See our complete guide: [Powered by Faraday — embedding predictive AI in your platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai)

_This customer story has been anonymized to respect the company’s competitive strategy._

A leading CRM used by roofers leveraged Faraday’s infrastructure to power their Lead Intelligence tool, which helps their customers see which leads are likely to convert so that no time is wasted on bad fits.

## Giving clients an edge

The product team at this CRM for roofers is focused on helping users find value in their product, and in the five years before adopting [customer predictions](/features/predictions), they observed a 10% drop in their user base’s conversion rate. After investigating why, the product team found that, as the customer's lead generation tools grew more advanced and varied, the lead data quality was reduced, and it became more difficult for their users to see what was valuable to them. With this in mind, they determined that they needed better ways for users to cut through the noise and surface their most valuable leads–and they settled on a solution in AI [lead scoring](/solutions/lead_management).

> “We wanted to be able to help our customers’ sales teams close more deals faster. Faraday’s lead scoring and data could help us do just that.” - **VP of Product at a leading CRM for roofers**

## Finding a solution and time-to-market that fits

Although the product team unearthed an excellent opportunity to help their customers, building AI lead scoring entirely in-house was out of the question, as they didn’t have the resources to do so, and they knew that time was of the essence, so the long timeline wasn’t going to work for them. They also knew they needed purpose-built infrastructure to scale up as they grew.

With Faraday, this roofing software company found an infrastructure designed to predict lead and customer behavior and comes with a [consumer identity graph](/features/data) that includes data on nearly every U.S. adult. This–plus Faraday’s 10+ years of experience–gave their product team the confidence to build and launch a successful product in their desired timeline.

> “If we’d had the resources to build this pipeline ourselves fully, we were looking at a time-to-market of over a year. We integrated with Faraday in about three months, and it’s been worth it for that alone–not to mention the data and expertise we’ve received.” **VP of Product at a leading CRM for roofers**

## Making predictions actionable

The roofing CRM’s software engineers embedded Faraday’s lead scores into their product and provided a way to filter lead results by lead rank. With lead rank, every lead and prospect in the roofing software is assigned a rank based on how likely they are to convert, which is displayed as the lead rank status. Before partnering with Faraday to surface this likelihood, The roofing CRM’s users might spend days nurturing a sales opportunity only to have it fall flat because there wasn’t a good fit.

Their users can easily filter by the highest lead rank, which perfectly fits the ultimate goal at the start–to surface actionable insights that help focus roofing sales teams.

[![Image of roofing CRM user interface](/images/blog/leading-roofing-crm-lead-scoring/roofing-crm-ui.png)](/images/blog/leading-roofing-crm-lead-scoring/roofing-crm-ui.png)

In the first six months since launch, the roofing CRM’s clients who have turned on the new feature close deals 10% faster than those who haven’t–netting an additional $3k+ per month for your average contractor. The product team at the CRM for roofers is thrilled with the results so far and are keen to leverage more of Faraday’s infrastructure to build additional features, focusing on [explainability](/blog/what-is-explainable-ai-why-is-it-important) to give users a clear sense of why an individual lead is receiving its score.

[Create a free Faraday account](https://faraday.ai/signup) today.

A leading CRM used by roofers powers their Lead Intelligence tool with predictive lead scores, which helps their customers see which leads are most likely to convert.

How a leading CRM used by roofers employs Faraday to help clients close deals 10% faster

**See our complete guide:** [Powered by Faraday — embedding predictive AI in your platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai).

---

We are excited to announce that [AccuLynx](https://acculynx.com/), a leading provider of cloud-based business management software for roofing contractors, has launched a new Lead Intelligence feature that uses AI to predict homeowner buying behavior.

[Lead Intelligence](https://acculynx.com/roofing-sales-lead-intelligence/), powered by [Faraday's prediction API](https://faraday.ai/developers), taps into responsibly sourced consumer data and machine learning to enrich and score Acculynx customers’ leads every week, and provides users with up-to-date lead rankings that can help prioritize their sales efforts and close more business efficiently.

