Drop-off Trigger

Activate when users fail to complete expected actions within a specified timeframe

How Drop-off Triggers Work

1. Monitor Events: The system tracks when users perform the initial event
2. Set Expectations: You define what actions users should take next
3. Wait and Watch: The system waits for the expected follow-up events
4. Trigger on Absence: If expected events don’t occur within your timeframe, the workflow starts

Configuration

Inputs

Initial Events · string[] · required

Select one or more events that start the drop-off monitoring. Examples: trial_started, cart_created, signup_initiated.

Expected Events · object[] · required

Configure the events you expect users to complete after the initial event.

Event Name · string · required

The specific event you expect users to complete (part of Expected Events configuration).

Delay Value · number · required

How long to wait before considering it a drop-off (number value).

Delay Unit · enum · required

Time unit for the delay. Options: seconds, minutes, hours, days, weeks, months.

Sort Order · number

Priority order for multiple drop-off events. Lower numbers have higher priority.

Outputs

event · object · required

The drop-off event data generated by Flywheel when the expected event didn’t occur.

Show Event Properties

event.event_id · string · required

Unique identifier for this drop-off event.

event.event_name · literal · required

Always $fw_drop_off for drop-off events.

event.timestamp · string · required

ISO datetime when the drop-off was detected.

event.custom_properties · object · required

Contains drop-off specific data and system properties.

event.custom_properties.$fw_event_source · literal · required

Always flywheel for system-generated events.

event.custom_properties.$fw_user_id · string · required

Internal user identifier.

event.custom_properties.last_completed_event_name · string · required

Name of the last event the user completed that triggered the drop-off monitoring.

event.custom_properties.last_completed_event · object · required

Full details of the original event that started the drop-off monitoring, including all event properties and custom data.

org_user · object · required

Comprehensive user information for the user who dropped off.

Show User Properties

org_user.id · string · required

Unique user identifier.

org_user.org_assigned_user_id · string

Your company’s external user identifier.

org_user.primary_email · string

User’s primary email address.

org_user.first_name · string

User’s first name.

org_user.last_name · string

User’s last name.

org_user.subscription_status · enum

Current subscription status: incomplete, incomplete_expired, trialing, active, past_due, canceled, unpaid, paused, inactive.

org_user.payment_processor · enum

Payment processor used: stripe or paddle.

org_user.mrr · number

Monthly recurring revenue from this user.

org_user.ltv · number

Lifetime value of the user.

org_user.first_payment_date · string

ISO datetime of user’s first payment.

org_user.next_payment_date · string

ISO datetime of user’s next scheduled payment.

org_user.cancels_at · string

ISO datetime when subscription will be cancelled (if applicable).

org_user.contact_info · object

Additional contact information and preferences.

org_user.custom_properties · object

Custom user properties you’ve defined.

The system creates drop-off events with the name $fw_drop_off for all drop-off triggers.

Use Cases

Trial Conversion

Initial Event: trial_started
Expected Event: subscription_created
Drop-off Delay: 7 days
Result: Triggers if user doesn't subscribe within 7 days

Cart Abandonment

Initial Event: cart_created
Expected Event: purchase_completed
Drop-off Delay: 2 hours
Result: Triggers if user doesn't complete purchase within 2 hours

Onboarding Completion

Initial Event: account_created
Expected Events:
  - profile_completed (24 hours)
  - first_project_created (3 days)
  - team_invited (7 days)

Best Practices

Choose Appropriate Timeframes

• Consider your typical user behavior patterns
• Account for different user segments (some may need more time)
• Start with longer delays and optimize based on data

Segment Your Audiences

• Use trigger conditions to create different drop-off flows for different user types
• Consider factors like subscription tier, user role, or geographic location

Monitor and Adjust

• Track which drop-off triggers are most effective
• Adjust timing based on actual user behavior data
• A/B test different delay periods