 👇

## Our latest update

As one of our incredible Data Engineers, Tom Caruso put it in our internal Slack announcement for this update:

_“This new update is cool because it uses the kind of context you can only get from dealing with national populations for years. The key insight we observed is that, beyond the traditional means by which matches are generated — like name \+ address or email \+ phone — many people in the U.S. have names that are actually unique within their geographic area. For instance, while there might be a dozen John Smiths in Los Angeles, there’s only one Tom Caruso in ZIP code 05405, and only one Ben Rose in all of Vermont.”_

By compiling these unique name \+ location pairs from the Faraday Identity Graph, we added a new matching source that kicks in when those other identifiers aren’t available. That means we can now match records confidently even when key fields like email, phone, or full address are missing.

This particularly benefits clients working with partial contact data — like name \+ email \+ state only — or with sparsely populated address fields, which are usually Faraday’s strongest component in identity resolution. By recognizing these unique combinations, Faraday can salvage records that would otherwise be lost, adding millions of verified people to campaigns. And because we only apply this method when a name-location pair is truly unique, the update strengthens our core promise: higher match rates without sacrificing confidence.

This is the critical distinction. Other vendors inflate match rates by accepting ambiguity — counting multiple possible matches as wins. Faraday’s approach adds coverage only when we’re certain it’s the right person.

**TLDR:** Faraday delivers identity matching you can trust. No noise, no padded numbers — just more real people you can actually market to.

## The proof in the numbers

Early testing of this update had already delivered major gains in match rates for multiple clients.

One DTC optics brand immediately saw an additional \+1.5 million matches on an existing dataset and another client, a performance marketing company, gained \+1 million immediately.

Every single match of these millions translates into fewer wasted dollars, more reliable models, and campaigns they could actually plan against with confidence.

For you, that’s the difference between “numbers on a slide” and ROI you can feel in the field and in the boardroom.

## Why marketing leaders need better matches

Identity resolution isn’t just about hitting a high match rate — it’s about whether you can trust those matches to guide your spend and strategy. That’s what sets Faraday apart.

- **Confidence at the core:** Every match is one you can confidently act on. We don’t inflate numbers with guesses or maybes.
- **Proven over time:** With 13+ years of experience and longitudinal data, we capture life changes and long-term patterns that competitors miss.
- **Built-in scale:** Our Identity Graph spans 240M U.S. adults and households, enriched with 1,400+ data points and continuously updated.
- **Innovation in motion:** Updates like unique name \+ location matching show that even after more than a decade, we’re still pushing for incremental lifts that flow straight to ROI.
- **Better outcomes downstream:** Trustworthy matches mean stronger predictions, more efficient campaigns, and marketing dollars that work harder.

## We’re always improving

This update is just the latest example of how Faraday treats identity resolution as a living system, not a one-and-done feature. Because of our longevity and ongoing innovation, our clients can trust their matches to be accurate today — and better tomorrow.

Identity resolution isn’t glamorous. But every percentage point of improvement falls directly to the ROI bottom line. And that’s what makes it worth obsessing over.

Stop guessing and start planning with confidence. [Get in touch with our team](/talk-to-sales).

Faraday delivers identity matches you can actually trust—clean, accurate, and enriched with real consumer context—so every record drives smarter predictions, campaigns, and ROI.

Is your identity match rate driving real value or just vanity?

**See our complete guide:** [Predictive AI for home services brands](/blog/home-services-complete-context-guide).

---

If you’re running marketing at a home services company, the pressures never let up. Your CFO is demanding lower CAC. Your sales team is tired of chasing unqualified appointments. Your installers are sitting idle after no-shows. And your call center is burning time on reschedules.

These pressures add up fast — in fact, for one national home services brand, they were burning more than $100,000 per month on junk leads. But by turning to Faraday, they cut that waste and freed their reps from chasing 13,000 bad prospects. Here’s how.

