If you’ve spent five minutes in the identity resolution world, you’ve probably heard someone throw out a number like *“we match 99% of records, guaranteed\!”*

Sounds great, right? Until you make campaign projections for your CMO that … don’t entirely pan out.

The thing is, flashy numbers might close deals, but do they drive value? Only if they’re accurate. 

So sure, Faraday can hit 99% matching in certain conditions (we’ll get to that later). But does that mean it’s the number you should plan your campaign around? Well, that depends: on what identifiers you’re looking for, and what you put in to find them.

[![Jack Black magic number gif - school of rock](/images/blog/magic-number.gif)](/images/blog/magic-number.gif)

## Match rates aren’t one-size-fits-all, and they’re not a magic number

We’ve written before about why [match rates matter](/blog/identity-matching-makes-or-breaks-predictions) (because they’re the foundation of enrichment and predictions) and why [transparency and accuracy matter even more](/blog/is-your-id-match-rate-real) (because vanity numbers break downstream models). But here’s the thing: when it comes to match rates, there’s no universal “magic number.” Anyone selling a consistent 90%+ is probably doing one of three things:

* Counting duplicates (two bad emails for the price of one\!)  
* Leaning on easy identifiers like household address and presenting that rate as always true  
* Or—a personal favorite—straight-up inflating numbers.

In reality, match rates depend almost entirely on **what identifiers you hand us**. Think of it like cooking: bring fresh ingredients, and you’ll get a great meal. Show up with a half-empty pantry and a can of expired beans, and we can only do so much.

Moreover, it’s not just about whether something matches—it’s about *at what level* they match. Faraday can resolve identities at the person or household level, and in some cases at a broader level like ZIP code or metro area. Which one matters depends entirely on your use case. Sending direct mail? Household-level matches are usually perfect. Calling or emailing individuals? Then person-level accuracy is what you need.

## So what can you actually expect?

With all of that context in mind, here’s what you can actually expect with Faraday:

* **Email-only datasets** → **\~60%+** individual match rate. Not bad, considering how many emails are throwaways, duplicates, or churned.  
* **Add phone numbers** → things stabilize. Old numbers still get recycled, but you’re closer to **\~65%+**.  
* **Bring addresses into the mix** → now we’re cooking. Household match rates can climb into the **80–99%**+ range depending on data quality, with **70–80%** at the individual level.

**And here’s an important point**: yes, on a clean dataset with addresses, we can technically match **99% of residences**. But do we advertise a 99% match rate? 

No. Because that would be misleading. Of course we can recognize residence level matches on that dataset—we literally have the addresses—but what we’re actually proud of in that example is that we can match **\~80%** of *individuals* on the same dataset. Is that less flashy? Sure. But is it accurate, actionable, and potentially value driving? Absolutely. 

And that means you won’t be backtracking later when projects built on inflated numbers don’t pan out. 

## Why this matters for CMOs

If you’re a CMO you already know where this is headed. Inflated numbers might look good in a pitch deck, but they don’t hold up when you’re presenting results to the board. Every padded “match” that isn’t real will:

* Waste spend in acquisition  
* Poison your enrichment  
* Undermine predictive models you rely on

That’s why Faraday takes the harder road: we dedupe, we verify, and we only count matches that are actually usable in production. For you, that means you can confidently plan campaigns and allocate budget knowing the foundation is solid—no backtracking when the numbers don’t pan out.

## So what’s the punchline?

At the end of the day, match rate isn’t about chasing the biggest number—it’s about trusting the one that will actually power your campaigns. Faraday’s approach gives you the clarity and reliability you need to move forward with confidence.

Stop planning campaigns on vanity numbers. Let's see what *actual* match rates look like with your data.

Faraday delivers real, verifiable match rates—not inflated vanity numbers—so CMOs can confidently plan campaigns on accurate foundations that actually drive value.

Where's my 99% match rate!? Well, right here, but it’s complicated…

If you’re leading marketing at a financial services brand—credit unions, regional banks, fintech lenders, mortgage providers—you already know how critical data is to keeping your funnel moving. You’ve built strong programs to generate leads, pre-qualify applicants, and feed loan officers. 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 prioritizing applications in the call center: better data means fewer unqualified prospects, more productive reps, and stronger portfolio growth. 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, loan origination system, and campaigns. 

That’s where [Faraday’s three-part data platform](/blog/stop-buying-more-data-get-the-right-data) comes in:

* [**Identity completion**](/blog/three-types-of-data-that-power-smarter-decisions): Fill in the blanks with verified names, addresses, emails, and phone numbers so every record maps to a real household.  
* [**Consumer data**](/docs/abstractions/data-appends): 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**](/templates): AI-trained scores built on your own history—like Likelihood to Refinance, Default Risk Proxy, or ValueScore—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 financial services, these packages are tuned to help you qualify applicants, segment portfolios, and drive smarter outreach.

