Title: Quick Start
Locale: en
URL: https://sensorswave.com/en/docs/user-operation/quick-start/
Description: Get started quickly with the Users module through a complete example
This guide walks you through a complete example scenario to help you quickly get started with the core features of the Users module.

## Example Scenario

**Goal**: Identify high-value users and analyze their behavioral characteristics

As a data analyst for an e-commerce product, you need to:
1. Find users who spent the most in the past 30 days
2. Analyze the behavioral characteristics of these users
3. Save these users as a cohort for subsequent targeted operations

## Step 1: Drill Down from Segmentation to User List

First, use Segmentation to find high-spending users.

### Create a Segmentation Analysis

1. Click **Insights** in the left navigation bar
2. Click the **New Analysis** button in the top right corner
3. Select the **Segmentation** model
4. Configure the analysis parameters:
   - Event: Select **Payment Successful**
   - Aggregation: Select **Sum of property values**, property: **Order Amount**
   - Time range: Select **Past 30 days**
5. Click the **Query** button

### Drill Down to User List

1. View the data table, sorted by order amount in descending order
2. Find a date with high spending (e.g., 2026-01-20)
3. Click the user count value in the table for that date to enter the User List page

Alternatively, you can access it directly through the Users menu:

1. Click **Users** > **User List** in the left navigation bar
2. Add filter conditions:
   - User behavior: **Payment Successful** event, **Sum of Order Amount** > 1000
   - Time range: Past 30 days

## Step 2: Explore User Profile

In the user list, select a typical high-value user to explore their behavior in detail.

### View User Profile

1. In the user list, click the **User ID** or the **View Details** button on a user row
2. Enter the User Profile page
3. View basic user information:
   - User ID, Device ID
   - First seen time, Last active time
   - User properties: membership level, registration channel, region, etc.

### Analyze Activity

1. Click the User ID to enter the user details page
2. View the user's recent event list
3. Analyze the user's typical behavior path:
   - How did the user discover the product? (Search, recommendations, category browsing)
   - How many products did the user browse before placing an order?
   - What was the payment method?

### View Associated Information

1. View the **Cohorts** list to see which cohorts the user belongs to
2. View the **Feature Gate** status to see which features are enabled for the user
3. View **Experiment participation** information to see which A/B Experiments the user is participating in

## Step 3: Create a Cohort

Save the high-value users as a cohort for subsequent use.

### Method 1: Save from User List

1. Return to the User List page
2. Configure filter conditions to select high-value users
3. Click the **Save as Cohort** button in the top right corner
4. Fill in the cohort information:
   - Cohort Name: High-Value Users_Past 30 Days
   - Cohort description: Users who spent more than 1,000 yuan in the past 30 days
   - Cohort type: Select **Dynamic Cohort** (users will be automatically updated)
5. Click **Confirm** to save

### Method 2: Create a Cohort Directly

1. Click **Users** > **Cohorts** in the left navigation bar
2. Click the **New Cohort** button in the top right corner
3. Select cohort type: **Dynamic Cohort**
4. Configure cohort rules:
   - Add behavior condition: **Payment Successful** event
   - Time range: Past 30 days
   - Aggregation: Sum of Order Amount
   - Condition: Greater than 1000
5. Click **Estimate Size** to see the number of matching users
6. Fill in the Cohort Name and description, then click **Save**

## Step 4: Apply the Cohort for Analysis

After creating the cohort, apply it to various analysis and operational scenarios.

### Use the Cohort in Segmentation

1. Create a new Segmentation analysis
2. In **Segment By**, select **Cohort**
3. Select the newly created **High-Value Users_Past 30 Days** cohort
4. Analyze the behavioral characteristics of high-value users:
   - Which product categories are most popular?
   - What is the distribution of active time periods?
   - What is the average visit frequency?

### Compare Cohort Differences

1. Add multiple segments in Segmentation
2. Segment 1: Select the **High-Value Users_Past 30 Days** cohort
3. Segment 2: Exclude the cohort (regular users)
4. Compare behavioral differences between the two user groups:
   - Visit frequency differences
   - Session duration differences
   - Feature usage differences

### Use the Cohort in Feature Gates

Enable exclusive features for high-value users:

1. Navigate to the **Feature Gates** module
2. Create or edit a Feature Gate
3. In the targeting rules, add a **Cohort** condition
4. Select the **High-Value Users_Past 30 Days** cohort
5. Enable VIP exclusive features for these users

## Complete Workflow Review

```
Segmentation ──drill down──▶ User List ──view details──▶ User Profile
    │                          │
    │                          │ Save as Cohort
    │                          ▼
    │                      Cohorts
    │                          │
    └────Cohort filter─────────┘
            │
            ▼
    Feature Gates / Experiments
```

Through this workflow, you have:

1. ✅ Drilled down from data metrics to specific users
2. ✅ Analyzed individual user behavioral characteristics in depth
3. ✅ Created a reusable cohort
4. ✅ Applied the cohort to analysis and operations

## Next Steps

Congratulations on completing the quick start for the Users module! You can now dive deeper into each feature:

1. **[User List](user-list.mdx)**: Learn more about filtering and operations
2. **[User Profile](user-profile.mdx)**: Master User Profile analysis techniques
3. **[Cohorts Overview](cohort-overview.mdx)**: Learn more complex cohort rules
4. **[Best Practices](best-practices.mdx)**: Reference cohort design and analysis tips

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**Last updated**: January 19, 2026