## The problem: you’re filling calendars with jobs that never happen

In home services, volume often wins the day. Marketing budgets flow into purchased leads, affiliates, and digital campaigns to keep the funnel full. But here’s the catch: industry research shows that [only about 12% of leads convert to customers](https://blog.gorizen.com/why-most-window-leads-dont-turn-into-sales).

- **Financing failures:** Many leads won’t qualify for credit with your financing partners, so your reps spend time setting appointments that never close.
- **No-shows:** Some prospects book, but when your crews arrive, no one’s home — burning time, fuel, and morale.
- **Last-minute cancellations:** Others cancel just before the appointment, wasting truck rolls and pushing up CAC and leaving installer schedules in chaos.

Every one of those unqualified or unreliable leads is money burned — in the form of call center hours, truck rolls, and lost opportunities.

## The solution: predictive lead suppression and qualification with Faraday

Faraday isn’t just a theory \- it’s a data platform built to plug into the exact systems you already use to buy and manage leads. That means we don’t just tell you what you _could_ do differently; we deliver the exact data points you need into your workflow (in batch or real time) so you stop wasting budget immediately.

But there are a range of ways Faraday’s data can become tangible ROI for your company. Here are just a few:

### Stage 1: Don’t buy the wrong leads

Most home services brands buy leads via [ping-post systems](https://faraday.ai/blog/so-what-is-a-ping-post-anyway). Here, you only get a partial “ping” (basic info like ZIP and homeowner status) before deciding whether to bid. Using [Faraday’s real-time Lookup API](https://faraday.ai/docs/features/lookup-api), you can request a custom predictive data point — like **Likelihood-to-Convert** — for each ping.

Through our integration with [ActiveProspect’s](https://faraday.ai/blog/dtc-homeservices-uses-credit-score-proxy) [LeadConduit](https://activeprospect.com/leadconduit/), low-scoring pings are automatically rejected before purchase \- so no money or time are wasted on them.

👉 Outcome: you never pay for the junk leads in the first place, saving thousands per week in wasted budget.

### Stage 2: Qualify appointments with financial signals

Even after rejecting bad leads, many home services CMOs face another pain: reps booking time with customers who won’t pass credit. Faraday helps here too by delivering [Credit Score Proxy](https://faraday.ai/blog/dtc-homeservices-uses-credit-score-proxy), one of 1,400+ prebuilt consumer data points from the [Faraday Identity Graph (FIG)](https://faraday.ai/features/faraday-identity-graph). Credit Score Proxy ranks U.S. households by purchasing power and conversion potential, allowing you to deprioritize or flag likely credit fails before time is put into a meeting, only to disqualify them.

👉 Outcome: fewer wasted appointments, less frustration in your sales org, and more predictable CAC.

### Stage 3: Enrich and prioritize at scale

Beyond suppression and credit qualification, Faraday’s platform can continually enrich your lead and customer records with new data points — both prebuilt consumer data and custom predictive models trained on your outcomes. That means your call center can prioritize its daily queue around the most winnable prospects, and your marketing team can target high-propensity households for direct mail or digital campaigns.

👉 Outcome: a cleaner funnel from start to finish, with fewer bad appointments and more closed deals.

## The business impact

When CMOs and marketing teams use Faraday to suppress and qualify leads, the results show up fast across the funnel. Here’s how:

- **Lower CAC at the top of the funnel:** Real-time suppression through the Lookup API and LeadConduit integration means you stop paying for the bulk of leads that won’t convert. That’s not abstract savings — it’s marketing dollars preserved before your reps even touch the phone.
- **Higher efficiency in the middle of the funnel:** Credit Score Proxy flags households unlikely to pass financing. The result is fewer wasted appointments, fewer dead-end truck rolls, and a sales team that spends more time closing deals.
- **Smarter outreach at the bottom of the funnel:** Enrichment with predictive and consumer data points powers cleaner call center queues, more targeted direct mail, and more effective digital audiences. Every touchpoint aligns with real conversion potential, not just guesses.