“Lead Intelligence is a game-changer for roofing companies,” said Mark Rattin, VP of Product at AccuLynx. “AccuLynx customers no longer have to guess which leads to call first, or only rely on their experienced sales people to quickly close deals. Now they can improve their sales win rates by focusing their efforts on the best leads.”

## Here’s how it works

Previously, Acculynx users could set a lead's priority level depending on what they thought of the lead. This was of course based only on a limited set of data that the user had in front of them and their intuition.

Lead Intelligence uses Faraday to analyze hundreds of data points on all of their customers’ leads, including demographic, financial, property data and more, which then provides a score that indicates the likelihood of a lead converting into a customer.

By leveraging Faraday's API, Acculynx was able to develop the Lead Intelligence feature in a matter of months, providing users with a game-changing tool included in all subscriptions and saving significant engineering and data science resources that would otherwise be required.

We at Faraday are proud to support Acculynx’s mission of providing roofing contractors with easy-to-use technology tools that help them work smarter and grow their business.

How AccuLynx uses Faraday’s prediction API to power a game-changing Lead Intelligence feature for roofing companies across the US.

Acculynx launches AI-powered lead intelligence with Faraday prediction API

Purchasing jewelry is traditionally positioned around the moment. Whether it’s for romance or a special occasion, jewelry campaigns tend to focus on everything but the actual wearer. [gorjana](https://gorjana.com/) is the fine jewelry company that says otherwise by providing jewelry you’ll live in.

With this focus on the wearer, it comes as no surprise that gorjana wanted to know who their ideal customers were, so they teamed up with Faraday to validate their customer profile and reach more potential customers across digital channels and direct mail.

We recently chatted with [Elton Graham](https://www.linkedin.com/in/eltonxgraham/), Chief Marketing Officer at gorjana, to further explore their challenges and how they worked with Faraday to solve them.

## Gaining customer insights by clustering for personas

Previously, gorjana had a pretty good hunch about who their core customers were, but were looking to confirm and deepen their understanding of them with more data. In came Faraday, unlocking a wealth of data that they could leverage to not only confirm their profile, but validate their core customer segments with the people-first focus they were looking for.

To do this they leveraged the [Faraday Identity Graph](https://faraday.ai/features/data), enriching their first-party data with 1400+ responsibly sourced attributes on nearly every adult consumer in the U.S. With this combination of data, gorjana was able to build brand-specific customer personas using Faraday’s built-in machine learning capabilities. These quantitatively developed personas enabled the gorjana team to identify similarities and differences between their core customer segments to further understand how their retail-only, ecommerce-only, & omni-channel customers compared.

## Calculating total addressable market (TAM) with potential buyer predictions

[Faraday’s potential customer predictions](https://faraday.ai/features/predictions) are often used to create custom audiences for customer acquisition campaigns. The gorjana team took it a step further and leveraged those predictions to calculate their TAM and penetration rates in designated market areas across the U.S. This prediction-based TAM analysis is currently helping to inform go-to-market decisions and campaign strategies across the country.

## Reaching more potential customers across paid digital and direct mail

With a stronger understanding of their customers, the gorjana team leaned into their potential customer predictions to create custom audiences of high propensity to buy consumers for their digital ad campaigns. The team saw meaningful improvements in cost per acquisition and return on ad spend with these custom audiences from Faraday.

When asked what’s on the horizon for gorjana, their response was to expand their use of Faraday’s potential customer audiences to direct mail, and to build new predictive outcomes to identify likely gifters.

We love working with gorjana - they’re a people-first brand making fine jewelry for everyday wear. [Check them out](https://gorjana.com/) to find the perfect pieces for your jewelry stack.

At gorjana, Elton Graham’s team used Faraday to gain customer insights, validate core customer segments, and reach more potential customers with data-driven campaigns.

