American Standard's contact center was making 20,000 dials a day and reaching almost no one — a contact rate of just 5.56%. After partnering with Faraday and outboundIQ, that number climbed to 20%, and high-score leads converted at 3x the rate of others. Here's how it happened.

When [Eric Kozak](https://www.linkedin.com/in/eric-kozak-11b32550/) returned to [American Standard](https://www.linkedin.com/company/lixil-global/), I heard through the grapevine about his skepticism for lead scoring. And as the "lead scoring" vendor for American Standard, that wasn't exactly the signal I was hoping to receive.

But when we finally sat down to meet, I could tell immediately that I was talking to a guy who knew his stuff. He wasn't skeptical because he thought lead scoring "didn't work". He was skeptical because he thought it was just a simple filter, a "lead blocker", that couldn't do anything else.

And right out of the gate he made this position clear, saying something like: "I get that you can reject leads, but simply rejecting leads isn't really that valuable to us."

But I didn't see this exchange as a problem. In fact it presented a big opportunity. **Eric was a leader with a vision for a totally overhauled customer journey at American Standard, and Faraday was the tool he needed to achieve that goal.**

Fast forward to Q4 2025… Eric and I sat down again. This time with a different story to tell. Eric joined me, our CEO, [Andy Rossmeissl](https://www.linkedin.com/in/rossmeissl/) and [Jason Shatzkamer](https://www.linkedin.com/in/jshatzkamer/) from [outboundIQ](https://www.outboundiq.com/) to walk through exactly what American Standard (powered by Faraday and outboundIQ) did to transform American Standard's outbound process and improve their lead to sale conversion rates by 3x.

But before we get into the results, we need to understand where things started. To see why a 3x lift mattered, you first have to see just how broken the contact center was when Eric returned.

## Inside the silent contact center

_"When I walked through the contact center, it was astounding, the silence that I was hearing. We were making twenty thousand dials a day, but we weren't making contact with anyone."_

On the panel, Eric described returning to American Standard and being pleased to see that the company had grown. But he also noted one part of the operation that had not kept pace: the contact center needed immediate attention.

It did not take long for him to rule out the usual suspects. This was not a staffing problem, and it was not a demand problem. It was a contact problem. A severe one.

**At the time, their contact rate sat at just 5.56%.**

Eric needed answers, so one of his first calls was to [Jason](https://www.linkedin.com/in/jshatzkamer), outboundIQ's CEO. Jason quickly pinpointed the problem: American Standard was optimizing for calls, not conversations.

On paper the team looked busy, making thousands of dials a day, but way too few of those dials turned into connections. The silence Eric heard on the floor wasn't about effort or volume; it was about contact. The tires were spinning, sure, but the car wasn't moving.

Jason had a solution — and once the system was tuned to actually reach customers, the difference on the floor was immediate.

## Then the problem shifted

The dialing fix worked, but it uncovered the next issue. Conversations were finally happening, but conversion rates were not rising with them. The routing system was now healthy, but the logic behind it was still running on basic filtration that relied entirely on limited first party data.

As Eric dug into the numbers, something clicked. The team finally had the throughput they needed, but they did not have the intelligence to match it. **The system could move leads around, but it could not tell anyone who was actually likely to buy.** The more he optimized the routing, the more obvious it became that the data at the center of it all needed to get smarter.

## Lead scoring isn't just a filter

This was the problem Eric was grappling with when we met. He did not need a tool that rejected leads at the top of the funnel. He needed to know how to route them intelligently at every stage of the process. And from his perspective, lead scoring did not solve that problem.

But I knew this was exactly where we thrive, so I wanted to understand where the disconnect was happening. After a quick look at the setup, the answer became clear.

Sure, the Faraday integration existed in their tech stack, but none of the deeper consumer context was turned on. No behavioral signals. No financial indicators. No household insights. And because of how the integration had been structured, Faraday was indeed only acting as a filter at the very top of the funnel. In practice we were functioning exactly how he suspected. We were a blocker and nothing else.