* **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](/blog/real-time-vs-batch-which-drives-roi). 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 financial services essential data package

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

| Data Trait | Description |
| ----- | ----- |
| Approximate household income tax rate | Approximate tax bracket for the household, assuming head of household is filing |
| Home market value v2.0 | Estimate of home market value |
| Household income | Median numeric value of narrow-band income |
| Living area | The finished square meters of the house |
| Living Area Square Feet (actual) | Indicates the square footage of the entire living area of the home (minus basement, garage, etc.) |
| Low/moderate income | True if a household qualifies as low or moderate income for their region under HUD guidelines |
| Mortgage liability | Primary property mortgage |
| 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 |
| Year built | The year that the house was originally built (see "Effective year built" for last extensive remodel) |
| Lifecycle \- millennials | This lifecycle segment contains households with adults born between 1981 and 2002 |
| Dwelling type | Advantage-class property type categorization |
| Homeowner or renter probability | Designation of person-to-property relationship (renter vs. owner), with probability |
| Mail Order Donor | Household donates through mail order |
| Net worth | Value equals household asset minus liabilities |
| Shopping styles | Household's preferred mode of shopping |
| Home market value trigger | Indicates the home market value has increased or decreased |
| Credit Score Proxy | Ranking that approximates household credit health, available in 5 or 12 buckets |
|  |  |

These datapoints give you immediate visibility into whether a lead or applicant is likely to be a fit for your business, before your loan officers ever pick up the phone.

## Why this matters

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

* **Save money** by rejecting unqualified applicants earlier in the funnel.  
* **Increase conversions** by prioritizing the right households for your products.  
* **Boost efficiency** by making sure your reps and loan officers aren’t wasting time on poor fits or low-probability approvals.  
* **Prove ROI** to the CFO with data-driven evidence that marketing spend is driving real portfolio growth.

## 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 financial services brand needs to compete.

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

Faraday’s financial services essential data package delivers curated, high-impact datapoints that help banks, credit unions, and fintechs qualify applicants faster, prioritize the right households, and drive portfolio growth.

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

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: which actually drives ROI?

It’s 2025 and it’s never been easier to collect data. Every click, every form fill, every purchase leaves a trail. In fact, most brands are drowning in it — thousands of rows, endless dashboards, endless promises that “more data” will finally unlock the answers and ROI that they seek

But that’s the trap. More isn’t better. It’s just more.

[![It's a trap Star Wars gif](/images/blog/its-a-trap.gif)](/images/blog/its-a-trap.gif)

The real challenge isn’t access to data — it’s in its use and the precision with which it can be taken advantage of. You don’t need another pile of generic attributes or a bigger warehouse. You need the specific datapoints that actually move the needle for _your_ business: things like complete, verifiable identities, essential datapoints that have been proven to drive value in your industry, and the predictive signals that tell you which leads are worth chasing, which products to put in front of which customers, and where to spend your next marketing dollar.

What modern marketing execs need isn’t “more data.” It’s the “right data,” along with a clear plan to use it to drive value. And that’s what Faraday can offer.

## The problem

So in a nutshell, the problem is that most brands already have data but it’s incomplete, scattered, and not built for action. You can’t personalize effectively when you’re missing identities. You can’t prioritize when you don’t know who’s a fit. And you can’t build AI-driven features when you’re piecing together an incomplete dataset.

The result? Campaigns that misfire. Teams spinning their wheels on the wrong priorities. AI projects that never get off the ground. More data alone doesn’t automatically solve these problems — you need the _right_ datapoints to fill the gaps and point the way forward.

And this isn’t some abstract problem. We’ve seen it firsthand:

- Call centers wasting hundreds of hours chasing unqualified leads because there was no way to filter in real time.
- Solar companies burning thousands on direct mail campaigns that landed in the mailboxes of renters who could never buy.
- Credit unions recommending products members didn’t want — eroding trust instead of strengthening wallet share.
- Brands succumbing to the cold start problem — unable to launch new AI or personalization projects because they didn’t have enough foundational data on prospects who haven’t already made a purchase.
- Teams who can’t even connect with their customers because their records are incomplete or outdated — missing key identifiers like emails, addresses, or phone numbers.

When the wrong datapoints drive the strategy, the outcome is predictable: wasted spend, frustrated teams, and missed opportunities. But the right datapoints flip the script — turning uncertainty into clarity and wasted effort into measurable growth.

## The solution

If you’ve read this far, you probably already suspect the solution we’re offering: the **right datapoints at the right time.**

That’s where Faraday comes in. We’re a modern consumer data platform and predictive layer. Instead of selling you another bulk list of generic fields, we enrich the records you already have with the signals that matter — closing the gaps so your campaigns and models actually work.

