Funnel charts show the progression through a series of steps in any journey, automatically calculating drop-offs between stages. They’re perfect for analyzing activation rates, onboarding flows, MRR growth, or any multi-step process where you want to see progression and drop-offs. Important: You must explicitly define the steps of your journey for the funnel to work properly.

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When to use funnel charts

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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

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Not ideal for:

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Example queries

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Predefined time periods

Show me MRR growth month over month as a funnel chart

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Onboarding with defined steps

Display onboarding drop-offs: signup, email verification, profile completion, first action

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User journey with explicit steps

Show me user activation journey: visit, signup, email verification, first login, first feature use

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Sales pipeline with clear stages

Create a funnel chart of sales pipeline: lead created, qualified, proposal sent, negotiation, closed won

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Marketing funnel with defined steps

Show me marketing funnel: website visit, page view, form fill, email signup, conversion

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Best practices

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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

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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

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Common use cases

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User journeys

  • Signup to activation flows
  • Onboarding completion rates
  • Feature adoption funnels
  • User engagement progression

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Business metrics

  • MRR growth month over month
  • Revenue progression by stage
  • Customer lifetime value growth
  • Subscription upgrade flows

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Process optimization

  • Sales pipeline analysis
  • Support ticket resolution
  • Manufacturing processes
  • Quality control workflows

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Advanced features

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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

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Drop-off analysis

Identify specific drop-off points:
  • Stage-by-stage conversion rates
  • Bottleneck identification
  • Optimization opportunities
  • Performance benchmarking

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Journey optimization

Improve process efficiency:
  • Identify problematic stages
  • Compare different time periods
  • Test process improvements
  • Track optimization results

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Common pitfalls

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Avoid these mistakes:

  1. Inconsistent definitions: Ensure stage definitions are clear
  2. Wrong time windows: Use appropriate time periods for each stage
  3. Missing context: Include baseline or comparison data
  4. Too many stages: Keep to 5-7 stages for clarity
  5. No drop-off analysis: Focus on where users leave

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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

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Example scenarios

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E-commerce optimization

Show me the conversion funnel from homepage visit to purchase completion

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SaaS onboarding

Create a funnel chart of user signup to feature activation

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Marketing campaign

Display lead generation funnel from ad click to qualified lead

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Customer support

Show me support ticket resolution funnel by priority level