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 datapoints 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 1,500+ consumer datapoints 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 datapoint [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 datapoints 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 datapoints 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 datapoints (including predictive datapoints) 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 datapoint 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 datapoint 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” datapoint 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 datapoints 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 1,500+ datapoints 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 1,500+ datapoints 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?

If you’re leading marketing at a retail or e-commerce brand—like apparel, furniture, or subscription boxes—you already know how critical data is to keeping the funnel moving. You’ve built strong programs to drive traffic, grow carts, and keep customers coming back. But there’s always another level of efficiency and ROI to unlock when you have richer context on the households you’re targeting.

Think about segmentation for campaigns: better data means fewer wasted impressions, more personalized offers, and higher conversion rates. That’s where Faraday comes in.

## The Faraday data platform

To take your efforts further, you don’t just need more data—you need the right data, delivered in a way that’s immediately usable across your CRM, ESP, and ad platforms. That’s where Faraday’s three-part data platform comes in:

- **Identity completion**: Fill in the blanks with verified names, addresses, emails, and phone numbers so every record maps to a real household.
- **Consumer data**: Tap into the [Faraday Identity Graph (FIG)](/features/faraday-identity-graph), which includes over 1,500 responsibly sourced datapoints covering 240M U.S. adults and households. These datapoints span demographics, lifestyle, property, and behavioral attributes that make segmentation, targeting, and prioritization easy—even before layering on custom predictions.
- **Custom predictive datapoints**: AI-trained scores built on your own history—like Likelihood-to-Purchase, Best-First-Product, or Credit Score Proxy—so you can prioritize and personalize at scale.

## Essential data packages

One way we make the consumer data layer easier to use is by curating industry essential data packages: sets of just the datapoints that we’ve determined to be most predictive for your industry. We identify these datapoints using our Faraday predictor [leaderboard](/leaderboard), which ranks datapoints by their predictive power in each particular industry vertical. For retail/e-commerce, these packages are tuned to help segment audiences, tailor offers, and boost conversions.

**How they’re chosen**: We analyze all 1,500+ datapoints in the FIG and surface the ones with the strongest predictive signals for your vertical. That way you get the attributes that matter most, without paying for data you don’t need.

**How they’re delivered**: Essential data packages show up just like any other Faraday datapoint—directly appended to your records in your CRM, ESP, or ad platform in real time through our API or in scheduled batch. No extra setup required.

**How they’re different**: Most data vendors hand you a pile of raw attributes. Faraday packages are curated, validated, and aligned to your outcomes. Our data is high-quality, responsibly sourced, and refreshed regularly—and we take on the work of figuring out what’s actually useful for you.

## The retail/e-commerce essential data package

Here’s what’s included in the standard package for retail & e-commerce brands:

| Data Trait                                      | Description                                                                                     |
| ----------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| Age                                             | The age of the individual                                                                       |
| Marital status                                  | Marital status                                                                                  |
| Gender                                          | Gender of individual                                                                            |
| Length of residence                             | The number of years the resident has lived at this location                                     |
| Household size                                  | The number of people living in the household, including adults and children                     |
| Children in household                           | Known or inferred presence of children in the household                                         |
| Number of adults                                | Number of adults in the household                                                               |
| Primary language                                | Inferred primary language of the household                                                      |
| Low/moderate income                             | True if a household qualifies as low or moderate income for their region under HUD guidelines   |
| Household income                                | Median numeric value of narrow-band income, modeled and validated against truth sets            |
| Activity – average dollars quintile             | Average dollars spent across all purchases, bucketed into quintiles                             |
| Books quintile                                  | Average dollars spent on books, bucketed into quintiles                                         |
| Channel preference – catalog quintile           | Relative share of catalog purchases, bucketed into quintiles                                    |
| Channel preference – online quintile            | Relative share of online purchases, bucketed into quintiles                                     |
| Activity – number of offline purchases quintile | Lifetime count of offline purchases, bucketed into quintiles                                    |
| Activity – number of online purchases quintile  | Lifetime count of online purchases, bucketed into quintiles                                     |
| Online household                                | Household has a connection to the internet (mobile or hard line)                                |
| Mail order buyer                                | Known to shop via general mail order channels, including online and traditional catalogs        |
| Niches 5.0                                      | Epsilon household segmentation system dividing U.S. consumers into 26 lifestyle/spending groups |

These datapoints give you immediate visibility into whether a prospect is likely to be a fit for your brand and how best to reach them, before you spend on acquisition or campaigns.

