My first dashboard had 47 metrics on it. Activation rate, trial conversion, DAU/MAU, feature usage by tier, onboarding completion, NPS, retention curves, cohort analysis, time-in-product, and dozens more.
Every morning I'd open it, stare at the sea of numbers, and have no idea what action to take.
More metrics didn't give me more clarity. They gave me more confusion.
The VP of Product asked me in a 1:1: "If you could only look at one number to know if we're succeeding, what would it be?"
I couldn't answer. Every metric seemed important.
That question haunted me. Over the next six months, I stripped my dashboard down to the essential metrics—the ones that actually drove decisions and predicted business outcomes.
I went from 47 metrics to 6.
These six metrics now tell me more about product health than my original 47 ever did. Here's what they are and why they matter.
Metric 1: Activation Rate (Within 7 Days)
Definition: Percentage of new signups who complete the activation trigger within 7 days
Our activation trigger: User connects real data + creates first meaningful analysis + exports/shares results
Why this metric matters:
Activation rate is the single best predictor of retention I've found. Users who activate in their first week retain at 78%. Users who don't activate retain at 19%.
If activation rate drops, retention will drop 30 days later. It's a leading indicator.
How I use it:
Every morning, I check yesterday's activation rate. Our baseline is 54%. If it drops below 50%, something broke.
Last month, activation dropped to 42% on a Tuesday. I dug into the data:
- Signups were normal
- Drop-off was happening at "connect data source"
- One specific integration (our most popular one) was throwing errors
I alerted engineering. They found a bug introduced in Monday's deploy. Fixed it in 2 hours. Activation recovered by Thursday.
Without daily monitoring, we might not have caught that for a week. We'd have lost hundreds of potentially activated users.
Weekly cohort view:
I also track activation rate by weekly signup cohort:
- Week of May 1: 54% activation
- Week of May 8: 52% activation
- Week of May 15: 48% activation ← Declining trend
- Week of May 22: 47% activation
Declining activation means something changed—onboarding friction increased, ICP shifted, product quality degraded, or competition improved.
This trend triggered an investigation. We discovered our trial signup funnel had started attracting lower-quality leads after a marketing campaign change. We adjusted targeting and activation recovered.
The single number I check first every morning: Yesterday's activation rate.
Metric 2: Time-to-Value (Median Days)
Definition: Median number of days from signup to first activation (completing the activation trigger)
Our baseline: 2.8 days
Why this metric matters:
Users who activate fast retain better than users who activate slow, even if they both eventually activate.
Day 1 activators: 84% retention at 90 days Day 3 activators: 71% retention at 90 days Day 7 activators: 52% retention at 90 days
Faster time-to-value = higher retention.
How I use it:
I track median time-to-value weekly. If it starts increasing, something is adding friction to onboarding.
Last quarter, median time-to-value crept from 2.8 days to 4.1 days over 6 weeks.
I investigated and found the cause: We'd added a new "recommended integrations" step to onboarding. It was supposed to help users, but it was actually slowing them down.
Users were getting overwhelmed by choices, spending 15 minutes deciding which integrations to connect, then getting tired and coming back later.
We removed the step and let users add integrations when they needed them. Time-to-value dropped back to 2.9 days.
Trend view:
I look at time-to-value distribution, not just median:
- <1 day: 23% of activators
- 1-3 days: 41% of activators
- 3-7 days: 28% of activators
- 7-14 days: 8% of activators
Goal: Shift more users into the <1 day and 1-3 day buckets where retention is highest.
How to improve it: Remove onboarding friction, use smart defaults instead of asking for configuration, show value with sample data immediately, make the quickest path to value obvious.
Metric 3: Feature Adoption Rate (Power Feature)
Definition: Percentage of activated users who've used our core power feature at least 3 times
Our power feature: Advanced analytics (the feature that drives expansion revenue and prevents churn)
Our baseline: 38% of activated users become power feature adopters
Why this metric matters:
Not all activated users are created equal. Users who adopt our power feature have:
- 91% retention at 90 days (vs. 58% for basic users)
- 3.2x higher expansion revenue
- 4.1x higher NPS
Power feature adoption is the bridge between activation and expansion.
How I use it:
I track the adoption funnel:
- Activated users: 100% (baseline)
- Discovered power feature: 62% (viewed it at least once)
- Tried power feature: 44% (used it once)
- Adopted power feature: 38% (used it 3+ times)
Biggest drop-off: Discovered (62%) → Tried (44%)
18% of users viewed the power feature but didn't try it. Why?
I interviewed 15 users in this segment. Common theme: "I saw it but didn't understand when I'd use it or how it's different from the basic features."
The fix: Contextual prompts showing power feature examples at the moment when users would benefit from it.
