We're thrilled to kick off 2025 with the launch of our new Sampling and Advanced Filtering feature, which aim to improve the precision, efficiency, and overall effectiveness of study distribution by enabling researchers to target specific segments of their community or panel.
Introducing: Sampling and Advance Filtering
Rival’s Precision Tools for Targeted Research
Expanding on the Rival platform’s current capability that already allows its users to filter participants by profile attributes, Sampling and Advance Filtering further expands on this capability.
Here’s a detailed breakdown of what this feature entails:
- Sophisticated filtering for precise targeting: Filter participants in the community or panel to identify and engage specific segments of your community or panel, to boost engagement and get better and relevant responses.
- Dynamic Participant Counting – Track participant numbers in real time to make sure you're hitting the right sample size for each study.
- Automated Sample Selection – Effortlessly generate representative samples from larger communities, enhancing data reliability and reducing the time spent on manual sampling. Representative sampling ensures the collected data reflects the broader community, making the insights more trustworthy and actionable.
- Intelligent Invitation Management – Maximize participant diversity and prevent duplicate invitations to strengthen data collection and improve insights. Ensuring diversity makes the sample more representative and minimizes bias, while avoiding duplicates preserves data integrity. This results in more robust, reliable data, better suited for making informed decisions.
Some use cases of sophisticated filtering include:
- Attributes: users filter community members by system and profile attributes, for detailed segmentation.
- Participation history: users target community members based on their interaction with a chat or study. It filters based on participation status like completed, partially completed, or not started, ensuring follow-ups reach the right audience
- Response patterns: researchers filter participants based on their responses to specific questions in past chats or surveys, helping identify the most relevant subset of the community and,
- Activity levels: users segment participants by chat activity, subscription dates, engagement duration, and response rates.
This release also supports exclusion criteria's, and selection of precise subsets based on nuanced user attributes, meaning you engage only the most relevant people and reduce survey fatigue.
Without the ability to efficiently segment and filter participants, researchers struggle to achieve the precision needed for meaningful and cost-effective studies.
At Rival, we believe every customer interaction is a brand interaction. By providing better participant experience, researchers will gather more quality insights, participants engage in future studies, and have a great experience with the brand.
Ready to See Rival’s New Features in Action?
Book a meeting discover how our platform can elevate your research to the next level.