**See our complete guide:** [What is the Faraday Identity Graph?](/blog/whats-in-faraday-identity-graph).

---

If you use Faraday — for predictions, appends, personas, or anything in between — you've been using the [Faraday Identity Graph (FIG)](/catalog) whether you knew it or 
not. FIG is the foundation everything runs on: a continuously refreshed collection of 1,400+ attributes on 240 million US consumers, curated and normalized from leading data vendors. Every lead score, every data append, every audience we help you build draws from it.

Consumer data is the foundation of modern marketing — and what businesses need from that foundation has changed. Models are getting more sophisticated and need richer, time-aware inputs to keep getting better. Marketers want to act on signals the moment they emerge, not weeks later. Data science teams want documentation and metadata they can actually build with. And operations teams want predictable release cadences that fit their change management processes.

FIG v2 is built for where consumer data needs to go next. It's a complete rebuild of our consumer data system that introduces historical data, an expanded and redesigned attribute catalog, continuous data refreshes, and release pinning — so the foundation underneath your marketing keeps pace with what your business is asking of it.

Here's what's new:

## 1. Hundreds of new attributes, with richer signal across the board

We expanded our underlying [attribute set](/catalog) significantly — adding hundreds of new data points to capture consumer signals we couldn't surface before. We also harmonized unwieldy groups of related attributes into cleaner replacements with more expressive, consistent names.

**What this means for you:** richer raw material for every prediction, append, and audience you build. More signal in the data means models can pick up on patterns that were previously invisible, which translates directly to better prospect targeting and higher marketing ROI.

## 2. A redesigned catalog you can actually navigate

We rebuilt our [data catalog](/catalog) from scratch around the expanded attribute set — retiring data points that weren't pulling their weight and reshaping what remains around the attributes that consistently drive better prediction quality. Every attribute now comes with detailed documentation: descriptions, visual data distributions, and technical metadata designed for both marketers and data scientists.

**What this means for you:** less time hunting for the right attribute, faster model iteration for your data science team, and a catalog that both marketers and analysts can actually use without a data science degree. 

## 3. Historical data

FIG v2 carries the [full history of values for every attribute](/blog/fig-v2-historical-data), each one timestamped and annotated with precision and source metadata. Rather than knowing someone's income is \$85,000 today, you now know it was \$62,000 five years ago, \$74,000 three years ago, and \$85,000 today.

This is possible because Faraday has been a careful steward of consumer data for over a decade. We've been collecting, normalizing, and preserving these observations the whole time. FIG v2 is the first time we've been able to surface all of that accumulated history in a structured, ML-ready format — every attribute is now a timeline of events rather than a single point in time.

**What this means for you:** dramatically better predictive models. Models trained on current-state data are actually learning what people look like *after* they convert — not before. Take a home remodeling brand trying to predict who'll buy new windows: after someone buys, there's a contractor transaction, possibly a HELOC, elevated home-improvement spend. Those signals weren't there when they were still a prospect. If your training data captures people as they look *after* converting, the model goes hunting for prospects who already bought.

With FIG v2, we can press rewind and train on what a person looked like just before they became a lead — so models learn what a genuine pre-purchase prospect looks like and get dramatically better at finding more of them.

For data science teams, every historical observation comes with the metadata you need to use it directly: data types, statistical types, directionality guidance, coverage breakdowns. If your team builds internal models and wants rich consumer context that goes back in time, this is the dataset to do it with.

## 4. Fresher data, delivered continuously

Our new infrastructure [ingests fresh data from vendors as soon as we receive it](/blog/fig-v2-freshness-release-pinning) — no more waiting for scheduled monolithic releases.

**What this means for you:** less stale targeting and fewer wasted impressions. This matters most for time-sensitive signals like recent movers, life events, and income changes — the kinds of triggers where a few weeks of lag can mean the difference between reaching someone at the right moment and missing them entirely.

## 5. Release pinning

We've heard from many of you that you love getting the most current data automatically. We've also heard from teams with strict change management requirements who need more control over when their data updates. FIG v2 supports both. You can now [pin your account to a specific FIG release](/blog/fig-v2-freshness-release-pinning), and new updates won't apply until you move your pin date forward.

**What this means for you:** stability when you need it, freshness when you want it. Teams with regulated workflows, locked-in model versions, or careful release calendars get to upgrade on their own schedule — without sacrificing access to the latest data when they're ready.

### **What this means for your account**

* **FIG v2 is live today for all new accounts**  
* **Existing accounts will be migrated over the coming months** — no account will be moved without coordination with your team  
* **Your current account keeps running as-is** throughout the process — there's no disruption until you're ready  
* **Questions about your migration or timeline?** Reach out to your account manager

## Ready for even better customer data?

FIG v2 is a fundamental shift in what consumer data can do inside Faraday — better models, fresher data, hundreds of new attributes, a catalog you can actually navigate, and a release cadence that respects your operations. 

We're working through migrations as quickly as we can and will be in touch when it's your turn. In the meantime, if you want to go deeper on what's changed and why, reach out to your account management team or [read the full FIG v2 series](/blog/series/figv2).

FIG v2 is a complete rebuild of Faraday's consumer data system, introducing historical data, redesigned attributes, fresher updates, and new tools to make customer data easier to discover, deploy, and operationalize.

Now live: FIG v2 - the biggest upgrade to our customer data system, ever

If you've been following our FIG v2 series, you've already read about the [overhauled data catalog](/blog/fig-v2-data-catalog) and the [historical data upgrade](/blog/fig-v2-historical-data) that makes your models dramatically more accurate. Today I want to close out the series with something that might sound like a backend plumbing detail—but has real consequences for anyone running production pipelines on Faraday data: how we handle data freshness, and the new release pinning controls that come with it.

## How most identity graphs handle updates (and why that's a problem)

To understand why this matters, it helps to know how data updates typically work in the identity data industry.