What Eric hadn't seen yet was the depth of context sitting behind that integration. Faraday's lead scores are built on the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) (FIG) — over 1,500 consumer data points spanning 240M U.S. adults and their households. That's the difference between knowing a lead's ZIP code and knowing their likely purchasing power, life stage, and propensity to convert. The data to power intelligent routing was already there; it just wasn't switched on.

<Pullquote>But Faraday is not a lead blocker. We're a lead optimizer.</Pullquote>

I talked Eric through this paradigm shift and explained that in this use case, the real value Faraday provides is using consumer context to power the routing system outboundIQ had created. High fit leads should rise to the top. Mid tier prospects should move into the right workflows. Lower fit records should shift into slower nurture paths instead of taking up agent time. Smart routing only works when it is powered by smart data. And yes, we can reject the worst leads before purchase, but that is just the tip of the iceberg.

Once we walked through how the lead score could prioritize instead of reject, Eric's skepticism had dried up and was replaced with excitement, he was ready to test it out\!

## Real results

When we circled up with Eric and Jason to hear how the test had gone, the numbers spoke for themselves:

- **Contact rate lift:** Contact rate increased from 5.56% to 20%
- **Conversion rate:** High Faraday score records converted at 3x the rate of others

Nothing had changed about their leads, the only thing that had changed was getting more context to power smarter dialing and routing. Transformational results.

Beyond the top-line numbers, Eric described the shift on the floor as well. When the contact rate increased, the energy changed immediately. As he described it:

_"The energy just goes through the roof… the people are encouraging each other. That unquantifiable morale boost is critical for a contact center… Turnover is down. The momentum is back. People have wins again, and that changes everything."_

This is what it looks like when your stack finally works the way it should. It might not appear on a dashboard, but operators feel it instantly. Real success is not just about the numbers. It is about creating a productive, motivated, and winning environment. You know how to hire the right people, but you need to give them the right tools to succeed too.

## Strategic recommendations for making your contact center actually work for you

If you want to bring the same lift American Standard saw into your own organization, start with the foundation: if your contact rates are struggling before you ever get to lead scoring, that's a dialing and routing problem first — and partners like outboundIQ specialize in exactly that. Get that right, then layer in intelligence.

### 1. Layer consumer context into 1 (or 2) points of your customer journey

Rule-based filters and crude sorting cannot keep up with modern compliance pressure or consumer behavior. Use a custom-built lead score at two points in your customer journey:

- If you are struggling with poor lead quality: add the lead score into ActiveProspect (read our article about how to do it [here](/blog/dtc-homeservices-uses-credit-score-proxy))
- If you are struggling with low conversion rates: add a lead score or credit score proxy to help reimagine how you prioritize leads in your contact center.

A lead score is simply a fancy data point that gives your dialer and CRM the intelligence they were designed to use. It's powered by the same consumer context in the Faraday Identity Graph — the property, demographic, financial, and life-event signals that tell you who's actually likely to buy. Obviously I'm partial to Faraday, but there are others out there. Find someone who can build a model quickly and iterate. If you can't refine the model quickly, you are going to think this process isn't working. It's the wrong conclusion, good modeling takes time and iteration.

If you run a home services contact center specifically, we've written a deeper playbook on where to layer predictive data into your funnel — from rejecting junk leads before purchase to qualifying appointments with financial signals. [Read how home services call centers stop burning money without lead scoring.](https://faraday.ai/blog/homeservices-call-centers-need-lead-scoring)

### 2. Measure everything

Your business is too critical for suffering through the gauntlet of making crappy tools work. If you simplify your tech stack, you can actually measure what matters: the current conversion rate at every stage of the funnel. When high-score leads rise to the top, reps focus their time where it matters, credit declines shrink, and your entire stack becomes more effective without adding new complexity.

## Bringing it all together

American Standard did not solve their problem by dialing harder. They solved it by putting their customers at the center and building a tech stack around figuring out how to reach the right people at the right time. Understanding which records were worth calling and focusing on those ones first enabled them to transform their journey.

