Title: Common Analysis Features
Locale: en
URL: https://sensorswave.com/en/docs/analytics/common-analysis-features/
Description: Common features and operation guide for all analysis models
Although Event Analysis, Retention Analysis, and Funnel Analysis in Sensors Wave have different analysis logic, they share a standardized interface. This design ensures that once you master one model, you can quickly get started with the others.

## Overall Interface Structure

The main interface of analysis models consists of three core areas:

1. **Query Configuration Area (Left)**: Used to build analysis conditions, including metric definitions, filters, and Group By.
2. **Visualization Area (Upper Right)**: Displays analysis results as charts, with support for time range and granularity switching.
3. **Data Detail Area (Lower Right)**: Displays detailed data in table format, with support for data download and user drill-down.

![Event Analysis Interface Example](/docs/assets/screenshots/event-analysis.png)
*(Figure: Overall interface of an analysis model, using Event Analysis as an example)*

## Query Configuration Area

This is the core area for analysis work, divided into three functional modules from top to bottom.

### 1. Metric Configuration

This is where the analysis models differ the most, determining "what to analyze."

- **Event Analysis**: Configure core events and their measures (e.g., Total Events, Average per User).
- **Retention Analysis**: Configure the **Starting Event** (initial behavior) and **Return Event** (subsequent behavior).
- **Funnel Analysis**: Configure the funnel's **conversion step** sequence and window period.

### 2. Segment By (Segmentation)

**Core purpose**: Equivalent to the `WHERE` clause in SQL. It helps you filter the exact subset of data you care about from massive datasets, eliminating noise and focusing on core groups.

**Typical use cases**:
- **Filter specific groups**: Only analyze the behavior of users whose "VIP Level" is "Gold Member", excluding regular users.
- **Exclude internal data**: Filter out internal test accounts with email suffixes like "@sensorswave.com" to ensure data authenticity.
- **Focus on specific channels**: Only view conversion results for users from the "TikTok Ads" channel.
- **Contextual analysis**: Filter high-value orders with "Payment Amount" greater than 1000 for in-depth analysis.

**Supported filter types**:
- **User Property**: Inherent user characteristics (e.g., gender, region, membership level).
- **Event Property**: Contextual information at the time of the behavior (e.g., product price, browser version, referral page).
- **Cohort**: Directly use pre-created user collections (e.g., high-risk churn group).

### 3. Group By (Grouping)

**Core purpose**: Equivalent to the `GROUP BY` clause in SQL. It breaks down a single aggregated value into distributions across multiple dimensions, helping you discover composition patterns and differences in the data.

**Typical use cases**:
- **Regional distribution insights**: Group by "Province" to quickly identify which region has the most active users or contributes the most revenue.
- **Version quality monitoring**: Group by "App Version" to compare crash rates or conversion rates between old and new versions.
- **Channel effectiveness evaluation**: Group by "Registration Source" to compare retention performance of users from different acquisition channels and optimize advertising strategy.

> **Tip**: You can set multiple Group By dimensions simultaneously (e.g., first by "Province" then by "Gender"), and the system will display cross-combination results for all dimensions, enabling more granular drill-down analysis.

## Visualization Area

Different business questions require different chart types to answer. Sensors Wave provides flexible visualization switching capabilities.

### Chart Types and Applicable Scenarios

| Chart Type | Applicable Scenario | Example Question |
| :--- | :--- | :--- |
| **Line** | **Trend analysis**. Observe how metrics change over time and identify fluctuation patterns. | "What is the DAU growth trend over the past 30 days?" |
| **Column** | **Distribution comparison**. Compare the size differences between different groups. | "Which city has the most registered users?" |
| **Stacked** | **Composition analysis**. See both overall trends and the proportion changes of internal components. | "How is the ratio of new vs. returning users changing among daily active users?" |
| **Metric** | **Core KPIs**. Focus only on the final aggregated result, not the process trend. | "What is the cumulative total sales this month?" |

### Time Controls
- **Time Range**: Determines the span of analysis data (e.g., Last 7 Days, January 1, 2024 - January 31, 2024).
- **Time Granularity**: Determines the level of data aggregation (e.g., by hour, by day, by week, by month).
  - *Example*: During a major promotional event, you might need to view traffic changes by "hour"; when reviewing annual financial reports, viewing by "month" is more appropriate.

## Data Detail Area

Visualization charts are used for discovering trends, while the Data Detail Area is for **verifying data** and **taking action**.

**Core capabilities**:
1. **Multi-dimensional data verification**: View the precise values behind charts to ensure analysis results are accurate.
2. **User list drill-down**: Seeing numbers alone isn't enough — you also need to know **which specific people** are behind the numbers.
   - *Example*: In Funnel Analysis, you discover an extremely high drop-off rate at a certain step. Click the drop-off count to directly view the list of lost users and analyze their common characteristics.
3. **Cohort save and application**:
   - *Action*: Save specific user groups discovered during analysis (e.g., "high-intent users with payment failures") as a **Cohort** with one click.
   - *Value*: After saving, you can directly target these users with precise push notifications, coupon recalls, or more in-depth behavioral analysis.
4. **Data export**: Supports downloading query results as CSV/Excel files for cross-department reporting or secondary processing.

## Next Steps

Now that you've mastered the common features of analysis models, choose a specific model to begin your data exploration journey:

- **[Event Analysis](event-analysis.mdx)**: Analyze trends and distributions of user behavior (the most commonly used analysis model)
- **[Retention Analysis](retention-analysis.mdx)**: Analyze user stickiness, return visits, and health
- **[Funnel Analysis](funnel-analysis.mdx)**: Analyze conversion efficiency and drop-off points in key business processes

If you encounter issues during use, check the [FAQ](faq.mdx) or refer to the [Best Practices](best-practices.mdx).