Segmentation Logic

Trackly SMS supports sophisticated audience segmentation with nested AND/OR logic. This guide explains how to combine conditions effectively.

Logic Basics

AND (All Must Match)

When conditions are combined with AND, a contact must satisfy every condition:

last_clicked_at within 7 days
AND send_count gt 3
AND custom_fields.state eq "California"

A contact must have:

Clicked in the last 7 days, AND
Received more than 3 messages, AND
Have custom_fields.state equal to “California”

Result: Only contacts meeting ALL criteria are included.

OR (Any Can Match)

When conditions are combined with OR, a contact must satisfy at least one condition:

custom_fields.state eq "California"
OR custom_fields.state eq "Texas"
OR custom_fields.state eq "Florida"

A contact with custom_fields.state matching California, Texas, OR Florida is included. Result: Contacts meeting ANY criterion are included.

Condition Groups

Creating Groups

Groups let you combine AND and OR logic. Each group is evaluated first, then groups are combined. Structure:

Group 1 (AND):
  - Condition A
  - Condition B

[AND/OR]

Group 2 (AND):
  - Condition C
  - Condition D

Example: Engaged OR New

Target engaged contacts OR brand new signups:

Group 1 (AND):
  - last_clicked_at within 7 days
  - click_count gt 2

OR

Group 2 (AND):
  - signup_date within 3 days
  - send_count eq 0

Matches:

Recent multi-clickers (engaged users)
Brand new signups who haven’t been messaged

Common Patterns

Geographic Targeting

Target multiple states or regions:

custom_fields.state eq "California"
OR custom_fields.state eq "Texas"
OR custom_fields.state eq "Florida"
OR custom_fields.state eq "New York"

For many values, consider using the in operator (e.g., custom_fields.state in ["CA", "TX", "FL", "NY"]) or creating a custom field like custom_fields.region eq "West" instead of listing states individually.

Engagement Tiers

Target by engagement level:

Highly Engaged
Moderately Engaged
At-Risk

last_clicked_at within 7 days
AND click_count gt 5

last_clicked_at within 30 days
AND click_count gte 2
AND click_count lte 5

last_clicked_at not_within 30 days
AND last_sent_at within 30 days
AND click_count gt 0

(Previously engaged but went quiet)

Revenue-Based Segments

High Value
Has Purchased
No Purchase Yet

revenue_total gt 100

has_revenue eq true

has_revenue eq false
AND click_count gt 3

(Engaged but hasn’t converted)

Reply-Based Segments

Target contacts by their inbound replies — what they said, when they last replied, and how often. These conditions read the contact’s reply history captured in the Inbox.

Field	Type	Operators
`last_reply_text` (Reply Text)	text	`eq`, `ne`, `contains`, `not_contains`
`last_reply_at` (Last Reply)	date	`within`, `not_within`, `gt`, `lt`
`reply_count` (Reply Count)	number	`eq`, `gt`, `gte`, `lt`, `lte`

Said a keyword
Active repliers
Asked for help

reply_text contains "yes"

(Reply-text matching is case-insensitive.)

reply_count gte 2
AND last_reply_at within 30 days

(Contacts who reply often and replied recently.)

reply_text contains "help"

(Surface contacts who need follow-up.)

Reply conditions match against the contact’s latest reply text and aggregate reply counts on ListContact. ne / not_contains require a reply to exist (contacts who never replied are excluded).

Re-Engagement

Target lapsed contacts who were once active:

Group 1 (AND):
  - last_sent_at within 60 days
  - last_clicked_at not_within 30 days

AND

Group 2 (OR):
  - click_count gt 5
  - has_revenue eq true

Matches: Contacts who:

Have been messaged recently
Haven’t clicked in a month
BUT previously showed strong engagement

Exclusion Logic

Exclude certain contacts using NOT conditions:

signup_date within 30 days
AND active eq true
AND last_sent_at not_within 24 hours

Matches: New signups who are active (not unsubscribed) and haven’t been messaged today.

Advanced: Nested Groups

For complex logic, nest groups within groups:

Group A (AND):
  |-- Group 1 (OR):
  |     - custom_fields.state eq "CA"
  |     - custom_fields.state eq "TX"
  |
  |-- AND
  |
  |-- Group 2 (AND):
        - last_clicked_at within 14 days
        - click_count gt 1

Matches: Contacts in CA or TX who clicked recently AND clicked more than once.

Deeply nested groups (groups within groups) are supported in the data model but are not currently exposed in the UI. The UI supports one level of group nesting.

Evaluation Order

Conditions are evaluated in this order:

Individual conditions within groups
Groups combined (AND/OR between groups)
Final audience membership

Example:

(A AND B) OR (C AND D)

Evaluate A AND B → Result 1
Evaluate C AND D → Result 2
Result 1 OR Result 2 → Final audience

Debugging Audiences

Audience Too Small

If your audience is smaller than expected:

Check that conditions aren’t too restrictive
Try removing conditions one at a time
Preview to see what’s filtering contacts out
Verify the field has data (e.g., custom_fields.state may be blank)

Audience Too Large

If your audience is larger than expected:

You may have OR where you meant AND
Check group logic (groups OR’d together include more)
Add additional qualifying conditions

Preview Tool

Always use the preview before sending:

Click Preview in the audience builder
Review the sample contacts
Check if they match your intent
Adjust conditions if needed

Performance Tips

Indexed fields are faster

Fields like last_clicked_at, send_count, and signup_date are stored on ListContact and are indexed for fast queries. Custom fields may be slower on large lists.Cross-collection lookups are slowest: Timezone, carrier, and DNC conditions require a $lookup join to the Contact collection, which is significantly slower than ListContact-only conditions. Use these sparingly in complex audiences.

Fewer groups = faster

Simple audiences with 2-3 conditions evaluate faster than deeply nested logic.

Cache audience sizes

For audiences used frequently, the cached size updates periodically. Don’t refresh constantly.

Getting Started

Guides

Account & Team

Integrations

Resources

Logic Basics

AND (All Must Match)

OR (Any Can Match)

Condition Groups

Creating Groups

Example: Engaged OR New

Common Patterns

Geographic Targeting

Engagement Tiers

Revenue-Based Segments

Reply-Based Segments

Re-Engagement

Exclusion Logic

Advanced: Nested Groups

Evaluation Order

Debugging Audiences

Audience Too Small

Audience Too Large

Preview Tool

Performance Tips

Next Steps

Dynamic vs Static

Campaign Targeting

​Logic Basics

​AND (All Must Match)

​OR (Any Can Match)

​Condition Groups

​Creating Groups

​Example: Engaged OR New

​Common Patterns

​Geographic Targeting

​Engagement Tiers

​Revenue-Based Segments

​Reply-Based Segments

​Re-Engagement

​Exclusion Logic

​Advanced: Nested Groups

​Evaluation Order

​Debugging Audiences

​Audience Too Small

​Audience Too Large

​Preview Tool

​Performance Tips

​Next Steps

Dynamic vs Static

Campaign Targeting

Logic Basics

AND (All Must Match)

OR (Any Can Match)

Condition Groups

Creating Groups

Example: Engaged OR New

Common Patterns

Geographic Targeting

Engagement Tiers

Revenue-Based Segments

Reply-Based Segments

Re-Engagement

Exclusion Logic

Advanced: Nested Groups

Evaluation Order

Debugging Audiences

Audience Too Small

Audience Too Large

Preview Tool

Performance Tips

Next Steps