While there are 1,500+ demographic features available per American adult (from companies like Faraday), feeding all this raw data to an AI agent would overwhelm its "attention budget" - the limited cognitive capacity LLMs have to process context. Anybody who has used a code copilot like [Cursor](https://cursor.com) or [Claude Code](https://www.claude.com/product/claude-code) knows the feeling when its attention has wandered... after too much information, the LLM is distracted, not helpful, pissed off, stuck up a creek without a paddle. This is because of attention scarcity:

> LLMs have an “attention budget” that they draw on when parsing large volumes of context. Every new token introduced depletes this budget...

Anthropic's Applied AI team recently wrote a [short essay about the evolution from "prompt engineering" to "context engineering"](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents). The upshot is that you must continuously manage the information available to the LLM, keeping it focused by discarding unnecessary tokens but keeping "high-signal" tokens. Advanced AI agents like Cursor and Claude Code are rapidly getting better at this. It's much harder to tire them out now, thanks to strategies like:

- Tools - using vector search and `grep` to find only relevant code, instead of reading the whole codebase.
- Sub-agents - "sub-agents [handling] focused tasks with clean context windows [returning] condensed, distilled summar[ies] of [their] work."
- Compaction (compression) - "the practice of taking a conversation nearing the context window limit, summarizing its contents, and reinitiating a new context window with the summary" - often with the output of the tools or sub-agents.

This is relevant to my industry, [demographic data](https://faraday.ai/features/faraday-identity-graph) and [customer behavior predictions](https://faraday.ai/features/customer-behavior-predictions), because the amount of data available about every American adult - at least 1,500 features are available from Faraday alone - would easily overwhelm most LLM attention budgets. If you want to [build AI agents that interact with American consumers](https://hightouch.com/demo-ai-agents) and take advantage of third party data, you need to keep the signal while discarding the rest.

Let's detail 3 ways to do context engineering with demographic data. The upshot is to use quantitative methods as tools - really, as deterministic sub-agents - to compress the surfeit of raw demographic features into high-signal tokens and maximize the attention budget left over for AI agents to do their job:

## Decision tree models

Decision tree models compress thousands of demographic features into a single propensity score by learning which combinations of features predict a specific outcome. They work by recursively splitting the population based on feature values—much like a flowchart—to find patterns that distinguish people who achieve an outcome from those who don't. **Instead of forcing an AI agent to reason about every demographic detail, the model distills this complexity into one number that captures everything relevant to the prediction.**

The input is historical examples: people who converted versus those who didn't, along with demographic and behavioral features describing them. The output is a percentile score (0-100) or probability for each person, indicating how likely they are to achieve the outcome. This compression is dramatic—from potentially 1,500 input features per person down to a single score that the agent can easily incorporate into its decision-making.

[![Decision trees efficiently compress thousands of demographic datapoints into high-signal tokens for LLMs](https://developers.google.com/static/machine-learning/decision-forests/images/DecisionTree.png)](https://developers.google.com/static/machine-learning/decision-forests/images/DecisionTree.png)
_Decision trees efficiently compress thousands of demographic datapoints into high-signal tokens for LLMs ([source: Google ML](https://developers.google.com/machine-learning/decision-forests/decision-trees))_

Decision trees give you numeric scores and lift that can go straight into your context:

[![Help the LLM understand how convinced the user is](/images/blog/finance-lift-table.png)](/images/blog/finance-lift-table.png)
_Help the LLM understand how convinced the user is ([source: Faraday blog](/Users/seamus/fdy/src/magic/src/content/blog_posts/how-to-optimize-ads-for-compliance-in-finance.md))_

Consider a credit union building an AI agent to help members with financial planning. Without propensity models, the agent would need to process 1,500 demographic fields per member every time it considers whether to recommend opening a savings account. With a decision tree model, the agent simply receives a score indicating each member's likelihood to open an account, preserving its attention budget for actual conversation and advice.

## Recommender systems

Recommender systems compress interaction history—purchases, clicks, views—into ranked predictions of which product or action comes next. These systems analyze the matrix of who-interacted-with-what across your entire customer base to find latent patterns: "people who bought A and B tend to buy C next." **Instead of an AI agent processing raw transaction logs and product catalogs to figure out what to recommend, it receives a pre-computed ranked list that captures all that complexity.**

The input is a transaction or interaction matrix (rows are people, columns are products or actions) plus any product metadata. The output is a ranked list of recommendations for each person—typically the top 5-10 options most likely to resonate. This compression is especially powerful because it captures collaborative filtering patterns (what similar people liked) without forcing the agent to analyze the entire interaction history every time.