This isn't theoretical AI. [One national home services brand put this process into practice.](https://faraday.ai/blog/dtc-homeservices-uses-credit-score-proxy) By scoring every ping in real time with Faraday’s Lookup API and automatically rejecting low-quality leads in LeadConduit, they cut more than 13,000 junk leads in just five months — saving over $500,000 in wasted spend in just the first 5 months. Building on that success, they layered in Credit Score Proxy at the appointment stage, which allowed them to flag low-likelihood households before scheduling in-home visits. The results were clear: fewer wasted appointments and a sales team free to focus on winnable prospects.

As their Sr. Director of Marketing put it:

“We now prioritize calls based on Faraday scores, kick back low-quality leads, and save thousands of dollars every week. A high Faraday score goes to the front of the line. It’s been a complete shift.”

## Why this matters now

For home services CMOs and VPs of Marketing, the mandate is clear: deliver more installs with less waste. Faraday helps you do exactly that by combining consumer enrichment and predictive scoring into a seamless data platform. From rejecting junk leads before purchase, to filtering out low-credit households before appointments, to prioritizing outreach across the funnel, Faraday ensures every marketing dollar works harder. That’s why leading brands now treat Faraday as the centerpiece of their go-to-market strategy — and why your call center doesn’t have to burn money on bad leads anymore.

If you’re tired of paying for leads your crews never install, let’s talk. Or see how Faraday plugs directly into your lead buying and call center workflows on our [home services landing page](https://faraday.ai/landing/homeservices).

Faraday helps home services brands cut wasted spend by rejecting junk leads, qualifying appointments with financial signals, and prioritizing call center queues using predictive data.

Home services call centers are burning money without lead scoring

Imagine this: You’re a brand with a list of leads, but no real way to tell which ones are actually in a financial position to become high-value customers. So you treat everyone the same. That means wasted spend, low conversion rates, and missed opportunities when your best reps chase the wrong people—or when a strong prospect drops out late in the funnel due to credit issues.

Now imagine you could suppress just the bottom 10%—the folks least likely to convert, based on financial behavior and purchasing power—without pulling a single credit score.

That’s where **Credit Score Proxy** comes in.

## So what is Credit Score Proxy?

Credit Score Proxy is a relative marketing value ranking. It segments 120 million U.S. households into 12 rank-ordered categories (A1 to E3), using behavioral and lifestyle data—like age, property characteristics, aggregate credit indicators, and more (we also offer a variant of this data point in which the 12 categories are collapsed to 5 more-manageable categories).

It can function as a predictive proxy for financial reliability, offering many of the same benefits as a **credit score** for marketers, without using actual sensitive credit data or requiring a credit pull.

This table shows how Credit Score Proxy tiers typically align with traditional credit score ranges (in the risk score column) but please note: these are illustrative estimates, not actual credit scores. Credit Score Proxy doesn’t use credit data or generate credit scores; these risk scores are included to help marketers understand how each tier relates to familiar financial benchmarks.

| Credit Score Proxy Rank | Illustrative Credit Score Range | % of Households in FIG\* | Marketing Profitability |
| ----------------------- | ------------------------------- | ------------------------ | ----------------------- |
| 1.0                     | 760                             | 17%                      | **Best**                |
| 2.0                     | 740                             | 10%                      | **Best**                |
| 3.0                     | 725                             | 10%                      | Above average           |
| 4.0                     | 710                             | 11%                      | Above average           |
| 5.0                     | 690                             | 11%                      | Average                 |
| 6.0                     | 675                             | 11%                      | Average                 |
| 7.0                     | 660                             | 10%                      | Below average           |
| 8.0                     | 650                             | 4%                       | Below average           |
| 9.0                     | 640                             | 4%                       | Below average           |
| 10.0                    | 625                             | 4%                       | **Poor**                |
| 11.0                    | 610                             | 4%                       | **Poor**                |
| 12.0                    | 590                             | 4%                       | **Poor**                |

**\* Note on distribution:** While the above table reflects approximate national distributions represented in FIG, the Faraday Identity Graph (FIG) shows a slight overrepresentation of higher-credit-score households and an underrepresentation of low-credit or underbanked individuals. This means your enriched dataset may lean more toward high-value prospects—helpful when prioritizing outreach, but worth keeping in mind for broad reach strategies.