[You can hear Eric, Jason, and Andy walk through this transformation in their own words.](https://faraday.ai/blog/smarter-data-dialing-overhauled-american-standards-contact-strategy)

## Frequently asked questions

**If the leads didn't change, why did conversion improve so much?**

That's the key insight from American Standard's story. The leads were the same — what changed was the context layered on top of them. By turning on Faraday Identity Graph signals (purchasing power, household attributes, propensity to convert), the routing system could finally tell which records were worth prioritizing. High-fit leads rose to the top of the queue instead of getting lost in the volume, so reps spent their time where it actually paid off.

**What's the difference between lead blocking and lead optimization?**

Lead blocking is a top-of-funnel filter: reject the worst leads before you pay for them. That's useful, but it's only the tip of the iceberg. Lead optimization uses the same predictive score across the *entire* funnel — prioritizing high-fit leads, routing mid-tier prospects into the right workflows, and shifting lower-fit records into nurture paths instead of burning agent time. Same data, far more value.

**How long does it take to see results?**

It varies by data volume and how many points in the funnel you're scoring, but American Standard saw a measurable lift once the deeper FIG context was switched on and routing was tuned around it. The bigger factor is iteration — good modeling takes a few cycles to refine. If you expect a perfect model on day one and bail when you don't get it, you'll draw the wrong conclusion.

**Does this only work for home services or contact centers?**

No. The contact-center framing is where American Standard saw it, but the underlying principle — using consumer context to prioritize instead of just reject — applies anywhere you're ranking leads or customers. That said, if you do run a home services funnel, we've mapped out the specific plays in our [home services lead scoring guide](https://faraday.ai/blog/homeservices-call-centers-need-lead-scoring).

## Ready to make your contact center work harder?

If you want to chat through how this could transform your organization, [talk to a Context Consultant](/contact). And if you'd rather dive in and start scoring leads yourself, you can [get started on buy.faraday.ai](https://buy.faraday.ai).

Faraday and outboundIQ helped American Standard shift lead scoring from a simple top-of-funnel blocker to a full-funnel routing optimizer, boosting contact rates from 5.56% to 20% and driving 3x higher conversion for high-score leads.

Don't trust lead scoring? Here's how we changed American Standard's mind, and how you can get the same results

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

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

## The challenge

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

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

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

## How Faraday helped: Enriching data and finding customers

The brand connected their e-commerce order data to the Faraday platform, which enriched every customer record with attributes from the [**Faraday Identity Graph**](https://faraday.ai/features/faraday-identity-graph) — a built-in dataset covering 1,500+ traits across 240M+ US adults.

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

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

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

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

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

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

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

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

## The results

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

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

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

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

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

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

## Why this matters

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

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

- **Data-rich** — grounded in 1,500+ real consumer attributes from the Faraday Identity Graph and, not assumptions
- **Behavior-validated** — tested through real market experiments on Instagram and consumer channels, not focus groups
- **Findable** — deployed as custom audiences through Faraday so you can reach these customers where they are
- **Conversational** — Heatseeker's AI persona avatars let anyone in the company chat with their customers, driving empathy and faster decisions
- **Living** — continuously updated as new data flows in and new experiments run

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

---

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

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

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

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

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

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

## The problem: Personalization at scale requires context

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

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

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

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

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

## The implementation: Using data to drive conversion

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

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

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

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

## The outcomes: Better outcomes, lower noise

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

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

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

## Scaling the boutique experience

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

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

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

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

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

_You lead customer retention at an omnichannel consumer brand and have been tasked with finding the optimal channels to engage customers to drive up repeat purchase rates._

_You lead customer analytics for a fast growing DTC brand and have a KPI of increasing AOV._

In each of these instances, arriving at the optimal decision has significant revenue implications for your company. For too many marketers, though, data-informed decisions that enable optimal outcomes run headlong into real world issues like data silos, quantitative expertise, and time-to-value tradeoffs.

This is where [Hazel](http://fromhazel.ai) comes in. Its platform provides an agentic AI coworker to marketers, so they can access and analyze relevant contextual data on their business, their customers, and the market in which they operate. Hazel unlocks for marketers the quantitative prowess and timely analysis needed for optimal decisioning.

