Customer Research Platforms: Dovetail vs. Gong vs. Spreadsheets

I had two customer research platforms. Gong ($12K/year) for call recordings and Dovetail ($8K/year) for research synthesis.

My boss asked: "What are the top 3 customer pain points we should address in Q4?"

I had 247 customer calls in Gong. I had 89 research notes in Dovetail.

And I had no answer.

To answer her question, I spent the next 6 hours:

• Reviewing Gong call highlights (1.5 hours)
• Re-reading Dovetail research notes (2 hours)
• Manually coding themes in a spreadsheet (1.5 hours)
• Synthesizing into top 3 pain points (1 hour)

I had $20K worth of customer research tools. And I was still manually synthesizing insights in Excel.

The platforms had recorded everything. They hadn't made insights actionable.

The Spreadsheet Research Days

Before Gong and Dovetail, customer research was entirely manual.

The process:

• Conduct customer interviews (2-3 per week, 30 min each)
• Take notes during calls (messily)
• Clean up notes after (30 min per interview)
• Tag key themes in spreadsheet (colors for different topics)
• Synthesize patterns monthly (4 hours)
• Present insights to stakeholders (2 hours building deck)

Weekly time investment: 8-10 hours

The output: Rich insights from deep listening, but limited scale (only 10-12 interviews per month).

The problem: When execs asked "is this representative?", I couldn't say. My sample was tiny.

I told my boss: "We need customer research platforms to scale this."

Buying Gong: The $12K Call Recording Solution

Gong's demo was compelling:

Problem: Can't scale manual note-taking Solution: "Auto-record and transcribe all customer calls. Never miss an insight."

Problem: Hard to find specific topics across calls Solution: "AI-powered search. Find every mention of 'pricing' across 1000 calls."

Problem: Insights stuck in PMM's notes Solution: "Stakeholders can listen to actual customer calls themselves."

ROI calculation:

• Currently reviewing 12 calls/month manually
• Gong gives access to 100+ sales calls monthly
• 8x more customer data = better insights
• Cost: $12K/year

Approved. I was excited to finally have comprehensive customer insights.

Month 1-3 with Gong: The Data Flood

Gong started recording every sales call. Demo calls, discovery calls, close calls, customer success calls.

Data coming in:

• 40-50 calls per week
• 2,000+ calls per quarter
• Hundreds of hours of customer conversations

This was amazing. I had comprehensive customer data for the first time.

The problem: I didn't have time to review 2,000 calls per quarter.

I tried Gong's AI features:

• Topic tracking (automatically tag calls mentioning "pricing," "features," "competitors")
• Highlight reels (AI-generated clips of important moments)
• Sentiment analysis (which calls were positive/negative)

These features helped. But they created a new problem:

Before Gong: 12 calls, deep understanding With Gong: 2,000 calls, superficial understanding

I could find every mention of "pricing" across 2,000 calls. But I couldn't understand the nuance of pricing concerns without manually reviewing calls.

Month 3 realization: Gong had solved data capture. It hadn't solved insight synthesis.

Buying Dovetail: The $8K Research Synthesis Solution

After 3 months with Gong, I recognized the gap: I could capture data but couldn't synthesize insights.

I evaluated Dovetail (research synthesis platform):

Problem: Can't synthesize insights from Gong data Solution: "Import call transcripts, tag themes, auto-generate insights"

Problem: Hard to track patterns over time Solution: "Tag taxonomy, pattern detection, trend analysis"

Problem: Insights stuck in my analysis Solution: "Stakeholder-friendly insight boards with evidence"

This would complement Gong perfectly. Gong for capture, Dovetail for synthesis.

Cost: $8K/year

Approved. Total customer research spend: $20K (Gong + Dovetail).

Month 4-6: The Synthesis Theater

I started importing Gong transcripts into Dovetail.

The workflow:

1. Export call transcripts from Gong (weekly batch)
2. Import into Dovetail
3. Review and tag key themes
4. Build insight boards showing patterns

This felt productive. I was doing "real" research synthesis using proper tools.

The reality:

Tagging 50 transcripts per week took 6 hours.

