Marketers have always used [personas](/docs/abstractions/persona_sets) to bring customer data to life. But too often, [persona](/blog/k-means-clustering-customer-personas) outputs end up feeling opaque, confusing, or overwhelming. Blurry averages flatten nuance, biased naming conventions create distraction, and long trait tables? They leave teams unsure what truly drives action.

At Faraday, we’re committed to making data and predictions easier to interpret and act on. Building on our recent [persona naming update](/changelog/2025-10-08-persona-naming-update), we’re now rolling out **salience-based persona interpretation features** right in our existing dashboard—a major step toward clearer, bias-free insights that help marketers move from analysis to strategy with confidence.


## What’s new in persona interpretation

With this release, every [persona](/blog/how-many-personas-do-you-need) in Faraday now includes:

* **Plain-English summaries**
A one-paragraph description of what defines the group is now found in the definitions tab so teams can immediately understand who they’re looking

[![persona summaries in definition tab](/images/blog/trait-salience/persona-summary.png)](/images/blog/trait-salience/persona-summary.png)

* **Salient traits (or datapoints)**  
Clear indicators of which characteristics distinguish this persona from the rest of the set are now found in the Analysis tab. In practice, this means you can see which of Faraday’s 1,500+ consumer datapoints most clearly set this persona apart from others — helping you understand what’s unique about them.

In your dashboard, these most distinctive traits are now flagged with a star. ⭐

[![trait salience in the analysis tab](/images/blog/trait-salience/trait-salience.png)](/images/blog/trait-salience/trait-salience.png)

* **Strategic recommendations**  
Practical ideas for how to engage each persona more effectively, which turn insights into action

[![persona recommendations tab](/images/blog/trait-salience/recommendations.png)](/images/blog/trait-salience/recommendations.png)

Together, these tools connect Faraday’s predictive data to clearer audience segmentation and campaign planning.

## Why interpretation matters  

Interpreting personas shouldn’t feel like decoding a spreadsheet. Historically, persona analysis has relied on modes (the most common values) and assumptions That leads to two familiar problems:

1. **Blurry signals** — Minor differences (like 51% vs. 49%) get overstated, while meaningful behavioral patterns are buried.  
2. **Too much noise** — Lengthy trait tables provide data, but not direction.

The new interpretation features in Faraday personas cut through that clutter. Each persona now highlights only the *salient* traits that truly define the group, accompanied by plain-English summaries and strategic recommendations. Combined with our recent move to neutral persona naming, these updates give you a clearer, more trustworthy picture of your audiences.

## Key takeaways  

✅ New interpretation features make persona insights clearer and more actionable.  
✅ Salient traits highlight what defines each audience segment.  
✅ Strategic recommendations turn analysis into immediate next steps.  
✅ New and rebuilt personas automatically include these updates.

## Explore our new personas features today

This feature is now live in both the Faraday Dashboard and API. Head to your personas to see the new summaries, salient traits, and recommendations in action.

If you have questions about how to get the most out of enhanced personas, your Faraday account manager is here to help.

## FAQ  

**Why are some cells that appear most different not starred?**  
Each cell represents the mode, or the most common value, of the trait for that persona. But modes don’t always tell the full story. Suppose Persona 1 shows 100K+ as its mode for income, while others show 80–100K. Persona 1 appears to be most different, yet it might actually include 51% individuals earning 100K+ and 49% earning under 80K. In that case, the mode alone doesn’t capture what distinguishes that persona’s income.

Instead, the salient labels identify which persona shows the most distinctive pattern within each trait. This process repeats for every trait. Specifically, traits that receive stars are those whose distributions most strongly differentiate that persona from others. This allows the system to highlight what truly distinguishes a persona within each trait, not just what values of traits occur most frequently.

**Do I need to rebuild existing personas to see the new interpretation features?**  
Yes. Existing personas stay as they are, but any new or rebuilt sets include summaries, salient traits, and recommendations automatically.

**Will this affect my API workflows?**  
No. There are no API changes—personas will simply include richer interpretation data when rebuilt.

**Where do I find these updates?**  
In the Faraday Dashboard: summaries in *Definitions*, salient traits in *Analysis*, and recommendations in *Recommendations*.

**Why do some traits have no starred values across any personas?**  
Some traits receive no stars because the data patterns for those traits are too similar. The system applies two rules before assigning a star. First, a value’s salience must exceed a minimum threshold. Second, it must be significantly higher than the salience of that same trait’s values in other personas. Only when both conditions are met does the system star a trait value.