Instead of generic "Try Advanced Analytics!", we show:
"The analysis you just ran took 10 minutes. Advanced Analytics can do it in 30 seconds and refresh automatically. Want to try?"
After the change:
- Discovered → Tried improved from 44% to 58%
- Overall power feature adoption: 38% → 49%
Weekly tracking: I monitor new power feature adopters each week. Target: 100+/week at our current signup volume.
If we fall below 80/week, I investigate what changed.
Metric 4: Product Stickiness (DAU/MAU Ratio)
Definition: Daily Active Users / Monthly Active Users
Our baseline: 42% (meaning 42% of monthly users use the product on any given day)
Why this metric matters:
Stickiness measures how often users return. A product that users need daily is more valuable (and harder to churn from) than a product they use weekly or monthly.
High stickiness = high engagement = high retention.
Industry benchmarks:
- Social media: 60-70% (very sticky)
- B2B SaaS tools: 30-50% (moderate stickiness)
- E-commerce: 10-20% (low stickiness)
Our 42% is solid for B2B SaaS, but there's room to improve.
How I use it:
I track stickiness by user segment:
- Power users (use power feature regularly): 68% stickiness
- Activated users (basic usage): 34% stickiness
- Trial users (not yet activated): 12% stickiness
The gap between power users (68%) and basic users (34%) tells me: If we can move more users to power features, we'll improve overall stickiness.
Trend monitoring:
I watch for stickiness declines:
- January: 44%
- February: 43%
- March: 41%
- April: 39% ← Declining trend
Declining stickiness means users are finding the product less valuable or competition is improving.
I investigated and found two causes:
- New competitor launched with better mobile experience (our mobile app was weak)
- We'd removed a notification feature users relied on to remember to check the product
Actions taken:
- Prioritized mobile app improvements
- Re-introduced notifications with better customization
Stickiness recovered to 43% within 8 weeks.
Leading indicator: Stickiness declines usually predict retention declines 60-90 days later.
Metric 5: 90-Day Retention (Cohort-Based)
Definition: Percentage of users from a signup cohort still active 90 days later
Our baseline: 54% overall, 78% for activated users
Why this metric matters:
Retention is the ultimate measure of product-market fit. If users keep using your product 90 days later, you're solving a real problem.
All the other metrics (activation, time-to-value, feature adoption, stickiness) exist to drive this one.
How I use it:
I track retention curves by cohort:
March signup cohort:
- Day 7: 68% active
- Day 30: 61% active
- Day 60: 57% active
- Day 90: 54% active
April signup cohort:
- Day 7: 71% active
- Day 30: 64% active
- Day 60: 60% active
- Day 90: 57% active (projected)
April cohort is trending 3 points higher than March. Why?
I looked at what changed between March and April:
- New onboarding flow launched mid-April
- Activation rate improved from 52% to 58%
- Time-to-value decreased from 3.4 days to 2.6 days
The onboarding improvements drove measurable retention lift.
Segmented retention analysis:
I also track retention by activation speed:
- Day 1 activators: 84% retention at 90 days
- Day 3 activators: 71% retention
- Day 7 activators: 52% retention
- Day 14 activators: 38% retention
- Never activated: 19% retention
This data informs prioritization: Improving activation rate and time-to-value has more impact on retention than any feature we could build.
Monthly review: In our monthly product review, we compare retention curves across recent cohorts to spot trends early.
Metric 6: Expansion Usage Signal
Definition: Percentage of activated users exhibiting expansion-ready behaviors
Expansion signals we track:
- Using product at usage tier limits (running out of capacity)
- Inviting additional teammates
- Requesting features only available in higher tiers
- Using integrations associated with enterprise use cases
Our baseline: 18% of activated users show expansion signals
Why this metric matters:
Retention is great, but growth requires expansion. Users who hit product limits or add teammates are prime expansion candidates.
This metric helps sales prioritize outreach.
How I use it:
Every Monday, I generate a list of accounts showing expansion signals in the past 7 days.
Example list:
- 12 accounts hit usage limits
- 8 accounts invited 3+ new users
- 5 accounts requested enterprise features
Sales reaches out to these accounts: "I noticed you've been inviting team members. Want to discuss how our Team plan could better support your growing usage?"
Conversion on these outreach emails: 34% (vs. 8% on cold expansion emails)
Timing matters:
I track how long after activation users typically show expansion signals:
- 30-60 days post-activation: 24% of expansion signals
- 60-90 days: 41% of expansion signals
- 90-120 days: 22% of expansion signals
- 120+ days: 13% of expansion signals
Peak expansion window: 60-90 days after activation
This tells sales when to start expansion conversations. Too early and users aren't ready. Too late and they might have already hit frustration points with limitations.
