Title: Integration with Analytics
Locale: en
URL: https://sensorswave.com/en/docs/user-operation/integration-with-analytics/
Description: Learn how to use the Users module with the Insights module
The Users module works closely with the Insights module, forming a closed-loop analysis workflow of "from data to users, and from users to data." This guide covers the integration methods and typical workflows between the two modules.

## From Analytics Models to the Users Module

### Drill Down to User List

In analytics models, drill down from aggregated data to specific users.

**Segmentation drill-down**:

1. Create a Segmentation analysis using "Unique Users" as the aggregation
2. In the data table, click a value
3. The system redirects to User List, displaying the corresponding users
4. You can further filter or view user details

**Supported aggregation methods for drill-down**:

| Aggregation | Drill-down Result |
|-------------|------------------|
| Unique Users | List of users who triggered the event |
| Per User Count | List of users who triggered the event |

**Filter conditions after drill-down**:

After drilling down, User List automatically carries over the following filter conditions:

- Event name
- Time range
- Segment By conditions configured in the analysis
- Specific Group By dimension values (if you clicked on a grouped result)

**Example**:

In Segmentation, analyze the "Payment Successful" event grouped by "Product Category" showing Unique Users. Click the user count for the "Electronics" category:

```
Filter conditions after drill-down:
- Event: Payment Successful
- Time range: Past 30 days
- Product Category = Electronics
```

**Funnel Analysis drill-down**:

1. Create a Funnel Analysis and configure conversion steps
2. In the funnel chart, click a step
3. Select to view "Converted users" or "Dropped-off users"
4. The system redirects to User List, displaying the corresponding users

**Drill-down scenarios**:

| Drill-down Location | User Scope |
|---------------------|------------|
| Converted users | Users who completed the conversion at that step |
| Dropped-off users | Users who dropped off at that step |
| Overall drop-off | Users who did not complete all steps |

**Retention Analysis drill-down**:

1. Create a Retention Analysis and configure the initial and return events
2. In the retention table, click a cell
3. The system redirects to User List, displaying the corresponding retained users

**Drill-down scenarios**:

| Drill-down Location | User Scope |
|---------------------|------------|
| Initial users on a date | Users who triggered the initial event on that date |
| Retention cell | Users who started on that date and returned in the corresponding period |

### Save as Cohort

In analysis results, save specific users as a cohort.

**Save from Segmentation**:

1. Drill down to User List from Segmentation
2. Click the **Save as Cohort** button
3. Select the cohort type:
   - **Dynamic Cohort**: Saves filter rules; users are automatically updated
   - **Static Cohort**: Saves the current user snapshot
4. Fill in cohort information and save

**Save from Funnel Analysis**:

1. Drill down to User List from Funnel Analysis
2. Click the **Save as Cohort** button
3. Select to save "Converted users" or "Dropped-off users"
4. Fill in cohort information and save

**Typical scenarios**:

| Analysis Scenario | Saved Cohort |
|-------------------|-------------|
| Discovered high-activity users on an anomalous date | Save as "Jan 20 High-Activity Users" Static Cohort |
| Identified funnel drop-off users | Save as "Cart Drop-off Users" Dynamic Cohort |
| Found high-retention users | Save as "7-Day High-Retention Users" Dynamic Cohort |

## From the Users Module to Analytics Models

### Cohort Filter

Use cohorts as filter conditions in analytics models.

**Configure a cohort filter**:

1. Create or edit an analysis (Segmentation, Funnel Analysis, Retention Analysis, etc.)
2. In the **Segment By** area, click **Add Filter**
3. Select **Cohort**
4. Select the operation: **Belongs to** or **Does not belong to**
5. Select the target cohort
6. Execute the query

**Use cases**:

| Scenario | Cohort Filter Configuration |
|----------|---------------------------|
| Analyze high-value user behavior | Belongs to "High-Value Users" cohort |
| Exclude test users | Does not belong to "Test Users" cohort |
| Analyze new user conversion | Belongs to "Registered in Past 7 Days" cohort |

### Cohort Comparison

Compare the performance differences of different cohorts in Segmentation.

**Method 1: Multi-segment comparison**

1. Add multiple Segment By filters
2. Each segment corresponds to a cohort
3. Execute the query and compare on the same chart

**Configuration example**:

```
Segment 1: Belongs to "High-Value Users" cohort → Named "High-Value Users"
Segment 2: Belongs to "Regular Users" cohort → Named "Regular Users"
Event: Browse Product
Aggregation: Per User Count
```

**Method 2: Group By dimension comparison**

1. In Group By dimensions, select **Cohort**
2. Select multiple cohorts to compare
3. Execute the query to display results by cohort

**Suitable scenarios**:

| Comparison Method | Suitable Scenarios |
|-------------------|-------------------|
| Multi-segment | Compare 2–5 specific cohorts with custom naming |
| Group By dimension | Compare multiple cohorts simultaneously for a quick overview |

### Cohort Trend Analysis

Track the trend of cohort size changes over time.

