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Signals are Trig’s proactive alerting system. They surface customers who need attention right now—identifying who is off track before problems compound.

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What are signals?

Rather than waiting for customers to churn or disengage, Signals detect early warning patterns and prompt timely intervention.
Core concept: Signals are point-in-time indicators that a specific action is needed across your customer base.
Think of Signals as a heat map showing where attention is needed most. They answer: “Of all my customers, who should I focus on today, and why?”

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Why signals matter

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The problem signals solve

Without Signals, finding customers who need help requires manual investigation:
  • Export data to spreadsheets
  • Calculate averages and compare
  • Identify outliers
  • Build target lists
  • Hope you caught everyone
This is slow, error-prone, and doesn’t scale.

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How signals change this

Signals automate the detection:
  • Continuously evaluate all customers against criteria
  • Surface those who are underperforming
  • Quantify the revenue impact
  • Enable immediate action via jobs
Instead of “go find problems,” Signals say “here are the problems—what do you want to do about them?”

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The insight to action loop

Stage → Objectives defined
                        ↓
              Trig calculates averages
                        ↓
              Identifies customers below average
                        ↓
              Surfaces as Signal
                        ↓
              User creates Job from Signal
                        ↓
              Measures intervention impact

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How signals work

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The foundation: objectives and averages

Signals build on stages and objectives. When you configure objectives, Trig tracks:
  • How many customers complete each objective
  • How long it takes on average
  • Which customers are still pending
This creates the baseline for “normal” behaviour.

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Detecting atypical customers

Signals identify customers who deviate from normal: Slow completers: Customers who haven’t completed an objective and have taken significantly longer than average (e.g., 2x the average completion time). 
Objective: Connect Payment Gateway
Average completion time: 6 days
Signal threshold: 2x average (12 days)

Signal: "15 customers have been pending 'Connect Payment Gateway'
for more than 12 days (2x average). Revenue at risk: £47,000"

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The flag system

Trig continuously evaluates:
  1. For every member of an objective, calculate average time to completion
  2. For anyone who hasn’t completed and exceeds threshold, set a flag
  3. Aggregate flags into Signals for display and action

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From flags to signals

Individual flags:
├── Customer A: slow on Objective 1 (flagged)
├── Customer B: slow on Objective 1 (flagged)
├── Customer C: slow on Objective 1 (flagged)
└── Customer D: slow on Objective 2 (flagged)

Signal 1: "3 customers slow on Objective 1"
Signal 2: "1 customer slow on Objective 2"
Each Signal represents a group with a common issue addressable with a single intervention.

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Signal types

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Objective slow completers

Identifies customers taking longer than expected to complete objectives. How it works:
  1. Calculate average completion time for an objective
  2. Identify pending customers who exceeded threshold (e.g., 2x average)
  3. Surface as Signal with count and revenue at risk
Example Signal: 
Signal: Slow Completers - "Create First Invoice"
Stage: Onboarding
Customers affected: 23
Revenue at risk: £127,000
Average completion: 4 days
These customers: 8+ days pending
Why this matters: Slow completers are at higher risk of:
  • Failing to realise value
  • Disengaging before activation
  • Churning before renewal

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Future signal types (planned)

  • Low completion rate: Objectives with completion percentage below threshold
  • Declining engagement: Customers whose activity has dropped significantly
  • Expansion indicators: Customers showing upsell-ready behaviours
  • Churn predictors: Patterns historically associated with churn

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Viewing signals

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The signals dashboard

Summary metrics:
  • Total customers at risk
  • Total revenue at risk
  • Number of active Signals
Signal feed:
  • List of current Signals
  • Count of affected customers
  • Revenue impact
  • Related objective/stage
  1. Quantity: How many Signals? Where’s the biggest problem?
  2. Clarity: Click into a Signal to see exactly which customers and why
  3. Activity: Create a job directly from the Signal

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Stage-level view

Within a stage:
  • Objectives and completion status
  • Signals associated with each objective
  • Quick stats: how many at risk, revenue impact

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Objective-level view

Drilling into an objective:
  • All members in this objective
  • Toggle to view only flagged customers
  • Create job targeting Signal audience

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Acting on signals

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Creating jobs from signals

From any Signal, you can:
1

View affected audience

See exactly which customers are flagged
2

Create a job

Launch with audience pre-populated from Signal
3

Define intervention

Configure messages for the specific issue

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Job strategy for signals

Message relevance: These customers are stuck on a specific objective. Address that blocker. Timing: They’re already slow. Act quickly once the Signal surfaces. Follow-up: If the first intervention doesn’t work, the Signal continues showing them.

