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.

How Bee’s Wrap landed a nationwide retail deal with help from Faraday personas

In our last blog, we explored why ROI analysis at Faraday isn’t just about proving value after the fact—it’s also about continually uncovering new opportunities. 

We shared what that looks like at a high level. But this isn’t just a philosophy expressed in our marketing; it’s how we work every day. So today, we’re sharing a story from the \~real world\~ that shows just how much insight you can unlock through our comprehensive ROI evaluation process.

It also drives home an important point: these evaluations can only achieve their full value if you actually engage with them. The top-line numbers might say “Yep, it’s going great,” but just because that’s good news doesn’t mean it reflects the whole story. 

Sure, it’s going great, but how great, and where is there room for improvement? Which parts of the campaign are driving the most value? The least value? Do we need to revise any major assumptions to be more accurate? 

Indeed when it comes to ROI analyses like these, digging deeper is where the real opportunity lies.

## A deeper dive reveals something unexpected

Hi, ROI extraordinaire Chris Waite here. After I came on full-time at the start of the year as Faraday’s Head of Customer Analytics, I kicked off ROI initiatives with several of our existing customers. The goal was simple: move beyond one-time reports and start treating ROI as an ongoing conversation—one that actively drives value, not just records it.

With one financial services client, we began with my usual process: aligning on key assumptions—value per conversion, attribution logic, unit costs—and setting up monthly check-ins to track progress and dig into results over time.

And the returns looked great. Performance was strong, and the model was holding up. But during one of those check-ins a couple of months ago, someone on their team made an offhand remark that caught our attention: some of their hardest-to-convert prospects—sometimes rejected due to their low predicted conversion rates—actually had the potential to generate the *most* value if they did convert.

Our interest was piqued, so we ran the numbers.

We analyzed the relationship between Faraday’s prediction scores and the average conversion revenue. Sure enough, there was a clear pattern: while certain low-scoring groups converted less frequently, they delivered more value when they did.

The selection process didn’t need a full overhaul—but it did need a refinement. We brought the results back to the client, and together we incorporated these insights into their approach. By adding an additional variable into our selection process, we could proactively identify the majority of these high-value individuals and ensure their inclusion in the campaign. Once updated, the campaign began to improve—conversion rates dipped slightly (by design), but total revenue went up.

This is a perfect example of how our ROI reporting process uncovers new and unexpected insights that drive value. In this case, we used it to fine-tune suppression logic and reframe scoring thresholds—without a full process overhaul. The outcome was a smarter, more mission-aligned strategy that helped the client prioritize not just likely conversions, but the ones with the greatest potential overall value.

And through that process, we uncovered something more. These high-impact conversions weren’t just good for business—they were cultural wins. Inside the company, signing one of these clients—often individuals carrying significant debt—was seen as a major moment. It represented the core of their mission: to support people who need it most. The whole team felt it.

So yes, ROI is about financial returns. But sometimes, it's also about morale, alignment, and purpose. In this case, their investment in Faraday didn’t just lead to better outcomes on paper—it reinforced the values that drive their business forward.

## From revenue recap to campaign shift

This paradigm shift is exactly Faraday’s goal when we present our clients with our ROI reports. We can talk about them driving value as much as we want but they drive the most value with buy-in and funding from the teams that use them.

In this case, thanks to the deeper ROI dive, the client was able to:

* Identify underutilized customer types  
* Refine their lead suppression strategy  
* Shift budget allocations across customer segments  
* Drive more high-impact conversions

None of that was the original intent. But that’s the point—when ROI becomes an ongoing process, not just a retroactive check-in, it drives value in real time.

## Why Faraday treats ROI like strategy

We’re not sharing this story to pat ourselves on the back—we’re sharing it because it highlights something bigger: most companies treat ROI as a postmortem. We treat it as a feedback loop.

At Faraday, ROI modeling isn’t a final deliverable—it’s built into every engagement from day one. We co-develop success metrics with our clients, revisit assumptions as new information emerges, and treat every report as a living document meant to drive change in real time. Because when ROI is treated as a collaborative, evolving process—not a static snapshot—it doesn’t just validate past performance. It unlocks new value, sharpens ongoing strategy, and turns every insight into an opportunity for future impact.

## Coming up next

In our next post, we’ll pull back the curtain on how we build some of our ROI models at Faraday—what goes into them, what we measure (and why), and how we handle the gray areas most companies avoid.

