Title: Best Practices
Locale: en
URL: https://sensorswave.com/en/docs/user-operation/best-practices/
Description: Master best practices and implementation tips for the Users module
This guide summarizes best practices for the Users module, helping you use User List, User Profile, and Cohorts more effectively.

## Cohort Naming and Organization Guidelines

### Naming Principles

**Use clear, descriptive names**:

```
✅ Recommended
High-Value Users_Past 90 Days_Spending Over 5000
At-Risk Churn_30 Days Inactive
New Users_Within 7 Days
VIP Members_Gold and Above

❌ Not recommended
Cohort 1
test
User Group
Temporary
```

### Naming Format

**Include key information**:

- User characteristic description
- Time range (if applicable)
- Key threshold (if applicable)

**Examples**:

```
[User Characteristic]_[Time Range]_[Threshold]

Active Users_Past 7 Days_Logged In 3+ Times
Paying Users_Cumulative Spending Over 1000
Churned Users_90 Days Inactive_Previously Active
```

### Naming Checklist

Before creating a cohort, check whether the name meets the following criteria:

- [ ] The name clearly describes the Cohort Definition
- [ ] Key filter condition information is included
- [ ] The length is reasonable (recommended 10–30 characters)
- [ ] Team members can understand its meaning

## Cohort Design Principles

### Start from Business Scenarios

**Clarify the cohort purpose**:

| Purpose | Cohort Design |
|---------|---------------|
| Targeted analysis | Filter target users, exclude interference |
| User operations | Define operation targets, support outreach |
| Feature rollout | Define rollout scope, control risk |
| Performance comparison | Define comparison groups, analyze differences |

**Example**:

```
Purpose: Analyze behavioral characteristics of high-value users
Cohort design:
- Core condition: Spending in the past 90 days > 5000
- Supplementary condition: Cumulative order count > 5 (exclude one-time large purchases)
- Exclusion condition: Does not belong to "Internal Employees" cohort
```

### Keep Rules Simple

**Recommended approach**:
- Keep rule conditions to 5 or fewer
- Avoid overly complex nested combinations
- Prefer simple, direct conditions

**Not recommended**:
- Accumulating too many conditions
- Complex multi-level nesting logic
- Rules that are difficult to understand and maintain

### Choose the Right Cohort Type

**Dynamic Cohort vs Static Cohort**:

| Scenario | Recommended Type | Reason |
|----------|-----------------|--------|
| Ongoing operations user group | Dynamic Cohort | Users are automatically updated |
| One-time campaign participants | Static Cohort | Fixed snapshot for analysis |
| Feature rollout targeting | Dynamic Cohort | New users are automatically included |
| Historical data comparison | Static Cohort | Maintains data consistency |
| Externally imported users | Static Cohort | Only supports static method |

### Leave Room for Extension

**Consider future needs**:

```
❌ Not recommended: Overly absolute conditions
Membership Level = VIP

✅ Recommended: More flexible conditions
Membership Level in [Gold Member, Platinum Member, VIP]
OR
Cumulative Spending > 10000
```

## Common Cohort Designs

### User Lifecycle Cohorts

**New users**:

```
Registration date within the last 7 days
```

**Active users**:

```
Past 7 days Any Event Total count > 0
```

**Silent users**:

```
Past 30 days Any Event Total count = 0
AND
Past 90 days Any Event Total count > 0
```

**Churned users**:

```
Past 90 days Any Event Total count = 0
AND
Historical Any Event Total count > 10
```

### User Value Cohorts

**High-value users**:

```
Past 90 days Payment Successful Order Amount sum > 5000
OR
Cumulative spending > 10000
```

**Paying users**:

```
Historical Payment Successful Total count > 0
```

**Potential users** (interested but not yet paid):

```
Past 30 days Add to Cart Total count > 3
AND
Historical Payment Successful Total count = 0
```

### Behavioral Characteristic Cohorts

**Search-oriented users**:

```
Past 30 days Search Total count > 10
```

**Frequent users**:

```
Past 7 days Any Event Active days >= 5
```

**Deep engagement users** (long session times):

```
Past 7 days Page View Total count > 50
```

### Operational Scenario Cohorts

**Re-engagement targets**:

```
Past 60 days Any Event Total count = 0
AND
Historical Payment Successful Total count > 0
AND
Does not belong to "Already Re-engaged Users" cohort
AND
Does not belong to "Marketing Opt-out" cohort
```

**Conversion guidance**:

```
Past 7 days Add to Cart Total count > 0
AND
Past 7 days Payment Successful Total count = 0
```

## User Profile Analysis Tips

### Quick Issue Identification

**Timeline analysis**:

1. Determine the time range when the issue occurred
2. Filter the Activity for that time period
3. Review events one by one, looking for anomalies
4. Focus on error events and failure states

**Event type analysis**:

1. Filter for specific event types (e.g., payment-related)
2. View Event Property details
3. Compare differences between successful and failed events
4. Focus on error codes, device information, etc.

