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](/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](/features/faraday-identity-graph), 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

**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 solar provider improved customer targeting with personas and market opportunity analysis

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