So how should you think about using Credit Score Proxy in practice?

## What Credit Score Proxy isn’t

It’s easy to think of Credit Score Proxy as essentially a predictive credit proxy—and that’s not entirely wrong. But it’s also not quite right.

Unlike FICO or other regulated credit scores, Credit Score Proxy wasn’t built for lenders or compliance-heavy decisions. It’s designed for marketers who want to prioritize outreach based on a household’s likely value—not just financial risk.

It uses lifestyle, demographic, and inferred financial behavior to segment households by marketing profitability. No tradeline data, no credit bureau pulls—just smart signals modeled for outreach.

In short: Credit Score Proxy is inspired by credit scores, but optimized for marketing. It’s a broader, privacy-safe alternative that aligns with overall consumer value, not just credit risk.

## How Credit Score Proxy fits into Faraday

Faraday is a modern data platform that helps consumer brands grow by enriching their first-party records with **verified identity data**, curated **consumer data points**, and **custom AI-powered predictions**. At the heart of this is the [Faraday Identity Graph (FIG)](https://faraday.ai/features/faraday-identity-graph), our rich database that contains 1400+ unique data points on over 240 million U.S. adults and their households. This data is prediction ready, privacy safe, and sourced from some of the best providers in the industry.

We use the FIG to enrich your customer data by appending new information, giving you a deeper understanding of your audience and how best to engage with them. These enriched data points generally take three forms:

- **Verified identity data** — Fill in the blanks: name, email, phone, address. These verified identities form the foundation of every enriched record.  
  **Consumer data points** — Curated, industry-relevant traits like income, homeownership, household size, or age—plus prebuilt predictive scores such as likelihood to buy luxury goods or subscribe to streaming services. These data points make segmentation, targeting, and prioritization easy—even before layering on custom predictions.
- **Custom predictions** — AI-powered scores trained on your historical outcomes, like Likelihood to Buy, Best Product Recommendation, or Persona assignment. Built specifically for your brand, these models use your first-party data alongside the 1,400+ behavioral data points in the FIG. Unlike off-the-shelf predictive insights, they’re tailored to your goals and go-to-market motion.

**Credit Score Proxy** is a textbook example of a prebuilt predictive **consumer data point**, and like any other data point from the FIG, it’s added as a new column to your dataset—ready to drive smarter targeting, prioritization, and ROI.

With Credit Score Proxy in place, you can:

- Build audiences by selecting only the highest-value tiers
- Filter out low-likelihood individuals for cost-efficient targeting
- Route leads or assign reps based on expected value

It’s one of the fastest ways to improve performance without adding complexity—and without pulling credit.

## Credit Score Proxy in action

For example, here’s how [a leading direct-to-consumer home services brand used Credit Score Proxy to sharpen its acquisition funnel.](/blog/dtc-homeservices-uses-credit-score-proxy)

- **At the lead purchase stage**, they paired Credit Score Proxy with a custom predictive score to reject low-quality leads in real time—saving over **$500,000 in wasted spend** within five months.
- **At the appointment stage**, they used Credit Score Proxy tiers to flag households likely to fail credit checks, which were failing about **75% of the time** in lower tiers. By suppressing these leads, they freed up their reps to focus on high-value prospects.

The result: fewer wasted appointments, lower acquisition costs, and a more efficient sales funnel overall.

## Why it matters to you

Many of our clients need a way to infer financial standing without crossing regulatory lines. Credit Score Proxy gives you a privacy-friendly path to do just that—especially useful in industries like:

- Home services
- Solar and energy
- Education
- Financial products (without lending)

It’s one of the most effective ways to suppress low-value outreach and drive higher ROI on every campaign.

Want to learn how Credit Score Proxy works inside your workflow? [Let’s talk.](https://faraday.ai/contact)

Credit Score Proxy helps brands prioritize high-value prospects by ranking U.S. households into predictive tiers of marketing value—without using sensitive credit data.