## Why this matters

When you add these essentials to your records, you:

- **Save money** by excluding low-fit audiences before they eat up ad budget.
- **Increase conversions** by personalizing campaigns to the right households.
- **Boost efficiency** by matching offers to the channels households actually prefer.
- **Prove ROI** to the CFO with data-driven evidence that marketing spend is driving sales and lifetime value.

## Closing: smarter data, real results

Industry essential data packages are how Faraday makes your records AI-ready, without heavy lifting from your team. Combined with verified identity completion and custom predictive datapoints, they give you the full modern data stack your retail or e-commerce brand needs to compete.

Ready to see how these datapoints can transform your funnel? [Get in touch with Faraday](/contact)\!

Faraday’s essential retail/e-commerce data package gives brands the curated datapoints—like household income, online vs. offline purchase ratios, and channel preferences—to qualify customers, cut waste, and boost conversions.

Just the essentials: get the datapoints retail & e-commerce brands need to make better decisions

If you’re leading marketing at a home services brand—kitchen, bath, remodeling, roofing, solar—you already know how critical data is to keeping the funnel moving. You’ve built strong programs to generate leads, feed the call center, and keep installers busy. But there’s always another level of efficiency and ROI to unlock when you have richer context on the households you’re targeting.

Think about qualifying appointments in the call center: better data means fewer no-shows, more productive reps, and more installs. That’s where Faraday comes in.

## The Faraday data platform

To take your efforts further, you don’t just need _more_ data—you need the _right_ data, delivered in a way that’s immediately usable across your CRM, call center, and campaigns. That’s where Faraday’s three-part data platform comes in:

- **Identity completion**: Fill in the blanks with verified names, addresses, emails, and phone numbers so every record maps to a real household.
- **Consumer data**: Tap into the [Faraday Identity Graph (FIG)](/features/faraday-identity-graph), which includes over 1,500 responsibly sourced datapoints covering 240M U.S. adults and households. These datapoints span demographics, lifestyle, property, and behavioral attributes that make segmentation, targeting, and prioritization easy—even before layering on custom predictions.
- **Custom predictive datapoints**: AI-trained scores built on your own history—like Likelihood-to-Purchase, Best-First-Product, or Credit Score Proxy—so you can prioritize and personalize at scale.

## Essential data packages

One way we make the consumer data layer easier to use is by curating industry essential data packages: sets of 20 datapoints that we’ve determined to be most predictive for your industry. We identify these datapoints using our Faraday predictor [leaderboard](/leaderboard), which ranks datapoints by their predictive power in each particular industry vertical. For home services, these packages are tuned to help qualify leads, score calls, and prioritize installs.

**How they’re chosen:** We analyze all 1,500+ datapoints in the FIG and surface the ones with the strongest predictive signals for your vertical. That way you get the attributes that matter most, without paying for data you don’t need.

**How they’re delivered:** Essential data packages show up just like any other Faraday datapoint—directly appended to your records in your CRM, call center platform, or lead management system in real time through our api or in scheduled batch. No extra setup required.

**How they’re different:** Most data vendors hand you a pile of raw attributes. Faraday packages are curated, validated, and aligned to your outcomes. Our data is high-quality, responsibly sourced, and refreshed regularly—and we take on the work of figuring out what’s actually useful for you.

## The home services essential data package

Here’s what’s included in the standard package for home services brands:

