Introducing Signals: Automatic risk and opportunity detection across your entire customer base.

Every customer success and account management leader knows the feeling: you walk into the week with a vague sense that some accounts need attention, but no clear picture of which ones, why, or how urgent the situation actually is.

So you start digging. You pull reports from your CRM, cross-reference product usage data, build spreadsheets to compare activity levels across accounts. By the time you've identified the customers who need help, half the morning is gone—and the list you've built is already out of date. Worse, you're still not sure if you've prioritized correctly. Is the account that hasn't logged in for two weeks more at risk than the one that's been active but stuck on a key milestone? How do you know?

This is the reality for most post-sales teams. The work of figuring out where to focus consumes the time that should be spent actually helping customers. And by the time you've figured it out, it's often too late to intervene effectively.

The hidden cost of manual prioritization

Most customer success and account management organizations have more data than they know what to do with: CRM records, product analytics, support tickets, engagement metrics. The problem is that making sense of it all requires constant, manual effort.

Someone has to pull the reports. Someone has to define what "at risk" even means for your business. Someone has to compare each account against that definition, weigh competing signals, and make judgment calls about where to spend limited time. And then someone has to do it all again next week, because the data has changed.

This creates two failure modes. The first is paralysis: you spend so much time analyzing that you don't have time to act. The second is gut-feel triage: you give up on rigorous analysis and just work the accounts that feel most urgent, hoping your instincts are right. Neither approach scales, and both leave money on the table in the form of preventable churn and missed expansion opportunities.

What if you had a data scientist watching every account?

Imagine having someone whose sole job is to monitor your entire customer base, continuously, looking for signs that accounts need attention. They know what healthy customer behaviour looks like at each stage of the journey. They know how long it typically takes customers to complete key milestones. And when someone falls behind, they flag it immediately—with context about who's affected, how much revenue is at stake, and what specifically has gone wrong.

That's what Signals does in Trig.

Signals are Trig's proactive alerting system. They continuously analyze your customer data against the benchmarks Trig has established for your business, and they surface the accounts that need attention before problems compound.

How Signals work

To understand Signals, you need to understand how Trig thinks about customer progress.

In Trig, every customer journey is broken into stages—onboarding, activation, expansion, renewal—and each stage has objectives: the specific milestones customers need to hit to succeed. Trig tracks when each customer completes each objective, and over time, it builds a picture of what "normal" looks like. How long does the average customer take to complete their first project? How many typically connect an integration within the first two weeks? What's the usual time from signup to inviting a second team member?

This data creates a performance distribution—a bell curve of customer behaviour. Some customers move quickly and complete objectives ahead of schedule. Some move slowly and fall behind. Most land somewhere in the middle.

Signals identify the customers who are significantly behind. If the average customer completes a key objective in ten days, and a specific account is on day twenty-five with no progress, that's a signal. Trig surfaces it automatically.

What you see in the Signals dashboard

When you open Signals in Trig, you see a prioritized list of issues that need attention. Each signal shows you:

• How many customers are affected. A signal might tell you that 543 customers are stuck on a particular objective. That's not a single account problem—it's a pattern that demands systematic intervention.
• How much revenue is at stake. Trig calculates the ARR associated with the affected customers, so you can prioritize based on business impact. A signal affecting fifty customers worth $2M in ARR gets different treatment than one affecting fifty customers worth $50K.
• What specifically is wrong. Signals are tied to specific objectives, so you know exactly where customers are getting stuck. It's not a vague "health score is declining"—it's "these customers haven't completed their integration setup, and they're taking twice as long as average."
• The trend over time. Signals show whether the problem is getting better or worse. If the number of affected customers is climbing week over week, that's a different urgency level than a stable or declining number.

This is the kind of prioritization that's nearly impossible to do manually. You'd need to define your own thresholds, run your own queries, calculate your own revenue impact, and track your own trends—for every objective, across every stage, every week. Trig does it automatically.

From signal to action

Surfacing problems is only useful if you can act on them. That's why Signals connect directly to jobs.

When you click into a signal, you see the list of affected customers. You can review who they are, understand the commonalities, and decide whether this warrants intervention. If it does, you can create a job directly from the signal—and the audience is already pre-populated with the affected customers.

Trig even drafts job actions for you based on the signal context. If customers are stuck on integration setup, the suggested intervention might include an email with setup instructions, a Slack notification to the account owner, and a CRM task to follow up. You can customize the approach, but you're not starting from scratch.

This workflow—from signal to job—takes minutes instead of hours. You go from "I think some customers might be struggling with onboarding" to "here are the 127 accounts stuck on integration setup, here's the intervention I'm running, and here's how I'll measure whether it works."

Prioritizing across signals

Most teams have multiple things competing for attention at any given time. Some customers are stuck in onboarding. Others are showing signs of declining engagement. Still others are approaching renewal without having adopted key features.

Signals help you prioritize across these competing demands. You can compare signals by magnitude (how many customers are affected), by revenue impact (how much ARR is at stake), and by trend (is this getting worse?). A signal with 50 affected customers and $500K in ARR that's been stable for three weeks gets different treatment than a signal with 30 affected customers and $1.2M in ARR that's doubled in the past week.

This kind of systematic, data-driven prioritization replaces the instinctive triage that most teams rely on. Instead of working the accounts that feel most urgent, you work the accounts where intervention will have the greatest impact on revenue and retention.

The shift from reactive to proactive

Without Signals, most customer success and account management work is reactive. You find out about problems when customers complain, when usage drops off a cliff, or when renewal conversations reveal that an account has been struggling for months without anyone noticing. By then, the damage is done, and you're in recovery mode.

Signals flip this dynamic. Problems surface when they're still small—when a customer is falling behind on a key milestone, not when they've already disengaged entirely. You intervene while there's still time to change the outcome.

This changes the nature of customer success work. Instead of spending your week figuring out where to focus, you spend it actually helping customers. The analysis happens automatically. The prioritization is built in. Your job is to review, decide, and act.

What this means for your team

When Signals are working well, a few things change.

First, your team stops spending hours on manual analysis. The work of identifying at-risk accounts, calculating revenue impact, and prioritizing across competing signals happens automatically. That time gets reallocated to actual customer conversations and strategic work.

Second, your coverage improves. Without Signals, teams naturally focus on the accounts they know best or the ones that make the most noise. Signals surface the accounts that are quietly struggling—the ones that would otherwise churn without anyone noticing until it's too late.

Third, your interventions become measurable. Because Signals connect to jobs, and jobs track completion rates, you can see whether your interventions are actually working. If a signal keeps reappearing with the same customers, that's a sign your approach needs to change. If customers are completing objectives after intervention, that's validation that you're solving the right problems.

Continuous, automatic, prioritized

The value of Signals comes from three properties working together.

Continuous: Signals update on whatever cadence you configure—daily, hourly, or in real time. You're never working off stale analysis.

Automatic: You don't have to define queries, run reports, or build spreadsheets. Trig calculates the benchmarks, identifies the outliers, and surfaces the issues.

Prioritized: Signals come with context—customer count, revenue impact, trend—that makes it clear where to focus first.

This combination is what makes Signals feel like having a data scientist watching your entire customer base. The analysis that would take a human hours happens automatically, continuously, in the background. All you have to do is act on what it finds.