Segmento allows you to segment and filter your audience based on different data fields such as text, email, number, date, and more. Filters help you create targeted user groups based on specific conditions.

Using filters, you can create segments like:

• Users whose email ends with @gmail.com

• Customers whose purchase amount is greater than ₹1000

• Users who signed up in the last 7 days

Each field type supports different filter conditions to help you build accurate segments.

Filters work by applying conditions to a specific field. Segmento checks the value stored in that field and returns only the records that match the selected condition.

Example:

This filter will return all users whose email domain is gmail.com.

Text filters are used for fields that contain text values, such as name, city, or tags.

Example:
City contains "Delhi"

Detailed Example:
If you want to target users from Delhi:
- Column: City
- Condition: contains
- Value: Delhi

This will include all users whose city field contains "Delhi".

Email filters are used to target users based on their email addresses.

Example:
Email ends with @company.com

Detailed Example:
If you want to target users from a specific company:
- Column: Email
- Condition: ends with
- Value: @test.com

This will include users whose email domain matches the given value.

Number filters are used for numeric values such as order count, user age, or total purchases.

Example:
Order Count greater than 5

Detailed Example:
If you want to target high-value users:
- Column: Order Count
- Condition: greater than
- Value: 5

This will include users who have placed more than 5 orders.

Boolean filters work with true or false values.

Common examples include:

• Email verified

• Active user

• Subscribed to notifications

Example:
Email Verified is true

Detailed Example:
If you want to target users who have verified their email:
- Column: Email Verified
- Condition: is true

This will include only users whose email verification status is true.

Decimal filters are used for values with decimal numbers, such as product price or rating.

• is exactly

• is not exactly

• is greater than

• is less than

• in

• not in

• is empty

• is not empty

Example:
Product Price greater than 499.99

Detailed Example:
If you want to target users who purchased high-value products:
- Column: Product Price
- Condition: greater than
- Value: 499.99

This will include users whose product purchase value is greater than 499.99.

Date filters allow you to segment users based on time-based conditions, such as signup date or last activity.

Example:
UpdateDate in the last 7 days

Detailed Example:
If you want to target recently active users:
- Column: Last Activity Date
- Condition: in the last
- Value: 7 days

This will include users who were active in the last 7 days.

These filters are used when the field contains percentage values, such as completion rate or discount percentage.

Supported filters include:

• is exactly

• is not exactly

• is greater than

• is less than

• in

• not in

• is empty

• is not empty

Example:
Completion Rate greater than 80%

Detailed Example:
If you want to target highly engaged users:
- Column: Completion Rate
- Condition: greater than
- Value: 80%

This will include users whose completion rate is above 80%.

Currency filters are used for monetary values, such as revenue or order value.

Supported conditions:

• is exactly

• is not exactly

• is greater than

• is less than

• in

• not in

• is empty

• is not empty

Example:
Total Purchase greater than ₹300

Detailed Example:
If you want to target high-spending customers:
- Column: Total Purchase
- Condition: greater than
- Value: ₹300

This will include users whose total purchase value exceeds ₹300.

Phone filters help segment users based on mobile numbers.

Example:
Phone number starts with +91

Detailed Example:
If you want to target users from India:
- Column: Phone Number
- Condition: starts with
- Value: +91

This will include users whose phone numbers begin with the Indian country code.

Object filters are used for nested data fields or structured data objects.

Example:
User Profile exists

Detailed Example:
If you want to target users who have completed their profile:
- Column: User Profile
- Condition: is exist

This will include users where the profile object is available.

Dropdown filters apply to fields where users can select values from predefined options.

Example:
Subscription Plan in Premium, Gold

Detailed Example:
If you want to target premium users:
- Column: Subscription Plan
- Condition: in
- Value: Premium, Gold

This will include users who are subscribed to Premium or Gold plans.

Presence filters help identify the activity status of users.

Example:
Presence is Online

Detailed Example:
If you want to target currently active users:
- Column: Presence
- Condition: Online

This will include users who are currently active on the platform.

Using Nested Filters (AND / OR Conditions)

Segmento allows you to combine multiple conditions using AND and OR to create more specific segments.

Example:

• Order Count greater than 5 AND City contains “Delhi”
  This will include users who match both conditions.

• City contains “Delhi” OR City contains “Mumbai”
  This will include users who match any one condition.

You can also create grouped (nested) conditions:

• (Order Count greater than 5 AND City contains “Delhi”)

• OR (Order Count less than 2 AND City contains “Mumbai”)

This helps in creating advanced audience segments.

Note:
The filter works only when the field contains a valid value (string format).

In cases where the field value is null (no value present), the filter condition will not return any results.

For example, in the above data, fields like "CurrentFortnightTicketCount" have null values, so applying the "is exactly" filter on them will not work.

Example:
Value = "13" → Filter works
Value = null → No results returned

• Combine multiple filters to create more precise segments

• Use date filters for time-based campaigns

• Use email domain filters for company-based targeting

• Avoid overly complex filter combinations that may reduce segment accuracy