Faraday’s personas now include plain-English summaries, salient traits, and strategic recommendations—making it easier than ever to interpret and act on your customer insights. Building on our recent persona naming update, this release adds a new layer of clarity and context that turns analysis into action.

What's new: Personas get interpretation features for faster insight

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…

Consumer brands today run on data. From personalization to retention, every marketing move depends on how well you know your audience. But here’s the catch: even with mountains of first-party data, not all of it is actually usable. 

Some data is locked behind privacy regulations, some is incomplete, and much of it simply isn’t actionable. Meanwhile, CMOs are under pressure to reduce CAC, prove marketing’s contribution to revenue, and differentiate in markets where competitors often have access to the same third-party sources. You can’t grow on data alone—you need data you can act on.

## Problem

And that was exactly where a leading DTC consumer eyewear brand we work with found themselves. They needed reliable customer intelligence to guide merchandising, lifecycle messaging, and geo activation. But much of their most informative first-party data—like prescriptions and exam info—qualifies as [Protected Health Information (PHI)](https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html) under [HIPAA](https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html).

HIPAA tightly governs how covered entities and their business associates handle PHI. Using PHI to segment, target, or personalize marketing requires explicit patient authorization or de-identification that meets strict standards. In practice, marketing teams are blocked from using this data to understand demographics, build segments, or activate campaigns. Even analytics teams face barriers when joining or sharing it downstream. The result: blind spots that inflate CAC, weaken segmentation, and slow campaign velocity.

This eyewear brand’s CMO and team had major gaps: their customer base appeared to skew much older than reality, household and location context was incomplete, and they lacked confidence to prioritize creatives, offers, and store-adjacent campaigns.

## Solution

But with Faraday, it doesn’t have to be this way. Even when first-party data is incomplete, restricted, or locked behind HIPAA, Faraday makes it actionable.

### Faraday’s data pillars

Faraday provides three complementary types of data that work together as a privacy-safe growth toolkit:

1. **Identity completion** – Filling in the missing fields that make a record usable (e.g., appending verified address, phone, or email to partial identifiers).  
2. **Consumer data** – Rich demographic, lifestyle, property, and geo datapoints that unlock segmentation and analytics without relying on PHI.  
3. **Custom predictive datapoints** – On-demand models like Likelihood-to-Convert, Best-First-Product, or Credit Score Proxy, built on top of the [Faraday Identity Graph](/features/faraday-identity-graph).

Unlike generic datasets available to any competitor, Faraday’s Identity Graph is proprietary—built from verified, diverse sources and continuously maintained for completeness. Together, these pillars help CMOs complete their records, enrich them with context, and predict what comes next.

### Why consumer data was the unlock

In this eyewear brand’s case, the **consumer data appends** pillar supplied the missing context—without touching PHI. Faraday’s U.S. identity graph linked public and commercially available consumer datapoints to their customer records, enriching them with:

* **Demographics** (age, income proxies, household composition)  
* **Lifestyle attributes** (interests, shopping behaviors, property/home tenure)  
* **Location context** (precise household-level geo, nearest store)

This enrichment enabled:

* A truer picture of the brand’s age mix and household dynamics.  
* Weekly **closest-store** data powering one-to-one geo messaging (email, SMS, push).  
* Append-driven cohorts aligned with survey-based attitudinal segments—without needing PHI.  
* An identity layer that could be extended across martech partners via Faraday’s [Universal ID](/blog/universal-id).

With these foundations in place, the next step was to operationalize the data.

## How we rolled it out

The rollout followed a clear progression. [Matching](/blog/match-boost-improves-enrichment) is the foundation—without reliable matches, enrichment and predictions fall apart. Deterministic keys like email and name+address ensure that the right consumer datapoints attach to the right individual, building trust in the insights that follow. [Strong matching](/blog/is-your-id-match-rate-real) also reduces wasted spend, prevents duplicate outreach, and gives teams confidence that when a segment is defined, it truly represents the audience they want to reach. To cement that foundation, the team validated coverage by cohort, compared age accuracy against survey and date-of-birth panels, and tuned household logic with clear match-rate expectations.

