"If one record is wrong, how can I trust the rest?" 

We sometimes hear this from clients, and we get why. You open a freshly enriched customer file, spot something you know is wrong — your own name at an address you left five years ago, a coworker tagged with an income that's clearly outdated — and suddenly the whole file feels suspect.

But that instinct — to judge the whole file by one bad record — is exactly the **wrong way to evaluate consumer data**. And it leads to worse decisions, not better ones.

## Spot-checking is the wrong test for data quality

First things first: spot checking for 100% accuracy is not a valid way to review data quality in a customer file.

Whether you have spotted an incorrect record in a 100k person audience for direct mail or you've identified an incorrect attribute from your customer file, the key is to go back to first principles: your ultimate goal is likely to drive an increase in conversion rates or reach more people. One bad row isn't going to spoil the bunch.No dataset at scale is 100% accurate, however, our mission is to have the best data set on the market. We rely on many upstream vendors and getting feedback on their data quality only helps us get better at our mission. 

## What to do when you spot a bad record

When you find one bad record, here’s what to do: 

- First, consider your use for the data. If the file is 95% accurate instead of 100% accurate, can you still achieve your goal?  
- Second, report it to us via [faraday.ai/data-corrections](http://faraday.ai/data-corrections). When a client flags something, the issue almost always originates with one of our upstream vendors — but because it ended up in your file, it's our job to act on it. Our team reviews every submission, escalates with the upstream vendor when needed, and applies corrections back into the pipeline. It's not a quality guarantee — it's a correction loop, and it's how the dataset stays trustworthy over time.

Our number one goal is to provide data that drives meaningful lift on your campaigns, and ultimately delivers significant ROI for your company. (Side note: we strive to be assessed by the ROI we deliver, check out some of our [case studies](/stories) to see just how much lift we generate). 

The reality is that chasing 100% accuracy at this scale would mean slower files, longer QA cycles, higher prices, and less freshness — all in service of a number that doesn't change the campaign outcome. So the right question isn't "is every record correct?" — it's "does this file help me reach more of my best customers than a random list would?"

## How to actually evaluate consumer data

If you want to know whether your file is doing its job:

* **Run a holdout test.** Pick a random subset of your enriched audience and a comparable subset without enrichment. Run the same campaign to both. The lift between them is your real answer.  
* **Look at downstream metrics.** Response rate, conversion rate, CPA, ROAS. These are the only metrics that reflect whether the file is working.  
* **Evaluate match rates in context.** Match rates depend on what identifiers you bring to the table (check out our [thoughts on this](https://faraday.ai/blog/wheres-my-99-match-rate)). Compare against realistic benchmarks, not vanity numbers.

None of these require inspecting individual rows. All of them tell you something a spot-check cannot: whether the file is actually delivering value.

## The bottom line

It’s just like life, don’t let one data point throw you off your game\! Stay focused on measuring what matters: response rate, CPA, lift versus a random list. If the numbers clear your benchmarks, the file did its job. If they don't, that's the signal worth investigating — not the one weird row that caught your eye.

When you do find a record that's off, [flag it](/data-corrections). We want to know. And if your campaign results aren't where they should be, [talk to us](/talk-to-sales). That's the conversation we want to be having.

When you spot a bad record in a 150,000-person file, your instinct is to question the whole dataset. The math says otherwise. Here's how to actually evaluate data quality.

Found a wrong record in your data? Here's what it actually means

Updated: May 2026

In [our last post](https://faraday.ai/blog/b2c-customer-data-stop-guessing-start-executing), we made the case that B2C brands are leaving value on the table because the data they have about their customers isn't complete enough to act on. Brands know what customers bought. They have a name, probably an email address and physical address. What they’re missing is the broader context — who the customers actually are, what their financial situation looks like, where they are in life — and that's exactly what's missing when campaigns underperform and outreach falls flat.

The first step to closing that gap is data enrichment: appending verified, high-quality external datapoints to your existing customer records so you're working with a complete picture instead of a partial one. That's what Faraday can do for your brand, and this post covers what data is available, how the enrichment process works, and how to get started.

## What data is available for enrichment through Faraday

Faraday's data enrichment is powered by the [Faraday Identity Graph (FIG)](http://faraday.ai/catalog) — a comprehensive, continuously maintained dataset covering **240M US consumers and their household, with over 1,500 verified, pre-cleaned, and feature-engineered datapoints.** Every record is tied to a verified consumer identity, which means the data you enrich actually belongs to the person you're trying to reach.