How gorjana fuels growth with responsible data and AI

Since 2009, [Momentum Solar](https://momentumsolar.com/) has been one of the fastest growing solar installers in the country, helping thousands of homeowners make the transition to solar.

## The problem: optimizing the lead scoring process

Momentum Solar relies on their state-of-the-art call center as their main conversion channel, reaching out to leads acquired from aggregators to set up on-site assessments for solar installations.

But Momentum didn't become one of the nation's largest installers by being satisfied with _good enough_.

A solar installation is a significant investment, and it can be easy to get cold feet. When a sales rep needs to dedicate an afternoon to an on-site visit that can be canceled at any time, the cost to acquire a new customer can get expensive quickly. To help offset those costs, the Momentum team was looking for areas of optimization with the goal of reducing acquisition costs. One area they looked at for improvement was a lift in their lead conversion rate. They believed that [better lead prioritization](/solutions/lead-management) that allowed their call center to focus on the best leads was key to driving this lift.

## The obstacle: an incomplete picture of a solar customer

Traditionally, Momentum had leveraged a rules-based approach to lead scoring that focused on a few data points, such as lead source, lead demographics, home ownership status, and location of residence, to determine lead priority. The challenge with a rules-based approach is these data points are often subjectively chosen, and don't account for the full spectrum of both intuitive and unintuitive data points that are key indicators of whether or not a lead will convert.

Additionally, Momentum had a large collection of leads that had not initially converted. A major issue with the rules-based approach to lead scoring is that it's static, even though people aren't. With Momentum's existing approach, a lead's score–and in turn, priority–would never change.

## Why Faraday?

### Fresh data + models

To optimize their lead conversion, Momentum used the Faraday platform to add a [predictive layer](/features/customer-behavior-predictions) to their lead prioritization system.

The value of predictive models is the quantitative, rather than qualitative, approach to lead scoring. Machine learning algorithms run millions of simulations to find patterns in large datasets, meaning they transcend subjective biases.

Instead of scoring a lead based on a few key points, Momentum used the Faraday platform to build a [lead scoring model](/blog/why-you-need-predictive-lead-scoring-today) that ranked leads based on their probability of becoming a customer. The model trained on Momentum's existing customer base and the [hundreds of first and third-party data points](/features/faraday-identity-graph) that Faraday associated with Momentum's customers.

Furthermore, the lead scores were updated as new customers' examples were added and lead behavior was observed. This ensured that Momentum's prioritization methodology was never static.

## The results

### Appointments set 33% faster

To test the efficacy of this new approach to prioritization Momentum split the scored leads into three groups: red, yellow, and green, with red being low probability leads and green being high probability leads. The goal was to see a _higher_ rate of lead-to-scheduled appointments rates for the yellow and green groups (comprising the top four deciles  of leads) and _lower_ conversion in the red group (bottom six deciles).

Each of these groups received the same call cadence and performance was measured against a baseline performance of 180 connected calls per scheduled appointment.

As expected, the green and yellow groups converted at increased rates compared to the baseline vs the red group, which converted at a lower rate. Specifically, the green group saw a **+191% lift in conversion rate** over baseline, while the yellow group saw a **+51% lift in conversion rate** over baseline. Equally as important, the red group converted 4% _lower_ than the baseline, further confirming where the call center should spend its time.

Ultimately, the call center needed **33% fewer outbound calls** to hit their scheduled appointment goal.

Momentum Solar used Faraday to add a predictive layer to their call center's lead scoring process, resulting in the call center needing 33% fewer outbound calls to achieve their appointment goal.

How Momentum Solar cut calls-per-appointment by 33% with predictive lead scoring

**See our complete guide:** [Predictive AI for banks and credit unions](/blog/banks-credit-unions-complete-context-guide).

---

[Community First Credit Union](https://www.communityfirstfl.org/) (CFCU) started in 1935 in a Jacksonville high school basement and has since evolved into 19 branches and over 135,000 members. CFCU's success is rooted in its commitment to its members, offering tailored products to meet individual needs and utilizing innovative technologies to deliver an exceptional banking experience.