Most identity graphs operate on a monolithic release model. Providers work with many data vendors, each of whom delivers updated data snapshots on their own schedule. The standard approach is to either wait for all vendors to deliver their latest data, or simply release on a fixed schedule—monthly, quarterly—regardless of when the underlying data actually arrived. Either way, the result is the same: a gap between when data is collected in the real world and when it's available to you.

The obvious problem: every day you spend waiting is another day the data sits there getting older. The release cycle itself introduces latency that has nothing to do with how fresh the underlying data actually was. **FIG v2 eliminates that bottleneck entirely.**

## Rolling releases: freshness without the wait

Now with FIG v2, instead of waiting for a full vendor sweep before publishing an update, we ingest each vendor's data the moment it arrives. Each new observation is processed, normalized, and added to the appropriate attribute's history vector—timestamped, sourced, and immediately available—without waiting for anything else.

The result is rolling releases. Every time even a single vendor delivers updated data, that data flows into FIG v2 right away. There's no more monolithic "release event" to wait for. The gap between when data is collected in the real world and when it's available to you in Faraday shrinks dramatically.

For most attributes, this is just a nice quality-of-life improvement—fresher data, more often. But for certain attributes, it's much more significant. Pre-mover signals, for example, are almost entirely time-sensitive. A pre-mover flag that's six weeks stale is considerably less valuable than one that arrived yesterday. With rolling releases, those high-velocity attributes stay sharp.

## Release pinning: you control when freshness flows in

Fresher data is almost always better — but there's a legitimate exception. If you're running a mission-critical pipeline and you've carefully validated the data distributions it depends on, constant rolling updates can feel like a liability. Think an automated risk scoring model at an insurance company, or a lead prioritization pipeline that's deeply integrated into a sales team's daily workflow — cases where an unexpected shift in data distributions could quietly change outputs in ways that are hard to detect and costly to diagnose. If you're in one of those situations (or something similar), you don't want things shifting under your feet without warning.

That's what release pinning is for.

In FIG v2, you can pin any individual pipeline to a specific date. What that means concretely: any new consumer data incorporated into FIG *after* that date is simply ignored for that pipeline. The pipeline keeps running—against exactly the data it was validated against—until you decide you're ready to move it forward.

The workflow this enables is deliberate and controlled. Say you're running a critical pipeline and a new round of vendor data has just been ingested into FIG. Rather than letting it flow in automatically, you:

1. **Make a copy of the pipeline.** The copy runs against the newer data.  
2. **Do your validation.** Check distributions, spot-check outputs, confirm that everything downstream still behaves as expected.  
3. **When you're satisfied, advance the pin date on your production pipeline.** The new data flows in. The copy gets retired.

You can do this on whatever cadence makes sense for your team—monthly, quarterly, or whenever a new vendor refresh comes in that you want to evaluate. You get the benefit of fresher data without ever being surprised by it.

For teams that don't need this level of control, the default behavior is simple: data stays fresh automatically, with no manual steps required.

## Freshness and stability, on your terms

The two ideas here—rolling releases and release pinning—are designed to work together. Rolling releases make FIG v2 the freshest consumer data platform we've ever built. Release pinning makes sure that freshness doesn't come at the cost of predictability for the teams who need it most.

Together, they represent something we think is genuinely new in the market: a consumer data system that treats data freshness not as a fixed property you accept or reject wholesale, but as a dial you can tune per-pipeline based on your actual operational needs.

If you want to learn more or start testing these capabilities in your account, reach out to your account management team. And if you missed the earlier posts in this series, you can catch up on the [data catalog deep dive](/blog/fig-v2-data-catalog), the [historical data deep dive](/blog/fig-v2-historical-data), or the [freshness and release pinning deep dive](/blog/fig-v2-freshness-release-pinning) on our blog.

And if you're new to the platform, reach out to [talk to a context consultant](/contact) to see what FIG v2 can do for your data.

FIG v2 features continuous rolling releases for immediate data freshness, while introducing release pinning to give teams precise control over when new data enters their mission-critical pipelines.

Deep dive: FIG v2 data freshness and release pinning

Most marketing teams aren't starting from zero. You have CRM records, campaign data, transaction history — probably more than you can act on. But having data and having the *right* data are two different things. And the gap between them is where budget gets wasted, campaigns underperform, and your team ends up doing work that better data would have made unnecessary.

The right question isn't "how much data do I have?" It's "do I have the data that lets my team stop compensating and start executing?"

## What goes wrong when the data is wrong

The same patterns show up for consumer brands across industry verticals — sometimes as a campaign that outright fails, more often as the quiet drain of time your team spends manually compensating for gaps the data should have filled. Here’s how that might look for you:

* A brand builds their entire acquisition strategy around a customer profile that turns out not to reflect their most valuable buyers — and spends years optimizing for the wrong person.  
* A paid media campaign misfires because the targeting is built on assumption-based profiles instead of actual purchase signals — the right message reaches the wrong people, and someone spends two weeks in spreadsheets trying to figure out why the numbers look off.  
* An inbound sales team wastes hours on leads that were never going to convert, because there's no signal to distinguish high-value prospects from low-value ones until a rep is already on the phone.  
* An outbound sales team can't reach half their customer list because the records are missing valid emails, phone numbers, or addresses — and somebody has to go fix that before anything else can happen.

In every case, the brand had data. They just didn't have the data that would have prevented the wrong decision.

## What the right data actually looks like

The right data isn't a single thing. It's three layers, each doing distinct work:

**Verified identity data.** Clean, foundational fields — name, phone, email, address. These make sure you can actually reach your customers, eliminate duplicates, and build a complete view of each household. Everything downstream depends on this being right.

**Customer profile datapoints.** Curated context like household income, homeownership, Credit Score Proxy, and industry-specific essentials packages (see our offerings for [home services](/blog/homeservices-essential-data), [retail/ecommerce](/blog/retail-ecom-essential-data), and [financial services](/blog/finservices-essential-data)). Use these to segment audiences, tailor messaging, and prioritize outreach based on signals that have actually been shown to drive value in your vertical. 

