[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

In 2026, we all know that effective marketing requires personalization. Faraday enriches leads with contextual data so you can personalize every interaction and drive conversion.

Years ago, one of our earliest clients wanted to confirm this premise for themselves. Instead of sending one generic email to their entire list, they created unique, hyper-tailored content variants specifically crafted for the distinct vocabularies and motivations of their unique Faraday personas. The results were undeniable: a 30% lift in conversion. 

Yet, despite the clear upside, they didn’t stick with the new system very long. Why? Because in order to actually use personas to personalize, you have to understand what makes each Persona special and then produce personalized materials. Doing both of these things manually is incredibly hard and tedious. 

This is the paradox of personalization: The more you tailor your messaging, the better your results—but the more manual work you have to do. Our clients want ROI through a lift in engagement, leading to better LTV and/or CAC, but the friction of execution often gets in the way.

We built our new "Talk to your personas" chat interface to solve that exact paradox. By preloading an AI agent with the in-depth, statistical details of your Persona distributions, we've made it possible to move instantly from passive data analysis to active content generation. 

**Pro-tip for ultimate creative control:** To ensure your outputs are always perfectly aligned with your current objectives, the agent operates as a fresh slate in every session. It relies solely on the deep, mathematical truths of your Persona sets rather than making outside assumptions about your brand. This gives you total freedom. Just drop in your current brand guidelines, your quarterly goals, or the specific dilemma you are facing today, and the AI will adapt its answers to your exact context.

Ready to start? Here is your tactical playbook for turning customer insights into action.

## Campaign matchmaking

Sometimes you know exactly what you want to sell, but you aren't sure who wants to buy it. When you are running a promotion for a specific product line, a new category, or a holiday sale, you need to know which group of customers will actually care.

Instead of relying on a hunch, you can lean on the AI to interpret the Persona data for you. Describe the campaign and the situation to the chat, and simply ask which Persona would be most receptive to this promotion. The AI looks at the underlying data distributions to match your product's value proposition with the group that mathematically shows the highest affinity for it.

## Offer brainstorming

On the flip side, you might have a specific segment of your audience that is underperforming. Maybe a Persona is smaller than you want it to be, or you feel like your recent campaigns just haven't pushed their buttons.

You can use the chat to brainstorm what kind of promotion that specific Persona would react well to. Instead of guessing what might incentivize a stubborn segment, the AI can cross-reference their salient traits to suggest uniquely tailored offers or messaging angles based on their known motivations.

## Producing personalized materials at scale

Writing one great email is hard. Writing four distinct variations of that same email to appeal to different subgroups is agonizing. Usually, the friction of this process outweighs the lift in revenue. 

Once your campaign and targeting have been decided, you can paste your baseline materials into the chat and ask the agent to adapt the copy so that it appeals specifically to your targeted personas. This workflow allows you to instantly spin up personalized positioning, subject lines, email/SMS copy, and landing page copy without the manual tedium. 

## Running synthetic panels

Brands have been running focus groups forever to test messaging, but hiring an agency to build a representative panel is incredibly expensive and takes a lot of time. With this feature, you can run a highly accurate focus group right from your dashboard by "faking it" in chat.

Tell the agent to create a synthetic panel that is proportionately allocated to your personas. The AI will generate incredibly detailed, mathematically representative fake customers—complete with names, ages, and backstories—that you can interview in real-time to test your ideas.

**Try this exact prompt:**
> *"I want to create a synthetic panel (focus group) so that I can ask questions. Please create 10 synthetic panelists, proportionately allocated to my personas. Invent a name, demographic details, and backstory for each one. Then I want to be able to ask panelists separately or altogether questions."*

## Sales training and roleplay

Marketing isn't the only team that benefits from understanding customer personas. Sales teams need reps who can pivot their pitches based on who they are talking to. New reps, or veteran reps with ingrained biases, need to respect the diversity of the customer base. 

You can tell the agent to act as a member of a specific Persona and use it as a sparring partner for your sales pitches. The AI will adopt a highly specific Persona and allow your reps to practice their pitch by asking what motivates their decisions, what might block a purchase, or what competitors they are looking at. 