Prioritize your most profitable prospects with predictive data points like Credit Score Proxy

See our complete guide: [How predictive lead scoring works — and why it outperforms traditional methods](/blog/predictive-lead-scoring-b2c)

Every marketer knows the feeling: you’ve invested heavily to drive demand, nurtured warm leads, and handed them off to the call center — only to watch conversion rates flatline.

The call center’s job is simple in theory: take warm leads and turn them into real dollars. But too often, reps are churning through lists, treating every lead the same, and burning time on the wrong prospects. The result? **Terrible conversion rates, frustrated teams, and wasted budget.**

## The real problem isn’t marketing — it’s targeting

Before you point the finger back at your CMO, it’s worth asking what really happens once a lead hits the call center.

The challenge might not be the number of leads or the skill of the reps — it’s whether the right prospects are even getting attention in the first place. Without a way to distinguish high-intent buyers from tire-kickers, reps waste valuable time chasing the wrong people while genuine opportunities slip away.

And the unfortunate reality today is that many call centers still operate on instinct and raw volume rather than intelligence, leaving conversion potential untapped.

## How predictive scoring fixes the problem

But it doesn't have to be this way anymore\! [Custom predictive data points](/blog/what-data-do-you-need-for-marketing) like **Likelihood-to-Convert** allow you to [score leads in real time](/blog/real-time-vs-batch-which-drives-roi), separating serious buyers from casual interest before they ever reach a rep.

Take [a national DTC home services retailer](/blog/dtc-homeservices-uses-credit-score-proxy) we work with as an example. They were seeing thousands of leads pour in, but their call center was overwhelmed and conversions lagged. Instead of asking their reps to just "work harder," they came to Faraday.

Together, we built a custom predictive data point using their own first-party-data as well as the [Faraday Identity Graph](/features/faraday-identity-graph) (FIG) which contains 1400+ data points on 240M+ US consumers and their households. This data point identified which households were most likely to purchase.

This allowed every new lead that entered the center to be automatically given a score on their likelihood to buy and the call center could focus its most experienced reps on those high-scoring prospects while still nurturing the rest with lower-cost resources.

The difference was immediate:

- **In 2 days:** 2% lift in contact rates
- **In 1 month:** 4% lift in conversions
- **In 3 months:** Predictive scoring became the nucleus of their tech stack — saving over $100K/month in wasted effort!

[![Diagram showing set rate vs. date](/images/blog/american-standard-chart.png)](/images/blog/american-standard-chart.png)

## Why it matters to you

For marketing executives at consumer brands, especially in high-consideration sectors like financial services, solar, and home improvement, this isn’t just a call center optimization trick.

It’s a way to:

- Protect marketing ROI by ensuring warm leads don’t go cold in the call center.
- Improve conversion efficiency without increasing ad spend.
- Build sustainable competitive advantage with predictive intelligence your competitors can’t replicate.

## Conclusion

Call centers don’t have to be a black hole where good leads go to die. By pairing your first-party data with predictive intelligence, you can ensure that your marketing investment actually turns into revenue. When you know which prospects are most likely to convert, you can transform your call center from a cost center into a growth engine. Smarter targeting protects ROI, boosts conversions, and builds lasting competitive advantage.

Find out the difference smarter lead scoring can make today. [Come say hi.](/talk-to-sales)

Predictive lead scoring transforms call centers from cost sinks into growth engines by ensuring reps focus on the prospects most likely to convert.

Leads dying on the call center floor? Here’s why you need lead scoring

**See our complete guide:** [Understanding why you need data (and what kind actually moves the needle)](/blog/what-data-do-you-need-for-marketing).

---

You probably know Faraday for our powerful AI predictions. But before we get to the models, let’s talk about something more foundational: data.

AI is only as good as the data you feed it. And for our partners, it’s Faraday’s data services that are driving some of the biggest returns on investment.

