Overview
This feature introduces sophisticated tools to filter, sample, and distribute research studies, ensuring that only the most relevant community members are engaged. By focusing on efficiency and relevance, it empowers users to optimize study distribution, reduce costs, and enhance participant experience.
Key Use Cases
Attribute-Based Targeting
Filter participants based on specific profile or system attributes, such as demographic information, interests, or other predefined criteria.
Participation Status Filtering
Target community members based on their participation history (e.g., completed, partially completed, or not started studies).
Question-Response Filtering
Narrow down participants by their answers to specific questions in past surveys or chats.
Activity-Based Segmentation
Engage participants based on their activity within the community, such as:
Number of studies completed in a set timeframe
Response rates over a specific period
Exclusion Criteria
Create more focused studies by excluding certain participants (e.g., those who didn’t respond to a previous study or who lack specific attributes)
Step by step guide to create an invitation to target a specific segment
Select the CREATE action from the invitations table to open the invitation drawer.
From the target dropdown menu, choose Create a filtered list of participants.
A dialog will open, allowing you to define rules to target participants.
Create the targeting rules by utilizing the available sources and datapoints. Refer to the "Sources and Datapoints" section for additional guidance.
Click the Calculate button to view the count of participants matching the defined rules.
Once the rules are finalized, click OK in the dialog to save the filter criteria
Return to the invitation drawer and click CREATE to finalize and save the invitation.
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.