**Try this exact prompt:**
> *"I'm a new sales rep for this brand ([Insert your brand], please look it up so you know what we do) and want to get a better handle on what motivates my customers. Can you choose one of our personas and invent a representative customer that belongs to it, with a name, demographic details, and backstory? Then you can act as that person and I can ask you questions about your perspective and motivations with regard to our brand."*

***

## Bridge the gap between insight and action

There isn't "one perfect analysis" that can illuminate all the different ways to use your personas. The true value of your customer data is unlocked when you start using it to drive your daily decisions and content creation. 

By talking directly to your personas, you completely remove the friction of personalization. You no longer have to choose between a high-converting, hyper-targeted campaign and your own productivity. 

Head to your Faraday dashboard, open a Persona set, and start chatting today.

A tactical playbook for using Faraday's new chat feature to generate targeted campaigns, run synthetic focus groups, and roleplay sales scenarios.

Turn your Personas into a marketing engine with "Talk to your Personas"

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.  
* **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](https://faraday.ai/blog/coming-soon-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.

Getting inside the FIG v2 data catalog

Even when top-line revenue looks healthy, a shrinking active customer base is a major warning sign. In today's direct-to-consumer landscape, where customer acquisition costs are at an all-time high, you cannot afford to have a leaky bucket.

A major DTC children's clothing company recently faced this exact dilemma. Like many established retailers, they possessed solid historical transaction records and had just rolled out a new marketing automation platform to power their engagement. Despite these massive investments, getting one-time buyers to come back was proving incredibly difficult.

The missing link? They fell into the classic "empty CRM" trap. They knew exactly what was purchased, but they didn't truly understand the people behind the purchases. Without a clear, multi-dimensional picture of the individual, even the most sophisticated automated outreach falls flat.

## The fix: foundational data enrichment

Before diving into complex predictive modeling or black-box AI, the organization took a step back to repair its data foundation. You can't personalize a customer journey if your database only consists of an email address and an order number.

They collaborated with Faraday to safely append external consumer intelligence to their internal records. This foundational data enrichment meant looking beyond the transaction and attaching the most relevant third-party demographic, psychographic, and lifestyle datapoints to their existing profiles.

By organizing these newly acquired details into distinct clusters, the brand successfully mapped comprehensive personas onto their audience. This granted the marketing team immediate, actionable visibility into the real people making up their market—shifting their perspective from "order #12345" to a complete view of a household's life stage and purchasing behaviors.

## A clear view of their customers

While the ultimate objective is always revenue growth and repeat purchases, the immediate victory here was absolute clarity.

This data enrichment proved vital for internal reporting, empowering leadership to finally comprehend the diverse segments within their ecosystem. Instead of treating all one-time buyers as a monolith, they could now diagnose the root cause of their retention struggles. They could clearly see the difference between a price-sensitive shopper who only converts during a major sale, and a high-value loyalist who simply needs a nudge about a new product line.

By establishing this identity-level understanding first, the marketing team is now fully unblocked to design highly personalized customer journeys. This solid groundwork acts as the perfect launchpad for their upcoming advanced initiatives, paving the way for dynamic product suggestions, highly tailored promotional strategies, and adaptive discounting.

The takeaway is simple: you cannot create personalized experiences at scale if you don't know who you are communicating with.

Want to learn more about how data enrichment could improve your retention? [Let's walk through it together](/talk-to-sales).

A DTC children's apparel brand used Faraday's data enrichment to integrate identity-level insights into their CRM, unblocking their predictive personalization strategy and gaining clarity on their customer base.

How one DTC apparel brand proved identity-level context is the key to unlocking retention

We’re excited to share a major product update that Faraday will be rolling out next month.

You’re probably familiar with the [Faraday Identity Graph (FIG)](https://faraday.ai/features/faraday-identity-graph), a collection of consumer profile attributes that we curate and normalize from leading data vendors and regularly refresh with new data. This data sits at the heart of Faraday’s platform: whether you use Faraday for predictions, appends, insights, or some combination, you’ve been using FIG all along.

Now we’re preparing to release **FIG v2**, the largest update we’ve ever made to our consumer data system.

