[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.

How Burrow uses customer predictions to drive intelligent growth

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

## The problem: optimizing their lead scoring methodology

A national home services brand was trying to optimize their lead-to-customer conversion rate. Their existing lead handling methodology was to engage all leads upon entry into their funnel via a call center, then an SMS and email engagement drip for those leads that did not convert. Their active lead engagement window was 19 days from funnel entry to lead conversion. Leads were scored using a rules-based methodology of first-in, first-out with identity verification completed upon form fill completion. Beyond this, all leads were treated the same.

This longer sales cycle resulted in sales representatives having more leads than they could consistently follow up with. The company believed this dynamic was negatively impacting conversion rates with sales reps spending too much time on prospects with a low chance of converting into customers, and too little time on those who were a great fit for their services.

## The obstacle: limitations of rules based scoring models

Conventional B2C lead scoring models commonly utilize a rules-based approach, where human judgment determines the rules for scoring leads. Typically, this involves calculating a lead score by establishing rules from a blend of basic demographic, property, or identity details (e.g. owner-occupied household, identity verified, etc.) and observed behaviors such as website visits, email interactions, form completions, and/or call length.

Although these rule-based models aid marketing and sales teams in prioritizing their efforts, they carry notable limitations.

1. The criteria for lead scoring in a rules based approach are often subjectively chosen, leading to inaccuracies in assessing a lead's actual potential.
2. There is a cold start problem for a new lead that has exhibited little to no behavior. A new lead that has just entered the sales funnel often can't be scored, or the score is inaccurate due to a lack of observed behavior.
3. Rules reliant on demographic, property, and/or identity criteria often involve either having to ask the lead for this information, which impacts conversion rates, or licensing 3rd party data, which can be costly and increase a company's privacy compliance and liability burdens.

As a result of these limitations, this national home services team started exploring alternatives to traditional lead scoring.

## Why Faraday?

### Quantitative rather than subjective lead scoring

Unlike a rule-based lead scoring approach, [predictive lead scoring](/blog/predictive-lead-scoring-b2c) uses machine learning to analyze historical lead data and create statistical models. This approach predicts the likelihood of a lead becoming a customer through quantitative analysis, rather than qualitative assessment.

Faraday's predictive modeling eliminates subjectivity from lead scoring by incorporating both intuitive and non-intuitive data points that traditional lead scoring might miss. The model updates continuously with new customer data, ensuring that changes in the market or product offerings are reflected in the score. Unlike rule-based scoring, which can become outdated, predictive scoring always evaluates leads based on the most current and relevant information.

Additionally, Faraday's [out-of-the-box identity graph](/features/faraday-identity-graph) eliminates the need to collect identity criteria or rely on expensive data vendors. With just an email or physical address, Faraday can instantly access over 1,500 data points on nearly every adult consumer in the country, both providing a deep well of data to find signals and eliminating the cold start problem of how to score leads with very little data.

## The results

### 4x higher lead conversion

The marketing and analytics team at this nationwide home improvement brand integrated their lead data into the Faraday platform. The data used for training these models included multiple years of lead, appointment, and customer records. The Faraday platform automatically associated these records with thousands of demographic, property, financial, and lifestyle attributes found in the Faraday Identity Graph.

From there, the brand quickly built predictive lead scoring models to assess and rank leads based on their likelihood to convert into paying customers. The models found objective patterns across the client and Faraday's data that clearly distinguished a good lead from a bad lead.

To validate the accuracy and effectiveness of Faraday's lead scores, the home improvement brand engaged the agency Bold Orange to conduct a thorough analysis of the efficacy of the predictive lead scores, comparing Faraday model predictions against actual lead conversions and outcomes.

[![Image of a third party holdout analysis](/images/stories/screenshots/bold-orange-analysis.png)](/images/stories/screenshots/bold-orange-analysis.png)

The analysis revealed that the conversion rate of leads in the top 10% of lead scores was **4x higher than the brand's baseline conversion rate**, confirming the model's ability to accurately identify high-potential leads.

With this third-party verification in place, the brand deployed the models into production. The models scored the brand's leads in real-time the moment they entered their sales funnel and used the scores to prioritize leads within their call center. Now, the sales team has the confidence to know that they are talking to the best opportunity at any given moment.

A third-party analysis revealed that the conversion rate of leads in the top 10% of lead scores was 4x higher than the brand's baseline conversion rate, confirming Faraday's ability to accurately identify high-potential leads.

