When to use funnel charts
Perfect for:
- Predefined time periods: MRR growth month over month
- Explicit journey steps: User onboarding with defined stages
- Clear process stages: Sales pipeline with specific milestones
- Drop-off analysis: Identify where users/items leave the journey
- Process optimization: Find bottlenecks in any workflow
Not ideal for:
- Vague journey descriptions: “User activation funnel” without defined steps
- Time series data: Use line charts instead
- Category comparisons: Use horizontal bar charts instead
- Proportions of a whole: Use pie charts instead
- Single metrics: Use number displays instead
Example queries
Predefined time periods
Onboarding with defined steps
User journey with explicit steps
Sales pipeline with clear stages
Marketing funnel with defined steps
Best practices
Journey definition
- Explicit step definition: You must clearly define each step of the journey
- Describe each step: Clearly define what each stage represents
- Natural progression: Ensure stages follow a logical order
- Consistent time periods: Use the same time window for all stages
- Clear stage names: Use descriptive, consistent naming
Automatic ordering
- Basedash ordering: AI automatically orders stages logically
- Drop-off calculation: Automatic calculation of drop-offs between stages
- Proportional display: Bars sized proportionally to stage values
- Clear progression: Visual flow from top to bottom
Common use cases
User journeys
- Signup to activation flows
- Onboarding completion rates
- Feature adoption funnels
- User engagement progression
Business metrics
- MRR growth month over month
- Revenue progression by stage
- Customer lifetime value growth
- Subscription upgrade flows
Process optimization
- Sales pipeline analysis
- Support ticket resolution
- Manufacturing processes
- Quality control workflows
Advanced features
Multi-source data
Pull from different data sources:- Different tables for each stage
- Various event types and sources
- Mixed data types and formats
- Cross-system journey tracking
Drop-off analysis
Identify specific drop-off points:- Stage-by-stage conversion rates
- Bottleneck identification
- Optimization opportunities
- Performance benchmarking
Journey optimization
Improve process efficiency:- Identify problematic stages
- Compare different time periods
- Test process improvements
- Track optimization results
Common pitfalls
Avoid these mistakes:
- Inconsistent definitions: Ensure stage definitions are clear
- Wrong time windows: Use appropriate time periods for each stage
- Missing context: Include baseline or comparison data
- Too many stages: Keep to 5-7 stages for clarity
- No drop-off analysis: Focus on where users leave
Data quality issues:
- Attribution problems: Ensure proper user/session tracking
- Duplicate counting: Handle multiple events per user
- Missing stages: Account for users who skip stages
- Sample size: Ensure sufficient data for each stage
Example scenarios
E-commerce optimization
SaaS onboarding
Marketing campaign
Customer support
Related chart types
- Line charts: For conversion trends over time
- Bar charts: For stage comparisons
- Number displays: For individual stage metrics
- Tables: For detailed funnel data