Because the better you understand your own ROI, the greater your ability to power new growth.

A routine ROI check uncovered a surprising insight that helped a financial services client shift strategy, boost revenue, and realign with their core mission.

This financial services brand’s ROI assessment empowered a whole new strategy

A leading marketing, technology, and data company that specializes in direct mail campaigns came to Faraday with a problem: their targeting was working, but it wasn’t scalable.

They were helping their clients send mailers based on narrow behavioral triggers from their websites, like cart abandonment or time spent on a product page. 

That kind of targeting can be effective—sometimes. But often, the real world is messier.

Maybe someone hops on an apparel site, intent on making a purchase only to get pulled away by their toddler. Maybe a renter clicks through a solar quote tool, strictly because they’re curious, even though they can’t make the final decision to install solar on their roof. 

Not all signals mean intent. And more importantly: not every potential converter sends a clear, discernible signal every time they are about to make a purchase. 

**Reactive targeting wasn’t enough**

This behavioral-only strategy limited the number of people eligible for mailers—capping both reach and return. By relying solely on website interactions, the company’s campaigns were targeting a very specific kind of customer—one who was already halfway down the funnel and showing overt signals of intent. While this worked for a subset of visitors, it left out a much larger group of people who looked like good fits but didn’t quite meet the behavioral criteria.

The result: capped growth and missed opportunity.

## The solution: smarter lead scoring powered by Faraday

That’s where Faraday came in. By shifting from reactive targeting to predictive lead scoring, the company could stop waiting for the perfect website behavior and start identifying the right people to mail—before they ever clicked “add to cart.”

At the heart of this was the [Faraday Identity Graph](/docs/abstractions/traits)—a rich third-party dataset covering nearly all U.S. consumers and their households. By connecting their first-party behavioral data to this built-in body of rich consumer datapoints, the company was suddenly able to understand far more about each identified website browser and score them accordingly. But that wasn’t all.

With Faraday’s AI-powered lead scoring, they could now evaluate:

* **Current prospects** not just by their actions—like browsing or abandoning a cart—but by their full profile. Was this someone who matched the traits of past converters? Did their income, lifestyle, or purchasing habits suggest they were ready to buy, even if their behavior didn’t scream it yet?  
* And, critically, **potential customers** who had never interacted with the brand at all—opening up entirely new audiences for outreach, no website visit or form fill required.

This opened up a whole new world of targeting. Instead of just reacting to a cart abandonment or a quote request, the company could proactively find high-potential leads who hadn’t yet taken any action—but still looked like strong matches.

No more waiting for someone to raise their hand. Now they could find and prioritize the right people, at the right time—and reach them with personalized mailers before the competition did.

## The result: 3x more mailers, same high conversion rate

Thanks to this predictive scoring approach, the company was able to triple its mailing volume—without compromising performance. That’s because every additional mailer was backed by a validated custom predictive datapoint: likelihood-to-convert.

Their clients benefited too: more prospects reached, more conversions captured, and better ROI on every campaign.

## Why it worked

Faraday’s AI didn’t replace the company’s behavioral targeting-but rather enhanced it. By layering in predictive signals based on real people’s demographics and past purchasing behavior, the model was able to widen the targeting net without watering the quality of the leads down.

Now, the company helps its clients:

* Expand their reach beyond narrow behavioral triggers  
* Identify new high-potential leads using consented, privacy-safe data  
* Maintain high conversion rates even as mailing volumes increase

It’s not just smarter targeting—it’s scalable performance.

## The impact

By enriching their behavioral data with Faraday’s Identity Graph and predictive datapoints, this direct mail platform was able to triple their reach—without compromising on performance. More qualified leads. More efficient targeting. More revenue for everyone involved.

**Ready to scale your outreach without sacrificing ROI?**

Let’s talk about how Faraday can help.

How a direct mail platform used Faraday’s AI to triple their reach with smarter, scalable lead targeting—without losing conversion performance.

Smarter prospecting at scale: How a direct mail provider grew reach without sacrificing performance

At Faraday, we’re always looking for ways to make our tools more accessible and valuable for our users. And with our latest update, you can now download [Faraday](http://faraday.ai) [Personas](https://faraday.ai/docs/abstractions/persona_sets) as CSV files directly from the dashboard, bringing even more power to your analysis and reporting processes.