[![Hazel website homepage, fromhazel.ai](/images/blog/hazellandingpage.png)](/images/blog/hazellandingpage.png)

Clint Dunn, Hazel’s Co-founder, talks about the triangulation of data for an optimal decision. To make an optimal decision, you need first-party customer data; you need the business-specific fundamentals like your brand’s ICP, inventory goals, and demand forecast; and you need third-party data to account for external market factors.

For Hazel to fulfill this mission, its agent must sit at the nexus of this data triangle.

### Problem

For the Hazel team, solving for two parts of this triangle, access to first-party customer data and business-specific data, was largely a matter of building out integrations with their clients’ data warehouses.

For third party consumer data, Hazel’s plan was to engage with traditional data brokers like Acxiom and TransUnion. Such an approach, though, carried high risk from an ROI perspective: Hazel faced a six-figure upfront licensing fee and a multi-month engineering investment to build out:

- ETL infrastructure to process hundreds of millions of consumer records every month
- identity resolution capabilities to match first- and third-party data
- privacy compliance infrastructure for data access and deletion requests

Even with these investments made, they’d face an ongoing resource drain of updating the data, all while knowing these brokers lacked both MCP support and an off-the-shelf API for real-time contextual data retrieval. These deficiencies would seriously limit the client use cases they could support.

For a startup with limited engineering resources and working capital operating in the emerging, hyper-competitive agentic marketing vertical, this was a problematic dilemma.

### Solution

With Faraday, Hazel was able to directly address these issues and reduce build-out to a matter of weeks.

Hazel gained access to the [Faraday Identity Graph](https://faraday.ai/blog/series/fig) through [Faraday’s API](https://faraday.ai/docs), giving their agent access to a high-quality source of third-party consumer data without having to take on the burden of hosting and maintaining such a dataset.

Within a day’s engineering time Hazel had a [working prototype](https://youtu.be/LoODRvfDxSs)—a significant improvement over the multi-month effort they expected. Within a few weeks they arrived at a production-ready offering.

Faraday’s usage-based pricing model allowed Hazel to scale their capital investment with client uptake.

[Faraday’s MCP](https://faraday.ai/docs/features/mcp) and [Lookup API](https://faraday.ai/docs/features/lookup-api) ensured Hazel’s agent could retrieve data in real time and in turn support a wide range of client use cases.

### Results

For Hazel, Faraday’s infrastructure dramatically accelerated its roadmap and its ability to fulfill its mission of enabling marketers to make optimal decisions. The results included:

- Upfront costs reduced from \$100k+ upfront to \$0 upfront; instead payments made on use.
- Initial engineering investments reduced from multi-month resourcing to a multi-week build out.
- Elimination of ongoing quarterly engineering investments to update third-party data sources.
- Automatic built-in privacy compliance infrastructure, including [logical account separation](https://faraday.ai/blog/how-to-create-a-subaccount-api).

### How it works

Hazel took the following steps to implement against Faraday’s API

- Step 1: establish a [Motherduck connection](https://faraday.ai/docs/how-to/connections/motherduck)
- Step 2: map [datasets](https://faraday.ai/docs/abstractions/datasets)
- Step 3: define [customer and lead cohorts](https://faraday.ai/docs/abstractions/cohorts)
- Step 4: establish data [pipeline](https://faraday.ai/docs/abstractions/pipelines), including [data appends](https://faraday.ai/docs/abstractions/data-appends) selection for relevant context
- Step 5: [deploy data](https://faraday.ai/docs/abstractions/deployments) back to Motherduck and make available via Lookup API for real-time retrieval
- Step 5: measure \+ iterate (track lift, tune thresholds).

### The takeaway

In the AI age, startups like Hazel are operating in a greenfield. They are in a race to cement relationships with clients and prove out value. The opportunity cost of time correlates directly to lost market share. Finding Faraday was a major accelerant for Hazel and gave it a competitive advantage over traditional SaaS analytics and BI tools—who are, in turn, working to transform themselves into agentic platforms. For Hazel, displacing these tools and winning clients will be easier due to their agent's unique ability to triangulate the relevant data needed for optimal decisioning.