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Iterative intervention

Once you’ve created a job from a Signal:
  • Customers in the job are being addressed
  • New customers falling behind appear in future Signal refreshes
  • Add new customers to existing job or create new ones

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Configuring for signals

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Prerequisites

To generate Signals:
  1. Stages configured with entry/exit criteria
  2. Objectives within stages with completion criteria
  3. Customers in stages actively progressing
  4. Sufficient data for meaningful averages

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Objective design for effective signals

Design objectives that:
  • Represent meaningful milestones (not trivial clicks)
  • Have clear completion criteria
  • Are achievable in reasonable time
  • Cover the critical path to value

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Threshold considerations

ThresholdSensitivityTrade-off
1.5x averageHighEarlier alerts, more noise
2x averageModerateBalanced detection
3x averageLowFewer alerts, later detection

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Signal workflow example

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Scenario: onboarding slow completers

Setup:
  • Stage: Onboarding (Days 1 to 30)
  • Objective: “Complete Profile Setup”
  • Average completion: 3 days
  • Threshold: 2x (6 days)
Signal appears: 
Slow Completers - Complete Profile Setup
12 customers | £34,000 revenue at risk
Investigation:
  • 8 are self-serve plan
  • 4 are enterprise
  • Mix of industries
Action: Create job targeting these 12:
  • Goal: profile_complete = true
  • Entry: “Here’s why your profile matters and how to do it in 2 minutes…”
  • Auto-exit: 7 days
Outcome:
  • 8 complete (success)
  • 4 exit without completing (need different approach)
  • Signal updates with new slow completers

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Signals vs other objects

ObjectPurpose
BehavioursTrack when customers achieve milestones (historical)
SignalsAlert when customers are failing to achieve milestones (ongoing)
CohortsStatic/dynamic audience segments
JobsIntervention mechanisms
Signals detect problems. Jobs address them.

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Best practices

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Start with core objectives

Don’t Signal everything. Focus on:
  • Objectives that predict success/failure
  • Early stage milestones
  • High-value customer segments

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Act quickly on signals

Signals surface customers already behind. Delay compounds the problem:
  1. Review same day
  2. Create job if warranted
  3. Don’t wait for perfect message

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Review signal patterns

If the same Signal keeps appearing:
  • Is the objective realistic?
  • Is there a product friction point?
  • Do customers need more support?
Recurring Signals may indicate systemic issues.

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Don’t over-intervene

  • Customers shouldn’t receive too many automated messages
  • Some slowness may be acceptable (enterprise moves slower)
  • Use exclusions to prevent job overlap

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Measure job impact

Track whether Signal-driven jobs improve outcomes:
  • Do recipients complete faster than non-recipients?
  • Does intervention reduce Signal population over time?
  • What message content works best?

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Common questions

Continuously as customer data updates. New slow completers appear as they cross the threshold.
Thresholds are configured by Trig. Future versions may expose configuration in the UI.
You need sufficient completions for meaningful averages. Generally 10+ completions for stability.
v0.1 focuses on risk detection. Positive signals (expansion opportunities) are planned.
Summed from ARR/contract value of affected customers (from CRM data).
They’re removed from the Signal. Job tracks this as completion.

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Summary

Signals transform Trig from observation to action:
  1. Built on objectives — Require stages and objectives as foundation
  2. Automated detection — Continuously evaluate all customers against thresholds
  3. Quantified impact — Revenue at risk makes prioritisation clear
  4. Direct to action — Create jobs directly from Signals
  5. Iterative — New customers surfaced as they fall behind
Signals show you which customers need help before it’s too late.