## What changed

There's now a "Download CSV" button on the Persona analysis page and under the Persona set ‘…’ menus. The CSV largely matches the layout of the Persona set analysis page. Although it’s less sortable than our internal Persona report, it’s easier to visually parse and can be used for a much broader range of applications after export.

You’ve always been able to do this in [API](https://faraday.ai/docs/reference/getpersonasets) but this new feature adds this ability to the dashboard—which makes this valuable workflow possible for non-technical marketers to use as well.

![/Graphic representation of FIG categories](/images/blog/downloadcsv.png) 

## How this features helps brands and agencies

This update makes it easier than ever for both brands and agencies to leverage Faraday’s in-depth Persona analysis. 

**For brands**: this means easier integration of Persona data into your internal reports, or an easier way to communicate the changes in your customers to your executive team. 

**For agencies**: this means Persona data can now be integrated into customer reports, BI tools, dashboards for clients, and ultimately can have a bigger impact on the downstream activations and creative choices. 

Previously, users had to manually extract Persona information and input it into reports to achieve these ends. This time-consuming process often meant only the most crucial insights were included—but as anyone who uses our Personas knows that they’re most valuable because of the depth of their analysis, so you really want to have access to everything\! Now, with just a click of the "Download CSV" button, you get a clean, visually accessible file that mirrors the layout of our Persona analysis page. 

## How EmpowerFi™ is using this new feature to drive results

*This all sounds great in abstract but has anyone actually generated results with this feature?*

Well as it happens, yes\! [EmpowerFi](https://empowerfi.org/)™, a data-driven marketing firm (and one of our favorite partners) is already seeing the tangible benefits of Faraday’s new CSV download feature through their Faraday integration, [IntelliFi](https://empowerfi.org/solutions/intellifi/)™. Erin Lutz, the VP of Operations at EmpowerFi™, shared how this simple update has saved her team a significant amount of time and money.

As an agency that works with multiple clients, including credit unions and banks, Erin historically had to manually extract and input Persona data into reports. This process was time-consuming, often limiting the scope of data they could include. But with the new CSV download feature, Erin can now effortlessly download, customize, and export the information—saving valuable time while delivering more comprehensive insights, directly from Faraday’s dashboard. 

This update has not only streamlined Erin’s reporting process but also allowed her to take much greater advantage of the robust Faraday Personas in a range of use cases. As a result, EmpowerFi™ can now offer clients a deeper, data-driven understanding of their target audiences, while advertising Faraday’s powerful Persona tool to their networks.

This feature has made Faraday’s Personas even more accessible, helping EmpowerFi™ continue to deliver exceptional value to their clients.

## Ready to get started\!?

Sounds pretty great, right? We think so too\! If you’re ready to get started today, reach out to me, [Dave Small](https://www.linkedin.com/in/davidtoddsmall/) and I’ll get you set up today\!

Faraday’s new CSV download feature makes it easier for customers to use Persona data outside of our platform—saving time, enhancing analysis, and making insights more accessible for non-technical marketers.

Faraday’s CSV download feature for Personas unlocks new possibilities for agencies like EmpowerFi™

[Direct mail remains a core acquisition strategy for many financial services providers](https://www.lob.com/blog/the-benefits-of-direct-mail-marketing-in-todays-economy), but it comes with a hefty price tag. Each mailer represents an investment, and when sent indiscriminately, costs can spiral with little return.

When this client first came to us at the end of 2024, like many in the industry, they relied primarily on instinct-based direct mail strategies, meaning a significant portion of their outreach was reaching consumers who were unlikely to convert. They knew they were overspending but lacked the tools to pinpoint which mailers were worth sending—and which could be cut without hurting revenue.

This client needed a solution that could optimize their outreach, reduce waste, and maintain strong enrollment numbers. Most of all, they wanted confidence in whatever decision they made—all without disrupting their existing workflow. So we provided them a custom predictive datapoint that did exactly that.


## Lead suppression with Faraday

And that’s where Faraday came in. As soon as we identified their challenges, we quickly devised a targeted strategy to help. 

The answer? Faraday’s [lead suppression](https://faraday.ai/templates/lead-suppression/recharge) template.