### Understanding User Behavior

**Analyze typical paths**:

1. View the user's complete Activity
2. Identify key conversion points
3. Analyze the path from browsing to purchase
4. Discover the user's decision-making patterns

**Compare different users**:

1. Select a few typical users for comparison
2. Compare behavioral differences between high-value and low-value users
3. Compare paths of converted users vs. dropped-off users
4. Summarize commonalities and differences

### Validate Data Accuracy

**Check instrumentation quality**:

1. Perform the target operation in the test environment
2. Check in User Profile whether the event was reported
3. Expand event details to verify property completeness
4. Compare with the instrumentation documentation to verify accuracy

## Do's and Don'ts

### Recommended Practices

**✅ Build a unified cohort system**:

- Establish unified cohort naming conventions
- Define standard user lifecycle cohorts
- Avoid creating duplicate similar cohorts

**✅ Use cohorts for analysis**:

- Prioritize using existing cohorts for filtering during analysis
- Ensure consistent analysis criteria
- Facilitate long-term tracking and comparison

**✅ Maintain cohorts regularly**:

- Regularly clean up unused cohorts
- Update outdated cohort rules
- Merge cohorts with similar functionality

**✅ Use Dynamic and Static Cohorts appropriately**:

- Use Dynamic Cohorts for ongoing operations
- Use Static Cohorts for historical snapshots
- Choose the appropriate type based on the scenario

**✅ Add descriptions to important cohorts**:

- Explain the business meaning of the cohort
- Document the usage scenarios
- Note the creation context

### Practices to Avoid

**❌ Creating too many temporary cohorts**:

- Not cleaning up after ad-hoc analysis
- Accumulating a large number of unused cohorts
- Impacting discoverability and maintenance

**❌ Using overly complex rules**:

- Rules with more than 10 conditions
- Hard-to-understand nesting logic
- Cohort Definitions that cannot be explained

**❌ Creating duplicate similar cohorts**:

- Multiple people creating cohorts with the same functionality
- Lacking unified cohort management
- Inconsistent Cohort Definitions

**❌ Neglecting cohort maintenance**:

- Not updating cohort rules for extended periods
- Not cleaning up unused cohorts
- Not checking cohort accuracy

**❌ Over-relying on Static Cohorts**:

- Using Static Cohorts for scenarios that need continuous updates
- Frequently manually updating Static Cohorts
- Leading to data inconsistencies

## Checklists

### When Creating a Cohort

- [ ] Business purpose is clearly defined
- [ ] Name is clear and follows conventions
- [ ] Rule conditions are concise and reasonable
- [ ] Correct cohort type is selected
- [ ] Estimated size is within a reasonable range
- [ ] Description information is added
- [ ] Placed in the correct folder

### When Using a Cohort

- [ ] Confirmed Cohort Definition matches analysis needs
- [ ] Checked that cohort size is reasonable
- [ ] Understand whether the cohort is dynamic or static
- [ ] Considered potential changes over time

### When Maintaining Cohorts

- [ ] Regularly review the cohort list
- [ ] Clean up cohorts unused for more than 3 months
- [ ] Update outdated cohort rules
- [ ] Merge duplicate cohorts
- [ ] Archive rather than delete potentially useful cohorts

## Team Collaboration Tips

### Establish Cohort Management Guidelines

- Define unified naming conventions
- Clarify folder organizational structure
- Define cohort approval workflows
- Set regular cleanup schedules

### Cohort Sharing and Reuse

- Encourage reusing existing cohorts
- Search for existing cohorts before creating new ones
- Add explanations when sharing cohorts
- Notify relevant team members before modifying shared cohorts

### Regular Review and Optimization

- Review the cohort list quarterly
- Track cohort usage statistics
- Clean up unused temporary cohorts
- Optimize rules for frequently used cohorts

## Next Steps

Now that you understand best practices for the Users module, you can:

1. **[FAQ](faq.mdx)**: View answers to frequently asked questions
2. **[Cohorts Overview](cohort-overview.mdx)**: Review core cohort concepts
3. **[Integration with Analytics](integration-with-analytics.mdx)**: Learn about using cohorts in analytics

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**Last updated**: January 19, 2026