> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trig.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Signals

> Proactive alerts when customers need attention

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

## What are signals?

Rather than waiting for customers to churn or disengage, Signals detect early warning patterns and prompt timely intervention.

<Note>
  **Core concept:** Signals are point-in-time indicators that a specific action is needed across your customer base.
</Note>

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?"

## Why signals matter

### 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.

### 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?"

### 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
```

## How signals work

### 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.

### 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"
```

### 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

### 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.

## Signal types

### 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

### 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

## Viewing signals

### 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

### Navigation: quantity to clarity to activity

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

### Stage-level view

Within a stage:

* Objectives and completion status
* Signals associated with each objective
* Quick stats: how many at risk, revenue impact

### Objective-level view

Drilling into an objective:

* All members in this objective
* Toggle to view only flagged customers
* Create job targeting Signal audience

## Acting on signals

### Creating jobs from signals

From any Signal, you can:

<Steps>
  <Step title="View affected audience">
    See exactly which customers are flagged
  </Step>

  <Step title="Create a job">
    Launch with audience pre-populated from Signal
  </Step>

  <Step title="Define intervention">
    Configure messages for the specific issue
  </Step>
</Steps>

### 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.

### 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

## Configuring for signals

### 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

### 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**

### Threshold considerations

| Threshold        | Sensitivity | Trade-off                     |
| ---------------- | ----------- | ----------------------------- |
| **1.5x average** | High        | Earlier alerts, more noise    |
| **2x average**   | Moderate    | Balanced detection            |
| **3x average**   | Low         | Fewer alerts, later detection |

## Signal workflow example

### 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

## Signals vs other objects

| Object         | Purpose                                                          |
| -------------- | ---------------------------------------------------------------- |
| **Behaviours** | Track when customers achieve milestones (historical)             |
| **Signals**    | Alert when customers are failing to achieve milestones (ongoing) |
| **Cohorts**    | Static/dynamic audience segments                                 |
| **Jobs**       | Intervention mechanisms                                          |

Signals detect problems. Jobs address them.

## Best practices

### Start with core objectives

Don't Signal everything. Focus on:

* Objectives that predict success/failure
* Early stage milestones
* High-value customer segments

### 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

### 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.

### 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

### 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?

## Common questions

<AccordionGroup>
  <Accordion title="How often do Signals refresh?">
    Continuously as customer data updates. New slow completers appear as they cross the threshold.
  </Accordion>

  <Accordion title="Can I customize the threshold?">
    Thresholds are configured by Trig. Future versions may expose configuration in the UI.
  </Accordion>

  <Accordion title="What if I don't have enough data for averages?">
    You need sufficient completions for meaningful averages. Generally 10+ completions for stability.
  </Accordion>

  <Accordion title="Can Signals detect positive patterns?">
    v0.1 focuses on risk detection. Positive signals (expansion opportunities) are planned.
  </Accordion>

  <Accordion title="How is revenue calculated?">
    Summed from ARR/contract value of affected customers (from CRM data).
  </Accordion>

  <Accordion title="What happens when customers complete the objective?">
    They're removed from the Signal. Job tracks this as completion.
  </Accordion>
</AccordionGroup>

## 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.