Flows - Cryptique

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.

Use Cases

User Journey Discovery

What do users do after connecting their wallet?
What actions lead to a first transaction?
Where do users go after viewing the pricing page?

Drop-off Analysis

Where do users go instead of converting?
What do users do after abandoning checkout?
Why are users leaving the swap page?

Feature Discovery

How do users discover feature X?
What’s the typical path to becoming a power user?
Do users explore multiple chains before settling?

Quick Start

Select Starting Event

Choose the event to center your analysis:

Starting Event: wallet_connect

Choose Direction

Analyze what happens before, after, or both:

Direction: Steps After
Number of steps: 5

Configure Display

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

Analyze Patterns

Identify common paths and unexpected behaviors

Flow Directions

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

Steps Before

See what users do before an event: Use for:

Understanding what leads to conversions
Identifying effective entry points
Discovering successful paths

Steps Before & After

See the complete journey around an event: Use for:

Complete journey understanding
Before/after comparisons
Holistic flow analysis

Reading Flow Diagrams

Sankey Structure

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

Multi-Step Flows

Configuration Options

Number of Steps

Control how many steps to display:

step: Immediate next action only
steps: Short journey
steps: Medium journey (recommended)
steps: Full exploration

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

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!

Filters

Focus on specific user segments or behaviors:

Event Filters

Filter the starting event:

Starting Event: wallet_connect
  └── where: wallet_type = "metamask"

Only MetaMask connections

User Filters

Filter by user properties:

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

US Pro users only

Time Filters

Analyze specific periods:

Time Range: Last 30 days
Granularity: Daily

See recent behavior patterns

Web3 Flow Examples

Post-Wallet Connection

Starting Event: wallet_connect · Direction: 5 Steps After

Pre-Transaction Path

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

Cross-Chain Exploration

Starting Event: chain_changed · Breakdown: new_chain

NFT Journey

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

Analyzing Flows

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?

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

Comparing Segments

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

Best Practices

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)

Hide Noise

Remove events that obscure patterns:

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

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

Segment Meaningfully

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

❌ Not actionable:
- random_id
- timestamp

Save Flow

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

Add to Board

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

​Overview

​Use Cases

​Quick Start

​Flow Directions

​Steps After

​Steps Before

​Steps Before & After

​Reading Flow Diagrams

​Sankey Structure

​Multi-Step Flows

​Configuration Options

​Number of Steps

​Hide Events

​Breakdown by Property

​Filters

​Event Filters

​User Filters

​Time Filters

​Web3 Flow Examples

​Post-Wallet Connection

​Pre-Transaction Path

​Cross-Chain Exploration

​NFT Journey

​Analyzing Flows

​Identifying Patterns

​Identifying Drop-Off

​Comparing Segments

​Best Practices

​Choose Meaningful Starting Events

​Hide Noise

​Use Appropriate Step Counts

​Segment Meaningfully

​Saving & Sharing

​Save Flow

​Add to Board

​Next Steps

Boards

Cohorts