I was spending 6 hours/week manually coding data—the same work I'd done in spreadsheets, just in a nicer interface.

Before Dovetail: Manual coding in Excel (6 hours/week) With Dovetail: Manual coding in Dovetail (6 hours/week)

The platform made the research look more professional. It didn't reduce the work.

Month 6 realization: Dovetail had made research artifacts prettier. It hadn't made research faster.

The Moment of Truth

Six months into using Gong + Dovetail ($20K combined), I tracked my actual time:

Time spent on customer research:

Before platforms: 10 hours/week

• 3 hours conducting interviews
• 3 hours taking/cleaning notes
• 4 hours synthesizing insights

With Gong + Dovetail: 14 hours/week

• 2 hours conducting my own interviews (fewer, since we have Gong recordings)
• 6 hours reviewing Gong calls and tagging in Dovetail
• 4 hours synthesizing insights (unchanged)
• 2 hours managing platforms (exports, imports, troubleshooting)

The platforms had increased my workload by 40%.

What went wrong?

Before platforms: 12 deep interviews, manual synthesis With platforms: 2,000 recorded calls, still manual synthesis

The platforms gave me 166x more data. But insights don't come from data volume—they come from synthesis quality.

I could review 2,000 calls superficially or 12 calls deeply. The latter produced better insights.

I'd scaled data collection without scaling insight generation.

Why Customer Research Platforms Often Disappoint

I talked to other PMMs about their research platforms:

Friend using Gong ($12K): "We have thousands of calls. Nobody has time to review them. Sales uses it, PMM doesn't."

Friend using Dovetail ($8K): "Beautiful research repository. But I'm still the one doing all the coding and synthesis work."

Friend using UserTesting ($15K): "Great for usability testing. Terrible for strategic insight generation."

Friend using spreadsheets ($0): "Manual but at least I understand my data. Research platforms create the illusion of insights."

The pattern:

Research platforms optimize for:

• Data capture (record everything)
• Data organization (tag, categorize, store)
• Data search (find specific topics)
• Data presentation (pretty insight boards)

Research platforms don't optimize for:

• Insight synthesis (connecting patterns)
• Actionability (what should we do based on this?)
• Distribution (getting insights to stakeholders who need them)
• Integration (connecting customer insights to product roadmap, competitive intelligence, messaging)

The core issue: Research platforms treat research as artifact creation, not decision-making input.

What Actually Mattered in Customer Research

After 6 months with Gong + Dovetail, I realized what actually mattered:

Not: Access to 2,000 call recordings Need: Synthesis of patterns from 20 high-quality interviews

Not: AI-tagged themes across calls Need: Deep understanding of why customers have specific pain points

Not: Beautiful insight boards in Dovetail Need: Insights that influence product roadmap, messaging, and competitive strategy

Not: More data Need: Better decisions

The best research platform isn't the one with most data. It's the one that turns research into action fastest.

The Integration Problem

After realizing Gong + Dovetail weren't reducing workload, I identified the deeper problem:

Customer insights lived in isolation.

When I discovered a customer pain point through research:

1. Document in Dovetail
2. Manually add to product feedback doc
3. Manually update messaging if relevant
4. Manually update competitive positioning if it's a competitive issue
5. Manually add to launch considerations if relevant

One insight = 5 manual distribution steps

Gong and Dovetail helped with step 1. Steps 2-5 were entirely manual.

What I needed: Customer insights that auto-flow to product, messaging, competitive, and launches.

What I had: Customer insights in a research silo.

The Consolidated Alternative

I started researching platforms that integrated customer research with product marketing workflow.

Platforms like Segment8 approached it differently:

Traditional approach (Gong + Dovetail):

• Capture customer data in Gong
• Synthesize insights in Dovetail
• Manually distribute insights to product, messaging, competitive

Integrated approach:

• Capture key customer insights
• Insights automatically tag product gaps, messaging opportunities, competitive intelligence
• Feedback flows directly to product roadmap, messaging frameworks, battle cards

Instead of research as standalone artifact, research as input to integrated workflow.

