The Research Hub turns your Voice of Customer data into structured intelligence — win/loss studies, sentiment tracking, competitive analysis, and product feedback research — with dashboards, trend analysis, and actionable findings. This guide covers how to get the most out of it. Adoption journey: First Project (Enabled) → Building a Program (Adopted) → Strategic Intelligence (Embedded)Documentation Index
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1. Setting up your first research project
Your first research project is where you build confidence in the platform. Choose something where you already have good data and real stakeholder interest. Pre-launch checklist- Confirm Research Hub is activated and accessible
- Verify you have 50+ VoC assets connected — interviews, surveys, reviews, feedback
- Identify your first research question (see recommended projects below)
- Confirm at least one stakeholder who will act on the results
- Set a timeline for your first findings review (3 weeks is a good starting point)
Win/Loss Analysis
Start here if you have deal outcome data. Sales and product marketing teams see value immediately. Research question: “Why are we winning and losing deals, and what patterns emerge across segments?”
Product Feedback Research
Start here if you have strong product usage data. Product teams can act on this right away. Research question: “What are our customers’ top pain points and feature requests, and how do they vary by segment?”
Customer Health Tracking
Start here if you have broad feedback from across your customer base. CS leadership can spot trends and risk signals. Research question: “What’s the overall sentiment trend, and which accounts show risk signals?”
2. Designing effective research projects
A well-scoped project produces findings people act on. A poorly scoped one produces interesting data that sits in a report. Project scoping framework| Element | What to define | Example |
|---|---|---|
| Research question | One clear question you want answered | ”Why are enterprise deals taking 30% longer to close?” |
| Data sources | Which VoC data to include | Win/loss interviews, call recordings, CRM notes |
| Time frame | What period to analyze | Last 2 quarters, compared to prior year |
| Segments | How to slice the analysis | By deal size, industry, or competitor mentioned |
| Stakeholders | Who will consume findings | VP Sales, Head of Product, CRO |
| Decision context | What decision this research will inform | Whether to restructure the enterprise sales process |
- Are your data sources connected and flowing?
- Do you have enough data points? (50+ for most projects)
- Is the data recent enough to be relevant? (6 months or less for most research)
- Are there known gaps? Consider launching an AI Interview campaign to fill them
3. Using AI Interviews within research
Research Hub includes AI Interview capabilities. Use them when your existing data identifies a pattern but you need to understand the “why” behind it. When to add AI Interviews to a project- Your research identifies a pattern but needs deeper qualitative context
- You have a hypothesis that needs validation from a larger sample
- A specific segment is underrepresented in your current data
- You want to test a new research question before committing to a full project
| Practice | Why it matters |
|---|---|
| Design interview questions from research findings | Ensures you’re asking about what the data is already telling you |
| Target interviews to fill specific gaps | Interview the segments where you need more signal, not everyone |
| Use interview quotes to enrich findings | Stakeholders connect with specific customer stories, not just numbers |
4. Presenting findings to stakeholders
Research only creates value when people act on it. Tailor your delivery to each audience.| Audience | Format | Content focus | Timing |
|---|---|---|---|
| C-Suite / Board | 1-page executive summary | Top 3 findings + strategic implications + recommended actions | Quarterly |
| Product Leadership | Prioritized insights report | Feature requests ranked by frequency and urgency | Monthly or per project |
| Sales Leadership | Battle card updates + competitive brief | Win/loss patterns, positioning shifts, proof points | Monthly |
| CS Leadership | Account health dashboard + risk alerts | Sentiment trends, at-risk accounts, expansion signals | Bi-weekly |
| Marketing | Messaging validation + competitive intel | How customers describe your product, perception shifts | Per project |
- Lead with the finding, not the methodology — “Customers say X” not “We analyzed 500 responses”
- Include 2–3 direct customer quotes to bring findings to life
- End every presentation with specific recommended actions
- Follow up in 2 weeks to check whether findings led to decisions
5. Building a research cadence
Move from one-off projects to a recurring research program that drives continuous intelligence.| Project type | Frequency | When it runs |
|---|---|---|
| Win/Loss Analysis | Continuous (rolling) | Always running; review findings monthly |
| Customer Health Tracking | Continuous (rolling) | Always running; review trends bi-weekly |
| Product Feedback Research | Quarterly | Aligned with product planning cycles |
| Competitive Intelligence | Quarterly or on-demand | Triggered by competitive moves or pipeline shifts |
| Annual Customer Intelligence Report | Annually | Aligned with annual planning or board meetings |
- At least 2 research projects running simultaneously
- Findings presented to stakeholders within 1 week of project milestones
- At least 1 strategic decision per quarter informed by research data
- Research requests coming inbound from stakeholders, not just initiated by your CSM
- Research Hub metrics reviewed in executive check-ins
6. Measuring success
| Metric | Target | How to track |
|---|---|---|
| Active research projects | 2+ running simultaneously | Research project dashboard |
| Time to first findings | Under 3 weeks from project start | Project timeline |
| Stakeholder reach | Insights shared with 3+ departments | Distribution tracking |
| Decisions influenced | 1+ strategic decision per quarter | Stakeholder follow-up |
| Research cadence | At least 1 new project per quarter | Project creation history |
| Inbound requests | Stakeholders requesting new research | Request log / CSM notes |
7. Troubleshooting
Findings seem surface-level
Findings seem surface-level
Check data volume and diversity. Most projects need 100+ data points across multiple source types. Consider launching AI Interviews to add qualitative depth to aggregate patterns.
Stakeholders aren't engaging with results
Stakeholders aren't engaging with results
You may be delivering in the wrong format. Ask each stakeholder what format they prefer. Lead with the “so what” — what it means for the business — before diving into data.
Not sure what to research next
Not sure what to research next
Ask your stakeholders: “What’s the biggest customer-related question you can’t answer today?” Their answer is your next project.
Too many projects, not enough depth
Too many projects, not enough depth
Consolidate. Better to run 2 rich projects than 5 thin ones. Prioritize by stakeholder demand and business impact.
Data from different sources is conflicting
Data from different sources is conflicting
This is actually a finding. Conflicting signals often mean different segments have different experiences. Slice by segment and you’ll find the real story.