How Faraday's predictive lead scoring helps a national brand identify leads with a 4X conversion rate

[![DataGrid header image](/images/stories/screenshots/salesrabbit-story-header-image.png)](/images/stories/screenshots/salesrabbit-story-header-image.png)

## Making a powerful tool even smarter

SalesRabbit is the leader in software for field sales teams. Their [DataGrid](https://salesrabbit.com/datagrid/) solution supplies canvassers with household data on specific properties and homeowners in their territories, providing essential context as they knock on doors. However, the SalesRabbit product team recognized that static data points on each property provided limited context for sales reps.

They realized that, if they could identify which attributes of a homeowner or property could predict the likelihood that a prospect would become a customer, then they could use that information to inform how their sales reps approached their day-to-day responsibilities. For example, solar clients would not only know which houses had the right amount of sun exposure for solar panels, but also which of them most closely fit the model of historical solar adopters. Pest control clients would not only know what material houses were made out of, they could also know if the homeowner would be more or less likely to say yes to regularly scheduled inspections or not, and so on.

Furthermore, if they had a way to predict which neighborhoods had the highest concentration of likely customers, then DataGrid could direct sales reps' efforts more efficiently.

## The challenges of building predictive AI models from scratch

SalesRabbit identified three main obstacles to them building effective predictive AI models in-house:

- A lack of data about consumers.
- The long ramp time to build effective predictive models.
- Resources to test models for accuracy and continue to provide transparency about the models efficacy.

### Licensing more customer data

Predictive models need a deep well of data to find an accurate signal. Despite the wealth of data SalesRabbit had on homeowners, adding an ML layer to DataGrid would require thousands more data points to ensure accurate models. Psychographic data, shopping behaviors, demographic data, financial information, and more would all need to be vetted, licensed, and reconciled with their existing data before predictions could be made.

### Extensive time and resources dedicated to prediction

To build a customer prediction pipeline in-house, the SalesRabbit team would also need to grow their existing data science team. They couldn't just reorganize other team members for a temporary sprint. Not only is the typical timeline for an ML program multiple years, they’d need to invest to continuously maintain those models over time.

### Limited transparency and scalability of cost

For the C-level team at SalesRabbit, it was critical for the solution to their challenge to be scalable. The original timeline for building this feature entirely in-house was multiple years and required a major investment from their data science team to get started.

Additionally, SalesRabbit's customer base includes both enterprise home service companies and small, regional installers. Creating a customer prediction pipeline comes with a tremendous computing cost, and running hundreds of millions of scenarios to build a model that works quickly can grow that cost exponentially–and unpredictably. How do you forecast the budget for the computing cost when the number of predictions made monthly for large and small organizations varies dramatically from month to month?

## The partnership

[![DataGrid mock](/images/stories/screenshots/datagrid-mock.png)](/images/stories/screenshots/datagrid-mock.png)

Realizing that an in-house build carried these risks, the SalesRabbit team shopped for existing best-in-class infrastructure that they could build on.

Faraday was the perfect fit for the team to implement customer predictions quickly, and at scale.

### Built-in consumer data for predicting customer behavior

Faraday has over [1,500+ attributes](/features/faraday-identity-graph) built into its platform, which mean all of this data was available on day one. Even better, it could be seamlessly combined with their existing data sets–no identity resolution required.

Employing this font of data on nearly every U.S. adult, SalesRabbit had everything they needed to understand the patterns and trends of their customers' ideal homeowners, regardless of which vertical their customers work in.

### Preserving existing data science resources and cutting product build time with Faraday recipes

Faraday’s plug-and-play [customer behavior predictions](/features/customer-behavior-predictions) meant there was no need to pull their data scientists off of other projects to build a customer prediction pipeline from scratch. SalesRabbit used pre-built prediction recipes as an accelerator to test and refine models before incorporating them into their product. This allowed them to shrink a multi-year roadmap to a matter of days, and free up their data science team to focus on other efforts.

### Peace of mind with transparency and scalability of cost

To the product team at SalesRabbit, building a customer prediction pipeline from scratch came with no small level of forecasting uncertainty. How much should they invest to launch? How much will it cost to scale up as their customer base grows?

With Faraday, they found pricing certainty, which allowed SalesRabbit to know exactly what to expect as they scale up.

## The results

[![DataGrid UI screenshot](/images/stories/screenshots/datagrid.jpg)](/images/stories/screenshots/datagrid.jpg)

To SalesRabbit, choosing to embed customer predictions into their software rather than build it in-house was a game-changer. Once SalesRabbit connected their data and created their initial predictive models, Faraday’s developer API made it easy to start embedding Faraday’s predictive features into DataGrid, and in a fraction of their original timeline.