Segmentation:
Users who adopted power features show expansion signals at 3x the rate of basic users:
- Power users: 42% show expansion signals
- Basic users: 14% show expansion signals
This reinforces why power feature adoption matters—it's not just retention, it's expansion revenue.
How These 6 Metrics Connect
These metrics aren't independent—they're a system:
Week 1: Activation Rate + Time-to-Value
- Did new users activate quickly?
- Are they experiencing value fast enough?
Week 2-4: Feature Adoption (Power Feature)
- Are activated users discovering advanced capabilities?
- Are they adopting features that drive retention?
Week 4-12: Stickiness (DAU/MAU)
- Are users coming back regularly?
- Is the product becoming habitual?
Day 90: Retention
- Did users stick around long enough to validate product-market fit?
- Did our activation and engagement efforts pay off?
Day 60-120: Expansion Signals
- Are retained users growing their usage?
- Are they ready for expansion conversations?
Each metric informs action at different stages:
Low activation rate? → Fix onboarding friction High time-to-value? → Remove setup barriers, use defaults Low feature adoption? → Improve discoverability, add contextual prompts Declining stickiness? → Investigate competitor changes, add engagement triggers Low retention? → Review activation quality, interview churned users Low expansion signals? → Improve power feature adoption, add team features
The Dashboard I Actually Use
My daily dashboard is one page with these 6 metrics:
Today's Snapshot:
- Activation rate (yesterday): 56% ↑
- Time-to-value (this week's cohort): 2.7 days ↓
- Power feature adoption (this month): 41% ↑
- Stickiness (30-day rolling): 43% →
- 90-day retention (most recent complete cohort): 57% ↑
- Expansion signals (this week): 23 accounts ↑
Arrows show trend vs. previous period.
Green arrows = celebrate Red arrows = investigate Flat = monitor
That's it. Six numbers that tell me product health in 30 seconds.
What I Removed From My Dashboard
Here are metrics I used to track that I removed because they didn't drive decisions:
Total signups: Doesn't matter if they don't activate. Focus on activation rate instead.
Onboarding completion rate: Doesn't correlate with retention for us. Focus on activation trigger instead.
Time in product: Doesn't predict retention. Some users get value in 5 minutes/day. Focus on stickiness instead.
Feature usage counts: Too granular. Focus on power feature adoption instead.
NPS: Lagging indicator, doesn't tell me what to fix. Focus on retention and expansion signals instead.
MRR: Finance metric, not product metric. Sales can track this.
None of these are bad metrics. They just didn't help me make better product decisions.
How to Build Your Product Adoption Dashboard
Don't copy my six metrics. Find the six that matter for your product.
Start with business outcomes:
- What predicts customer retention?
- What predicts expansion revenue?
- What predicts customer satisfaction?
Work backwards to leading indicators:
- What early behaviors predict those outcomes?
- What can you measure daily or weekly vs. waiting 90 days?
Test correlation:
- Pull data on 500-1,000 users
- Test which metrics actually predict the outcomes you care about
- Remove metrics that don't correlate
Prioritize actionability:
- If the metric drops, what would you do?
- If you don't know, it's not actionable—remove it
Simplify:
- Start with 20 metrics
- Remove anything you haven't used to make a decision in 30 days
- Keep removing until you hit 6-8 core metrics
The goal: A dashboard you can review in under 5 minutes that tells you exactly what action to take.
The Uncomfortable Truth About Metrics
Most product teams track too many metrics because they're afraid of missing something important.
The result: They can't see what actually matters through all the noise.
I've watched product managers spend 2 hours reviewing dashboards with 40+ metrics and walk away with no clarity on what to do.
More metrics = more confusion, not more insight.
The best product leaders I know track 5-10 core metrics obsessively and ignore everything else.
They know:
- Which metric predicts retention (for me: activation rate)
- Which metric shows early problems (for me: time-to-value)
- Which metric drives expansion (for me: power feature adoption)
- Which metric indicates product-market fit (for me: retention curves)
Everything else is noise.
If you can't answer "what action would I take if this metric drops?" for every metric on your dashboard, remove that metric.
Your dashboard should drive decisions, not just display data.
The six metrics I track:
- Activation rate (within 7 days)
- Time-to-value (median days)
- Feature adoption rate (power feature)
- Product stickiness (DAU/MAU)
- 90-day retention (cohort-based)
- Expansion usage signals
These six tell me everything I need to know about product adoption. They predict business outcomes. They're actionable. They're measurable daily or weekly.
And most importantly: They help me make better decisions.
Start with these six, adapt them to your product, and remove anything that doesn't drive action.
Your dashboard should make product strategy obvious, not complicated.