**Custom predictive signals.** Predictive scores trained on your specific outcomes and customers — [likelihood to convert](/templates/lead-prioritization), [best product recommendation](/templates/next-best-offer), [persona assignment](/docs/abstractions/persona_sets). Because they're built on your data, not generic benchmarks, they reflect your business and get sharper over time. Use them to tell your team exactly where to focus: which leads to call first, which product to surface, which customers to retain.

The key is knowing which layer solves which problem. You don't throw all of it at your CRM at once and see what sticks. An informed data strategy means identifying which specific categories of data — and which specific datapoints within those categories — actually drive value for your business right now. When you get that right, here's what becomes possible.

## What this actually changes

Once you have the right data foundation, the same problems that were draining time and budget become solvable:

* **Know your real ICP.** Instead of building an acquisition strategy around an assumed customer profile, you have the data to understand who actually buys from you and where to find more of them.  
* **Personalize at scale.** Instead of generic messaging that treats every customer the same, you have the context to tailor offers, timing, and creative to the specific people most likely to respond.  
* **Score and prioritize leads.** Instead of burning hours on leads that were never going to convert, you have predictive signals that tell your team exactly where to focus their time.

## Ready to get the right data?

Most brands we talk to are a few targeted datapoints away from campaigns that actually work. Explore what's available at [buy.faraday.ai](https://buy.faraday.ai) — or [talk to a context consultant](/talk-to-sales) if you want help figuring out exactly which ones move the needle for your business.

Faraday gives B2C brands the verified identity data, customer profile attributes, and predictive signals they need to stop patching data gaps and run campaigns that actually work.

Get the B2C data your brand needs to stop patching gaps and start executing

**See our complete guide:** [Powered by Faraday — embedding predictive AI in your platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai).

---

Even the most powerful model in the world can’t drive real value if it doesn’t know who it’s talking to. As our CEO, [Andy Rossmeisl](https://www.linkedin.com/in/rossmeissl), [put it](/blog/ai-needs-consumer-data-to-give-context):  “Without the specific identity and history of the customer it’s talking to the world's most advanced AI can do little more than offer a polite, generic greeting."

That's not a model problem. It's a context problem.

## The real issue isn't your model — it's what your model knows

Most AI agents start every conversation blind. Without history or clear signal about who's actually on the other end. So they default to generic responses. Not because they're poorly built, but because they have nothing to work with.

The instinct is to throw more data at the problem. More attributes. More integrations. More inputs. But raw data doesn't tell your agent anything useful about this specific person, right now. You can have a thousand attributes about someone and still not know whether they're price-sensitive, in-market, or a high-value prospect worth a personalized offer.

[As Andy puts it](/blog/ai-needs-consumer-data-to-give-context): "Context is the guiding light that turns a cold interaction into a warm conversion. It is the vital distinction between having a list of names and possessing the clarity to know exactly who a prospect is, and how to best engage them."

That shift — from dumping data at your model and hoping it figures things out, to feeding it curated signals — is what separates agents that plateau from agents that keep getting smarter.

## What Faraday does

Faraday enriches incoming identities against the [Faraday Identity Graph](/catalog) — 1,400+ consumer attributes across 240 million U.S. adults. You send us PII. We return a rich consumer profile, appended to your customer record, ready to pass directly to your agent or model.

For you, it’s as simple as a clean API call that turns an anonymous identity into someone your agent actually knows.

## What this means for what you're building

**Your agent personalizes from message one.** No more [cold-start](/stories/dtc-retailer) problems. The moment an identity hits your system, it can be enriched with the context your agent needs to skip the generic greeting and get to the relevant conversation. This is exactly what [Hazel](/stories/hazel) did with our data — their agentic marketing platform went from knowing nothing about a user to having real customer context on every interaction, with a working prototype in a single day.

**Your agent acts on signal, not noise.** Curated traits — household composition, lifestyle indicators, behavioral factors — slot directly into your agent's reasoning. Platforms like [Heatseeker](/blog/how-dtc-brands-build-living-personas-faraday-heatseeker) work with us to turn their customers' flat transactional records into rich consumer profiles overnight to power smarter personas, better targeting, and a shared language across the org.

**Your roadmap moves at API speed.** What would take a multi-year build internally — sourcing data, hosting it, modeling against it, maintaining it — becomes a few API calls. There's no infrastructure overhead, no six-figure procurement cycle, and no contract to renegotiate as you grow. Brands like [Hazel](/stories/hazel) and [SalesRabbit](/blog/salesrabbit) collapsed timelines from months or years down to days — and scaled on the same infrastructure they prototyped with.

## The bottom line

Your agent isn't underperforming because it's poorly built. It's underperforming because it doesn't know who it's talking to.

Faraday fixes that with a single API call — enriching every incoming identity with 1,400+ consumer attributes before your agent says a word. No data sourcing, no infrastructure build, no long procurement cycle. Just context, from day one.

If you’re ready to accelerate your roadmap and get the context your agent needs today, [start here](https://buy.faraday.ai) — or [talk to a context consultant](https://faraday.ai/talk-to-sales) about what enrichment looks like for your use case.

Faraday's API gives AI agents the customer context they need to personalize from day one — no data sourcing, no infrastructure build, no long procurement cycle.

Stop sending your AI agent into conversations blind: get customer context via the Faraday API

If you've been following our FIG v2 series, you already know about the [overhauled data catalog](https://faraday.ai/blog/fig-v2-data-catalog) and the broader [announcement of what's coming](https://faraday.ai/blog/now-live-faraday-identity-graph-version2). Today I want to go deep on what I think is the most technically significant upgrade in the entire release: historical data.