We deliver this through three core flavors of data:

- **Verified identity data** 👉 Clean, foundational fields like name, phone, email, and address. These basics make sure you can actually reach your customers, eliminate duplicates, and build a complete view of each household.
- **Essential consumer datapoints** 👉 Curated context like household income, homeownership, and buying signals. These include pre-modeled predictive datapoints — like [Credit Score Proxy](/blog/prioritize-with-credit-score-proxy) — as well as industry-specific “essentials packages” we’ve developed based on which datapoints we’ve observed drive the most value for specific industry verticals, so you’re not left guessing which fields will drive results.
- **Custom predictive datapoints** 👉 AI-powered scores and datapoints trained on your own outcomes, customers, and prospects. Whether it’s Likelihood-to-Convert, Best-Product-Recommendation, or Persona-Assignment, these are datapoints designed specifically for your business, built from your own data. And because they’re trained on your reality — not generic benchmarks — they evolve with your business, getting sharper and more valuable over time.

Even better, with Faraday delivery is effortless. Every datapoint flows directly (and automatically) into your systems — in batch or real-time depending on the use case — via simple API integrations. No heavy IT lift. No endless wrangling. Just curated, ready-to-use data that helps your team spend less time fighting spreadsheets and more time driving growth.

All this results in not just “more” data but **smarter data tailored to your business goals and outcomes**.

## Why this matters to you

In the age of AI, the difference between growth and waste isn’t who has the most data. It’s who has the right datapoints.

With Faraday, you stop guessing and start acting with confidence, whatever your role:

- **Marketing leaders (like CMOs, VPs of Growth, and Heads of Strategy),** can make confident bets, prioritize audiences, allocate spend wisely, and finally understand what’s driving ROI.
- **Data and operations professionals (like Heads of Data, Marketing Ops, and Analytics Leads)** can stop cleaning and reconciling messy datasets, and instead deliver enriched, predictive records that unlock performance.
- **Hands-on marketers and data activators (like Lifecycle Marketers, Channel Managers, and Acquisition Leads)** running campaigns can launch precise, personalized journeys, trigger outreach at the right moment, and optimize spend against the audiences most likely to respond.

And beyond the specific needs of each of these roles, what this really means to all Faraday customers is freedom: freedom to stop pouring money into broad campaigns that don’t convert, freedom to launch AI projects without worrying about cold starts or patchy data, and freedom to trust that the datapoints powering your business are transparent, compliant, and built to deliver value.

Faraday turns incomplete, static records into a strategic advantage. No heavy data science lift required—just actionable, AI-ready datapoints that drive impact from day one.

## Ready to get the right data?

Your business doesn’t need more noise — it needs clarity. With Faraday, you’ll know exactly who to reach, what to offer, and when to act.

Start by exploring how our platform can enrich your records with the datapoints that matter most. The right data is out there — and it’s the key to unlocking your next stage of growth.

Want to learn just how easy getting this data into your CRM could be? We want to show you. [So reach out](/talk-to-sales/), and let’s strengthen your marketing efforts, together.

Faraday helps brands move beyond “more data” by enriching records with verified identities, essential consumer data, and custom predictive datapoints—turning incomplete datasets into AI-ready insights that drive measurable growth.

Stop buying “more data.” Start getting the right data that actually moves the needle.

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

What happens when predictive segmentation meets banking reality? Find out 👇

<iframe width="560" height="315" src="https://www.youtube.com/embed/wiytGhyrX_Y?si=giMPhr2dLg5lzte-" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

In this conversation, our own Direction of Partnerships, [David Small](https://www.linkedin.com/in/davidtoddsmall/) and [Synpulse's](https://www.synpulse.com/en) [Matthew Cecil](https://www.linkedin.com/in/mcecil/) cover the full journey: identity resolution across fragmented systems, building propensity-based segments that map to actual products, and activating those signals in call centers, direct mail, and digital channels with transparency baked in.

## In this video, you’ll learn

- **How to build segments that perform:** move from static demographics to propensity-based [predictive datapoints](/blog/holdout-testing-with-faraday) (e.g., likelihood to open an auto loan).
- **Why explainability isn’t optional:** model documentation, per-person reason codes, and [bias mitigation](/blog/faraday-bias-reporting) controls for regulated use cases.
- **Identity resolution during M&A:** unify records and features to power product recommendations across newly merged portfolios.
- **Real outcomes:** one lender [cut $600k in 6 months](/stories/debt-consolidation-company) in direct-mail spend while maintaining conversion by suppressing low-propensity households; a call-center program saw **+20% conversion** by prioritizing high-likelihood leads during peak volume.
- **A practical path to “segment of one”:** start with data completion (emails, addresses, profile enrichment), layer predictive datapoints, then automate downstream workflows for timely offers.

## Who should watch

Retail banks, credit unions, and agency partners looking to operationalize segmentation with compliant, explainable predictive models—and turn data groundwork into measurable ROI.

Faraday’s Dave Small sits down with Matthew Cecil, Associate Partner at Synpulse, to unpack what client segmentation really looks like when you combine a bank’s first-party data with predictive datapoints.

Smarter segmentation for banks: from data foundations to real-time action

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.