[![Recommenders use matrix factorization to efficiently compress product affinity and transaction history into high-signal tokens for LLMs](https://developers.google.com/machine-learning/recommendation/images/Matrixfactor.svg)](https://developers.google.com/machine-learning/recommendation/images/Matrixfactor.svg)
_Recommenders use matrix factorization to efficiently compress product affinity and transaction history into high-signal tokens for LLMs ([source: Google ML](https://developers.google.com/machine-learning/recommendation/collaborative/matrix))_

Imagine an e-commerce site building an AI shopping assistant. Instead of the agent analyzing every past purchase across thousands of products to determine what to suggest next, it receives "most likely next 5 products: [Product A, Product B, Product C, Product D, Product E]" and can focus its attention on how to present these recommendations conversationally based on the current chat context.

## Cluster models

Cluster models group similar people together and identify the defining characteristics of each group, compressing individual-level variation into persona-level patterns. Rather than treating each consumer as a unique snowflake across thousands of attributes, clustering finds natural segments where people share meaningful similarities. **An AI agent can then work with a handful of personas instead of reasoning about every individual's complete demographic profile—a massive reduction in cognitive load.**

[![Don't tell your LLM all the dots... tell it the cluster](https://developers.google.com/static/machine-learning/clustering/images/clustering_example.png)](https://developers.google.com/static/machine-learning/clustering/images/clustering_example.png)
_Don't tell your LLM all the dots... tell it the cluster ([source: Google ML](https://developers.google.com/machine-learning/clustering/overview))_

The input is your customer population with demographic and behavioral features. The output is typically 3-7 personas, each with defining characteristics and a label for every individual indicating which persona they belong to. For example, a model might identify distinct groups like "Budget-Conscious Families" and "Premium Singles," automatically determining which datapoints (income, household size, urban vs. rural) meaningfully distinguish these personas.

Consider a subscription service building an AI agent for customer support. Instead of the agent processing each customer's unique demographic and usage profile from scratch, it receives "this customer belongs to the Budget-Conscious Families persona" and can immediately apply relevant context about typical concerns, preferences, and communication styles for that segment. The agent's attention is freed up to focus on the specific support issue rather than figuring out who this customer is.

## Faraday implementation notes

For readers interested in how these compression techniques are implemented in practice, here's how Faraday handles each approach:

**Decision tree models**: Faraday [Outcomes](https://faraday.ai/docs/abstractions/outcomes) handles this compression using multiple algorithms including [random forests](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-random-forest) and gradient boosted trees. The system trains separate models for different customer lifecycle stages (how long someone has been in the sales funnel), handles both vendor-enriched demographic data and customer-only data, and applies fairness adjustments to ensure protected demographic groups aren't disadvantaged by the predictions.

**Recommender systems**: Faraday [Recommenders](https://faraday.ai/docs/abstractions/recommenders) uses a two-stage approach: [matrix factorization](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-matrix-factorization) finds collaborative filtering patterns, then propensity models (similar to Outcomes) refine those recommendations using demographic data. The system handles cold-start scenarios (new customers with no purchase history) by falling back to demographic-based predictions, and adapts to different data availability levels—working whether you have rich behavioral data or just basic customer information.

**Cluster models**: Faraday [Persona Sets](https://faraday.ai/docs/abstractions/persona_sets) implements this using [k-means++](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-kmeans) clustering. The system automatically selects the optimal number of groups (balancing too few vs. too many personas), removes outliers before clustering to prevent extreme cases from distorting the personas, and identifies which specific datapoints distinguish each persona—importantly, not forcing every datapoint to matter when some simply don't differentiate between groups. It also builds lifetime value predictions for each persona, helping prioritize which segments deserve more attention.

## Conclusion

When you use AI copilots like Cursor or Claude Code, you can feel them efficiently quantifying and summarizing your code base. They use tools like vector search and `grep` to pull out the high signal tokens and leave themselves lots of attention budget to write code. This is similar to what's necessary with AI agents that want to use third party data to interact with consumers. You have to selectively compress information about the consumers; decision trees, recommenders, and clusters are a simple quantitative way to do this.