## What "historical data" actually means

The [Faraday Identity Graph](https://faraday.ai/catalog) has always given you an up-to-date snapshot of every US adult—what we know about them *right now*. FIG v2 changes that fundamentally. Instead of a single current value for each attribute, we have the full history of values going back many years, each one timestamped and annotated with precision and source metadata.

Think of it this way: rather than knowing that someone's income is \$85,000, you now know that their income was \$62,000 five years ago, \$74,000 three years ago, and \$85,000 today. Each of those data points has a timestamp, a confidence level, and information about where it came from. For you, this means every data attribute isn’t just a point-in-time number, but instead a timeline of historical events.

This is possible because Faraday has been a careful steward of consumer data for over a decade. We've been collecting, normalizing, and preserving these observations the whole time. FIG v2 is the first time we've been able to surface all of that accumulated history in a structured, ML-ready format.

## Why this matters most for modeling

The biggest immediate payoff from historical data is better predictive models. To understand why, it helps to think through how model training actually works.

Let's start with a propensity-to-buy model (i.e., a lead score model). When you build that model, you feed the algorithm historical examples: leads that converted and leads that didn't. The algorithm finds patterns—the characteristics that consistently differentiated the converters from the non-converters—and encodes those patterns into a model. Later, when new leads come in, the model applies those patterns to score them based on which is most likely to buy.

Here's the problem that a model without historical data has: if it’s trained without historical data then it is actually training on what the person looks like *after* they purchased, NOT the actual time of purchase. Take a home remodeling brand trying to predict which prospects are likely to purchase new windows. To train that model, you feed it historical examples: people who bought windows and people who didn't. But here's the catch—a person looks very different in the data *after* buying windows than they did *before*. After the purchase, there's a contractor transaction on their record, a home equity line of credit may have appeared, and their home improvement spend has spiked. Those signals weren't there when they were still a prospect. If your training data captures people as they look *after* converting rather than just before, the model learns the wrong patterns entirely—and the prospects it finds will be people who match a post-purchase profile, not a pre-purchase one.

Until now, this was a problem we could only partially solve. If a brand had leads going back three years, we could append current FIG data to those leads, but "current" data for a lead from three years ago is not the same as that person's data *at the time they became a lead*. A lot has changed in three years.

With FIG v2, we can press rewind and assemble a training dataset based on what they looked like just *before* they submitted the lead form and what they looked like after they purchased. The model learns what a genuine pre-purchase prospect actually looks like—and gets dramatically better at finding more of them.

## A first-of-its-kind resource for data science teams

Better internal modeling is the most immediate benefit, but FIG v2's historical dataset also opens up something that hasn't really existed before commercially: a comprehensive, historical consumer dataset that's fully ML-ready out of the box.

Every historical observation comes with the kind of metadata that data scientists actually need—data types, statistical types, directionality guidance, coverage breakdowns, and more. If your team is building your own internal models and wants to incorporate rich consumer context that goes back in time, this is the dataset to do it with.

## What's coming next: change detection

We're also excited about something that historical makes possible further down the road: monitoring when a person's profile changes in a meaningful way.

The most valuable signal isn't always the value of an attribute—it's the fact that the attribute just changed. Someone who *just became* a new homeowner, or *just had* their first child, or *just crossed* an income threshold is a fundamentally different prospect than someone who has been in that state for years. Life events create moments of opportunity, and right now, most brands have no reliable way to know when those moments arrive.

Having a full historical vector for every attribute makes this kind of change detection possible. We'll be sharing more about how we're building that capability in Q2.

## Try it this month

FIG v2 is in beta now and releasing this month. If you want to get your hands on the historical data and see how it affects your model performance, reach out to your account management team. And keep an eye out—we have more deep dives coming before the full release.

FIG v2 adds historical timelines to consumer data, enabling far more accurate predictive models by training on pre-purchase profiles instead of post-purchase ones.

Deep dive: FIG v2 historical data and what it means for your models

A high match rate is a vanity metric if you don't distinguish between where someone lives (address-level matching) and who they are (person-level matching). And even a perfect match doesn't guarantee useful enrichment — those are two independent dimensions of precision, and conflating them is one of the most common and costly mistakes teams make.

## The two dimensions of data precision

There are two independent dimensions of precision in any data enrichment process: **match precision** and **enrichment precision**. They are not the same thing, and a strong score on one does not imply a strong score on the other.

- **Match precision** describes how confidently a data provider recognizes the record you sent them. It has two levels: **person-level matching** (the provider has identified a specific individual — e.g., Jane Smith at 123 Main St, Unit 4B) and **address-level matching** (the provider has identified the household or residence, but not the specific person living there).  
    
- **Enrichment precision** describes the quality of the data being appended to that matched record. It also has two levels: **observed data** (the provider has actual, directly measured values for that person or household) and **imputed data** (the provider doesn't have the value directly, so they estimate it).  
    
- **Imputation** is the standard data science technique used when a value is missing. The system may look at everyone else known to live at the same fully qualified address — a spouse, an adult child, a roommate — take the values that are available for them, and average those to produce a best estimate. It's often better than returning nothing, but the resulting data is residence-level, even when the match itself was person-level.

These two dimensions combine into four possible outcomes. The quadrant below shows what each combination is good for:  

[![2x2 grid that describes enrichment precision and match precision.](/images/blog/enrichment-precision-diagram.png)](/images/blog/enrichment-precision-diagram.png)

## What matching precision actually means in practice

Both levels of matching are valid, depending on what you're doing. If you're running a direct mail campaign based on property characteristics — say, targeting homeowners with pools — address-level matching is perfectly fine. You care about the house, not the person. But if you're personalizing outreach based on individual behavior or demographics, person-level matching is what you actually need.

Most providers report a single "match rate" number without distinguishing between these levels. A 95% match rate sounds great — until you realize a significant portion of those matches are address-level, not person-level, and your personalization strategy depends on the latter.

## What enrichment precision actually means in practice

Not all attributes are created equal. Some are naturally person-level — things like age, income, or purchase behavior. These describe an individual. Others are naturally residence-level — like bathroom count, square footage, or home value. These describe a property.