Hazel is an agentic marketing platform that helps omnichannel and DTC brands make better retention and AOV decisions by triangulating first-party data, business fundamentals, and third-party consumer context. By streaming real-time consumer data from Faraday’s API, Hazel bypassed six-figure broker fees and months of engineering work, shipping a production-ready offering in weeks.

Optimizing agentic marketing with context: The story of the Hazel e-commerce assistant

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

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

## Problem

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

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

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

## Solution

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

### Faraday’s data pillars

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

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

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

### Why consumer data was the unlock

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

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

This enrichment enabled:

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

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

## How we rolled it out

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

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

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

## Why CMOs choose Faraday consumer data

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

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

## Conclusion

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

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

DTC eyeglasses brand unlocks growth with Faraday's consumer data

Marketing agencies today are in a bind.

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

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

## The problem

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

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

## The solution

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

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

## The outcome

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

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

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

## What does this mean to you?

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

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

## Ready to differentiate your agency?

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

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

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

For over a century, this leading DTC home services brand has delivered innovative, high-quality products that improve everyday life. But as a forward-thinking company, they’re constantly evolving their customer acquisition strategy to stay ahead. And in today’s market, that means solving one of the toughest challenges in lead buying: separating high-potential prospects from those that will never convert.

## The challenge of ping-post

Like many brands in home services, this Faraday client acquires leads from third-party sources using a real-time bidding system called [ping-post](/blog/so-what-is-a-ping-post-anyway). In this system, lead sellers send basic lead details (the "ping") to potential buyers, who then evaluate the lead based on predefined criteria. If a buyer decides to proceed, they purchase the full lead details (the "post").

While this system allows for rapid lead distribution, it also presents a major challenge: identifying which leads have real potential versus those that are unlikely to convert. Without an efficient filtering process, businesses risk wasting marketing dollars on low-quality leads that never became customers. And at over $40 per lead (in this brand’s case), these mistakes can quickly add up to a serious financial loss.

Although this brand had always been able to hit their numbers with their existing lead engagement strategy, they weren’t happy with “good enough.” They wanted excellence, and in this case that meant actively assessing the quality of all leads before they even entered their sales funnel to maximize the value of their lists.

## Real-time lead rejection with Faraday

To add this functionality to their tech stack, this brand partnered with Faraday to integrate custom predictive datapoints trained on their own historical data directly into their lead buying workflow—allowing them to [reject](/templates/lead-rejection) the least-likely-to-convert prospects before ever placing a bid.

This process works by scoring each ping in real time. When a lead becomes available for purchase, the brand automatically requests a custom datapoint: **likelihood-to-convert,** through Faraday’s real-time [Lookup API](/docs/features/lookup-api). The result is routed back via our integration with [ActiveProspect’s](https://activeprospect.com/) lead routing software [LeadConduit](https://activeprospect.com/leadconduit/), so leads that fall below their selected threshold can be rejected—avoiding the purchase entirely.

The results were immediate: in just the first five months, more than **13,000 low-quality leads** were rejected, saving over **$500,000** in wasted spend.

If you’d like to learn more about our integration with LeadConduit, [check out this blog from our team and ActiveProspect.](/blog/smarter-lead-acquisition-starts-here)

## Extending value with Credit Score Proxy

Building on this success, the brand wanted to reduce wasted time at the appointment stage. The answer came from another type of Faraday data: an **off-the-shelf consumer datapoint** called **Credit Score Proxy**, delivered through the **Faraday Identity Graph (FIG)**.

FIG contains over [1,500 consumer datapoints available from day one](/features/faraday-identity-graph), covering everything from demographics to financial proxies. Unlike custom predictive datapoints—which are trained on a brand’s historical outcomes and tuned to specific goals—consumer datapoints come prebuilt and can provide immediate predictive value.

Credit Score Proxy, for example, ranks U.S. households by purchasing power and conversion potential. It serves as a privacy-safe stand-in for financial reliability, helping brands flag leads likely to fail credit checks—without requiring homeowner consent or a bureau pull.

