The Essential Growth Marketing KPIs: Building a Dashboard That Drives Decisions
Learn which metrics actually matter for growth marketing and how to build a dashboard that drives better decisions. Includes templates and benchmarks for SaaS, e-commerce, and marketplaces.
The Metrics That Actually Matter
Growth teams drown in data. The challenge isn't getting metrics—it's knowing which metrics drive decisions and which are just noise.
After building growth dashboards for a dozen companies, I've learned that the best dashboards are opinionated. They surface what matters and hide what doesn't.
Tier 1: The North Star
Every growth team needs one metric that the entire company rallies around:
SaaS: Monthly Recurring Revenue (MRR) or Weekly Active Users (WAU)
E-commerce: Monthly Revenue or Gross Merchandise Value (GMV)
Marketplace: GMV or Transactions
Consumer: Weekly Active Users or Daily Active Users
Your North Star should:
- Capture customer value delivery
- Be measurable weekly or monthly
- Connect to business outcomes
- Be influenced by multiple teams
Tier 2: Unit Economics
These metrics determine whether growth is sustainable:
LTV:CAC Ratio
How much value you create per acquisition dollar.
Calculation: Customer LTV ÷ Customer Acquisition Cost
Benchmark: 3:1 minimum, 5:1+ for efficient growth
Frequency: Monthly
CAC Payback Period
How long until you recover acquisition investment.
Calculation: CAC ÷ (Monthly ARPU × Gross Margin)
Benchmark: <12 months for most businesses
Frequency: Monthly
Gross Margin
What percentage of revenue is profit after direct costs.
Calculation: (Revenue - COGS) ÷ Revenue
Benchmark: 60%+ for software, 30-50% for e-commerce
Frequency: Monthly
Tier 3: Acquisition Metrics
Track how you're filling the top of funnel:
Traffic and Visitors
Total visitors, by channel, and trends over time.
Segments: Organic, Paid, Direct, Referral, Social
Frequency: Weekly
Lead Volume
Marketing Qualified Leads (MQLs) and Sales Qualified Leads (SQLs).
Key Cuts: By channel, by campaign, by content piece
Frequency: Weekly
Conversion Rates
Conversion at each funnel stage.
Key Stages:
- Visitor to lead
- Lead to MQL
- MQL to SQL
- SQL to customer
Frequency: Weekly
Cost Per Acquisition (CAC)
Fully-loaded cost to acquire a customer.
Calculation: (Marketing + Sales Costs) ÷ New Customers
Key Cuts: By channel, blended vs. new customer only
Frequency: Monthly
Tier 4: Retention Metrics
Track how you're keeping customers:
Monthly/Annual Churn Rate
Percentage of customers lost.
Calculation: Churned Customers ÷ Starting Customers
Benchmark: <3% monthly for SaaS, varies by business
Frequency: Monthly
Net Revenue Retention (NRR)
Revenue retained including expansion and contraction.
Calculation: (Starting MRR - Churn + Expansion) ÷ Starting MRR
Benchmark: 100%+ is healthy, 120%+ is excellent
Frequency: Monthly
Customer Lifetime Value (LTV)
Total value a customer generates.
Calculation: ARPU × Gross Margin × Customer Lifespan
Key Cuts: By acquisition channel, by segment, by cohort
Frequency: Quarterly (cohort analysis)
Tier 5: Engagement Metrics
Track how customers use your product:
Daily/Weekly/Monthly Active Users
Users engaging with your product.
Calculation: Unique users with qualifying action
Key Cuts: DAU/MAU ratio, by cohort, by segment
Frequency: Weekly
Feature Adoption
Which features customers use and how often.
Key Cuts: By user segment, by tenure
Frequency: Monthly
Activation Rate
Percentage of new users reaching first value moment.