Here's a key insight: more precision isn't always better. If you ask for bathroom count for everyone in your customer base, you're going to get residence-level data — and that's exactly what you want, because bathroom count is fundamentally a property of the home, not the person. Asking for it at the person level wouldn't even make sense. The "right" level of precision depends on what the attribute actually describes.

When everything aligns perfectly, you get a person-level match with observed person-level data for a person-level attribute. You know who Jane is, and you have her actual income. That's the best case.

But data is inherently sparse. Even the best providers don't have every attribute for every person. So when a provider has matched Jane at the person level but doesn't have her income specifically, imputation kicks in — and the value you get back is residence-level, even though the match itself was person-level.

This is the distinction most teams miss: a person-level match does not guarantee person-level enrichment.

## Why this matters for your strategy

If you're buying data to fuel AI models, personalization engines, or predictive analytics, the quality of your outputs depends entirely on understanding what went in. And "quality" here isn't just about volume or match rates — it's about the right kind of precision for your use case.

This connects to a broader problem we see across the industry: teams buy more data thinking it will solve their performance issues, when what they actually need is the right data at the right level of precision. A massive dataset with high match rates but mostly imputed enrichment data isn't necessarily more useful than a smaller dataset with genuine person-level attributes.

Here's a practical framework for thinking about it:

When address-level precision is fine:

* Direct mail campaigns targeting properties (not people)  
* Geographic or neighborhood-level analysis  
* Property-based segmentation (home value tiers, housing type)

When you need person-level precision:

* Individualized outreach or personalization  
* Predictive models based on personal demographics or behavior  
* Lookalike audience building based on individual customer profiles  
* AI agent workflows that need to understand a specific consumer

Questions to ask your data provider:

* What percentage of my matches are person-level vs. address-level?  
* For enriched attributes, how much of the data is directly observed vs. imputed?  
* For the specific attributes I care about, are they naturally person-level or residence-level?  
* How do you handle sparsity for the attributes that matter most to my use case?

## Transparency builds better outcomes

Most data providers don't explain any of this. They give you a match rate, they give you enriched fields, and they let you assume the best. That works fine — until your AI model underperforms and you can't figure out why, or your personalized campaigns feel generic despite having "complete" customer profiles.

At Faraday, we think the answer is transparency. When you understand exactly what level of precision you're getting — for both matching and enrichment — you can make smarter decisions about how to use it. You can weight imputed data differently in your models. You can choose the right attributes for your use case. You can stop over-investing in data volume and start investing in data precision.

Because ultimately, the question isn't "how much data do I have?" It's "do I have the right data, at the right level of precision, for what I'm trying to do?"

Want to understand how Faraday handles matching and enrichment precision for your data? [Grab a time to talk to one of our context consultants](https://faraday.ai/talk-to-sales).

Match rate alone doesn’t tell you whether enriched data is actually useful, teams need to separate match precision, meaning person-level vs. address-level matching, from enrichment precision, meaning observed vs. imputed data. The right level of precision depends on the use case.

Understanding enrichment precision: what your match rate is actually telling you (and what it's not)

Some brands know their customers by the numbers — average order value, purchase frequency, seasonal spikes. But numbers alone don't tell you _why_ someone buys, what job they're hiring your product to do, or what keeps them coming back. That's the gap this story is about closing.

A leading DTC children's apparel brand had years of valuable transactional data. They knew what sold, when it sold, how often customers returned, which customers were driven by discounts, and what marketing channels were profitable. What they didn't have was a clear, company-wide picture of who their customers actually were and what motivated them to transact.

## The challenge

The brand's core problem wasn't a lack of data — it was a lack of _meaning_ in that data. Their customer records were transactional: order histories, product SKUs, shipping addresses. Useful for operations, but not enough to answer the strategic questions the team was asking.

Who are our highest-value customers, really? What life stage are they in? What motivates their purchases — convenience, values, gifting, something else entirely? And how do we make those insights accessible to everyone in the company — from the CMO to the junior copywriter and performance marketer — not just the data team staring at dashboards?

They needed their customers to come alive. Not as static slide decks pinned to a wall, but as something the whole company could engage with.

## How Faraday helped: Enriching data and finding customers

The brand connected their e-commerce order data to the Faraday platform, which enriched every customer record with attributes from the [**Faraday Identity Graph**](https://faraday.ai/features/faraday-identity-graph) — a built-in dataset covering 1,400+ traits across 240M+ US adults.

Overnight, flat transactional records became rich consumer profiles. The brand could now see household composition, lifestyle indicators, shopping preferences, property details, and estimated income brackets layered onto their existing customer base. The brand could see how their customers were clustering across these socio-demographic dimensions through Faraday derived [personas](https://faraday.ai/docs/how-to/predictions/customer-personas).

This enrichment didn't just add data points — it added _dimension_. For the first time, the team could see patterns they'd only guessed at: which customer segments over-indexed on sustainability values, which ones were driven by gifting occasions, and where geographic concentrations of high-value buyers actually lived.

But Faraday didn't stop at insight. Using their **custom audiences** capability, the brand could deploy these enriched segments directly to ad platforms, pushing predictive, behavior-based audiences into channels like Meta and Instagram. This meant the brand wasn't just _understanding_ their customers better — they were _finding more of them_ on the channels where they actually spend time.

## How Heatseeker helped: validating Personas through real market experiments

Enriched data tells you _who_ your customers are. But it doesn't tell you what message will make them act. That's where Heatseeker came in.

