You open your analytics dashboard. 47 different metrics. 12 charts. Endless data.
Product asks: "How's the new feature doing?"
You panic-click through dashboards trying to find an answer.
This happens because most PMMs drown in analytics instead of focusing on the 5-7 metrics that actually inform product marketing decisions.
Good product analytics for PMM isn't about tracking everything. It's about tracking the right things to answer key questions: Is this feature being adopted? Is it driving value? Should we invest more in promoting it?
Here's the framework for product analytics that PMMs actually need.
The PMM Analytics Framework
Product managers track: Everything (feature usage, technical performance, user flows)
Product marketers track: Adoption, activation, value delivery, and marketing effectiveness
The difference: PM focuses on product health. PMM focuses on go-to-market effectiveness.
The 7 Core PMM Metrics
Metric 1: Feature Adoption Rate
What it measures: % of users who have tried a feature at least once
Why it matters: Tells you if your launch and messaging reached users
Calculation:
Adoption Rate = (Users who used feature) / (Total active users) × 100
Example:
- Total active users: 1,000
- Users who tried new feature: 300
- Adoption rate: 30%
Benchmark:
- Week 1: 20-30% adoption = good launch
- Month 1: 40-60% adoption = successful
- Month 3: 60%+ adoption = hit feature
What it tells PMM:
- Low adoption (<20% week 1) = messaging didn't reach users, need more promotion
- High adoption (>50% week 1) = strong launch, keep momentum
Where to track: Amplitude, Mixpanel, custom dashboard
Metric 2: Activation Rate (Feature)
What it measures: % of users who completed core action that delivers value
Why it matters: Adoption doesn't mean value. Activation means they got the outcome.
Example:
- Feature: Email templates
- Activation: User created email from template AND sent it
- Not just: Clicked on templates
Calculation:
Activation Rate = (Users who completed activation action) / (Users who tried feature) × 100
Example:
- Users who tried email templates: 300
- Users who created AND sent email: 180
- Activation rate: 60%
Benchmark: 50-70% activation = good product-market fit for feature
What it tells PMM:
- Low activation (<30%) = users don't understand how to get value, need better onboarding
- High activation (>70%) = clear value, feature is sticky
Metric 3: Time to Value
What it measures: How long from first use to activation (value delivered)
Why it matters: Faster time to value = better user experience = higher retention
Calculation:
Time to Value = Time from first feature use to activation event
Example:
- User tries new feature: Day 1, 10am
- User completes activation action: Day 1, 10:15am
- Time to value: 15 minutes ✓ (good)
vs.
- User tries feature: Day 1
- User completes activation: Day 7
- Time to value: 7 days ✗ (too slow)
Benchmark:
- Same session: Excellent
- Same day: Good
- Same week: Okay
-
1 week: Problem
What it tells PMM:
- Long time to value = need better onboarding, in-app guidance, or documentation
- Short time to value = feature is intuitive, good UX
Metric 4: Feature Retention (30/60/90 day)
What it measures: % of users still using feature after 30/60/90 days
Why it matters: Adoption is vanity, retention is value. Sticky features = happy customers.
Calculation:
30-Day Retention = (Users active in feature on Day 30) / (Users who adopted) × 100
Example:
- Users who adopted feature in January: 500
- Users still using it in February (Day 30): 300
- 30-day retention: 60%
Benchmark:
- 30-day retention: 40-60% (good)
- 60-day retention: 30-50% (good)
- 90-day retention: 25-40% (good)
What it tells PMM:
- Low retention = feature isn't delivering ongoing value, users tried and abandoned
- High retention = sticky feature, core to workflow
Metric 5: Feature Influence on Expansion/Retention
What it measures: Do users of this feature expand or renew at higher rates?
Why it matters: Proves feature drives business value, justifies continued investment
Calculation:
Compare:
- Expansion rate (users who use feature) vs. (users who don't)
- Retention rate (users who use feature) vs. (users who don't)
Example:
- Users who use analytics feature: 70% renewal rate
- Users who don't use it: 50% renewal rate
- Feature influence: +20 percentage points
What it tells PMM:
- High influence (+15-25 pts) = core value driver, emphasize in messaging
- Low influence (<5 pts) = nice-to-have, not differentiator
Metric 6: Launch Effectiveness (MQL/Trial Impact)
What it measures: Did the launch drive new customer interest?
Why it matters: Measures whether your launch messaging reached market
Track:
- Week-over-week MQLs (did launch spike lead gen?)