More than just fresh data, FIG v2 represents a complete rebuild of the infrastructure that powers our consumer data. The new system introduces capabilities many of our customers have asked for while making the data easier to understand, deploy, and operationalize across marketing and data science teams.

Here’s a preview of what’s coming:

### 1. Fully redesigned collection of 1,500+ attributes 

We’re using more expressive and consistent names, and we’ve consolidated unwieldy groups of related attributes into harmonized replacements, making it easier for internal teams to operationalize data and build models faster. We’ve also added hundreds of new details along the way to provide richer consumer signals, ultimately driving better prospect targeting and higher marketing ROI.

### 2. Transparent data catalog

We're launching a new data catalog with more detailed descriptions and better clarity to enable **you** to find the right attributes for your needs. This data documentation enables faster experimentation and model deployment. For an early example, check out the version of the catalog we built for our current [FIG v1](https://faraday.ai/catalog).

### 3. More frequent updates

Our new infrastructure allows us to deploy fresh data from vendors as soon as we receive it, without waiting for scheduled monolithic releases. Fresher data reduces stale targeting and inefficient campaigns. That way you’ll always have the most current data possible for your models and appends so you can react and engage in real time for more timely engagement throughout the customer lifecycle. This is especially important for “trigger” data like recent movers.

### 4. Historical data

Faraday is surfacing the entire history of each datapoint for each person in our graph. Consider this: your own consumer data profile *today* is very different from what it was just before you bought a house, had a child, or graduated from university. Until now, you’ve only been able to get “current day” data from Faraday. Because FIG v2 uses a person's entire data history, models will be more predictive and able to recognize when consumers are moving closer to purchase. Appends will also be more relevant, and outputs will be ML-ready for your data science team, ultimately driving higher acquisition and conversion rates, alongside better forecasting and lifecycle planning.

### 5. Release pinning

We’ve spoken with many of you who are excited to be always receiving the most up-to-date FIG data. But some clients want to have more control over their refreshes. That’s why we’ll be allowing you to “pin” your account to a specific date, ensuring organizations can adopt new data updates on their own timeline without disrupting production systems. Subsequent FIG releases won’t be applied to your account until you move your pin date out, providing operational stability and allowing leadership teams to plan growth strategies with confidence while maintaining compliance and model consistency.

## Availability and beta access

FIG v2 will enter beta soon, and we’re currently looking for customers interested in early access. If you’re curious, talk to your account management team.

General availability is planned for **April**, at which point our client services team will work with customers to plan their transition. Organizations that prefer to remain on FIG v1 initially will have time to do so.

FIG v2 represents nine months of work across the entire Faraday team, and we’re excited to begin rolling it out. If you'd like to learn more about how this will impact your account, reach out! We can't wait to tell you more.

Faraday Identity Graph v2 is a complete rebuild of Faraday’s consumer data system, introducing redesigned attributes, historical datapoints, faster data updates, and new tools to make consumer data easier to discover, deploy, and operationalize.

Coming soon: Faraday Identity Graph v2

AI agents are reshaping how brands interact with consumers. Shopping assistants, recommendation engines, agentic marketing tools—all of them promise to deliver personalized experiences at scale. But there's a fundamental problem: personalization requires context, and context requires identity. For the vast majority of your website traffic, you have neither.

Roughly 95% of website visitors are effectively anonymous. Safari's Intelligent Tracking Prevention, Firefox's Enhanced Tracking Protection, Chrome's evolving privacy restrictions, widespread ad blocker adoption, and incognito browsing have collectively dismantled the cookie-based identity model that personalization has historically relied on. Without cookies, there's no persistent identifier. Without an identifier, your agent has no idea who it's talking to—and falls back to the same generic experience it would serve anyone else.

This is the bottleneck for personalized agentic experiences. It doesn't matter how sophisticated your agent is if it has zero context about the person it's serving. The agent needs to know _something_ about the visitor—what they're likely interested in, how price-sensitive they are, whether they're a first-time browser or a high-intent buyer—and right now, for almost all of your traffic, it knows nothing.