Using Heatseeker's market experiment engine, the brand ran structured experiments **on real consumer channels like Instagram** — not in focus groups or surveys, but in the wild, where customers actually make decisions. Specifically, they:

- **Tested [jobs-to-be-done](https://strategyn.com/jobs-to-be-done/) (JTBD) hypotheses in-market** — running experiments on Instagram to determine which JTBD statements resonated most with different customer segments. Was the primary job "find durable, cute clothes that survive active kids"? Or "buy gifts that feel personal and thoughtful"? Real consumers gave real signal — not survey responses, not guesswork.
- **Quantified pain points with real engagement data** — rather than assuming what frustrated their customers, they tested pain point messaging against Faraday-enriched audience segments on consumer channels to see which problems actually drove clicks, engagement, and conversion.
- **Mapped enriched data to behavioral personas** — Faraday's demographic and psychographic enrichment was layered with Heatseeker's behavioral experiment data, creating personas grounded in both _who customers are_ and _what they actually care about_.
- **Brought personas to life with AI-powered avatars** **and synthetic panels**— and here's where it gets groundbreaking. Heatseeker's AI persona technology doesn't just produce a PDF or a slide deck. It creates **persona avatars that talk like real customers** — because they're grounded in real behavioral and quantitative data from market experiments. Anyone in the company can open a conversation with a persona and ask questions: "What would make you switch brands?" "What's the hardest part about shopping for kids' clothes?" "Would free shipping or a loyalty discount matter more to you?" The personas respond with the voice, priorities, and pain points of the real customer segments they represent.

This is a fundamentally different kind of persona. Not a made-up archetype from a workshop. A living, conversational representation of actual customer behavior.

## The results

The brand went from having a spreadsheet of customer segments to having a **living persona system that the entire company could rally around — and actually talk to.**

**For marketing**, personas informed messaging strategy, creative direction, channel selection, and segment identification — grounded in validated JTBD, pain points from real Instagram experiments, and real time assignment of new customers to a persona.

**For merchandising**, knowing which persona segments over-indexed on gifting vs. everyday wear helped shape product assortment and seasonal planning. They could literally ask the persona avatar "what would you want to see in a holiday collection?" and get an answer rooted in real data.

**For the executive team**, persona avatars became a shared language for strategic conversations about growth, retention, expansion, and the evolution of their customer base — accessible to anyone, not locked behind an analytics dashboard.

**For customer empathy across the org**, something shifted. When a product designer, a customer service rep, or a new hire can actually _chat_ with a data-grounded persona, they develop an intuitive understanding of the customer that no deck or report can replicate. Empathy stops being an abstract value and becomes a daily practice.

The personas weren't static documents filed away after a single workshop. Because they're built on continuously enriched Faraday data and validated through Heatseeker's ongoing market experiments, they evolve as the customer base evolves.

## Why this matters

Most companies treat persona creation as a one-time exercise: run a few interviews, build some slide decks, pin them to a wall, and move on. The problem is those personas age out the moment customer behavior shifts — and nobody in the org actually uses them.

This case demonstrates a fundamentally different approach. By combining Faraday's identity-level consumer enrichment, clustering algorithm, and custom audience deployment with Heatseeker's ability to experimentally validate what actually drives customers — and then making those insights conversational — the brand created personas that are:

- **Data-rich** — grounded in 1,400+ real consumer attributes from the Faraday Identity Graph and, not assumptions
- **Behavior-validated** — tested through real market experiments on Instagram and consumer channels, not focus groups
- **Findable** — deployed as custom audiences through Faraday so you can reach these customers where they are
- **Conversational** — Heatseeker's AI persona avatars let anyone in the company chat with their customers, driving empathy and faster decisions
- **Living** — continuously updated as new data flows in and new experiments run

The combination of _knowing who your customers are_ (Faraday), _finding them where they are_ (Faraday custom audiences), _proving what they care about_ (Heatseeker experiments), and _making them someone you can talk to_ (Heatseeker AI personas) is what turns personas from a strategy exercise into a competitive advantage.

---

**Ready to bring your personas to life?** Faraday and Heatseeker work together to turn your customer data into validated, living personas that drive real decisions — and real empathy. [Get started with Faraday](https://faraday.ai/signup) | [Try Heatseeker](https://heatseeker.ai)

By combining Faraday's deep consumer data enrichment with Heatseeker's real-world market experiments, this DTC brand transformed static transactional data into living, AI-powered Personas that growth and empathy.

This DTC children's apparel brand brought their customer Personas to life with Faraday and Heatseeker

[John Hardy](https://johnhardy.com/) has always been more than just a jewelry brand; it’s a boutique experience. But even for a brand with such a legacy, the modern digital landscape presents a challenge: how do you maintain that high-touch, one-to-one feeling when you're communicating with thousands of customers at once?

In a boutique, associates tailor recommendations based on what they know about a client’s preferences, occasions, and past purchases. That depth of understanding drove conversions. But replicating that level of personalization online was the real challenge.

To continue delivering the same high-touch experience that had brought them so much success as they scaled, they needed to move past generic blasts and toward the same thing that had always driven their success: unique, personalized experiences that lead to actual conversions.

## The problem: Personalization at scale requires context

We recently sat down with [Jonas Malpass Caligan](https://www.linkedin.com/in/jonas-malpass-caligan-540b6b35/), John Hardy’s VP of Ecommerce and Digital marketing, and her framing of this problem really captured the friction:

“To me, personalization is really the only way to break through the noise we are seeing in the market. Many of our greatest successes have been thanks to the one-to-one relationship building we’ve been able to create through clientelling, and Faraday allows us to do that at scale for all of our customers.”

This is a challenge we hear from brands all the time. At Faraday, we have a word for it too: it’s a **context gap**. When a sales associate is selling in a boutique, they have a massive advantage: **small talk**. Through simple conversation, they learn that a customer has a daughter with a birthday coming up, or that they prefer silver over gold for everyday wear. That simple conversation provides the context that informs a perfectly timed, highly effective offer.

Automated digital workflows don’t have the benefit of small talk. They are typically limited to **first-party data**—the information a brand can collect directly through transactions and clicks. And while vital, that data only represents a fraction of the customer’s actual life and narrative.

