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

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 returned to American Standard, 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 and Jason Shatzkamer from outboundIQ 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, 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 (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.

But Faraday is not a lead blocker. We're a lead optimizer.

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)
• 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.

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.

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.

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. And if you'd rather dive in and start scoring leads yourself, you can get started on buy.faraday.ai.