By mapping Credit Score Proxy into their lead management system, this brand now flags low-likelihood leads at the appointment confirmation stage. The results are clear: **leads in the bottom tiers** (as identified by Credit Score Proxy) **fail credit checks about 75% of the time.** By identifying these upfront, this brand avoids wasted appointments and ensures their sales team stays focused on prospects most likely to succeed.

## Don’t just take it from us

We're excited about our partnership with this client—but we’ll let their team speak for themselves. Here’s what their Director of Marketing had to say:

"In just six months, Faraday has become the centerpiece of our entire go-to-market strategy. We now prioritize calls based on Faraday scores, kick back low-quality leads, and save thousands of dollars every week.

We treat every lead according to its potential value and a high Faraday score goes to the front of the line. And now, we’re even applying Faraday’s scores to qualifying appointments. It’s been a complete shift."

## Here’s why it matters

This brand’s approach shows how both custom predictive datapoints and prebuilt consumer data can optimize various stages of the funnel. In this case a custom model filters out low-quality leads before bids are placed and Credit Score Proxy suppresses appointments unlikely to succeed, preserving sales resources.

What’s important is that this wasn’t about more data for its own sake. Faraday worked with this brand to identify which specific datapoints would matter most at specific stages of the funnel. The team cut wasted spend, improved close rates, and freed up staff to focus on the prospects most likely to convert.

Working with Faraday doesn't just mean getting best-in-class data, it means getting the strategic guidance to use it to drive concrete value, and this client \- as well as all our other clients \- are seeing that value every day.

Want to see how Faraday’s predictive datapoints can transform your funnel and drive similar value for your brand? [Let’s talk.](https://faraday.ai/contact)

One of the most trusted names in DTC home services transformed their acquisition strategy using Credit Score Proxy to qualify prospects’ financial readiness at the appointment stage and saving over 100K/month.

How a leading DTC home services brand cut $100K+/month in wasted leads and qualified appointments with Credit Score Proxy

[Product recommendation](http://faraday.ai/templates/next-best-offer) engines are everywhere these days—from Netflix queues to Amazon homepages. But in subscription commerce, where customers commit to recurring deliveries, recommenders aren’t just nice to have—they’re essential. Sending the wrong product doesn’t just hurt engagement. It increases churn, reduces LTV, and erodes trust in the brand.

And that’s why a leading subscription box company set out to improve how they matched products to people, and to solve this problem, they of course came to Faraday first.

## The problem: Manual rules and missed opportunities

Before working with Faraday, this brand was manually assigning monthly boxes using basic written rules. While the logic was informed by customer traits and preferences, it was still deterministic, rigid, and prone to human bias. It also couldn’t scale effectively across an ever-growing catalog or subscriber base.

Most importantly, the team didn’t have a reliable way to test whether these rules were actually the best way to drive retention and increase revenue.

## The solution: AI-powered product recommendations

To modernize their process, the brand partnered with Faraday to implement a machine learning–based recommendation engine.
Unlike traditional scoring models that evaluate products in isolation, Faraday uses a two-sided matching framework—scoring both:

- Customer affinity: How likely a specific person is to engage with a product
- Product performance: How well that product has performed with similar audiences

This leads to smarter, high-confidence pairings optimized for both personalization and conversion—at scale. With Faraday, the brand was able to deliver more relevant recommendations, reduce bias, and adapt to new products and preferences as their catalog evolved.

## The results: Higher revenue and lower churn

The team ran a clean A/B test comparing Faraday’s algorithmic recommendations to their manual rules.

The outcome:

- 📈 ~5% initial lift in revenue per customer
- 📉 ~3% reduction to churn rate
- 💰 Over 5x ROI generated monthly with Faraday
- 🗓️ Four consecutive months of our recommendations outperforming their rules-based alternatives

These weren’t marginal gains—they were statistically significant improvements that validated the shift toward AI. And what’s more, this is just the start. The more this brand uses our models, the more they’ll learn and the better they’ll get, meaning they can expect even better results over time.