With this groundwork in place, Faraday’s consumer data began to deliver tangible value. Weekly closest-store feeds powered geo-personalized campaigns, while enriched cohorts aligned cleanly with the brand’s attitudinal segments. These quick wins proved that non-PHI consumer data could fill critical gaps and unlock action right away, giving marketing teams the confidence to activate without regulatory risk.

The natural next step was to extend beyond enrichment into prediction. Faraday’s custom predictive datapoints drove repeat-purchase and product-interest segments, and surfaced growth opportunities through a lite [Market Opportunity Analysis](/docs/abstractions/MOA). By layering prediction on top of trusted enrichment, the brand gained a clear path from raw data to measurable outcomes: age distribution rebalanced to align with survey ground truth, store-adjacent messaging became a high-engagement staple, and campaign testing velocity increased without waiting on PHI approvals.

## Why CMOs choose Faraday consumer data

In a market crowded with data vendors making big promises, Faraday stands apart by focusing on what actually moves the needle for marketing leaders. Many providers chase inflated match rates or push generic attributes that look good in a deck but fail in the field. Faraday’s advantage lies in pairing a privacy-safe foundation with exclusive, activation-ready data tied to a proprietary identity graph.

* **Privacy-safe by design:** Non-PHI append workflow that respects HIPAA.  
* **Action-ready:** Datapoints map cleanly to one-to-one channels, geo plays, and predictive models.  
* **Proven impact:** Keys, QA, and repeatable playbooks turn enrichment into lower CAC, stronger pipeline contribution, and higher LTV—not dashboards.

## Conclusion

Consumer brands don’t need more raw data—they need the right data they can actually use. Faraday delivers exactly that: privacy-safe enrichment, predictive insight, and proven rollout. If you’re ready to turn non-useful records into measurable growth, [we should talk](/contact).

A leading DTC eyewear brand turned restricted and incomplete first-party data into growth by using Faraday’s consumer data to enrich records, enable geo-personalized campaigns, and drive measurable results.

DTC eyeglasses brand unlocks growth with Faraday's consumer data

Marketing agencies today are in a bind.

Their brand clients expect clearer data, sharper predictions, and measurable ROI—but most agencies are working with the same third-party datasets as their competitors. That makes it hard to differentiate, hard to prove ROI, and harder still to retain high-value accounts.  

Agencies that can’t surface unique, predictive insights risk looking like commodities in an industry where brands are paying for strategic advantage, not generic analytics.

## The problem

One performance marketing agency faced this challenge firsthand. Their brand client’s repeat purchase rates had plateaued, and the team realized that their audience assumptions were largely untested.  

They believed their “younger” online buyers were driving growth—but without deeper enrichment, they lacked visibility into who these customers actually were. Their internal dashboards showed the numbers, but not the people behind them.

## The solution

Using **Faraday’s demographic enrichment**, the agency reshaped how their client understood its core audience. The enriched data revealed that what the brand assumed were “younger” online buyers were actually **five to ten years older** than expected.  

Armed with this new demographic clarity, the agency refined welcome flows, personalized product recommendations, and built messaging that reflected the audience’s true profile and motivations.

## The outcome

Within just five months, the client saw a **17% lift in repeat purchases** compared to the same period the year before. Beyond the lift itself, these refinements strengthened customer lifetime value and gave the marketing team newfound confidence in their audience strategy.  

> “We always had these customer personas in our minds,” said the client’s marketing lead. “It was interesting to see how what we thought were younger online customers weren’t as young as we thought.”

By helping the agency uncover and act on these insights, Faraday enabled them to elevate from an execution partner to a **strategic growth driver**—delivering proprietary, data-backed understanding their competitors couldn’t match.

## What does this mean to you?

For agency CMOs, this is what defensible differentiation looks like: unique, predictive enrichment that drives measurable performance improvements and scales across clients without adding complexity.  

With Faraday, agencies can move beyond generic reporting and offer clients proprietary insights that directly impact growth and retention.

## Ready to differentiate your agency?

Faraday helps agencies move beyond generic data to deliver proprietary predictive insights that win and retain high-value clients. Let’s talk about how we can power your dashboards with enrichment and predictions that set you apart.

Faraday enabled a performance marketing agency to boost repeat purchases by 17% by enriching their client’s customer data and revealing new insights that reshaped segmentation and messaging.

Performance marketing agency drives 17% lift in repeat purchases with Faraday’s demographic enrichment

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.

All posts