To enact this strategy, we started by inputting the provider’s recent lead data into our system (maintaining rigorous data privacy standards like HIPAA, GDPR, and SOC-2 compliance throughout the process). This data was then enriched through our [identity resolution](https://faraday.ai/blog/identity-resolution) process with 3rd party data from the built-in Faraday Identity Graph (FIG), which [includes over 1,500 attributes on more than 240 million U.S. consumers and their households](https://faraday.ai/features/faraday-identity-graph), and features nuanced demographic and behavioral indicators that influence consumer actions—ranging from property ownership to life events and purchasing habits.

From there, new predictive models were created and trained on this historical enrollment data (now enriched with 3rd party insights), identifying patterns and trends that distinguished high-value leads from low-value ones. Then, using advanced machine learning techniques, the system assigned each lead a predictive score, ranking them into ten distinct deciles based on their likelihood to convert. With this scoring system in place, a [cut-off threshold](https://medium.com/swlh/determining-a-cut-off-or-threshold-when-working-with-a-binary-dependent-target-variable-7c2342cf2a7c) was established to determine the point at which the cost of printing and sending a mailer outweighed the expected return, ensuring that outreach efforts remained cost-effective.

Finally, Faraday delivered an optimized, high-fit mailing list back to the provider and armed with this refined list, they were able to move forward with confidence, ensuring that every mailer sent had a greater likelihood of driving enrollment. For a visual breakdown of this process, check out our recent blog on lead suppression, [here](https://faraday.ai/blog/holdout-testing-with-faraday).

So how did it go? 


## The results (so far)

If you noted the timestamp on this article, you might be thinking to yourself, “Well, they reached out at the end of 2024 and this was published on Feb 21, 2025, so did they even get the models rolled out yet?” 

Well, despite the short timeframe, they not only deployed the models and sent direct mail based on their predictions, but have also already seen a significant reduction in costs, all while maintaining strong engagement with high-quality leads. 

Indeed, by suppressing low-likelihood recipients, they were already able to send over **80,000 fewer mailers**, producing **a net savings of nearly $40,000**, demonstrating the immediate financial benefit of a more targeted approach.

These early financial wins were certainly affirming, but it had only been a month. How did we know that they were actually the result of our model and not just an uptick consumer interest this year?

### Ongoing model validation

At Faraday, we take the [validation](https://faraday.ai/docs/reference/getscopeefficacy) of our models very seriously. We do this before predictions are deployed through our [holdout testing process](https://faraday.ai/blog/holdout-testing-with-faraday) and continue testing on an ongoing basis even after successful implementation. In this case even though we were already seeing positive results, we wanted to see data that confirmed (or challenged) our assumptions. So we conducted further analysis on our models after we started getting results back to confirm that our predictive scoring was actually aligning with real-world outcomes. 

And we found what we were looking for: Leads with the lowest predictive scores showed below-average conversion rates, validating the suppression strategy. Meanwhile, leads in the highest-scoring segments consistently outperformed the average, reinforcing the value of prioritizing the right audience. And what’s even better is that this correlation is only expected to strengthen as more data flows through the model, further refining predictions and maximizing efficiency.

While these financial results would be impressive on their own, they also highlight how quickly Faraday’s models can be deployed and optimized. The contract was signed in late-December, and within weeks, the provider was already seeing meaningful returns. This quick turn around, combined with the ability to continuously refine and improve targeting over time, positions them for even greater efficiency and cost savings in the months and years ahead.

## Next steps

With direct mail suppression already demonstrating clear value, the provider is now looking to further optimize their customer outreach efforts. A key next step? [Call center list scoring](https://faraday.ai/stories/call-center).

Similar to how Faraday’s predictive models helped refine direct mail targeting, they can also help [prioritize leads](https://faraday.ai/templates/lead-prioritization) for call center campaigns. By focusing call center agents’ efforts on high-likelihood prospects, the provider can reduce wasted calls, improve conversion rates, and increase overall campaign efficiency.

We can’t wait to see how they use this technology to generate even more value for their business\!

## In conclusion

In just a short time, this financial services provider has revolutionized its direct mail strategy, reducing unnecessary costs while maintaining strong engagement with high-value leads. Faraday’s predictive modeling has enabled them to move beyond broad, instinct-driven targeting to a data-backed approach, ensuring each mailer sent delivers greater value. And with tens of thousands of dollars saved in only a few weeks, the provider has already realized the immediate financial benefits of a more targeted outreach strategy.

What’s more, looking forward, the provider is well-positioned to refine their targeting approach even further. By continuously updating and optimizing their predictive models, and exploring additional use cases like call center list scoring, they can maximize the efficiency of all customer acquisition channels. 

