How to Build a Predictive AI Agent for Customer Retention

You know that sinking feeling when you pull up a lost deal report and spot a $200,000 account you didn't even realize was at risk? Your fraud systems flag suspicious transactions in milliseconds. Your infrastructure auto-scales before servers crash. But somehow, that high-value recurring revenue account quietly disengaged, and you found out 30, 60, maybe 90 days too late.

That frustrating gap between a customer checking out and you actually knowing about it? That's exactly what predictive AI agents for retention are built to fix. And the good news? You probably already have all the info you need to get one going.

In the Domo BUILD 2026 session Replacing Reactive Retention with Predictive AI Agents, Brian Jacobson, CFO at GeoComm, showed us how his team built an AI agent called CARE (Customer Account Retention Engine) to surface churn risk signals before customers go dark. Brian explained that GeoComm's problem wasn't missing data; it was that all the pieces weren't connected. The warning signs were there, scattered across different systems, but no one had brought them all together into one clear picture.

What follows is a practical framework for building your own retention agent, drawn straight from how GeoComm approached the challenge.

Start with signals, not systems

Identify the data sources that reveal customer health

Before you worry about AI models or dashboards, map out where your customer health signals actually live. GeoComm connected four categories of data that, together, painted a complete picture of account health.

The key signal categories to unify include:

• Commercial data: Contracts, annual recurring revenue (ARR), net promoter scores, and support ticket notes from your CRM or ERP (GeoComm used NetSuite)
• Engagement data: Platform login activity, including frequency, recency, and unique visitors
• Support burden: Case history, severity levels, time to resolution, and sentiment analysis
• Usage patterns: Feature adoption rates, especially whether customers adopt new features or stick with the same functionality they signed up for years ago

Brian noted that the insight came during a hackathon when the team realized, "We had a connection problem. We didn't have a single source of truth bringing together these important aspects into one location." The data existed. It just wasn't connected.

Build a unified health score

Transform fragmented signals into prioritized action

Once your data sources are connected, you need a mechanism to translate raw signals into something your customer success team can act on. GeoComm's approach uses ETL (extract, transform, load) processes to join data and generate a health score from zero to 100 for each account.

That score then feeds into priority segmentation, so CSMs (customer success managers) know which accounts need attention and how urgently. The dashboard lets CSMs filter by health score, rating, priority level, and renewal timeline (from 30 days out to 180 days plus).

Here's what makes this work in practice:

1. Define your scoring logic collaboratively: Involve CSMs in determining what signals should weight more heavily. This builds trust in the output.
2. Segment by urgency, not just risk: A low health score matters more when renewal is 30 days away than when it's six months out.
3. Make the score decomposable: Your team needs to see why an account scored the way it did, not just that it scored low.

Route explainable insights to workflows

Give CSMs summaries, rationale, and recommended next steps

A health score alone isn't enough. Your retention agent needs to operationalize that score by routing actionable summaries directly into CSM workflows. GeoComm's CARE agent analyzes priority accounts, summarizes the reasons behind the prioritization, and recommends specific next steps.

The agent also provides a chat interface so CSMs can dig deeper, asking follow-up questions or drafting outreach emails without switching contexts. The goal is to make the agent a daily tool that replaces the manual process of pulling data from multiple sources and trying to remember when you last checked in on a customer.

This shift compresses time-to-action dramatically. Brian described it as the ability to "execute these targeted actions in minutes, not hours or days," which means CSMs spend less time building reports and more time building relationships.

Keep humans in the loop

Automate routing and drafting, not judgment

Here's where governance becomes critical. GeoComm's agent auto-generates triage emails for priority accounts, but the keyword is "generating." The system doesn't automatically send those emails. CSMs review, modify, and decide whether to send.

Brian put it simply: "Automate routing and not judgment." The agent handles the heavy lifting of surfacing risk and drafting responses, but the human owns the relationship and the decision.

This approach serves two purposes. First, it maintains the quality of customer relationships by keeping a human accountable for communication. Second, it builds trust in the system. When CSMs know they're still in control, they're far more likely to adopt the tool and rely on it daily.

Measure impact on outcomes that matter

Track ARR protection, capacity gains, and forecast accuracy

GeoComm frames early business impact around three areas that translate directly to the metrics leadership cares about:

• ARR protection: Measured by increases in renewal and retention rates, plus proactive identification of risk before it results in churn.
• CSM capacity expansion: The same team can manage and maintain more accounts as the business scales, because they're not drowning in manual data gathering.
• Improved renewal forecasts: Forecasts tied to engagement signals rather than gut feel or educated guesses.

That last point is important. When renewal forecasts are based on actual customer behavior, like login frequency, feature adoption, and support sentiment, they become reliable enough to plan around. Wide best-and-worst-case ranges shrink into something actionable.

The framework above gives you a starting point for building your own predictive AI agent for retention. GeoComm also emphasized the value of cross-functional collaboration during the build process, including a dedicated 24-hour hackathon that brought together business, customer success, engineering, and technical teams to align on the solution.

Brian also noted that you don't need a perfect AI strategy to start, just a real problem, the data you already have, and a team willing to build something valuable.

Watch the full session to see how GeoComm brought CARE to life and what they learned along the way.