The distribution filter lets you target a specific segment of your community for an invitation. You can build rules using profile attributes, participation history, question responses, and activity data to reach exactly the right participants.
Key Use Cases
Attribute-based targeting - Filter participants based on profile attributes such as demographics, interests, or other predefined criteria.
Participation status filtering - Target members based on their participation history (e.g., completed, partially completed, or not started).
Question-response filtering - Narrow down participants by their answers to specific questions in past chats.
Activity-based segmentation - Engage participants based on activity such as number of studies completed in a timeframe or response rates.
Exclusion criteria - Exclude participants who have already completed a chat or who do not meet specific conditions.
Step-by-step Guide
Open your chat and go to the Distribute section from the left-hand menu.
Scroll to Invitations and click Create.
Fill in the Name and optional Description.
Select the Engagement Locale. For single-locale chats this is prefilled. For multi-lingual chats, choose which locale version of the chat participants will receive.
From the Target dropdown, choose Create a filtered list of participants.
A dialog opens where you can define targeting rules using the available sources and datapoints (see Sources and Datapoints section in the current article for the full reference).
Click the Calculate button to view the count of participants matching your rules.
Once finalized, click OK to save the filter criteria.
Configure the Delivery timeframe and Message content.
Click CREATE in the invitation drawer to create the invitation.
Preferred Locale Toggle
Inside the Target dropdown, there is a toggle: "Only target members with the same preferred locale as the chat locale". This adds an additional layer of filtering on top of your custom rules.
When enabled (blue): The system applies your filter rules first, then further narrows the results to only include participants whose PreferredLocale profile attribute matches the selected Engagement Locale. For example, if you filter for participants aged 25-34 and set the Engagement Locale to French (Canada) with the toggle enabled, only participants aged 25-34 who also have PreferredLocale set to French (Canada) will receive the invitation.
When disabled: Your filter rules are the only criteria. All matching participants receive the invitation in the selected Engagement Locale, regardless of their PreferredLocale.
Tip: You can also use PreferredLocale as a filter source directly in your targeting rules via the Profile Attribute source, giving you even more control over locale-based segmentation.
Sources and Datapoints
To enhance the functionality of the distribution filter and support the use cases discussed, new sources and datapoints have been introduced:
Source: Profile Attribute
This existing source enables users to create filters based on various participant profile attributes. The following datapoints are now available within this source:
SubscribedAt: Filter participants based on their subscription date to the community.
Single Choice Profile Attributes: Target participants based on attributes where only one option can be selected (e.g., gender, region).
Multiple Choice Profile Attributes: Target participants based on attributes where multiple options can be selected (e.g., interests, skills).
Numeric Profile Attributes: Filter participants based on numeric values in their profile (e.g., number of members in the family).
Text Profile Attributes: Create filters based on text-based attributes in participant profiles (e.g., custom tags or descriptions).
Source: Chat
This source enables users to create filters based on responses received in chats or system variables associated with chats in the research domain.
The following datapoint types are available:
Question: Create filters based on participant responses to specific questions in chats. This functionality mirrors the display logic builder in the chat interface.
Input Variables: Create filters based on input variables
Hidden Variables: Apply filters using hidden variables that are predefined and not visible to participants during a chat.
System Variables: Use system-defined variables for filtering. Notable examples include:
Participation Status: Filter participants based on their interaction with a specific chat, such as completed, entered, incomplete, disqualified, or overquota.
Invitation Status Delivered: Filter participants who received the chat invitation.
Source: Participation Data
This source enables filters based on participant activity data from research and engagement chats.
Key datapoints include:
Last Active Date: The last time a member completed, was disqualified (DQ), or overquota (OQ) in a chat. Users can filter by chat type (research, engagement, or both).
Last Completion Date: The last time a member completed a chat, with an option to filter by chat type.
Last Invited Date: The last time a member was invited to a chat, with an option to filter by chat type.
Chats Invited: The total number of chats a member has been invited to, filterable by chat type and duration.
Chats Completed: The total number of chats completed by a member, filterable by chat type and date range.
Response Rate: The ratio of chats completed, DQ, or OQ to chats invited.
Engagement status: Classify members as Active, Inactive, New and Not engaged members based on their interaction
Additional Filters for Participation Data:
Chat Type: Filter datapoints based on research or engagement chats, or both.
Date: Filter aggregate datapoints by a specific time period to focus on recent or historical activity.
These sources and datapoints allow for highly granular targeting, ensuring researchers can define participant groups based on detailed activity metrics, improving study relevance and efficiency.