For this company, this is just the beginning of a smarter way to decide who’s worth reaching—and when.

If you'd like to know how Faraday can produce similar results for your business, **sign up for free**!

In their first month with Faraday, this financial services provider used lead suppression to cut 80,000 low-value mailers and save nearly $40,000.

How this financial services provider optimized their direct mail outreach in just one month

A leading **debt consolidation** company relied on direct mail as a key channel for acquiring new customers. However, a significant portion of their marketing budget was being spent on sending mail to leads who were unlikely to convert. This inefficiency not only increased costs but also diluted the impact of their outreach efforts, making it difficult to connect with high-intent buyers.

## Smarter targeting with Faraday's lead suppression

To address this challenge, the company partnered with Faraday to implement a [predictive lead suppression model](https://faraday.ai/templates/lead-suppression/recharge). This process began with Faraday ingressing their lead data to assess the key characteristics of their existing customer base. Then, through careful analysis of their historical conversion patterns—as well as the inclusion of custom predictive datapoints, built using [rich 3rd party data and attributes on 240M+ US consumers](https://faraday.ai/features/faraday-identity-graph) that comes preloaded through the [Faraday Identity Graph (FIG)](https://faraday.ai/docs/abstractions/traits)—we were able to predict which of their leads were most likely to convert.

Using this analysis, Faraday’s model identified the bottom 10%—the least-likely-to-convert leads—and suppressed them from the company’s direct mail campaigns. This approach immediately reduced wasted marketing spend and allowed the company to reallocate resources toward higher-value prospects.

## Or see Faraday's lead suppression process broken down visually

1. ![Faraday Identity Graph](/images/blog/lead-supp-panel-0.png)

   ### Don’t start from scratch

   Faraday comes preloaded with 1500 attributes on 240M+ US consumers and their households.

   This powerful dataset provides high-signal datapoints—including property data, demographics, life events, and buying habits—that can shape smarter decisions.

2. ![client data](/images/blog/lead-supp-panel-1.png)

   ### Your lead data is shared with Faraday

   Your lead data is collected and ingressed into Faraday securely.

   This guarantee is achieved through our robust data security policies, which include HIPAA, GDPR, and SOC-2 compliance.

3. ![model is trained and predictions begin](/images/blog/lead-supp-panel-2.png)

   ### We train predictive model by analyzing client data for patterns

   Faraday’s Identity Graph & AI/ML predictive modeling enables us to identify unique trends in your data set and specifically assess your leads’ likelihood to convert.

4. ![leads are sorted into deciles](/images/blog/lead-supp-panel-3.png)

   ### We identify how likely each lead is to convert

   We identify 10 deciles to be able to help you determine which leads are the best (most likely to convert after receiving mail) and the worst (those that will cost more in postage/print than they will generate in value, or simply won’t convert).

5. ![cut-off threshold is established](/images/blog/lead-supp-panel-4.png)

   ### We establish a cut-off threshold

   Faraday then helps you to calculate the “cut-off” threshold. This determines where the cost to print/send the mailer is greater than the likelihood to convert to revenue.

6. ![targeted client list](/images/blog/lead-supp-panel-5.png)

   ### We return the targeted list

   Once Faraday has removed the low-fit leads, you can use the new list to target your direct mail process and proceed with your next mail drop.

## Optimized results, supported by data scientists

Although this model was already delivering significant value, our data science team wasn’t satisfied.

That was because this company recognized four distinct customer segments within their broader population—each with unique characteristics. Although the one-size-fits-all approach had made sense initially to drive results, we hypothesized that a single model that handled all leads at once was likely not actually able to provide the highest level of precision possible. Instead, we considered creating four additional models which would be individually tailored to each unique customer segment.

While we were confident that this more granular approach would improve results, at Faraday, we prioritize empiricism over intuition so we decided to run rigorous validation tests. And indeed, after comparing conversion trends between the original model alone and all five models together, we were able to prove—rather than assume—the added value of the more granular approach. For the client, this meant even better precision in their targeting and more return on their direct mail spend.

While this process required a deeper investment of our data science team’s time, the outcome was clear: greater accuracy, better optimization, and ultimately, higher value. At Faraday, this isn’t just a one-off added service—it’s how we operate. Strategic data science investment is the only way to unlock insights at this level, and this project stands as a testament to that philosophy.