FIG’s datapoints cover a range of categories including:

- **Demographics** — age, gender, household income, education level, marital status  
- **Property and housing** — homeownership status, home value, roof type, garage spaces, length of residence  
- **Financial signals** — Credit Score Proxy, spending percentiles, investment behavior, debt indicators  
- **Life events** — recent move, new homeowner, new parent, marriage, retirement  
- **Lifestyle and interests** — hobbies, brand affinities, vehicle ownership, charitable giving patterns  
- **Contact and identity** — verified name, address, email, phone, household composition

We recently completed a major refresh of the entire FIG catalog as part of FIG v2, updating datapoints across every category. You can browse everything that's available at [faraday.ai/catalog](https://faraday.ai/catalog) or purchase data directly at [buy.faraday.ai](https://buy.faraday.ai).

## How data enrichment actually works

A data enrichment is the process of matching your existing customer records to FIG and appending the datapoints you select. The process is straightforward:

1. **Select your datapoints.** Choose the specific attributes that address your business question — whether that's segmenting your audience, scoring leads, expanding into direct mail, or understanding your ICP. You can select individual fields or use one of Faraday's pre-built essentials packages, curated by vertical for home services, retail and ecommerce, and financial services.  
     
2. **Match against FIG.** Faraday resolves your customer records to verified identities in FIG using a combination of name, email, phone, and address fields. This is deterministic matching — not probabilistic — so you can trust the appended data actually maps to the right person.  
     
3. **Receive enriched records.** Your data comes back in a clean, structured format ready to load into your CRM, CDP, data warehouse, ad platform, or wherever you need it.

[![Data-appends diagram](/images/blog/data-appends-diagram.png)](/images/blog/data-appends-diagram.png)

## How brands use data enrichment

Data enrichment can unlock your entire marketing strategy — [better segmentation](https://faraday.ai/blog/smarter-segmentation-for-banks), [smarter messaging](https://faraday.ai/blog/how-dtc-brands-build-living-personas-faraday-heatseeker), [new channels](https://faraday.ai/blog/match-boost-improves-enrichment) you couldn't reach before. But here are the three ways we see brands put it to work first:

**Fill contact gaps to expand your reach.** A brand that's built its entire customer relationship through email has email addresses — but no physical addresses, no phone numbers. If they want to run direct mail or a call center program, enrichment is what makes that possible. You get the missing contact fields enriched to records you already own.

**Understand your customers beyond the transaction.** Your purchase history tells you what people bought. It doesn't tell you who they are — their financial situation, life stage, household context, or what else they might be in the market for. Enrichment fills that picture in so your segmentation and messaging are based on something real.

**Research your market before you move into it.** If you're evaluating a new geography, a new channel, or a new customer segment, enrichment lets you analyze the landscape before you commit — understanding what the population looks like and where the opportunity actually is.

## Why not just find the data yourself?

Some companies attempt to enhance their marketing efforts by purchasing third-party data directly from various vendors themselves, rather than using packaged and processed services like Faraday’s data enrichment. However, this approach often presents significant challenges:

**Contracting complexity**: Sourcing offline data can involve lengthy negotiations and large upfront commitments.

**Security risks**: Handling sensitive data requires strict compliance frameworks, such as SOC-2 certification. Unlike many vendors, all Faraday data enrichment is privacy-compliant, ethically sourced, and adhere to high standards of data protection, ensuring your customers' personal information remains secure while providing actionable insights.

**Regulatory challenges**: Companies storing offline data must navigate consumer privacy laws, including opt-out and deletion requests. Faraday manages these complexities for you, so you can focus on what matters—leveraging new insights to drive revenue.

**Data science overhead**: Raw offline data often requires significant processing, feature selection, and bias mitigation before it can be effectively used. This can result in costly time and resource investments as companies try to integrate third-party data into their models.

Faraday handles all of this before the data reaches you. The trade-off isn't 'Faraday or DIY' — it's spend your team's time building and maintaining data infrastructure, or spend it running better campaigns.

## How to get started

Getting started with Faraday’s data enrichment is simple. Once you’ve identified the data points you need, we’ll work with you to integrate them into your existing systems. Whether you’re looking for a one-time data boost or ongoing data enrichment, our flexible pricing and easy integration make it simple to scale.