Calculation: Users with activation event ÷ All new users
Benchmark: Depends on product complexity
Frequency: Weekly
Building the Dashboard
Structure
Page 1: Executive Summary
- North Star metric with trend
- LTV:CAC and payback
- Top-line acquisition and retention
- Key insights or alerts
Page 2: Acquisition Deep Dive
- Traffic and leads by channel
- Conversion funnel
- CAC by channel
- Campaign performance
Page 3: Retention Deep Dive
- Cohort retention curves
- Churn analysis
- NRR and expansion
- Health score distribution
Page 4: Product Engagement
- Active users
- Feature adoption
- Activation rates
- User segments
Tools
For Early Stage:
- Google Analytics + Spreadsheets
- Mixpanel or Amplitude for product analytics
- Simple dashboards in Notion or Google Data Studio
For Growth Stage:
- Dedicated BI tool (Looker, Metabase, Mode)
- Data warehouse (BigQuery, Snowflake)
- Integrated marketing analytics (Segment)
For Scale:
- Custom data infrastructure
- Real-time dashboards
- Automated alerting and anomaly detection
Common Dashboard Mistakes
Mistake 1: Too Many Metrics
If everything is a KPI, nothing is. Limit to 10-15 metrics that actually drive decisions.
Mistake 2: No Comparisons
Raw numbers mean nothing without context. Always show:
- Trend over time
- Comparison to target
- Comparison to benchmark
Mistake 3: Vanity Metrics
Likes, followers, and pageviews rarely connect to business outcomes. Include only if they're proven leading indicators.
Mistake 4: No Segmentation
Blended metrics hide important variations. Cut by channel, segment, cohort, and geography.
Mistake 5: Stale Data
Dashboards with week-old data don't drive decisions. Aim for daily refreshes at minimum.
Interpreting the Dashboard
Weekly Review Rhythm
What changed?
- Significant movements in any metric
- Anomalies or unexpected trends
- New records (good or bad)
Why did it change?
- External factors (seasonality, market, competitors)
- Internal factors (campaigns, product changes, pricing)
- Data issues (tracking, sampling, definitions)
What should we do?
- Double down on what's working
- Investigate what's not
- Update forecasts and plans
Monthly Deep Dives
Cohort Analysis:
- How are recent cohorts performing vs. historical?
- Are we acquiring better or worse customers?
- What's driving the difference?
Channel Analysis:
- Which channels are improving or declining?
- Where should we shift investment?
- What's the marginal return on spend?
Segment Analysis:
- Which customer segments are most valuable?
- Where are we underperforming?
- What segments should we pursue or deprioritize?
Case Study: Dashboard at PartnerSlate
When I joined PartnerSlate, the marketing team tracked 50+ metrics across multiple disconnected tools. Here's how we simplified:
Before:
- Metrics everywhere, insights nowhere
- Weekly reporting took 8+ hours
- Team looked at different numbers
- Decisions based on gut, not data
The Rebuild:
- Defined the North Star: Qualified demos booked
- Identified Core Metrics:
- Acquisition: Traffic, MQLs, SQLs, demos, customers
- Efficiency: CAC, LTV:CAC, payback
- Retention: Churn, NRR
- Engagement: DAU, activation rate
- Built the Dashboard:
- Single source of truth in Looker
- Daily refreshes
- Automated weekly email
- Mobile-friendly views
- Established Rhythm:
- Daily: Quick metrics check
- Weekly: Team review and decisions
- Monthly: Deep dive analysis
- Quarterly: Cohort and trend analysis
After:
- Clear metrics aligned to goals
- Weekly reporting took 15 minutes
- Whole team on same page
- Decisions backed by data
The Right Metrics for Your Stage
Pre-Product/Market Fit
- Focus: Engagement and activation
- Key metrics: Activation rate, retention curves, qualitative feedback
- Avoid: Revenue metrics that distract from learning
Product/Market Fit to Scale
- Focus: Unit economics and efficiency
- Key metrics: LTV:CAC, CAC payback, channel ROI
- Avoid: Pure growth metrics without efficiency context
Scaling
- Focus: Efficiency at volume
- Key metrics: Marginal CAC, segment economics, market share
- Avoid: Aggregate metrics that hide segment issues
Conclusion
Great dashboards are simple, opinionated, and action-oriented. They answer "what's happening?" and lead to "what should we do?"
Build your dashboard around the metrics that matter for your stage, review religiously, and iterate as your business evolves. The goal isn't perfect data—it's better decisions.