## The result

The impact of Faraday’s predictive lead suppression was immediate and substantial:

- **At least 600,000 fewer pieces of wasted direct mail** sent to low-intent leads monthly
- **Over $100,000 saved each month** in direct mail costs
- **An additional \$20,000 in monthly revenue, or $240,000 annually**, thanks to these targeted models’ better precision in predicting which customers would convert

With these enhancements, the company is now seeing a **10x return on investment (ROI)** from Faraday’s predictive modeling.

## Beyond cost savings: data-driven growth

By investing in Faraday’s predictive modeling and data science expertise, the company didn’t just cut costs—they redefined the way they acquire customers. Suppressing low-intent leads was just the beginning. By refining their approach with segment-specific models, they turned direct mail into a high-precision growth engine, ensuring that every dollar spent reached the right audience.

With this new strategy, the company isn’t just saving money—it’s driving measurable, sustained growth. Faraday’s predictive lead suppression has become a core part of their marketing playbook, proving that the right data science investment doesn’t just optimize campaigns—it transforms businesses.

Through predictive lead suppression, this company was able to cut at least 600,000 pieces of least-likely-to-convert direct mail each month.

How this debt consolidation company optimized direct mail with lead suppression to save over $100K each month

The renewable energy sector is growing rapidly as consumers and businesses increasingly turn to sustainable solutions. While this growth presents a wealth of opportunities, it’s also been a bumpy ride, with market uncertainties and shifting dynamics creating challenges for providers. In this ever-changing landscape, success hinges on the ability to identify, prioritize, and place their bets on the right horse—or, in this case, the consumer segments with the most value-generating potential.
When a prominent solar energy provider faced this challenge in 2024, they realized that finding the best answer alone wasn’t necessary, or the best use of their specialized team’s energy. Instead they turned to Faraday.

## The problem: they were tired of guessing and wanted to make data-driven decisions they could feel confident in

This solar company faced a wide-ranging set of problems as they tried to grow their sales pipeline in 2024. Although they were determined to scale efficiently and outpace competitors, their existing targeting approach lacked precision, which led to wasted marketing spend in low-potential areas and with the wrong consumers. Additionally, they were overwhelmed by an abundance of raw data that made it difficult to derive actionable insights. Finally, rapid market growth had also attracted competitors, heightening the urgency for smarter acquisition strategies.

These challenges translated into tangible pain points like lower conversion rates, wasted time, and inefficient marketing spend. Without a clear roadmap to identify and prioritize high-potential customers, their ability to compete effectively in the fast-evolving solar market was under threat.

## The solution: Faraday’s predictive analysis tools gave them data driven answer and confidence

Realizing these challenges could hinder their growth, the company decided to seek outside expertise and while these problems might seem insurmountable, they’re what we get up for.

Almost immediately after onboarding and hearing about their issues, we identified two significant ways we could support their expansion goals and help them to better understand their market: Persona Analysis and Market Opportunity Analysis.

### Persona Analysis

Faraday’s [Persona Analysis](https://faraday.ai/templates/thematic-personalization) is a process that helps companies uncover the traits and behaviors most closely associated with their ideal customers. By analyzing first-party data from the client as well as rich third-party data from our proprietary Faraday Identity Graph (FIG- will link), we create detailed profiles of a company’s highest-value personas. These represent distinct socio-demographic groups that share common characteristics and behaviors, making it easier to design marketing strategies tailored to their specific needs.

For this solar provider, Persona Analysis revealed the unique traits that drive solar adoption. By understanding these factors, the company could refine its messaging, focus on the most relevant customer segments, and craft outreach strategies that resonated with their target audience.

But that on its own might not be enough. This tools allows us to know what _type_ of person to target, but not necessarily how many of them exist and are reachable

### Market Opportunity Analysis

That’s where [Market Opportunity Analysis (MOA)](https://faraday.ai/blog/unlocking-growth-with-market-opportunity-analysis) comes in. Building on these insights and persona profiles, Faraday conducted a comprehensive Market Opportunity Analysis, a process which combines the client’s first-party data with broader market trends and dynamics to quantify their existing market. To conduct this analysis, Faraday builds a complex and individualized predictive model which compares that quantified market to the population at large, represented through [Faraday’s Identity Graph](https://faraday.ai/features/faraday-identity-graph)—a robust and proprietary dataset containing detailed information on nearly every U.S. adult. This enables the identification of the totality of potential new customers who resemble the company’s top-performing personas available in the U.S. An example is pictured below.