| Data Trait                           | Description                                                                                                                                                                                                                                                                              |
| :----------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Number of adults                     | Number of adults in the household                                                                                                                                                                                                                                                        |
| Age                                  | The age of the individual                                                                                                                                                                                                                                                                |
| Basement                             | Property has a basement                                                                                                                                                                                                                                                                  |
| Number of bathrooms                  | Total count of bathrooms in domicile                                                                                                                                                                                                                                                     |
| Number of bedrooms                   | Total count of bedrooms in domicile                                                                                                                                                                                                                                                      |
| Children in household                | Known or inferred presence of children in the household                                                                                                                                                                                                                                  |
| Low/moderate income                  | True if a household qualifies as low or moderate income for their region under HUD guidelines                                                                                                                                                                                            |
| Project home renovation quintile     | Average dollars that were spent on the project home renovation category bucketed into 5 quintile groupings                                                                                                                                                                               |
| Dwelling type                        | Property type categorization                                                                                                                                                                                                                                                             |
| Gender                               | Gender of individual                                                                                                                                                                                                                                                                     |
| Homeowner or renter probability      | Designation of person-to-property relationship (renter vs. owner), with probability                                                                                                                                                                                                      |
| Household income                     | Median numeric value of narrow-band income; computed based on highly accurate multi-sourced models which take into account modeled self-reported incomes, property values and other proprietary sources; calibrated to and validated against truth sets prior to release every six weeks |
| Household size                       | The number of people living in the household, including adults and children                                                                                                                                                                                                              |
| Mortgage amount                      | Original mortgage amount at the time of purchase                                                                                                                                                                                                                                         |
| Living Area Square Feet (actual)     | Indicates the square footage of the entire living area of the home (minus basement, garage, etc.)                                                                                                                                                                                        |
| Marital status                       | Marital status                                                                                                                                                                                                                                                                           |
| Credit Score Proxy                   | Ranking that approximates household credit health, available in 5 or 12 buckets                                                                                                                                                                                                          |
| Property lot size in acres (actual)  | Acreage representing entire property; square footage of lot (43,560 square feet \= 1 acre)                                                                                                                                                                                               |
| Single-family household or townhouse | Category of domicile                                                                                                                                                                                                                                                                     |

These datapoints give you immediate visibility into whether a lead is likely to be a fit for your business, before your sales team ever picks up the phone.

## Why this matters

When you add these essentials to your leads and customer records, you:

- **Save money** by rejecting unqualified leads earlier in the funnel.
- **Increase conversions** by prioritizing the right households.
- **Boost efficiency** Make sure your reps aren't wasting time on unqualified leads, low-fit or no show appointments.
- **Prove ROI** to the CFO with data-driven evidence that marketing spend is driving installs and revenue.

## Closing: smarter data, real results

Industry essential data packages are how Faraday makes your records AI-ready, without heavy lifting from your team. Combined with verified identity completion and custom predictive datapoints, they give you the full modern data stack your home services brand needs to compete.

Ready to see how these datapoints can transform your funnel? [Get in touch with Faraday](/contact)\!

Faraday’s essential home-services data package gives brands 20 curated datapoints—like household income, property type, and credit score proxy—to qualify leads, cut waste, and boost installs.

Just the essentials: get the datapoints home services brands need to make better decisions

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 datapoints 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 datapoint — 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](http://faraday.ai/blog/dtc-homeservices-uses-credit-score-proxy), one of 1,500+ prebuilt consumer datapoints 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 datapoints — 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 datapoints 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 datapoint 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 datapoints**, 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 1500+ unique datapoints 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 datapoints 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 datapoints** — 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 datapoints 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 1,500+ behavioral datapoints 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 datapoint**, and like any other datapoint 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 datapoints like Credit Score Proxy

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 datapoints](/blog/stop-buying-more-data-get-the-right-data) 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 datapoint using their own first-party-data as well as the [Faraday Identity Graph](/features/faraday-identity-graph) (FIG) which contains 1500+ datapoints on 240M+ US consumers and their households. This datapoint 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

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 datapoints, 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 datapoints, 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 datapoints:** The second pillar is essential consumer datapoints. These are curated, industry-relevant attributes like income, homeownership, household size, and age, drawn from the 1,500+ datapoints in the [Faraday Identity Graph](/features/faraday-identity-graph), which covers 240 million U.S. consumers and their households. We also provide prebuilt predictive datapoints like likelihood to buy luxury goods or subscribe to streaming services. Even before you bring custom AI/ML modeling and datapoints into the picture, these datapoints 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 datapoints give you enough signal to begin segmenting, targeting, and personalizing right away.

**Custom predictive datapoints:** 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 1,500+ datapoints 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 datapoints 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 datapoints, 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 datapoints, and custom predictions. Put together, they give your team the confidence to prioritize, personalize, and grow.

Want to see how Faraday’s varied datapoints 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 datapoints, and custom predictive datapoints—delivered directly into their tools for more accurate, actionable, and efficient decision-making.

The three types of data that power smarter growth

For over a century, this leading DTC home services brand has delivered innovative, high-quality products that improve everyday life. But as a forward-thinking company, they’re constantly evolving their customer acquisition strategy to stay ahead. And in today’s market, that means solving one of the toughest challenges in lead buying: separating high-potential prospects from those that will never convert.