To operate with the same effectiveness as a seasoned sales associate, automated systems need more than transaction history. They need the kind of background context an associate gathers naturally through conversation—the signals that explain not just what someone bought, but why they might buy next. Without that, automation defaults to generic. Faraday closes that context gap by providing privacy-safe third-party consumer data that supplies the missing background, allowing digital workflows to behave less like blunt instruments and more like informed, intentional advisors.

## The implementation: Using data to drive conversion

The John Hardy team decided to put this theory to the test with one of their most important annual initiatives: their **VIP gift card program**. For four years, this program had relied on a manual selection process based on basic purchase history. It worked, but it lacked the nuance the team knew was necessary to maximize its potential without over-spending on discounts.

Working with Faraday, the team replaced manual guesswork with a predictive [adaptive discount](https://faraday.ai/templates/adaptive-discounting/segment)ing strategy powered by Faraday’s data. Instead of looking at past purchases alone, they used AI-driven propensity scores to identify which specific discounts online customers would need to redeem an offer and convert.

This effectively automated the intuition of an in-store associate:

- **Predictive targeting:** They identified the exact customers who needed a nudge to buy, rather than wasting a gift card on someone who would have purchased at full price.
- **Data-driven "soft cues":** They integrated life-event data into their clientelling workflows, giving remote sales teams a natural, data-backed reason to reach out that felt personal, not promotional.

## The outcomes: Better outcomes, lower noise

The results of this data-driven approach were immediate and clear. By provisioning their workflows with better context, the John Hardy team proved that you can drive more growth with less "noise."

| Metric                 | Result |
| :--------------------- | :----- |
| Year-over-Year Demand  | \+17%  |
| Total Redemptions      | \+25%  |
| Audience Size Increase | \+9%   |

To validate the model, the team monitored a **holdout group**—high-propensity customers identified by Faraday who were intentionally _not_ sent a gift card. Even without the incentive, this group converted at a rate of **14%**, confirming that the models were accurately identifying the brand's most naturally engaged customers. But even better, when that same high-propensity audience _did_ receive the gift card, the conversion rate jumped to **34%**. This 20-point lift proved the measurable power of the targeted offer and demonstrated that the team was successfully finding the exact customers who were ready to be moved to a purchase.

## Scaling the boutique experience

For the John Hardy team, the success of the V VIP program was only the beginning. The goal remains the same: to act smarter and faster by bridging the context gap across every channel.

As they look ahead, the team is already planning to weave these predictive insights deeper into their broader strategy—from informing **wholesale expansion** to providing daily **clientelling cues** for their global sales teams. By combining a savvy marketing vision with the right data context, John Hardy is ensuring that "boutique" remains a feeling, no matter how large the brand scales.

John Hardy used Faraday’s predictive data to tailor offers more precisely, increasing conversions and demand while reducing unnecessary discounting.

Personalization at scale: How John Hardy tailored their offers and won with Faraday

[![diagram showing how marketing stacks have changed in 2026](/images/blog/bowties-diamonds/bowties-diamonds1.png)](/images/blog/bowties-diamonds/bowties-diamonds1.png)

Something very interesting has happened with the overall topology of a cutting-edge brand’s martech stack. Over the past year or so the pattern I’m seeing among leading brands (including many of our own clients) is a shift from “bowtie”-style stacks, like you see at the left, to “diamonds” like on the right. Here’s how and why this revolution is underway.

## The 2010s martech stack: Hub-and-spoke data warehouse models

Throughout the 2010s it was fashionable (and useful) to imagine any technology-forward organization’s stack as a “hub and spoke” with the data warehouse in the middle. Every component would pull the data it needed from the warehouse and, in return, supply the warehouse with data that other components might need.

In practice this was never quite so easy. Not every software product a brand needed to actually engage its customers natively talked to the warehouse. Sometimes this was for straightforward reasons—it’s hard to build and maintain countless native connectors to every “source of truth” across every vertical—and other times the end product saw the warehouse as a threat: they wanted to own the workflow _and_ the data.

[![diagram showing hub and spoke style marketing stack](/images/blog/bowties-diamonds/bowties-diamonds4.png)](/images/blog/bowties-diamonds/bowties-diamonds4.png)

## The early 2020s: ETL tools and the bowtie marketing stack

In the end, the market spoke, and robust Extract Transform and Load (ETL) tools like [Fivetran](https://www.fivetran.com/) became the crucial connector to feed data into the warehouse. Complementary Reverse ETL tools like Hightouch served the natural inverse role: delivering data from the warehouse back to the engagement tools marketers actually use.

It’s easy to see how central this ETL/RETL role became in the stack’s topology. So much so, in fact, that the “mental model” would often place it, rather than the warehouse itself, in the middle. That’s how we got the bowties of the early 2020s.

[![diagram showing bowtie style marketing stack](/images/blog/bowties-diamonds/bowties-diamonds2.png)](/images/blog/bowties-diamonds/bowties-diamonds2.png)

## 2026 martech stacks: Middleware and the diamond data model

The centrality of the ETL/RETL position did not escape its occupants’ notice. In many cases it was the _only_ tool with visibility on 100% of all of the organization’s data. The incumbent ETL/RETL vendors started to spread their wings, offering value-added services within this prime location—see Hightouch’s decisioning product and its latest agentic offering as examples. Other vendors like [OfferFit](https://www.braze.com/resources/articles/what-is-offerfit) (now a B[raze](https://www.braze.com/) product) sprung up in this “middleware” zone, leveraging data from the warehouse to orchestrate workflows within the engagement platforms.

Faraday, of course, thrives here as well, adding context to an organization’s data on its way to the engagement tools. Products like [cube](https://cube.dev/) are starting to imagine the middleware zone itself as multi-layered, offering normalized semantics that make its neighbors’ jobs easier.