## How Faraday recommendations work

Faraday’s recommender system doesn’t just surface “popular” products. It predicts the mutual affinity between a person and a product, using thousands of behavioral signals—purchase history, tenure, geography, psychographics, and more. It also incorporates product-level attributes and past performance to ensure the best-fit items rise to the top.

What’s more, clients can tune the system with constraints like inventory availability, product diversity, or subscription cadence—ensuring not just personalization, but operational feasibility.

## In conclusion

Want to see how our recommenders could work for your business? Just reach out and talk to sales, they’ll tell you exactly how this use case, as well as a range of [other AI use cases](https://faraday.ai/templates) could help your business thrive\!

A leading subscription box company replaced manual product rules with Faraday’s AI-powered recommendations—boosting revenue and retention through smarter, scalable personalization.

How one subscription box brand leveled up their product recommendations with Faraday’s AI-powered data

At Faraday, we love when our tools help a mission-driven brand grow in tangible ways, and when that growth could be described as “massive,” we’ll take that too. That’s exactly what happened with [Bee’s Wrap](https://www.beeswrap.com/)—a sustainable alternative to plastic wrap—who built a pitch deck with help from Faraday’s [Personas](https://faraday.ai/docs/how-to/predictions/customer-personas) features which landed them a “massive” (there’s that lovely description in action) placement in over 550 Target locations around the country.

[![Bees wrap product design](/images/blog/bees-wrap.jpg)](/images/blog/bees-wrap.jpg)

## What Bee’s Wrap needed

Bee’s Wrap was preparing for a high-stakes sales pitch to Target, a major national retailer—perhaps you’ve heard of them. The team needed to prove that their customer base was the right kind of shopper that Target wanted to be ‘Target-ing”. For this they needed to show real data that would resonate with a national buyer.

Bee’s Wrap had a major data asset to leverage: their first-party DTC sales data from [Shopify](https://www.shopify.com/). However, this data only tells part of the story. Order data is not going to tell you why someone buys, who they are demographically, or how many more people like them exist across the country. That’s where Faraday came in.

## How Faraday helped

Bee’s Wrap connected their order data to the Faraday platform, and in just one afternoon, they had:

- Detailed demographic and psychographic segments
- Purchase propensities broken down by geography
- A clear, visual representation of who their customers really are—and where to find more of them
- A ZIP-code-level view of where potential was highest—helping them identify which store locations to prioritize in their pitch

This gave Bee’s Wrap a concrete, compelling story to bring into their pitch—one that resonated with Target’s team.

## The results

That pitch didn’t just land—it turned into a deal.

Bee’s Wrap is now rolling out nationwide in over 550 Target stores, starting with an initial wholesale shipment worth well into six figures. It's a massive milestone for the brand—and one they credit, in part, to having the right data in hand at the right moment.

## Why this matters

As [Brendan Taylor](https://www.linkedin.com/in/bttaylor13/), Bee’s Wrap’s CEO, put it:

> “We approached Faraday with a simple but elusive set of questions about our consumers and their demographics. Armed with only our DTC sales data – in one afternoon – Faraday identified crisp consumer segments with actionable demographic and psychographic details. Their easy-to-use interface also provided powerful predictions about propensity to purchase based on geography and other demographic filters."

The Faraday suite can synthesize incredibly powerful insights and predictions out of disparate data sets that previously left marketers and sales leaders scratching their heads. If you have lots of data and want to convert it to knowledge and insights, you’ve come to the right place.”

But here’s the thing, this story isn’t just about one brand—it’s about what happens when companies make their data work for them. With Faraday Personas, even a single afternoon of analysis can unlock game-changing insights.

Whether you’re pitching a retailer, planning a campaign, or just trying to understand your customers better, we’re here to help you tell a data-backed story that lands.

Ready to learn more? [Let’s talk](https://www.linkedin.com/in/davidtoddsmall/).

Bee’s Wrap used Faraday Personas to turn their DTC data into a compelling retail pitch—landing a nationwide deal with over 550 Target stores.

Customer stories