### Pixel providers: resolving anonymous visitors

A growing category of tools—often called pixel providers—directly addresses this gap. The concept is straightforward: you install a lightweight JavaScript snippet in your site's `<head>`, typically via Google Tag Manager or a platform-specific integration like a Shopify app. The pixel fires on every page load and resolves anonymous visitors to a known identity—email address, name, mailing address—using a combination of device fingerprinting, IP intelligence, and the provider's own identity graph.

Numerous providers operate in this space ([reach out to ask about our recommended partners](/talk-to-sales)), each with their own identity graph and installation flow. The specific mechanics vary—some offer configurable collection rules to filter for high-intent visitors before resolution, others resolve on every page view—but the output is the same: a resolved identity record delivered to your systems via webhook, API, or warehouse sync.

### Adding consumer context with Faraday

A resolved identity is a critical first step, but an email address alone doesn't tell your agent much. To truly personalize, you need rich consumer context: demographic and lifestyle attributes, purchase propensities, persona classifications, predicted next-best-offer. This is where Faraday comes in. Faraday takes the resolved identity from your pixel provider and enriches it against the [Faraday Identity Graph](https://faraday.ai/blog/series/fig)—returning 1,500+ consumer attributes alongside custom predictive scores you've configured (propensity to convert, persona membership, cohort affinity, and more) via the [Lookup API](https://faraday.ai/docs/features/lookup-api).

![Batch and real-time workflows both work](/images/blog/pixel-providers-agentic-personalization/workflow.png)
_Batch and real-time workflows are both available_

The combination of pixel-based identity resolution and Faraday's consumer context enrichment creates a complete pipeline: anonymous visitor → known identity → rich, actionable context → personalized agentic experience.

### Option A: batch enrichment

The batch path is ideal when you're running scheduled enrichment, building audiences, or doing offline analysis.

Your pixel provider delivers identified visitor records—email, name, address—to your data warehouse (Snowflake, BigQuery, Redshift, Postgres, S3, or similar). You connect that warehouse to Faraday as a [data source](https://faraday.ai/docs/abstractions/datasets). Faraday resolves the identities against FIG, enriches each record with consumer attributes and predictive scores, and [deploys](https://faraday.ai/docs/abstractions/deployments) the results back to your warehouse or to downstream systems via managed connections.

From there, your agent applications query the enriched data directly from the warehouse. Because the enrichment happens on a schedule, this patyh works well for use cases where near-real-time isn't critical: bulk audience segmentation, periodic model retraining, cohort-level analysis, and offline campaign planning.

### Option B: real-time enrichment

The real-time path is where things get interesting for agentic experiences that need to personalize on the fly.

The pixel fires on page load, and the provider resolves the visitor to a known identity in real time—typically within seconds of the page rendering. Your application backend immediately calls Faraday's Lookup API (`POST /v1/targets/{target_id}/lookup`) with the resolved PII (email, address, phone + last name, or any combination). Faraday returns the full enrichment payload—propensity percentiles, persona classifications, cohort membership, FIG attributes—in milliseconds.

Your agent now has everything it needs. A shopping assistant can recommend products based on predicted category affinity. A retention tool can calibrate discount depth based on price sensitivity scores. An agentic marketing platform can select the optimal channel and message for a given persona. All of this happens while the visitor is still on the page, transforming what would have been a generic interaction into a contextually rich, personalized experience.

### Getting started

To get this workflow started, reach out and [talk to sales](/talk-to-sales). Or, if you'd like to get moving immediately, you can [sign up for Faraday](https://faraday.ai). Our [documentation](https://faraday.ai/docs) covers the full setup for both batch and real-time integration paths.

AI agents need consumer context to personalize, but most website visitors are anonymous. Pixel providers resolve anonymous traffic to known identities -- and Faraday enriches those identities with 1,500+ attributes and predictive scores so agents can deliver real-time, personalized experiences.

How pixel providers unlock consumer context for AI agents

*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.

Boost revenue by learning exactly what your customers want

*Faraday automatically groups your customers into distinct, actionable subgroups based on shared traits. To ensure your segments are highly useful, we dynamically scale the number of Personas based on your total audience size. This gives you the perfect level of granularity for personalized marketing.*

In 2026, marketers know that a generic message is a campaign killer. Your buyers are unique. Blasting your entire list with the same offer won't win the deal anymore. In this context, the question is: how do you effectively segment a massive, messy audience into groups you can actually talk to? And the answer is generally by building customer [Personas](https://faraday.ai/docs/abstractions/persona_sets).