If you're not sure where to start, browse what's available at our new data [catalog](faraday.ai/catalog) — or talk to a [context consultant](/talk-to-sales) who can help you identify which datapoints are most likely to move the needle for your specific business before you commit to anything.

And if you just want the data today, you can find it yourself in our new self service [marketplace](http://buy.faraday.ai).

Faraday's data enrichment appends verified third-party datapoints from FIG to your customer records, filling contact gaps, deepening customer understanding, and powering smarter targeting and expansion decisions.

How data enrichment actually works with Faraday, and what data you can find

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](http://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

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) — 1,500+ 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 1,500+ 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](http://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

[![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.

What's new: Talk to Personas

We recently announced the biggest update to the Faraday platform ever: an overhauled version of the [Faraday Identity Graph (FIG)](https://faraday.ai/features/faraday-identity-graph). FIG is the foundational dataset on which all other services at Faraday are built and if you want the big picture, check out our [main announcement](https://faraday.ai/blog/coming-soon-faraday-identity-graph-version2).

But today, I want to zoom in and talk about one specific, highly anticipated aspect of FIG v2: the data catalog.

## Starting from scratch

Over the last decade, we’ve learned a lot about how to structure data, and we’ve been continuously improving that whole time. But for FIG v2, rather than continue to make incremental updates, we took those years of learnings and rebuilt the dataset from scratch—ensuring every attribute is standardized, strictly categorized, and crystal clear.

Nowhere is that rigor more obvious than in our new data catalog.

## A better data dictionary

This new data catalog goes far beyond a standard data dictionary. It features an entire dedicated page for every single attribute in FIG v2.

[![FIGv2 data catalogue](/images/blog/fig-v2-data-catalog/data-catalog.png)](/images/blog/fig-v2-data-catalog/data-catalog.png)

Historically, a lot of this highly detailed data has only existed internally at Faraday and clients were required to go through their account management team to get these details. Now, for the first time, we are publishing it so that all of our users can get direct value out of it.

[![FIGv2 data catalogue example](/images/blog/fig-v2-data-catalog/data-catalog-example.png)](/images/blog/fig-v2-data-catalog/data-catalog-example.png)

When you land on these new attribute pages, you won't just find basic metadata like names and descriptions. We are showing you the true depth of the data by surfacing the really important technical details and historical context you need to make decisions, including:

- **Data distribution:** Exactly how many people are observed in each category, or how a numerical attribute is distributed across a histogram.
- **Technical properties:** The API name, category, unit, data type, statistical type, allowed values, and deprecation status.
- **Coverage breakdowns:** Exactly how many individuals have a known value versus a value imputed from their address or geography.
- **Data derivation:** Whether a value was directly collected/observed, modeled, or survey-based — so you know exactly how each attribute was produced.
- **Time series and contribution history:** A look at how many observations we've collected over time and the authorities that contributed them.
- **Directionality and interpretation:** Guidance on how to use the data (e.g., how missing values should be interpreted—does a missing value mean "we don't know," or should you assume it's "false"?).

## Built for marketers and data scientists alike

How you use this level of transparency really depends on what you're trying to build.

**If you’re a marketer,** let's say you’re looking for a specific piece of context to personalize an email campaign. In the past, finding that exact attribute usually meant digging through a dense, traditional data dictionary or—more likely—having to reach out to your account manager and wait for an answer. Now, you can skip the middleman, search the catalog yourself, and find exactly what you need to make a campaign feel bespoke.

**If you're a data scientist**, say using Faraday's data for internal modeling, things like data type and directionality matter immensely—they have a massive impact on the resulting accuracy of the models you're building. Instead of hunting for those specs, all of those crucial technical details are now front and center.

## Easier to browse, more to discover

When you start searching through the new data catalog, you might notice the data feels much cleaner. By doing a massive amount of consolidation and normalization behind the scenes, we’ve made FIG v2 much easier to navigate. Better yet, that cleanup created headroom for us to bring in additional vendors and add entirely new attributes.

So go ahead and explore—there is a lot more context about American consumers waiting for you.

**Interested in a specific data point?** If you're looking for a particular attribute or curious about our coverage for a specific demographic, we’re happy to pull those details for you. Just send a note to your account management team directly.