Just as code copilots like Claude Code use tools like vector search to pick out relevant code (saving attention for writing code), consumer-facing AI agents should use predictive models to pick relevant demographic signals (saving attention for conversation and decision-making).

Context engineering and demographic data

## Who this guide is for

This post is written for marketing and operations leaders in the home-services industry—the people responsible for filling calendars, scheduling appointments, and keeping call centers busy at roofing, solar, and plumbing companies. Whether you’re running campaigns in [ServiceTitan](https://www.servicetitan.com/), [Housecall Pro](https://www.housecallpro.com/), [Salesforce](https://www.salesforce.com/) or [HubSpot](http://hubspot.com), your challenge isn’t collecting more leads. It’s figuring out which ones will actually book jobs.

If you’re a CMO, VP of Marketing, or Marketing Ops leads in home services, this piece will show how a predictive data layer can plug into your existing [CRM](https://www.salesforce.com/crm/what-is-crm/) to help your team focus on high-intent prospects—and stop wasting time on leads who were never going to convert.

## The problem: Your CRM knows what happened before but not what happens next

Your CRM is great at logging calls, tracking appointments, and storing customer info. But it’s not helping you anticipate what will happen next, who is likely to convert, or how much they might buy. Your team is still guessing who to call first. Campaigns are firing blind. Reps are wasting hours chasing low-quality leads.

The problem isn’t your CRM—it’s what it’s missing: predictive context. You’ve got historical data, but not behavioral or financial signals that show who’s ready to book. That’s where adding data AND predictions comes in.

## The solution: The predictive data layer: what it is and why it matters

A predictive data layer adds real-world behavioral and financial signals drawn from verified consumer data to each contact in your CRM. In addition to first party data you collect from web form fills and past interactions, you get third party data points and scores based on how likely each lead is to book right now or in the next 30, 60, or 90 days.

Faraday’s predictive data layer integrates directly into your CRM—no replacement required—and automatically adds key datapoints that reveal intent, readiness, and fit. The result: your reps spend more time on leads that close, not leads that clog up the queue.

## What smarter CRMs deliver

Companies that add predictive enrichment to their see immediate results:

* [Fewer wasted calls as reps focus on high-intent prospects](/stories/call-center). If you could sort your call list, why wouldn't you?  
* Higher close rates across campaigns. By avoiding bad leads, or leaving them for last, you get better results sooner.  
* Cleaner funnels and more predictable revenue forecasting. You can calculate an "estimated value" (EV) for every lead, even if it just appeared in your pipeline.

## Faraday has the datapoints that change everything

Most CRMs weren’t designed to predict—they were designed to record. Faraday fills that gap by adding a predictive data layer that plugs directly into your existing tools. You don’t need a data science team, a new platform, or an overhaul. Just a connection to the [**Faraday Identity Graph**](/features/faraday-identity-graph), which powers over 1,500 verified consumer datapoints—everything from financial readiness to life-stage signals.

While our platform is chocked full of data that drives value, some are particularly useful for home services brands like yours. Here are the high-impact datapoints Faraday adds to your CRM:

**1️⃣ Credit score proxy:** Predicts a household’s financial readiness without a hard credit pull. Perfect for screening prospects before offering financing options.

**2️⃣ Likelihood-to-Convert:** Scores leads based on real behavioral patterns, showing who’s statistically most likely to book a job this week.

**3️⃣ Dwelling type:** Distinguishes renters from homeowners—critical for knowing whether to push service contracts or new installs.

**4️⃣ Household income & equity:** Adds key context for premium upsells, like full HVAC replacements or solar upgrades.

### So in short, with Faraday’s data, you can:

✅ Predict who can pay  
✅ Predict who will book  
✅ Predict what to offer next

## How to add predictive data to your existing CRM

Adding the datapoints you need to your CRM with Faraday is easy and fast. In a few careful steps, you can turn your existing customer database into a predictive engine that shows you who’s ready to book, who needs nurturing, and who’s not worth chasing—without touching your current workflows.