Testing the Integrated Approach

I ran a 30-day test:

Week 1: Manual deep-dive interviews

• Conducted 8 customer interviews (45 min each)
• Focused on quality over quantity
• Time: 6 hours + 2 hours note cleanup = 8 hours

Week 2: Insight synthesis (traditional approach)

• Tagged insights in Dovetail (3 hours)
• Created insight board (1 hour)
• Distributed to stakeholders:
  • Product feedback doc (1 hour)
  • Messaging update (1 hour)
  • Competitive intelligence (30 min)
  • Launch considerations (30 min)
• Total distribution: 4 hours
• Total: 8 hours synthesis + distribution

Week 3: Insight synthesis (integrated approach)

• Logged insights in consolidated platform (1 hour)
• Tagged as product feedback, messaging update, competitive intelligence
• Platform automatically:
  • Added to product roadmap feedback
  • Flagged messaging framework updates needed
  • Updated competitive battle cards if relevant
  • Surfaced for active launches
• Total distribution: Auto (included in 1-hour logging)
• Total: 1 hour synthesis + distribution

Time saved on distribution: 87%

Week 4: Cost comparison

Gong + Dovetail:

• Tool cost: $20,000/year
• Weekly time: 14 hours (reviewing calls + tagging + synthesis + distribution)
• Annual time cost: 14 hours × 50 weeks × $80/hour = $56,000
• Total: $76,000/year

Consolidated platform (manual interviews + integrated distribution):

• Tool cost: $2,400/year (includes research + product + messaging + competitive)
• Weekly time: 4 hours (interviews + integrated synthesis)
• Annual time cost: 4 hours × 50 weeks × $80/hour = $16,000
• Total: $18,400/year

Annual savings: $57,600

What I Do Now

I cancelled Gong and Dovetail after 12 months.

Current research approach:

Capture:

• Manual interviews (8-10 per month, 45 minutes each)
• Focus on deep understanding over volume
• Record key insights, not every word

Synthesis:

• Tag insights as I capture them (product feedback, messaging, competitive)
• Platform auto-distributes to relevant workflows
• No separate "synthesis phase"

Distribution:

• Product gaps appear in product roadmap integration
• Messaging opportunities update messaging frameworks
• Competitive insights update battle cards
• Launch teams see relevant research automatically

Results:

• Time investment: 14 hours/week → 4 hours/week (71% reduction)
• Insight quality: Better (deep interviews vs. superficial call review)
• Stakeholder usage: Higher (insights in their workflow, not separate research repo)
• Tool cost: $20,000 → $2,400 (88% reduction)
• Annual savings: $57,600

Do You Need Customer Research Platforms?

Here's the test:

You might need dedicated research platforms if:

• You have dedicated researchers (not PMM doing research among other duties)
• You conduct 50+ interviews monthly (enterprise scale)
• Research artifact creation is more important than decision impact
• You have separate teams for research vs. product vs. messaging

You probably don't if:

• You're a PMM doing research as one part of your role
• Quality of insights matters more than quantity of data
• You want research to influence product, messaging, and competitive strategy
• You're manually distributing insights across tools

Most PMM teams fall into the second category.

For them, research platforms create two problems:

Problem 1: Scale data collection without scaling insight synthesis More calls doesn't mean better insights. Often means more noise.

Problem 2: Isolate research from action Insights live in research tools, disconnected from product roadmap, messaging, competitive intelligence.

The Better Question

Instead of "What research platform should we buy?" ask:

"How do we turn customer insights into action faster?"

For most PMM teams, the answer isn't more call recordings. It's:

• Fewer, deeper interviews (quality over quantity)
• Integrated insight distribution (research updates product, messaging, competitive automatically)
• Connected workflow (insights flow to decision-makers in their existing tools)

That's not a research platform. That's a consolidated PMM workflow where research is one input to integrated decision-making.

I spent $20,000 and 12 months learning that lesson.

Customer research platforms are great for scaling data collection. But PMM teams don't need more data. They need better insights and faster distribution to stakeholders.

Quality interviews + automated distribution beats automated recording + manual synthesis.

That's the research system that actually works.