Launched as “DataGrid AI,” SalesRabbit’s predictive lead scoring tool guides canvassing teams to best-fit territories, helps with route planning, and leaves reps with clear guidance on which households to prioritize when in the field. And because all of Faraday’s predictions are cached in advance, overall app performance improved once they were integrated.

Launched as “DataGrid AI,” SalesRabbit’s predictive lead scoring tool guides canvassing teams to best-fit territories, helps with route planning, and leaves reps with clear guidance on which households to prioritize when in the field.

How SalesRabbit uses Faraday to enhance their field sales app with data & 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

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

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 400+ 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

[Advia Credit Union](https://www.adviacu.org/)'s mission is to provide financial advantages to its members through solutions that address life's evolving needs, including mortgage refinancing, debt consolidation, and auto loans.

## The problem: engaging customers with the right message

The average American holds eight financial products, yet only [19% of Americans](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/financial-services/us-kickingitupanotch-092614.pdf) have three or more products with their primary financial institution. For many credit unions, including Advia, growing their "wallet share" with [existing members](/solutions/customer-engagement) is key to their long term stability and success. The challenge is how to do this effectively. There is brand risk in simply bombarding members with unsuitable offers that could result in members ignoring all communications from the credit union. Worse, it could erode trust, create frustration, and sow overall member dissatisfaction.

For Advia, they needed a way to understand the personalized financial journeys for each of their members so that they could surface the [next best product recommendation](/features/customer-behavior-predictions) for each member. Such an approach would ensure optimal use of their marketing budget, minimize member dissatisfaction, and help grow wallet share with their members.

## The obstacle: using data and remaining compliant

Advia understood that their member data likely held the key for developing next best product recommendations. Unfortunately, their lead data scientist had recently left the organization, and Advia was looking at a lengthy ramp period to hire and onboard someone new.

Additionally, Advia needed to navigate a complex regulatory landscape. Advertising lending products comes with stringent compliance requirements to ensure fair lending practices and protect consumer privacy. The Equal Credit Opportunity Act (ECOA), for example, prohibits discrimination in credit transactions based on race, color, religion, national origin, sex, marital status, age, or receipt of public assistance. This means any data used for segmentation in advertising must not incorporate these protected classes, either intentionally or unintentionally, to avoid violations.

## Why Faraday?

To address these challenges, Advia partnered with Faraday. Advia used the Faraday platform to determine which of their primary retail financial products should be surfaced to each member. They initially tested this by looking at which members should receive information on their auto loan product.

Advia linked its historical member data with Faraday's identity graph, including information on member adoption of Advia's auto loan. With this training data, Advia created a set of predictive models that scored each member's propensity for an auto loan. They also leveraged Faraday's [bias identification and mitigation features](/blog/faraday-bias-management-announcement) to address historical bias in their training set, aiding their fair lending compliance efforts.

With Faraday's product recommendations in hand, Advia could segment their audience more intelligently. They identified the top percentile of members most likely to complete an auto loan application and targeted them through both personalized email and direct mail campaigns. This targeted approach ensured that the most relevant offers reached the right members, enhancing engagement and increasing the likelihood of loan applications.

## The results

The success of Advia's auto loan campaign was measured by the percentage of recipients that completed auto loan applications. Before using Faraday, Advia's benchmark for success was a 1.19% application rate.

**30-Day results**: After just 30 days of running the campaign that used Faraday's recommendation, Advia achieved a 2.41% application rate. This significant improvement resulted in **$1.1 million in new auto loans**.

**90-Day results**: The application rate continued to grow over the next 60 days achieving a 5.18% application rate after 90 days. This increase in engagement led to a total of **$2.7 million in new auto loans**, far surpassing the initial benchmark.

Advia Credit Union's use of Faraday's platform demonstrates the power of personalization and predictive analytics. Faraday gave Advia the ability to target the right members with relevant offers, leading to higher engagement and significantly improved loan application rates.

This approach not only boosted Advia's campaign performance, but also boosted Advia's regulatory compliance efforts, while safeguarding member trust. Advia's use of the Faraday platform exemplifies how financial institutions can harness data to drive better outcomes, provide superior service to their members, and use [AI responsibly](/features/ai-safety-and-responsible-ai).

The team at Advia Credit Union used customer predictions to deliver the right product to the right people in a direct mail campaign while using AI responsibly.