1. **Connect your records.** Faraday integrates directly with platforms like ServiceTitan, HubSpot, or your internal CRM through a secure API. Our [developer-friendly API](/docs/reference) and [detailed documentation](/docs) are a good starting point- and we'll give you a hand getting it set up. You can also give it a try using simple CSV uploads and downloads, to get comfortable with the data.  
2. **Enrich with Faraday’s Identity Graph.** We fill in the missing details—household, property, demographic, and behavioral data that CRMs alone can’t provide.  
3. **Deploy predictive datapoints.** Append fields like Credit Score Proxy or Likelihood-to-Convert so your team can instantly see which leads matter most.  
4. **Automate routing and prioritization.** Use these datapoints to filter and prioritize your call queues or lead-routing automations—whether that’s directly inside your CRM or through integrations like [ActiveProspect’](https://activeprospect.com/)s [LeadConduit](https://activeprospect.com/leadconduit/). This ensures your highest-value prospects are called first and passed to the right reps in real time, reducing manual triage and wasted dials.

> Implementation should take a couple weeks—after that, your CRM doesn’t just record what happened—it starts predicting what’s about to happen next.

## FAQ

1. **Do I need to replace my current CRM (like ServiceTitan or HubSpot) to use Faraday?** No. This is the most common misconception. Faraday is a predictive data layer, not a new CRM. It plugs into your existing system and enriches the customer records you already have. Your team keeps working in the tool they know, but with new, predictive data at their fingertips.

2. **What’s the difference between Faraday’s predictive scoring and my CRM's built-in lead scoring?** Traditional lead scoring is manual and behavior-based (e.g., "opened email" \= \+5 points). It only knows what a lead does with you. Faraday’s predictive scoring is AI-driven and market-based. It compares your leads to a massive, verified consumer dataset to find patterns. It answers a different question: not "Is this lead engaged with my marketing?" but "Does this lead look like someone who will actually book a job?"

3. **Do I need a data science team or data engineers to implement this?** It makes it easier, but not necessarily. Faraday has integrated with hundreds of marketing and operations teams and we can provide more or less help depending on your situation because we connect via pre-built integrations and APIs, you don't need data scientists to build or maintain models. Your ops team can map the new data fields (like Likelihood-to-Convert) into your CRM, and your marketing team can start using them in automated workflows immediately.

4. **How does a call center rep actually use a "Likelihood-to-Convert" score?** Faraday appends this score directly to each lead record in your CRM or routing system.

* **Qualification:** The system can filter out bad leads before they ever make it to your call center, if you want.  
* **Prioritization:** Reps can sort call queues by highest Likelihood-to-Convert, so they start with leads most likely to book—no guesswork or manual filtering.  
* **Routing:** Through integrations like **LeadConduit**, high-scoring leads can be automatically routed to top reps or live-transfer partners, while lower scores move to nurture sequences. The result: reps spend time where it counts—on the leads that actually book.

5. **Is Credit Score Proxy compliant?** The Credit Score Proxy is a modeled attribute, not actual credit bureau data. It is privacy-safe, does not require a "hard" or "soft" credit pull, and does not use or access any data from credit bureaus, making it fully compliant for marketing and prospecting use cases. More information can be found [here](/blog/prioritize-with-credit-score-proxy). 

## Putting it all together

So what should you walk away with? Your CRM isn’t broken—it’s just incomplete. By layering in Faraday’s powerful and varied datapoints, you give your team the visibility they’ve always needed: who’s likely to book, who’s financially ready, and who’s worth a follow-up later.

The result? Smarter routing, cleaner funnels, and a marketing team that can finally prove its impact in booked jobs—not dashboards.

If you’re ready to make your CRM predictive, reach out or take a look at our [API docs](/docs/reference).

Faraday’s predictive data layer turns your homeservices CRM into a tool that shows which leads are most likely to book, helping your team focus on high-intent prospects and close more jobs efficiently.

Your home-services CRM needs predictive data to predict who will actually book

If you’re using predictive models, the number one question you’ll eventually ask is: *Are these predictions actually working?*

At Faraday, we love that question. We’re not here to spin our wheels—we want to generate tangible value for each and every client. That ethos is built right into our sales process: before any client signs on the dotted line, we offer to run a custom [holdout](/blog/holdout-testing-evaluate-prediction-accuracy) analysis on their data to prove our models can accurately predict how their customers behave. That’s **transparency** and **trust** from the very beginning.