But how many Personas do you need? If you have a customer list of 1,000 people, organizing them into a few distinct personas makes sense. But what if your list is 100,000 people? Or 10 million?

If you are still squeezing a massive, diverse audience into those same basic Personas, you aren't personalizing at all. You are just generalizing at a slightly more granular scale.

## The problem with traditional Persona clustering

Historically, many data scientists have used a technique called the “elbow method” to help decide how many personas (or clusters) to create. The method tests different numbers of groups—one, two, three, four, and so on—and measures how tightly each customer fits within each grouping. As you add more clusters, the fit improves, but the improvement slows over time; each additional cluster helps a little less than the one before it. The “elbow” is the point where adding another group no longer meaningfully improves the separation.

In theory, that elbow represents an optimal number of personas.

In practice, real customer data rarely produces a clean, obvious elbow. Human behavior overlaps. Traits blend together. Instead of a sharp bend in the curve, you often get a gradual flattening. When that happens, the algorithm generally defaults to a standard output: three clusters. And often, three is not what you need

## The myth of the "true" persona

The elbow method breaks down because it is optimized to find a mathematically correct answer. It is hunting for what data scientists call “true clusters”.

But when our product team talks to clients, we often say: “In marketing there is no such thing as correct personas, only useful personas”.

For a mathematically true persona to exist, your audience data needs a massive, undeniable gap. For example, if your brand sells two entirely unrelated things—like skateboards for teenagers and mobility aids for seniors—your buyers will naturally split into two separate demographic groups. The math easily finds that gap. But if your groups are that distinct, you probably didn't need clustering tools to point them out to you at all.

For almost every other brand, human data is messy. Your customers have overlapping traits and nuanced behaviors. Because those massive gaps don't exist, the elbow method gets confused and tends to lump everyone into the default number of buckets: three. But often, three personas is not what you actually need.

## Segmenting for usefulness, not perfection

To build personas that drive value, we realized we needed to stop chasing mathematical perfection and start optimizing for utility.

Instead of relying on an algorithm that forces arbitrary limits, Faraday has rewritten the rules. We now dynamically scale your persona count based directly on the size of your audience. Using a proportional sizing model, Faraday now assigns a persona count ranging from a minimum of 2 up to a maximum of 8 based on the volume of your cohort.

Here is why this changes the game for your campaigns:

* **Granularity that scales with you:** An audience of 1,000 yields 3 personas. 10,000 yields 4\. 100,000 yields 5\. Massive lists finally get the rich segmentation they deserve.  
* **Characteristics shine through:** Because large audiences are no longer crammed into three tiny boxes, the unique traits that define your buyers become visible and actionable.  
* **No more micro-segments:** Conversely, smaller cohorts will never be artificially chopped into too many tiny, useless personas. Your groupings stay distinct and easy to manage.

Marketing isn't about hitting an arbitrary number of segments. It's about finding the best way to speak directly to your customers' unique characteristics. With proportional personas, you have the exact right number of segments to make your next campaign your highest-converting one yet.

**Ready to see your new segments?** Log in to Faraday to explore your dynamically scaled persona sets today, or read the updated docs for more details on analyzing your audience.

Faraday has replaced traditional persona clustering with a dynamic scaling model that automatically adjusts the number of personas based on audience size, ensuring marketing segments are always granular enough to be actionable without becoming overly complex.

All posts

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

Bowties to diamonds: 2026’s new marketing stack

Turn your Personas into a marketing engine with "Talk to your Personas"

What's new: Talk to Personas

ChangelogSee all

Talk to your Personas now available

Proportional sizing for persona sets

Improved invite experience for account members

Email verification added for Dashboard access

Getting inside the FIG v2 data catalog

How one DTC apparel brand proved identity-level context is the key to unlocking retention

Coming soon: Faraday Identity Graph v2

How pixel providers unlock consumer context for AI agents

Boost revenue by learning exactly what your customers want

Smarter segmentation: why we rewrote the rules on Persona sizing