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Overview

Flows (also called Sankey diagrams) visualize the paths users take through your product. See what users do before and after any event, discover common patterns, and identify unexpected behaviors.

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Use Cases

  • What do users do after connecting their wallet?
  • What actions lead to a first transaction?
  • Where do users go after viewing the pricing page?
  • Where do users go instead of converting?
  • What do users do after abandoning checkout?
  • Why are users leaving the swap page?
  • How do users discover feature X?
  • What’s the typical path to becoming a power user?
  • Do users explore multiple chains before settling?

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Quick Start

1

Select Starting Event

Choose the event to center your analysis:
Starting Event: wallet_connect
2

Choose Direction

Analyze what happens before, after, or both:
Direction: Steps After
Number of steps: 5
3

Configure Display

  • Set number of steps to show
  • Choose to hide specific events
  • Apply breakdowns
4

Analyze Patterns

Identify common paths and unexpected behaviors

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Flow Directions

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Steps After

See what users do after an event: Use for:
  • Understanding what users do after key milestones
  • Identifying next-step opportunities
  • Finding unexpected behaviors

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Steps Before

See what users do before an event: Use for:
  • Understanding what leads to conversions
  • Identifying effective entry points
  • Discovering successful paths

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Steps Before & After

See the complete journey around an event: Use for:
  • Complete journey understanding
  • Before/after comparisons
  • Holistic flow analysis

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Reading Flow Diagrams

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Sankey Structure

Wider paths = More users taking that route Percentages = Share of users from previous step

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Multi-Step Flows

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Configuration Options

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Number of Steps

Control how many steps to display: 
1 step: Immediate next action only
3 steps: Short journey
5 steps: Medium journey (recommended)
10 steps: Full exploration

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Hide Events

Remove noise by hiding irrelevant events: 
Hide events:
- page_view (too noisy)
- scroll (not meaningful)
- mouse_move (irrelevant)

Keep:
- wallet_connect
- swap_completed
- transaction

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Breakdown by Property

Split flows by a property: 
Breakdown: device_type

Mobile:
connect → swap (25%) → success

Desktop:
connect → swap (45%) → success

→ Desktop users convert better!

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Filters

Focus on specific user segments or behaviors:

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Event Filters

Filter the starting event: 
Starting Event: wallet_connect
  └── where: wallet_type = "metamask"

Only MetaMask connections

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User Filters

Filter by user properties: 
User Filter:
├── country = "US"
├── AND plan = "pro"

US Pro users only

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Time Filters

Analyze specific periods: 
Time Range: Last 30 days
Granularity: Daily

See recent behavior patterns

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Web3 Flow Examples

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Post-Wallet Connection

Starting Event: wallet_connect · Direction: 5 Steps After

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Pre-Transaction Path

Starting Event: Transaction (any) · Direction: 5 Steps Before

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Cross-Chain Exploration

Starting Event: chain_changed · Breakdown: new_chain

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NFT Journey

Starting Event: nft_viewed · Filter: collection = “CoolCats”

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Analyzing Flows

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Identifying Patterns

High-Volume Paths: Look for the thickest flows—these are your main user journeys. 
If 60% of users go: landing → docs → wallet_connect
→ Optimize this path! It's your main funnel.
Unexpected Paths: Look for surprising flows. 
If 20% of users go: checkout → settings → checkout
→ Are they looking for payment options?

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Identifying Drop-Off

Exit Points: Where do users leave? → Users leaving before completing—investigate UX Detours: Where do users go instead of converting? → Users need more information before swapping

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Comparing Segments

Use breakdowns to compare: → Mobile users leaving more—mobile UX issue?

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Best Practices

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Choose Meaningful Starting Events

✅ Good starting events:
- wallet_connect (key milestone)
- purchase_completed (conversion point)
- feature_used (engagement signal)
- signup_completed (lifecycle event)

❌ Poor starting events:
- page_view (too broad)
- click (too generic)
- scroll (not meaningful)

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Hide Noise

Remove events that obscure patterns: 
Hide:
- Generic page_views (keep specific ones)
- Scroll events
- Mouse events
- Internal/system events

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Use Appropriate Step Counts

Immediate impact: 1-2 steps
Journey analysis: 3-5 steps
Full exploration: 5-10 steps

More steps = more complexity
Start small, expand as needed

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Segment Meaningfully

✅ Actionable breakdowns:
- device_type (fix mobile?)
- traffic_source (optimize channel?)
- user_tier (different needs?)

❌ Not actionable:
- random_id
- timestamp

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Saving & Sharing

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Save Flow

  1. Configure your flow analysis
  2. Click Save
  3. Name it: “Post-Wallet Connect Journey”

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Add to Board

Include flows in dashboards for ongoing monitoring. All saved flows can be added to boards.

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Next Steps

Boards

Create dashboards with flows

Cohorts

Analyze flows for specific segments