## The problem

Most brands are swimming in data, but it’s incomplete, messy, or not built for decision-making. Identity gaps, missing data points, and a lack of predictive signals make it almost impossible to personalize outreach, optimize marketing spend, or build the data-driven features customers expect.

## The solution

Faraday acts as your modern data platform and predictive layer. We enrich your existing records with verified identity, curated consumer data points, and custom predictions—so your team always has the exact data you need to drive action. In short: we turn your existing data into a strategic advantage.

## The three pillars of the Faraday data platform

**Identity completion:** The first pillar is verified identity data. This is the foundation—filling in the blanks with name, email, phone, and address. With this in place, you can actually reach the people you want to talk to.

**Essential consumer data points:** The second pillar is essential consumer data points. These are curated, industry-relevant attributes like income, homeownership, household size, and age, drawn from the 1,400+ data points in the [Faraday Identity Graph](/features/faraday-identity-graph), which covers 240 million U.S. consumers and their households. We also provide prebuilt predictive data points like likelihood to buy luxury goods or subscribe to streaming services. Even before you bring custom AI/ML modeling and data points into the picture, these data points make segmentation and targeting straightforward and give you a much more complete picture of your consumers.

This also helps solve the [cold start problem](/stories/dtc-retailer). In other words, even if you don’t yet have large volumes of first-party data, these foundational data points give you enough signal to begin segmenting, targeting, and personalizing right away.

**Custom predictive data points:** The third pillar is custom predictions. These are AI-powered scores trained on your historical outcomes: Likelihood to Buy, Best Product Recommendation, Persona assignment, and more. Unlike off-the-shelf scores, these predictions are tailored to your brand and goals, built on your first-party data plus all 1,400+ data points in the Faraday Identity Graph. All of this is responsibly sourced, privacy-safe, and refreshed regularly to maintain signal quality.

## How the data is delivered

Enriched records don’t just sit in a report—they’re delivered directly into the tools your team already uses. You can access them in your CRM, ESP, lead platform, or warehouse in whatever format works best: batch uploads, clean APIs, or cloud shares. For certain use cases, like the ping-post process, we can even deliver enriched data points in real time through services like LeadConduit. That means you can avoid wasting money bidding on low-value leads and make smarter decisions on the fly.

## Why you need all three

Each pillar adds value on its own, but together they unlock a data foundation that’s accurate, actionable, and AI-ready. Consider a [case study from one of our DTC partners in home services](/stories/dtc-home-services). They were struggling with call center efficiency: too many missed connections and wasted calls. With identity completion, Faraday helped them verify phone numbers and addresses from website leads, so the call center could actually follow up. With essential data points, they could prioritize outreach to homeowners in relevant geographies. With custom predictions, they scored every lead on likelihood to convert—so agents could focus on the best opportunities first. The result was faster follow-ups, fewer wasted calls, and hundreds of thousands of dollars saved per year.

## Who this helps

Faraday supports consumer brands that want to enrich customer data, boost conversion rates, and eliminate wasted spend on low-fit leads. We also work with SaaS platforms and agencies embedding predictive data into their products to launch features faster and drive more client value.

## The takeaway

AI isn’t magic—it’s data, structured and trained to work for you. Faraday provides the building blocks: verified identity, essential data points, and custom predictions. Put together, they give your team the confidence to prioritize, personalize, and grow.

Want to see how Faraday’s varied data points can transform your funnel and drive value for your brand? [Let’s talk.](/contact)

Faraday helps brands grow smarter by enriching their data with verified identity completion, essential consumer data points, and custom predictive data points—delivered directly into their tools for more accurate, actionable, and efficient decision-making.

The three types of data that power smarter growth

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 data points. These new data points 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 data points 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

See our complete guide: [Predictive AI for financial services (debt, mortgage, lending)](/blog/debt-mortgage-lending-complete-context-guide)

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 an AI model to deliver each lead a custom predictive data point: 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 1,400+ 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 while cutting 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 data points are reshaping debt settlement

**See our complete guide:** [Customer personas explained — how Faraday builds them and what they unlock](/blog/what-are-ai-customer-personas).

---

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.