**Configuration method**:

1. Create a Segmentation analysis
2. Select "Any Event" for the event
3. Select "Unique Users" for the aggregation
4. In Segment By, select the target cohort
5. Set the time granularity to "By day"
6. Execute the query to view the trend

**Analysis example**: Track the user count trend of the "At-Risk Churn Users" cohort

```
Event: Any Event
Aggregation: Unique Users
Filter: Belongs to "At-Risk Churn Users" cohort
Time range: Past 30 days
Time granularity: By day
```

## Typical Workflow Examples

### Workflow 1: Anomaly Investigation

**Scenario**: Discovered that paying user count dropped by 20% yesterday, and you need to identify the cause.

**Steps**:

1. **Segmentation**: View the "Payment Successful" event's Unique Users trend
2. **Drill down to users**: Click yesterday's data point to drill down to the user list
3. **Comparative analysis**: Compare yesterday's and the previous day's users by different dimensions (platform, channel, region)
4. **Identify the issue**: Discovered that iOS users dropped significantly
5. **Save as cohort**: Save yesterday's iOS paying users as a cohort
6. **Deep dive**: View the Activity of these users to locate the specific issue
7. **Ongoing monitoring**: Create a Dynamic Cohort "iOS Paying Users" to monitor subsequent trends

### Workflow 2: Conversion Optimization

**Scenario**: Need to improve the conversion rate from cart to payment.

**Steps**:

1. **Funnel Analysis**: Configure a "Browse → Add to Cart → Place Order → Payment" funnel
2. **Identify drop-off**: Found that the Add to Cart to Place Order step has the highest drop-off rate
3. **Drill down to drop-off users**: View the list of users who dropped off at the Add to Cart step
4. **Save as cohort**: Save as "Cart Not Ordered Users" Dynamic Cohort
5. **Behavior analysis**: Analyze the behavioral characteristics of these users
   - In Segmentation, filter by this cohort and analyze their product browsing preferences
   - Compare behavioral differences between converted and dropped-off users
6. **Develop strategy**: Design promotional or push notification strategies for the cohort
7. **Apply cohort**: Enable a promotional popup for this cohort in Feature Gates

### Workflow 3: User Tier Operations

**Scenario**: Need to develop differentiated operational strategies for users of different value levels.

**Steps**:

1. **Create cohorts**: Create user value cohorts based on spending amounts
   - High-value users: Spending in the past 90 days > 5000
   - Medium-value users: Spending in the past 90 days 1000–5000
   - Low-value users: Spending in the past 90 days  5 (previously active)
6. **Re-engagement**: Export cohort users for re-engagement campaigns
7. **Track results**: In Retention Analysis, compare retention changes before and after re-engagement

## Integration Best Practices

### Build a Cohort System

Before starting analysis, build a comprehensive cohort system:

**Foundation cohorts** (by user lifecycle):
- New users, active users, silent users, churned users

**Business cohorts** (by business characteristics):
- Paying users, high-value users, VIP users

**Behavioral cohorts** (by behavioral characteristics):
- Search users, recommendation users, frequent users

### Prioritize Using Cohorts in Analysis

When performing analysis, prioritize using existing cohorts:

**Recommended approach**:
- Use the "High-Value Users" cohort for filtering instead of repeatedly configuring spending conditions
- Use the "Active Users" cohort for comparison instead of defining activity rules each time

**Benefits**:
- Ensures consistent analysis criteria
- Reduces repetitive configuration
- Facilitates tracking and comparison

### Save Valuable Users Promptly

When you discover valuable user groups during analysis, save them as cohorts promptly:

**When to save**:
- When you find users corresponding to anomalous data
- When you identify user groups with distinct characteristics
- When you need to track a group of users continuously

**Naming conventions**:
- Include time information (e.g., "20260203_High-Activity Users")
- Indicate the cohort source (e.g., "Funnel Drop-off_Cart Step")

### Regular Cleanup and Optimization

Regularly review cohort and analysis usage:

- Clean up temporary cohorts that are no longer in use
- Merge cohorts with similar functionality
- Optimize complex cohort rules
- Update outdated cohort definitions

## Next Steps

Now that you understand how the Users module integrates with analytics models, you can:

1. **[Best Practices](best-practices.mdx)**: Reference cohort design and analysis tips
2. **[FAQ](faq.mdx)**: View answers to frequently asked questions about integration
3. **[Segmentation](../analytics/event-analysis.mdx)**: Learn more about Segmentation

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**Last updated**: January 19, 2026