![Example of our Market Opportunity Analysis](/images/blog/moa_map.png)

The analysis doesn’t stop there though. It then visually maps these high-potential customers by state, zip code, and even down to the individual level. These dynamic predictions enable clients to focus on specific regions and neighborhoods with the highest concentration of ideal customers. These insights are paired with additional predictive tools, such as lead routing and direct mail targeting, ensuring marketing efforts can be directed with precision and confidence.

Through this dual approach, the solar provider gained actionable insights about market potential and customer behaviors. These findings informed advertising campaigns, local outreach initiatives, and third-party logistics strategies, enabling the company to allocate resources strategically and maximize their impact.

## The results

The impact of Faraday’s Persona and Market Opportunity Analysis changed the company’s entire outlook. Where as before, they had marketed broadly based on vague demographic factors, they could now target their spend down to the top zipcodes, and even to the individuals most likely to convert, suggesting products that were more likely to sell and talking to their ideal consumer in a way that they were most likely to respond to.

A particularly telling example of the power of these tools came when the solar provider, who primarily relies on their installation trucks for marketing, discovered through Market Opportunity Analysis that some of the highest-potential zip codes were in rural areas—places where the trucks wouldn't typically reach. To ensure these high-fit prospects were engaged, they decided to run a targeted direct mail campaign in those regions, making sure they could be seen by the people who mattered most.

Faraday's tools didn’t just provide actionable data—they gave the solar provider the confidence to act without hesitation. In this case that meant switching up their marketing strategy significantly. By moving away from broad, guesswork-based targeting, they were able to focus their efforts and dollar specifically on high-potential customers and this makes all the difference.

## Conclusion

Faraday's persona and market opportunity analysis provided clarity that allowed the company to make informed decisions on where to invest their resources, cutting out low-value areas and optimizing their target audience. With the right data guiding them, they gained the confidence to expand in competitive markets and scale efficiently. This success story underscores the value of data-driven insights in driving market expansion and delivering long-term success.

What's next for them? We're going to predict (with _high confidence_) more sales\!

Using Faraday's predictive tools, this company transformed their marketing strategy by targeting only the highest-potential customers.

How a leading solar provider improved customer targeting with personas and market opportunity analysis

[Burrow](https://burrow.com) is an innovative furniture brand founded in 2016\. From starting out with thoughtfully designed modular couches to recently expanding their offerings to shelving, coffee tables, and even rugs, they have continuously pushed to understand their customers and cater to their needs.

## The goal

Burrow wanted to personalize its customer journey to drive better conversion rates and more purchases as they expanded their product lines.

## The challenge

Like most companies, Burrow had a cold start problem: they knew a lot about their current customers, but very little about brand new customers or potential customers who had signed up for their newsletter.

## Faraday \+ Iterable

Enter Faraday \+ Iterable. With [Faraday’s Identity Graph](https://faraday.ai/features/faraday-identity-graph), the Burrow team suddenly had access to 1,500+ data points on almost every adult in the US they could combine with their contact data. And with Faraday’s AI tools, the team could identify the patterns and trends of their customers to build segments of leads who looked just like them.

All of these insights then [flow directly to Iterable](http://faraday.ai/docs/integrations/iterable), where their automated journeys enable customer engagement across multiple channels with minimal manual effort. By leveraging automation, informed by Faraday, Burrow delivers timely, relevant content that enhances the customer experience while saving time and resources.

## The results

By partnering with Faraday, Burrow was not only able to know what all of their leads looked like, but which looked like customers who had taken specific actions in the past—whether it was to purchase from a specific category, spend a certain dollar amount, or even prefer a specific color couch over another.

In this way, the Burrow team could engage leads who previously only had provided an email address and deliver highly tailored messaging that matched their consumer profile, with suggested products based on what they were most likely to care about.

All of this messaging was delivered with Iterable, which ensured it was delivered at the right time, and by the right channel for an optimal engagement experience.

With Faraday \+ Iterable, Burrow is building a [predictive customer experience](https://faraday.ai/blog/predictive-customer-experience-is-the-future) that makes it enjoyable for everyone to shop—whether you’re a repeat customer or simply signed up on the newsletter.

Burrow used data and machine learning to power personalization and drive higher conversion rates.

Customer stories