[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.

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

Thirstie is a technology company that offers an end-to-end solution for the alcohol industry, connecting brands with consumers through an integrated e-commerce platform. Their services include regulatory compliance, delivery logistics, and data analytics to streamline the online sale and delivery of alcoholic beverages.

## Challenge: Pioneering direct channel consumer data for the alcohol industry

E-commerce has revolutionized the way alcohol brands reach their consumers, offering a direct sales channel that bypasses traditional distribution models.

This direct relationship unlocks the most valuable data asset for a brand, a list of end customers. Prior to the emergence of an e-commerce channel for alcohol, alcohol beverage brands' customer insights were limited to the data they could garner from their distributors and retail sales data.

Recognizing the opportunity within this order data lie, Thirstie set out to make consumer insights a central component of their service offering. Their goal was to go beyond mere transaction data, providing brands with deep, actionable insights into a given brands’ customer base.

## Obstacle: Going beyond transaction data with predictions

While Thirstie had ample transaction data, they lacked any additional data on these buyers. Thirstie’s ability to deliver customer insights meant they would need access to 3rd party consumer data and the analytical resources to make sense of it all.

Challenge one, was licensing and reconciling thousands of transactions with a large consumer database would be both expensive and complex, involving substantial investment in ETL and computing power.

Challenge two, was identifying meaningful patterns and trends consistently and at scale.

## Why Faraday: Plug and play persona analytics

Facing a long development cycle in a competitive market, Thirstie looked to Faraday as an infrastructure provider who could not only handle data enrichment and ETL, but also the modeling workload to derive insights from this combined dataset. With access to Faraday’s Identity Graph, Thirstie only needed an email address associated with an online transaction to tap into hundreds of data points on every customer in their database. The consumer data covered the full spectrum of demographic, financial and psychographic data, ensuring a complete view of each brand's customers could be built.

With this combination of transaction and consumer data, Thirstie used Faraday’s k-means clustering models to produce customer persona sets by alcohol beverage type.

Each time a transaction was observed, Thirstie used Faraday to assign each buyer to the appropriate persona, populating this information automatically in their data warehouse. Instead of building a bespoke solution, Thirstie could link Faraday’s consumer data with their warehouse, ensuring data is refreshed daily.

Thirstie displayed these personas in an analytics dashboard in their product, providing its clients with a rich understanding of the distinct socio-demographic segments within their customer base.

Thirstie also made this data available at the order level, so that their clients could leverage it for personalized engagement and advertising..

## Results: A Faraday powered core offering reduces Thirstie’s sales cycle by 30%

By partnering with Faraday, Thirstie was able to quickly bring deep insights and customer personas to its users, on scalable infrastructure. They did this without having to increase their expenditures on data science resources or the licensing of third party data assets.

Since partnering with Faraday in 2020, Thirstie has seen its client base more than quintuple, and the length of its sales cycle has dropped by 30%.

In May of 2024, Thirstie was acquired by Cocktail Courier.

Thirstie, an e-commerce platform for beverage brands, built a new analytics feature, powered by  Faraday, that drove adoption.

Customer stories

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

How e-commerce platform Thirstie reduced their sales cycle by 30% with an analytics feature powered by Faraday