- Trial signups mentioning new feature
- Demo requests for new feature
Example:
- Average weekly MQLs before launch: 50
- Week of launch: 120 MQLs
- Week after: 80 MQLs
- Launch impact: +140% spike, sustained +60% lift
What it tells PMM:
- Strong spike = successful launch, messaging resonated
- No spike = launch didn't reach target market, need more promotion
Metric 7: Feature Attachment in Deals
What it measures: % of deals where feature was demo'd or discussed
Why it matters: Shows if feature helps close deals
Track:
- Tag deals in CRM where feature was shown
- Win rate comparison (deals with feature vs. without)
Example:
- Deals where new feature demo'd: 45% win rate
- Deals without feature demo: 30% win rate
- Feature impact: +15 pts win rate
What it tells PMM:
- High win rate impact = strong sales enablement asset, train all sales reps
- Low impact = not a differentiator in sales process
The PMM Analytics Dashboard
Build one dashboard with these 7 metrics:
Feature Performance:
- Adoption rate (30%)
- Activation rate (60%)
- Time to value (15 min avg)
- 30/60/90 day retention (60%/45%/35%)
Business Impact:
- Expansion rate influence (+18 pts)
- MQL impact (+60% sustained lift)
- Win rate impact (+15 pts in deals using feature)
Status: ✓ Healthy (all metrics on target)
Update: Weekly review, share with product and sales
How to Set Up PMM Analytics
Step 1: Define Activation Events
For each major feature, define:
- Adoption event: User clicked/opened feature
- Activation event: User completed value-driving action
Example: Email Templates Feature
- Adoption: Clicked "Templates" tab
- Activation: Created email from template AND sent it
Example: Analytics Dashboard
- Adoption: Viewed dashboard
- Activation: Created custom report OR exported data
Step 2: Instrument Tracking
Work with engineering to track:
- Feature adoption events
- Activation events
- Timestamps (for time to value calculation)
Tools: Segment, Amplitude, Mixpanel, custom events
Step 3: Build Dashboard
Use: Amplitude, Mixpanel, Looker, Tableau, or custom
Views:
- Overall product health (all features)
- Individual feature deep-dive
- Launch performance tracking
Step 4: Set Up Automated Reports
Weekly email with:
- Feature adoption trends
- New feature performance
- Red flags (declining retention, low activation)
Monthly review with product and sales:
- Feature ROI (which features drive retention/expansion)
- Launch effectiveness
- Roadmap prioritization based on data
How to Use Analytics for PMM Decisions
Decision 1: Should We Promote This Feature More?
Look at:
- Adoption rate (<30% = yes, need more promotion)
- Activation rate (>60% + high retention = yes, it's working)
Action: If low adoption but high activation/retention, create campaign to drive awareness
Decision 2: Do We Need Better Onboarding?
Look at:
- Activation rate (<40% = yes)
- Time to value (>1 day = yes)
Action: Create in-app guides, tooltips, tutorial videos
Decision 3: Is This Feature Worth Highlighting in Sales?
Look at:
- Win rate impact (+10 pts or more = yes)
- Customer retention impact (+15 pts = yes)
Action: Add to sales deck, create demo script, build ROI calculator
Decision 4: Should Product Invest More in This Feature?
Look at:
- 90-day retention (>40% = yes)
- Expansion influence (+15 pts = yes)
- Adoption rate (>60% = strong demand)
Action: Make case to product for continued investment with data
Common Analytics Mistakes for PMM
Mistake 1: Tracking vanity metrics
You track page views and sessions instead of value delivery
Problem: Activity doesn't equal value
Fix: Track activation and retention, not just usage
Mistake 2: No baseline
You launch feature but don't know pre-launch metrics
Problem: Can't measure impact
Fix: Document baseline before launch
Mistake 3: Only looking at aggregates
You only see total adoption, not segmented
Problem: Miss insights (maybe SMB adopts but enterprise doesn't)
Fix: Segment by customer type, industry, plan tier
Mistake 4: Not connecting to business metrics
You track feature usage but not impact on retention or expansion
Problem: Can't prove business value
Fix: Correlate feature usage with retention, expansion, win rates
Mistake 5: Analysis paralysis
You track 50 metrics and can't make decisions
Problem: Overwhelmed by data
Fix: Focus on 7 core metrics that drive decisions
The Analytics Review Cadence
Weekly (15 min):
- Quick dashboard check
- Any red flags? (declining adoption, low activation)
- What needs attention?
Monthly (60 min):
- Deep dive on new features
- Feature performance review
- Identify promotion opportunities
Quarterly (2 hours):
- Feature portfolio health
- ROI analysis (which features drive retention/expansion)
- Roadmap input (what to build/promote next)
Quick Start: Set Up PMM Analytics in 1 Week
Day 1-2: Define events
- List all major features
- Define adoption + activation events for each
Day 3-4: Implement tracking
- Work with engineering to instrument events
- Verify events firing correctly
Day 5: Build dashboard
- Set up in analytics tool
- Add 7 core metrics
- Test and validate
Day 6-7: Establish rhythm
- Weekly automated report
- Monthly review meeting scheduled
- Share access with stakeholders
Impact: Data-driven PMM decisions vs. gut-feel
The Uncomfortable Truth
Most PMMs ignore product analytics because they find it overwhelming or think it's "product's job."
They rely on:
- Anecdotal customer feedback
- Sales team opinions
- Gut feel
They miss:
- Which features actually drive retention
- Whether launches are successful
- What to prioritize promoting
What works:
- Track 7 core metrics (not 50)
- Review weekly (stay on top of trends)
- Connect to business outcomes (retention, expansion, win rate)
- Use data to prioritize (what to promote, what to kill)
The best PMMs:
- Check analytics weekly (not quarterly)
- Can answer "how's feature X doing?" with data
- Use retention/expansion data to prioritize messaging
- Prove launch ROI with metrics
If you can't tell me your top feature's adoption and retention rates, you're product marketing blind.
Measure what matters. Review regularly. Make data-driven decisions.