## The problem: finding the right members

A mere 19% of Americans have three or more products with their primary financial institution–that is, where their checking account lives–despite the average American holding [eight financial products](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/financial-services/us-kickingitupanotch-092614.pdf).

In order to remain competitive & ensure long-term success, CFCU knew that they had to grow their "wallet share" with [existing members](/solutions/customer-engagement). More members adopting CFCU's comprehensive financial solutions deepens the financial relationship between the credit union and its members, fostering loyalty and long-term engagement. This diversification helps in spreading risk and maintaining a more stable financial base, while reducing the need for costly marketing and acquisition efforts.

The challenge they faced was how to accomplish this effectively. CFCU lacked the resources to compete directly with larger banks and fintech companies. That meant simply shoveling more ad spend into their [direct mail](/blog/personalized-direct-mail-strategy) program wasn't an option - CFCU needed a more efficient approach that could increase response rates from both new and existing members _without_ increasing spending.

## The obstacle: reaching the right members efficiently

Before Faraday, CFCU used traditional segmentation, like a member's current product mix, their income, and identifying members to whom they could extend pre-approved offers of credit. What they found is that this approach often failed to capture the nuanced preferences and [behaviors](/features/customer-behavior-predictions) of individual members. Because segments were built off broad categories, the generic messages tailored to these segments didn't resonate with recipients. [Personalized](/blog/personalization-at-scale-guide) and relevant content were essential to better find and engage loyal members.

CFCU understood that AI offered opportunities for more effective segmentation, on top of an unlock for personalizing a member's journey.  Their existing member data provided the historical examples of customers who had grown wallet share from one product to many. Their existing direct mail providers had exposed CFCU to the immense trove of additional third party data that could be leveraged to create distinct segments.

At the same time, they knew that were signficant costs associated with this data, the infrastructure needed to run AI models, and the in house talent needed to develop that level of modeling.

## Why Faraday? 

### Identity resolution & easy product recommendations

Partnering with Faraday turned out to be the unlock that CFCU needed to realize this vision of using AI to grow wallet share amongst their members.

They used the Faraday platform to connect their member data to Faraday's identity graph, giving them instant access to [hundreds of data points](/features/faraday-identity-graph) beyond those they had used to segment members in the past. Using Faraday's modeling engine, CFCU was able to build product recommendations that were personalized for each member and for all prospects in their charter territory.

With this segmentation in hand, CFCU used Faraday to easily build mailing lists of the individuals they most wanted to target in their direct mail campaigns.

## The results

### Acquisition campaign

CFCU used the Faraday platform to predict which consumers were most likely to become their next loyal members, defined as a member with a high account balance and strong net interest revenue. To test the model, CFCU ran a direct mail campaign with a 50/50 audience split (50% from high-scoring model predictions; 50% from traditional segmentation techniques).

The new members generated using Faraday's targeting saw **61% higher average account balances** and **24% higher net interest revenue** than the new members acquired using traditional segmentation techniques.

### Next product recommendation

Faraday also predicted which members were most likely to need a checking account, auto loan, first mortgage, and more. To test the model's predictions and get in front of members with the highest potential for engagement, CFCU ran a direct mail campaign with the same technique in a 50/50 audience split (50% based on Faraday's next product recommendation; 50% from traditional segmentation techniques).

The members contacted with offers recommended by Faraday saw **54% higher net interest revenue** and **88% lift in projected ROI**.

With Faraday, Community First Credit Union used customer predictions to drive highly effective acquisition and engagement campaigns with great results.

Customer stories

How a leading CRM used by roofers employs Faraday to help clients close deals 10% faster

Acculynx launches AI-powered lead intelligence with Faraday prediction API

How gorjana fuels growth with responsible data and AI

How Momentum Solar cut calls-per-appointment by 33% with predictive lead scoring

How Community First Credit Union unlocks 61% higher account balances using Faraday’s predictive AI