## The challenge of ping-post

Like many brands in home services, this Faraday client acquires leads from third-party sources using a real-time bidding system called [ping-post](/blog/so-what-is-a-ping-post-anyway). In this system, lead sellers send basic lead details (the "ping") to potential buyers, who then evaluate the lead based on predefined criteria. If a buyer decides to proceed, they purchase the full lead details (the "post").

While this system allows for rapid lead distribution, it also presents a major challenge: identifying which leads have real potential versus those that are unlikely to convert. Without an efficient filtering process, businesses risk wasting marketing dollars on low-quality leads that never became customers. And at over $40 per lead (in this brand’s case), these mistakes can quickly add up to a serious financial loss.

Although this brand had always been able to hit their numbers with their existing lead engagement strategy, they weren’t happy with “good enough.” They wanted excellence, and in this case that meant actively assessing the quality of all leads before they even entered their sales funnel to maximize the value of their lists.

## Real-time lead rejection with Faraday

To add this functionality to their tech stack, this brand partnered with Faraday to integrate custom predictive datapoints trained on their own historical data directly into their lead buying workflow—allowing them to [reject](/templates/lead-rejection) the least-likely-to-convert prospects before ever placing a bid.

This process works by scoring each ping in real time. When a lead becomes available for purchase, the brand automatically requests a custom datapoint: **likelihood-to-convert,** through Faraday’s real-time [Lookup API](/docs/features/lookup-api). The result is routed back via our integration with [ActiveProspect’s](https://activeprospect.com/) lead routing software [LeadConduit](https://activeprospect.com/leadconduit/), so leads that fall below their selected threshold can be rejected—avoiding the purchase entirely.

The results were immediate: in just the first five months, more than **13,000 low-quality leads** were rejected, saving over **$500,000** in wasted spend.

If you’d like to learn more about our integration with LeadConduit, [check out this blog from our team and ActiveProspect.](/blog/smarter-lead-acquisition-starts-here)

## Extending value with Credit Score Proxy

Building on this success, the brand wanted to reduce wasted time at the appointment stage. The answer came from another type of Faraday data: an **off-the-shelf consumer datapoint** called **Credit Score Proxy**, delivered through the **Faraday Identity Graph (FIG)**.

FIG contains over [1,500 consumer datapoints available from day one](/features/faraday-identity-graph), covering everything from demographics to financial proxies. Unlike custom predictive datapoints—which are trained on a brand’s historical outcomes and tuned to specific goals—consumer datapoints come prebuilt and can provide immediate predictive value.

Credit Score Proxy, for example, ranks U.S. households by purchasing power and conversion potential. It serves as a privacy-safe stand-in for financial reliability, helping brands flag leads likely to fail credit checks—without requiring homeowner consent or a bureau pull.

By mapping Credit Score Proxy into their lead management system, this brand now flags low-likelihood leads at the appointment confirmation stage. The results are clear: **leads in the bottom tiers** (as identified by Credit Score Proxy) **fail credit checks about 75% of the time.** By identifying these upfront, this brand avoids wasted appointments and ensures their sales team stays focused on prospects most likely to succeed.

## Don’t just take it from us

We're excited about our partnership with this client—but we’ll let their team speak for themselves. Here’s what their Director of Marketing had to say:

"In just six months, Faraday has become the centerpiece of our entire go-to-market strategy. We now prioritize calls based on Faraday scores, kick back low-quality leads, and save thousands of dollars every week.

We treat every lead according to its potential value and a high Faraday score goes to the front of the line. And now, we’re even applying Faraday’s scores to qualifying appointments. It’s been a complete shift."

## Here’s why it matters

This brand’s approach shows how both custom predictive datapoints and prebuilt consumer data can optimize various stages of the funnel. In this case a custom model filters out low-quality leads before bids are placed and Credit Score Proxy suppresses appointments unlikely to succeed, preserving sales resources.

What’s important is that this wasn’t about more data for its own sake. Faraday worked with this brand to identify which specific datapoints would matter most at specific stages of the funnel. The team cut wasted spend, improved close rates, and freed up staff to focus on the prospects most likely to convert.

Working with Faraday doesn't just mean getting best-in-class data, it means getting the strategic guidance to use it to drive concrete value, and this client \- as well as all our other clients \- are seeing that value every day.

Want to see how Faraday’s predictive datapoints can transform your funnel and drive similar value for your brand? [Let’s talk.](https://faraday.ai/contact)

One of the most trusted names in DTC home services transformed their acquisition strategy using Credit Score Proxy to qualify prospects’ financial readiness at the appointment stage and saving over 100K/month.

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