## What comes next

As we noted in our [main announcement](/blog/now-live-faraday-identity-graph-version2), FIG v2 is set to officially release in April. However, we are currently looking for beta testers to get in early. If you're interested in getting your hands on the new data catalog and taking it for a spin, reach out to your account management team to learn more.

And keep an eye out—we'll be dropping more deep dives into FIG v2 in the coming weeks\!

Explore the new Faraday Identity Graph (FIG) v2 data catalog to access detailed attribute pages, visual data distributions, and technical metadata designed for marketers and data scientists.

Deep dive: FIG v2 data catalog

_Understanding what your customers truly want is the surest way to grow revenue and strengthen retention. Today’s top-performing companies use consumer data not just to sell, but to anticipate needs, personalize experiences, and build lasting loyalty. By connecting behavioral, demographic, financial, property, and transactional insights, you can design offers that feel natural and timely to each buyer. This guide explores how to define measurable goals, map the customer journey, analyze insights, and operationalize personalization strategies that scale—all within an ethical, privacy-first data framework._

## Define revenue and retention goals

Every customer-focused growth initiative starts with a clear vision of success. Revenue and retention goals define where to aim your efforts and how to measure progress.

Revenue goals address short-term and long-term performance, such as a 15% increase in average order value or reaching a specific monthly recurring revenue target. Retention goals measure how well your company maintains relationships over time. Customer retention—the percentage of customers you keep in a given period—is one of the most reliable profit levers. Even modest retention uplifts can dramatically improve profitability.

When setting objectives, align your team around practical metrics:

- Reduce churn to below a defined benchmark.

- Increase repeat-purchase rate quarter over quarter.

- Expand customer lifetime value through product bundles or upgrades.

These KPIs keep teams anchored to the outcomes that matter most for sustainable growth.

## Map the customer journey and buying cycle

A mapped journey turns vague assumptions about buyers into actionable knowledge. By visualizing how prospects move from first discovery to loyal advocacy, you can optimize engagement and remove friction.

Typical buying cycle stages include:

| Stage         | Objective               | Key behaviors to track                 |
| ------------- | ----------------------- | -------------------------------------- |
| Awareness     | Capture attention       | Visits, clicks, ad engagement          |
| Consideration | Educate and build trust | Resource downloads, demo requests      |
| Purchase      | Convert interest        | Checkout completions, time to purchase |
| Retention     | Strengthen loyalty      | Repeat orders, subscription renewal    |
| Advocacy      | Encourage referrals     | Reviews, referral program activity     |

Analyzing behavior at each step uncovers where people hesitate or drop off. Behavioral segmentation—grouping users by observed actions and timing in the cycle—lets marketers deliver precise messages and incentives to move them forward.

## Select frameworks to structure customer insights

Frameworks provide structure and repeatability for translating data into strategy. Among those most widely used:

- **AARRR (Pirate Metrics)** – Focuses on Acquisition, Activation, Retention, Revenue, and Referral. Useful for SaaS and growth-stage businesses.

- **STEPPS** – Emphasizes viral triggers: Social Currency, Triggers, Emotion, Public, Practical Value, and Stories. Ideal for campaigns seeking organic reach.

- **Lean Analytics** – Guides companies through Empathy, Stickiness, Virality, Revenue, and Scale stages to measure each growth phase before scaling.

| Framework      | Primary focus          | Best use case                    |
| -------------- | ---------------------- | -------------------------------- |
| AARRR          | Conversion and revenue | Subscription models              |
| STEPPS         | Shareability           | Social or community-led products |
| Lean Analytics | Iterative growth       | Product development cycles       |

Choosing the right model brings consistency and clarity to how customer data becomes actionable insight. Faraday plugs directly into these frameworks, turning first-party datasets into clear, measurable predictions that guide each stage of growth.

## Collect mixed-methods customer data

Strong insights come from blending numbers with narrative, with a foundation in behavioral, demographic, financial, and property data. Mixed-methods research combines quantitative and qualitative approaches to reveal not just what customers do, but why they act that way.

