> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cryptique.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Flows
> Visualize the paths users take through your product
## 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
* 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?
## Quick Start
Choose the event to center your analysis:
```
Starting Event: wallet_connect
```
Analyze what happens before, after, or both:
```
Direction: Steps After
Number of steps: 5
```
* Set number of steps to show
* Choose to hide specific events
* Apply breakdowns
Identify common paths and unexpected behaviors
## Flow Directions
### Steps After
See what users do **after** an event:
```mermaid theme={null}
flowchart LR
A[wallet_connect] --> B[swap_completed
40%]
A --> C[page_view: /pools
25%]
A --> D[page_view: /docs
20%]
A --> E[session_end
15%]
```
**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:
```mermaid theme={null}
flowchart LR
A[page_view: /landing
30%] --> E[wallet_connect]
B[page_view: /docs
25%] --> E
C[referral_link
25%] --> E
D[direct_visit
20%] --> E
```
**Use for:**
* Understanding what leads to conversions
* Identifying effective entry points
* Discovering successful paths
### Steps Before & After
See the complete journey around an event:
```mermaid theme={null}
flowchart LR
subgraph Before["Before"]
A1[landing 30%]
A2[docs 25%]
A3[referral 25%]
A4[direct 20%]
end
subgraph Event["Event"]
B[wallet_connect]
end
subgraph After["After"]
C1[swap_init 40%]
C2[pool_view 25%]
C3[docs 20%]
C4[leave 15%]
end
A1 --> B
A2 --> B
A3 --> B
A4 --> B
B --> C1
B --> C2
B --> C3
B --> C4
```
**Use for:**
* Complete journey understanding
* Before/after comparisons
* Holistic flow analysis
## Reading Flow Diagrams
### Sankey Structure
```mermaid theme={null}
flowchart LR
A[Event A
1000 users] --> B[Event B
500 users, 50%]
A --> C[Event C
300 users, 30%]
A --> D[Event D
200 users, 20%]
```
**Wider paths** = More users taking that route
**Percentages** = Share of users from previous step
### Multi-Step Flows
```mermaid theme={null}
flowchart LR
subgraph Step1["Step 1"]
A[connect]
end
subgraph Step2["Step 2"]
B1[swap]
B2[pool]
B3[leave]
end
subgraph Step3["Step 3"]
C1[success 80%]
C2[fail 20%]
C3[add_liq 60%]
C4[leave 40%]
C5[end]
end
A --> B1
B1 --> C1
B1 --> C2
A --> B2
B2 --> C3
B2 --> C4
A --> B3
B3 --> C5
```
## Configuration Options
### 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
```
### 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
```mermaid theme={null}
flowchart TB
A[wallet_connect] --> B[swap_initiated 40%]
A --> C[pool_viewed 25%]
A --> D[docs_viewed 20%]
A --> E[session_end 15%]
B --> B1[transaction swap 75%]
C --> C1[liquidity_added 50%]
D --> D1[wallet_connect 30%]
```
### Pre-Transaction Path
**Starting Event:** Transaction (any) · **Direction:** 5 Steps Before
```mermaid theme={null}
flowchart TB
A1[token_selected 90%] --> A2[swap_quoted 85%] --> A3[swap_initiated 60%] --> T[Transaction]
B1[pool_viewed 75%] --> B2[liquidity_add_clicked 20%] --> T
C1[staking_page_viewed 80%] --> C2[stake_clicked 20%] --> T
```
### Cross-Chain Exploration
**Starting Event:** `chain_changed` · **Breakdown:** new\_chain
```mermaid theme={null}
flowchart LR
subgraph Before["Before → Arbitrum"]
A1[swap_failed Ethereum 40%]
A2[price_comparison_viewed 35%]
A3[gas_estimate_viewed 25%]
end
E[chain_changed]
subgraph After["After"]
B1[swap_completed 65%]
B2[returned_to_ethereum 35%]
end
A1 --> E
A2 --> E
A3 --> E
E --> B1
E --> B2
```
### NFT Journey
**Starting Event:** `nft_viewed` · **Filter:** collection = "CoolCats"
```mermaid theme={null}
flowchart TB
A[nft_viewed] --> B[additional_nft_viewed 35%]
A --> C[wallet_connect 30%]
A --> D[collection_explored 20%]
A --> E[session_end 15%]
C --> C1[mint_initiated 60%]
C1 --> C2[transaction mint 70%]
```
## 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?
```mermaid theme={null}
flowchart LR
A[swap_initiated] --> B[session_end 25%]
```
→ Users leaving before completing—investigate UX
**Detours:**
Where do users go instead of converting?
```mermaid theme={null}
flowchart LR
subgraph Expected["Expected"]
A1[swap_page] --> B1[swap_completed]
end
subgraph Actual["Actual"]
A2[swap_page] --> B2[docs 40%]
end
```
→ Users need more information before swapping
### Comparing Segments
Use breakdowns to compare:
```mermaid theme={null}
flowchart TB
subgraph Mobile["Mobile after wallet_connect"]
M[wallet_connect] --> M1[swap 25%]
M --> M2[leave 40%]
M --> M3[browse 35%]
end
subgraph Desktop["Desktop after wallet_connect"]
D[wallet_connect] --> D1[swap 45%]
D --> D2[leave 20%]
D --> D3[browse 35%]
end
```
→ 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
```
## Saving & Sharing
### Save Flow
1. Configure your flow analysis
2. Click **Save**
3. Name it: "Post-Wallet Connect Journey"
### Add to Board
Include flows in dashboards for ongoing monitoring. All saved flows can be added to boards.
## Next Steps
Create dashboards with flows
Analyze flows for specific segments