Rally’s People Database is your entire research participant database. It holds all the people you plan to contact, have contacted in the past, or need to avoid contacting for research. The database is organized by populations to make recruiting and participant management easier.

Overall Guidance for Building Your People Database

When building a research participant database, focus on quality over quantity. While it is tempting to import every customer or user from your CRM, doing so often leads to a "noisy" database that increases administrative overhead and participant fatigue.

Use the mindset of "curating a community for insight.” Rather than including all users, prioritize individuals who can provide the most actionable feedback and that you need to target the most for research.

Who to Include: High-Signal Profiles

A high-signal profile typically meets one or more of these criteria:

• Opted-In Advocates: Only include people who have explicitly consented to be contacted for research. This increases "show rates" (often >95% for dedicated panels) and ensures legal compliance.

• The Power Users (and Churned Users): Include those at the extreme ends of your product lifecycle. Power users help identify advanced feature needs, while churned or "at-risk" users provide the most critical data on pain points and competitors.

• Ideal Customer Profile (ICP): Focus on users who match the strategic direction of the business. If the company is moving upmarket to enterprise, prioritize decision-makers over smaller account users.

• Niche Roles: In B2B contexts, prioritize "hard-to-find" roles (e.g., CEOs, DevOps Engineers, or specialized Surgeons). These individuals are your most valuable research assets because they cannot be easily replaced by external panels.

Look at the research recruitment needs you will have for the upcoming year to prioritize who you need to target to build your Rally People Database.

Where to Find High-Signal Candidates

A common mistake is treating the research database as a mirror of the entire customer base. To locate "high-signal" candidates, you must look beyond basic demographics (who they are) and focus on behavioral and transactional data (what they do). High-signal users are those whose actual product usage suggests they have a deep "need" or a specific "pain point" that your research aims to solve.

Here are the key sources, data categories and specific attributes to examine:

Product Engagement Data

This data identifies your most (and least) active users, helping you target either "Power Users" for advanced feature feedback or "At-Risk Users" for churn research.

• Recency (Last Login/Action): Prioritize users who have used the product within the last 30 days to ensure their feedback is fresh.

• Frequency (DAU/MAU): Identify Daily Active Users (DAU) for usability testing or Weekly/Monthly Active Users for general feedback.

• Breadth of Use: Look for users who use a wide variety of features vs. those who only use one specific tool. This helps in understanding "Feature Discoverability."

Feature-Specific Events

If you are researching a specific part of your product, examine "Event Data" to find people who have interacted with that specific workflow.

• Success Events: Users who successfully completed a key task (e.g., "Exported a Report," "Added a Team Member").

• Friction Signals: Look for "Rage Clicks," multiple refreshes, or users who started a workflow but didn't finish it (Funnel Drop-off). These are your highest-signal candidates for usability improvements.

• New Feature Adoption: Target the "Early Adopters" who tried a newly released feature within the first 48 hours.

Transactional & Account Data

This data provides the business context behind the user, which is vital for B2B research or pricing studies.

• Plan Type/Tier: Segment by "Free," "Professional," or "Enterprise" to ensure your feedback aligns with your revenue goals.

• Tenure: Distinguish between "New Customers" (for onboarding research) and "Long-term Loyalists" (for brand sentiment).

• Spend/Lifetime Value (LTV): High-spend customers are critical for "Strategic Roadmap" interviews, while low-spend or "shrinking" accounts are great for identifying competitive threats.

Self-Reported / Attitudinal Data

If you have access to existing feedback loops, use them to pre-filter for "Expressive" participants.

• NPS/CSAT Scores: Use "Promoters" (Score 9-10) for advocacy and "Detractors" (Score 0-6) for critical problem-solving.

• Support Ticket Volume: Users who contact support frequently are often highly motivated to see the product improve and have a list of specific pain points ready to share.

• Event Attendance: Attendees of your organization’s events can be good candidates for research because they have moved beyond passive usage into active brand engagement or interested non-users.