But after those lines are signed and once those models are live, it can sometimes become harder for clients to get that same level of visibility. Sure we have a variety of other [explainability and transparency](/features/ai-safety-and-responsible-ai) features but here at Faraday, we aren’t really ones for leaving well-enough alone. 

Trust in AI isn’t built on promises, it’s built on proof. So that’s why we’ve released **Efficacy Reporting**, a new feature in the Faraday dashboard that gives you yet another layer of insight into how your models are operating.

## What it does

Just like a holdout test, Efficacy Reporting measures the correlation between Faraday’s predictions and what actually happens. Reports are autogenerated for pipelines that have an [Outcome](/docs/abstractions/outcomes) in the payload (which must have eligibility explicitly configured). 

> 📌 Note: Efficacy Reports require a live, regularly updated dataset connection. One-off uploads won’t generate meaningful reports, since we need to verify outcomes over time.

Each report breaks down performance by decile—the top 10% of predicted scorers, the next 10%, and so on—so you can quickly see how outcomes line up against model expectations. A strong model will show the classic “lift” shape: higher attainment in the top deciles, tapering off toward the bottom.

[![Model efficacy report in Faraday dashboard](/images/blog/efficacy-reports-in-dashboard.png)](/images/blog/efficacy-reports-in-dashboard.png)

## Where to find it

Efficacy reports live in the [Pipeline tab](https://app.faraday.ai/pipelines) of the Dashboard for any qualifying pipeline. As they’re auto-generated, there’s no extra setup required, and they’re tied directly to the models they evaluate, so you’re always looking at performance in context.

## Who it’s for and how to use it

Efficacy reporting is designed for the teams and leaders who rely on predictive insights to make decisions. 

* Marketing and growth teams can use it to build confidence that their campaigns are aligned with reality.   
* Data teams can use it to validate that models are holding up over time without pulling ad‑hoc reports.   
* Executives can use it to get clear, ongoing evidence that predictive AI is delivering ROI.

In practice, efficacy reports let you validate Faraday models over time without extra manual work, spot potential performance issues early, and build confidence with internal stakeholders that predictions are worth scaling.

## What’s next for transparency at Faraday?

Efficacy reporting is just one step toward making the performance of your predictions as visible and explainable as possible. We’ll keep expanding this framework so you can always see, in concrete terms, how Faraday is driving real outcomes.

Because at the end of the day, predictive AI should be more than a [black box](/blog/the-ai-blackbox-is-a-liability) — it should be something you can measure, trust, and build on.

Efficacy reporting lets you see how well your Faraday models are performing in the real world by comparing predicted outcomes to actual results.

What's new: Efficacy reporting makes modeling even more transparent

We’re excited to launch a new email notification feature in Faraday that keeps you seamlessly informed when your most important resources complete a build or refresh, or enter an error state. No more manual checking; now you’ll know exactly when things wrap up (or need attention).

### What’s new
- **Email notifications for key resources** — Receive an email when important resources, like Outcomes or Datasets, finish building, refresh, or encounter an error.  
- **Selective rollout** — Everyone has access to the new *Settings → Notifications* feature, but only some users will have notifications pre-enabled. All other users can opt in at any time.  
- **Opt-in controls** — Notifications are off by default for new users.   

### Why it matters

Resources in the Faraday dependency graph often pass through intermediary states and may take hours—even a day or more—to complete. Until now, users had no automatic way of knowing when a resource built, refreshed, or failed, which could leave downstream workflows hanging. With these notifications, you can stay aligned in real time and act faster when things finish (or fail).

### Get started

If notifications aren’t already enabled for your account, visit *Settings → Notifications* in the Faraday app to turn them on and start receiving alerts.

Faraday now supports customizable email notifications, letting users opt in to alerts when key resources finish a build, refresh, or encounter errors.

What's new: Email notifications for key resource states

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.

Product

Context engineering and demographic data

Your home-services CRM needs predictive data to predict who will actually book

What's new: Efficacy reporting makes modeling even more transparent

What's new: Email notifications for key resource states

ChangelogSee all

Model efficacy for pipelines added

Email notifications now available

FIG data update

Trait appends now can resolve at multiple resolutions

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

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

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

Real-time predictions vs. batch: which actually drives ROI?

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

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