Fundrise scales 1:1 personalization with AI agents to drive investment growth

As Fundrise grew to over 400,000 customers and expanded into venture and private credit investments, they ran into one of the problems of success: how to scale high-value, personalized customer experiences without drastically increasing operational costs.

“At Fundrise, we only succeed when our investors do, because our fees are charged as a small percentage of assets under management. We want to pass as much savings on to our investors as possible, so adding more headcount and expanding our staff and costs proportionately to our customer base simply wasn’t a real option,” explained Fundrise’s CPO, Luke Ruth.

Fundrise saw AI Decisioning as an opportunity to transform their approach to investor communications while testing the potential impact AI agents could have on their marketing programs. It quickly evolved from a test to a change in how the company approaches personalization at scale across campaigns.

Scaling personalization for 400,000+ customers

Building enduring customer relationships through personalized communications isn’t just a marketing goal for Fundrise — it’s core to their business. However, the marketing team had hit a limit with traditional segmentation for audience-based campaigns, and delivering relevant, engaging content to each individual was growing increasingly complex, especially when given their need to leverage hundreds of attributes, test multiple messaging and creative variants, and orchestrate communication across channels.

“We refused to let growth force us to cut corners on personalization, but our manual workflows just couldn’t keep pace. As we added more investors, delivering truly tailored messages felt impossible,” explained Lindsay Kaplan, a Senior Director of Lifecycle Marketing at Fundrise.

Fundrise’s true focus is on building relationships with their customers as they grow their investments and use the platform over time, and their marketing strategy and technology needed to reflect this long-term plan.

Fundrise had already been experimenting with emerging technologies and exploring ML/AI techniques when they decided to try an AI Decisioning platform. Hightouch uses reinforcement learning and AI agents to make personalized decisions for every customer, learning from all of Fundrise’s data to continuously optimize the content, timing, creative, and channel of every message.

Within two to three months of launching AI Decisioning, the team saw significant growth in investments and a future look into how AI could scale their marketing capacity. Here’s the story of how they went about this…

Winning back dormant users

Fundrise started with a critical business challenge: winning back dormant users who had stopped engaging with their platform. Customer acquisition can be very costly in financial services, so winning back dormant users is a material growth lever for Fundrise.

This win-back use case offered a chance to see how AI Decisioning stacked up against Fundrise’s traditional campaigns. To set the agents up for success, Hightouch and Fundrise first audited every messaging asset — both existing and new — and defined the guardrails that would govern the AI agents’ decisions across:

• Who can receive which messages
• How often to send messages
• Which creative to use
• Which days messages can be sent

Very early on, the importance of properly weighting different goals within AI Decisioning to power the reinforcement learning was critical.

“We have a mix of short and long-term goals for our marketing, and needed AI Decisioning to handle that effectively. We wanted to know about email clicks, but ultimately optimize for orders placed and order value, while keeping retention and account growth in view. The ability to weigh goals meant that we could maintain focus on building relationships, not just driving transactions,” said Luke Ruth.

The platform gives the Fundrise team control through goals to ensure the system is optimizing toward the right outcomes and avoiding the wrong ones.

As they implemented AI decisioning and began seeing early results, they realized that some of their operational constraints were self-imposed rather than determined by investor needs.

“A lot of what we thought were calendar-based or time-sensitive messages actually weren’t. We had organized campaigns around calendar dates that made sense for our workflows and historical approaches, not necessarily when they would be most relevant to individual investors,” said Lindsay Kaplan.

With AI Decisioning, the marketing team organizes around the optimal time for each investor to maximize outcomes for both the customer and the company.

Growing investments

Within a month of launching AI Decisioning, the dormant-user pilot delivered clear wins: for that specific campaign, investment orders grew substantially, investments increased by 4x, and click-through rates jumped. Compared to standard marketing campaigns using the same content, AI Decisioning delivered a 4x higher amount invested on roughly the same number of orders placed. Even aside from the immediate revenue potential for Fundrise, higher investment amounts lead to investor “stickiness” and allow Fundrise to develop longer relationships with their customers.

Discovering unexpected insights

Beyond the financial impacts, AI Decisioning delivered unexpected insights into how specific customers respond to different types of messages. Based on its experimentation and analysis, AI Decisioning automatically surfaces significant and unusual correlations.

Here’s what the marketing team discovered:

• The best messages significantly outperform the worst: The best messages have 6x the average CTR compared to the worst, highlighting the importance of continuous testing and refinement.
• Some geography matters: Investors in New York and California responded better to particular messages, indicating potential for regional targeting.
• One content theme regularly outperformed others: Messages about new investment opportunities routinely outperformed other messages, revealing investors’ strong interest in portfolio diversification.
• Age matters for content themes: Customers born after 1990 showed higher engagement with podcast-related messages, providing actionable insight beyond the immediate campaign.

This has been a huge breakthrough for the marketing team because they can now take these insights to refine future campaigns and identify high-value opportunities to enhance product and marketing efforts across the company.

Shifting team operations from execution to strategy

The most profound improvement for Fundrise is the evolution in how their marketing and lifecycle teams operate, shifting from building and managing campaign calendars and journey flows to outcome-based marketing.

“This shift has been a turning point for our team. Rather than being bogged down in building triggered journeys or executing batch campaigns, we’re digging into investor needs and how we can best serve them,” shared Lindsay Kaplan.

Execution constraints had limited the lifecycle team to what they could accomplish given their personnel, time, and tools. Now they can focus on strategic functions, such as understanding the “why” behind message performance, analyzing patterns in investor behavior, and creating opportunities for deeper customer engagement.

“Our marketing team has undergone a similar transformation to our customer success team when we implemented Intercom,” explained their CPO. “In the same way that it freed the support team from answering repetitive questions to focus on deeper investor support, AI Decisioning has shifted our lifecycle team from tactical execution to strategic orchestration.”

All this has been achieved without adding more headcount — another proof point that AI isn’t here to replace marketers but to improve their capabilities.

Future vision & expansion

Fundrise has established a foundation for AI-powered personalization with their win-back success. They’re now expanding applications across other parts of the customer journey.

“We’re rethinking what’s possible across the entire investor journey, starting with applying this technology to lead nurturing and encouraging existing investors as they explore new asset classes. Personalization is the backbone of marketing, and with AI Decisioning, we’ve laid the groundwork to power and deliver any experience for years to come,” explained Luke Ruth.

AI Decisioning is a new cornerstone in Fundrise’s strategy to scale personalization as they continue to grow. They plan to leverage strategic insights from their initial implementation to enhance marketing decisions across the entire customer lifecycle, maintaining their tech-forward position in an industry not known for innovation.

To learn more about how AI Decisioning can deliver value for your team, check out how Whoop used AI Decisioning to drive 10% lift in cross-sell conversions or request a demo today.