> ## Documentation Index
> Fetch the complete documentation index at: https://knowledge.deeto.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Research Hub: Best Practices
> How to design effective research projects, present findings to stakeholders, build a continuous intelligence program, and measure Research Hub impact.
The Research Hub is the **analyze** layer of Deeto's research stack. It turns VoC data collected through AI Interviews into structured intelligence — win/loss studies, sentiment tracking, competitive analysis, and product feedback research. If you haven't set up AI Interviews yet, start with [AI Interviews — Overview](/docs/platform-guides/ai-interviews-overview).
The Research Hub turns your Voice of Customer data into structured intelligence — with dashboards, trend analysis, and actionable findings organized into projects. This guide covers how to get the most out of it.
**Adoption journey:** First Project (Enabled) → Building a Program (Adopted) → Strategic Intelligence (Embedded)
***
## 1. Setting up your first research project
Your first research project sets the foundation for your customer intelligence program. Choose a project 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 recommended)
**Recommended first projects**
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?"
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?"
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?"
Start with a project where you already have a hypothesis. Checking whether the Research Hub confirms what you already suspect builds trust in what you'll find later.
***
## 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 this quarter?" |
| Data sources | Which VoC data to include | Win/loss interviews, sales call recordings, CRM notes |
| Time frame | What period to analyze | Last 2 quarters, with trend comparison 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 |
**Data quality checklist**
* 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
The most common mistake is trying to answer too many questions in one project. Start narrow. You can always expand scope or launch a follow-up project.
***
## 3. Using AI Interviews within research
Research Hub and AI Interviews work together. Use AI Interviews 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 VoC data
* You want to test a new research question before committing to a full project
**Integration best practices**
| 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 |
| Review interview results within the research project | Keep all insights connected rather than analyzing interviews in isolation |
| Use interview quotes to enrich findings | Stakeholders connect with specific customer voices, not just aggregate patterns |
Launch a targeted AI Interview campaign at the midpoint of a research project. The first-half findings will tell you exactly what questions to ask.
***
## 4. Presenting findings to stakeholders
Research only creates value when people act on it. Tailor your delivery to each audience.
This table covers presenting **structured research project findings** — synthesized across multiple data sources over time. For sharing individual campaign results right after they close, see [AI Interviews: Best Practices](/docs/platform-guides/ai-interviews-best-practices#3-analyzing-and-sharing-results).
| Audience | Format | Content focus | Timing |
| ------------------ | ---------------------------------------- | ------------------------------------------------------------------------- | ------------------------- |
| C-Suite / Board | 1-page executive summary | Top 3 findings + strategic implications + recommended actions | Quarterly or at milestone |
| Product Leadership | Prioritized insights report | Feature requests ranked by frequency and urgency, roadmap recommendations | Monthly or per project |
| Sales Leadership | Battle card updates + competitive brief | Win/loss patterns, competitive positioning shifts, proof points | Monthly or per project |
| CS Leadership | Account health dashboard + risk alerts | Sentiment trends, at-risk accounts, expansion signals | Bi-weekly or continuous |
| Marketing | Messaging validation + competitive intel | How customers describe your product, competitive perception shifts | Per project or quarterly |
**Presentation best practices**
* 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
The single most important thing for Research Hub adoption is closing the loop. When stakeholders see that research leads to decisions, they request more research.
***
## 5. Building a research cadence
Move from one-off projects to a recurring research program that drives continuous intelligence.
This cadence covers ongoing **research projects** inside the Research Hub — how often you run and review each project type. For scheduling individual AI Interview campaigns, see [AI Interviews: Best Practices](/docs/platform-guides/ai-interviews-best-practices#4-scaling-to-multiple-campaigns).
| 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 |
**Program maturity checklist**
* 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
Avoid running too many projects simultaneously with insufficient data. Better to run 2 well-scoped projects with rich data than 5 shallow ones.
***
## 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 |
The strongest proof of Research Hub value is tracking which decisions changed because of research insights. Keep a simple log: "Finding X led to decision Y."
***
## 7. Troubleshooting
Check data volume and diversity. Most projects need 100+ data points across multiple source types. Consider launching an AI Interview campaign to add qualitative depth to aggregate patterns.
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.
Ask your stakeholders: "What's the biggest customer-related question you can't answer today?" Their answer is your next project.
Consolidate. Better to run 2 rich projects than 5 thin ones. Prioritize by stakeholder demand and business impact.
This is actually a finding. Conflicting signals often mean different segments have different experiences. Slice by segment and you'll find the real story.
***
## Support
Contact your Deeto Customer Success Manager or email [support@deeto.ai](mailto:support@deeto.ai).
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