How Advia Credit Union generates $2.7 million in new auto loans with Faraday’s consumer product recommendations

In the competitive landscape of direct-to-consumer (DTC) ecommerce, [personalization](/blog/personalization-at-scale-guide) is a key differentiator.

[Boll & Branch](https://www.bollandbranch.com/), a prominent DTC linens company, used Faraday to provide customer predictions that guide their marketing strategies.

## The problem

Boll & Branch was looking for a more accurate way to effectively segment their customer base so they could deliver more personalized and relevant content.

With a wide range of products and a diverse customer demographic, it was crucial to understand more about their customers, their preferred products, and channels.  They found that their initial segments had no correlation to specific products or marketing channels, limiting their ability to launch an effective personalization strategy.

## The solution

To address this challenge, Boll & Branch used Faraday's persona predictions to identify customer personas that represented distinct socio-economic clusters. Each persona had preferred products and marketing channels.

This approach differed from a traditional persona exercise in which a brand must either form personas from a qualitative process (e.g. surveys, focus groups, etc.) or leveraging pre-computed personas like PRIZM codes.

With [qualitatively derived personas](/blog/predictive-personalization-machine-learning), a brand can't easily assign new customers to a persona, unless a customer responds to survey questions. Given most customer surveys have single digit response rates, this limits a brand's ability to segment their customer base.

Pre-computed personas have the benefit of being universal, but are generic demographic clusters vs. specific patterns derived from a brand's data. This often results in persona segments with little variation in customer behavior.

Faraday's persona models leverage [rich data](/features/faraday-identity-graph) on nearly every U.S. adult–allowing for universal assignment–paired with an AI clustering algorithm that builds personas from a training set provided by a client's own customer data. The results are customer personas that can be applied to all known contacts in a brand's customer base, while also reflecting the socio-demographic nuances across this dataset.

### Data integration and persona development

Boll & Branch used the Faraday platform to identify three distinct personas based on a set of socio-demographic factors. They then were able to see that these segments had distinct purchasing behavior, preferences, and other relevant metrics.

These personas were then assigned to individual customers in Boll & Branch's [BigQuery](/integrations/predictions-with-bigquery) data warehouse, providing the metadata needed to support their personalization initiatives.

### Personas + attribution

Traditionally, Boll & Branch thought their demographic skewed older, and as a result they invested heavily in radio advertising. Pairing Faraday's personas with Boll & Branch's internal attribution system allowed them to see what kinds of customers were coming from which channels, what discount codes different personas were responding to, and what products they were purchasing.

An example of this was the marketing team's identification of their highest value persona, whom the team nicknamed "Jen": a wealthier, settled, homeowner, that preferred clean modern aesthetics.

This qualitative insight gave Boll & Branch the confidence to organize marketing towards the Jen persona - from branding, product design, prospecting, all the way down to engagement and retention.

As Daphne Chow, Senior Manager of Consumer Strategy & Analytics puts it, Faraday helped Boll & Branch "see what kind of consumer we are seeing as crucial to our success, and our whole organization has rallied around that goal."

For example, they could see that individuals in the Jen persona were heavy users of social media, and as a result began being more strategic with their channel mix.

Knowing Jen also allowed them to conduct focus groups and surveys with members of that cohort, where they found they were influenced by interior design personalities - guiding them to a successful partnership with the star designer, Nate Berkus.

### Personas + email campaigns

Because every contact in their system was assigned a persona, Boll & Branch could also use these assignments to power their email campaigns. The team designed personalized content for each persona: not only did they use creative that aligned to the socio-demographic makeup of each persona, they also were able to draw insights about what products each of their customer personas over-indexed on, allowing them to select highly relevant product recommendations for each persona.

This approach ensured that each customer received content that resonates with their unique preferences and needs.

## The results

The implementation of Faraday's personas into Boll & Branch's marketing strategy yielded impressive results. By delivering personalized experiences through targeted email campaigns, the company achieved a **30% lift in conversion rates**. This significant improvement underscored the value of utilizing advanced predictive analytics to drive personalized marketing efforts.

Boll & Branch's partnership with Faraday exemplifies the power of data-driven personalization in ecommerce. By identifying distinct customer personas and tailoring marketing messages accordingly, the company was able to enhance customer engagement and drive higher conversion rates.

Or, in the words of their Chief Digital Officer, Katia Unlu, "Faraday is in our DNA."

Boll & Branch used customer predictions to drive hyper-personalized customer experiences, resulting in a 30% lift in coversion rates.

How Boll & Branch achieved a 30% lift in conversion rates with customer predictions

[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