**Quantitative methods** include analytics dashboards, transaction histories, and heatmaps that pinpoint where visitors linger or exit. **Qualitative methods** go deeper through open-ended surveys, interviews, and focus groups that surface drivers, barriers, and preferences.

| Data type            | Example tools                     | Key use                                     |
| -------------------- | --------------------------------- | ------------------------------------------- |
| Behavioral           | Faraday, Hotjar, Google Analytics | Identify engagement patterns                |
| Feedback             | Typeform, SurveyMonkey            | Measure satisfaction and sentiment          |
| Social and sentiment | Brandwatch, Idiomatic             | Discover emerging themes and market signals |

Blending these perspectives enables richer, data-driven stories about your customers—stories that turn insights into real business actions.

## Analyze data to identify high-impact opportunities

Collecting data is only the first step; the real value emerges in the analysis. Key Driver Analysis helps determine which factors most influence loyalty or satisfaction. Combine this with health scoring and topic modeling to isolate high-impact issues.

A simple workflow might include:

- Aggregate metrics from analytics, survey, and CRM sources.

- Model correlations between behaviors and revenue outcomes.

- Prioritize interventions by potential impact on lifetime value.

- Share results through dashboards and internal briefings to accelerate adoption.

Effective analytics programs emphasize clarity and ease of use. Faraday’s predictive modeling enables business teams to surface high-value opportunities quickly—without requiring heavy technical support—driving faster, more measurable ROI.

## Run controlled experiments and measure impact

Data-driven experimentation removes guesswork from growth. Controlled tests—like A/B experiments—compare one change at a time to reveal what truly affects conversion.

A repeatable process ensures learning compounds:

- **Form a hypothesis:** “If we personalize onboarding emails, conversion from trial to paid will rise.”

- **Run the test:** Split traffic between control and variant groups.

- **Measure metrics:** Compare conversion, average revenue per user, or retention over time.

- **Document learnings:** Record tactics that work and scale them to other segments.

Cross-functional alignment matters here. Product, marketing, and sales teams should share an experimental mindset anchored in the same revenue and retention KPIs.

## Operationalize learnings into customer-facing workflows

Turning insight into repeatable practice is where strategy becomes growth. Operationalizing means weaving customer knowledge directly into your workflows so teams act on insights automatically.

Practical methods include:

- Embedding trigger-based actions in your CRM or marketing automation platform.

- Creating digestible knowledge hubs that summarize research and feedback.

- Setting automated alerts that prompt sales or success teams to act on key signals.

- Providing training modules so employees know when and how to apply customer data.

Leading brands like Amazon, Nordstrom, and L’Oréal excel at this: they translate insights into real-time personalization that keeps customers engaged. Faraday helps operationalize this same principle by integrating predictive intelligence directly into existing sales and marketing systems for immediate, scalable action.

## Personalize marketing outreach effectively

True personalization makes each customer feel understood rather than targeted. It uses enriched profiles, predictive modeling, and timing analysis to deliver messages that resonate.

A simple personalization workflow:

- Define the audience segment.

- Use predictive scores to rank by likelihood to convert or renew.

- Tailor creative and offers across email, SMS, direct mail, or ads.

- Automate trigger-based delivery through your marketing platform.

- Measure engagement and iterate.

Faraday makes this personalization process practical and scalable by unifying consumer data, enriching profiles, and providing reliable predictive scores—all in real time, with privacy compliance built in. The result: outreach that consistently drives revenue and strengthens loyalty.

## Frequently asked questions

### What are the best survey questions to understand customer satisfaction?

CSAT and NPS questions such as “How satisfied are you with our service?” and “How likely are you to recommend us?” quickly gauge loyalty and overall experience.

### How can open-ended questions reveal deeper customer needs?

They surface drivers, barriers, and preferences that quantitative metrics miss, revealing what most influences purchasing decisions.

### What techniques help prioritize which customer insights drive revenue?

Key Driver Analysis and predictive scoring with Faraday highlight the factors most tied to satisfaction and growth potential.

### How do customer interviews improve marketing personalization?

Interviews add human context to behavioral data, helping marketers design offers and messages that feel relevant and trustworthy.

### What metrics should I track to measure retention and revenue growth?

Track retention rate, churn, customer lifetime value, revenue per user, and expansion revenue for a complete profitability view.

---

By defining smart goals, analyzing data with precision, and personalizing customer interactions at scale, businesses can align every touchpoint to actual human needs. The companies that succeed in learning exactly what their customers want are the ones that grow faster—and keep growing.

Move beyond guesswork by mapping customer journeys, leveraging proven growth frameworks, and using predictive data to deliver the personalized experiences your buyers actually want.