The “source of truth,” meanwhile, at the bottom of the diagram, has crystallized in my mind to a single point. As Scott Brinker so eloquently [put it](https://newsletter.chiefmartec.com/p/the-saas-moats-are-crumbling-but-the-opportunity-is-bigger), once you “strip away the features, the interfaces, the proprietary data lock-in, the ecosystem lip service,” these systems essentially resolve to the same thing: the organization as represented by _data_.

[![diagram showing modern marketing stack](/images/blog/bowties-diamonds/bowties-diamonds3.png)](/images/blog/bowties-diamonds/bowties-diamonds3.png)

## The future of martech: AI agents and constellation structures

As the agentic paradigm deepens its hold on our industry, this diagram—already a far from perfect reflection of reality—will inevitably shift again. Where it goes is anyone’s guess, but I do believe it will be far less linear. Certainly agents will orchestrate everything, but I imagine their purview will extend beyond unidirectional customer engagement. A “constellation” structure is likely to emerge, with agents gathering data from every interaction and disseminating learnings throughout the stack.

The result may look eerily familiar — a modern recreation of the hub-and-spoke structure from a decade ago. Can’t wait to see where we go from there.

Martech has evolved from warehouse-centric “hub-and-spoke” systems to ETL-driven “bowtie” stacks and now to middleware-powered “diamond” models, with the next shift likely toward AI agent–orchestrated, more fluid “constellation” architectures.

Bowties to diamonds: 2026’s new marketing stack

**Update, May 2026:** This feature has been deprecated. The ability to interact with your Faraday personas and predictions is now available in a more powerful form through our [MCP server](https://faraday.ai/docs/features/mcp), which is currently in beta and gives AI agents real-time access to your Faraday data and predictions directly. We recommend exploring the MCP as the successor to this feature.

---

In 2026, we all know that effective marketing requires personalization.

But this wasn’t always accepted as fact. Years ago, one of our earliest clients—[a major home goods brand](https://www.bollandbranch.com/)—wanted to confirm the premise for itself. So they ran a rigorous experiment to find out just how much value Faraday’s predictive [Personas](/docs/abstractions/persona_sets) could generate. They committed to a strategy that most marketing teams of that era only dreamed of: instead of sending one generic email to their entire list, they created unique content variants for every single campaign. They had their team write a generic control version, as well as three hyper-tailored versions specifically crafted for the distinct vocabularies and motivations of their unique Personas.

The results were undeniable: [A 30% lift in conversion](/stories/boll-and-branch).

By every revenue metric, the experiment was a massive success. Yet, despite the clear upside, they didn’t stick with the new system very long.

Why would a business walk away from a 30% lift? The answer is simple: because the manual labor required to understand what makes each Persona special, and then write, edit, and stage unique versions of every single email, was simply too hard and tedious to sustain. The friction of the process outweighed the lift in revenue.

This is the paradox of personalization: The more you tailor your messaging, the better your results—but the more work you have to do.

## Talk to your Personas

We built a new chat interface to solve that tedium.

Faraday has always been great at helping you understand _who_ your customers are. We have a range of tools—from [AI-generated summaries to salience trait detection](/blog/new-release-trait-salience)—that help you investigate exactly how they differ. But at the end of the day, you still have to figure out how to apply those insights and actually _talk_ to them.

Now, you actually can. Our new chat feature provides a familiar AI interface, but we preload the chatbot with all the in-depth details about your Personas. You don't have to teach the AI who your customers are. It already knows!

However, the agent does _not_ natively know anything about your brand. This is by design—it allows you to give the chat whatever context, guidelines, or quarterly goals you want so you get highly relevant outputs. You just chat!

[![Chat with personas feature in Faraday's dashboard](/images/blog/persona-chat/persona-chat-unfilled.png)](/images/blog/persona-chat/persona-chat-unfilled.png)

## Power up your marketing

This feature allows you to move from passive analysis to active execution immediately. Here is a sneak peek at a few ways you can start using it today:

- **Match Personas to campaigns:** Describe a specific product launch or holiday promotion and ask the chat which Persona in your set is most likely to respond.
- **Brainstorm tailored offers:** Identify an underperforming segment and ask the AI to suggest specific promotions or incentives that align with that Persona’s unique motivations.
- **Generate personalized copy at scale:** Paste your baseline messaging into the chat and instantly receive variants for subject lines, email body copy, and landing pages tailored to each Persona.
- **Build synthetic focus groups:** Save time and budget by asking the AI to generate a panel of virtual customers that proportionately mirrors the distributions of your actual Persona set.
- **Enable Sales roleplay:** Improve outreach by asking the agent to "act" as a specific Persona so your sales team can practice overcoming objections and perfecting their pitch.

_(Note: The chat does not currently retain memory or history between sessions, so you will want to provide your brand context in each new conversation.)_

## How to talk to your Personas

This feature is included at no additional charge for clients on our usage-based pricing (UBP) plans.

You can find the new chat feature directly within your existing Persona sets:

1. Go to a Persona set on the Dashboard.
2. Click the **Chat** tab at the top of the page, or simply add `/chat` to the end of the URL.
3. Start talking to the interface as you would with any other LLM.

[![Chat with personas feature in Faraday's dashboard](/images/blog/persona-chat/persona-chat-filled.png)](/images/blog/persona-chat/persona-chat-filled.png)

## A note on privacy and safety

We know that using AI with customer data requires trust. The chat agent **does not have access to PII (Personally Identifiable Information)**. The model operates on the aggregate patterns and statistical definitions of your Persona groups, not the private data of individuals.

## Ready to start?

The days of choosing between personalization and productivity are over. By talking directly to your Personas, you can finally operationalize the deep insights you already have, turning static data into dynamic content in seconds.

_Keep an eye out for our next post, where we will dive deep into step-by-step playbooks and actual prompts for getting the most value out of these new use cases!_

Faraday’s newest feature lets you talk to your Personas using an AI agent—preloaded with your unique Persona definitions—to instantly generate targeted marketing copy, subject lines, and campaign strategies without manual tedium.

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