Title: Funnel Analysis
Locale: en
URL: https://sensorswave.com/en/docs/analytics/funnel-analysis/
Description: Analyze conversion funnels and optimize key conversion paths
Funnel Analysis is a core analysis model for measuring user conversion efficiency in key business processes. With Funnel Analysis, you can track user behavior across a series of ordered steps, identify the conversion rate and drop-off rate at each stage, and precisely locate conversion bottlenecks and drop-off points. Whether it's a registration flow, purchase process, or feature usage path, Funnel Analysis helps you quantify the efficiency of every step and provides data-driven support for product optimization and operational strategies.

Funnel Analysis supports flexible definition of conversion steps, conversion window settings, comparison across user groups, and user list drill-down capabilities, enabling you to see not just the numbers but also understand the specific users and behavior patterns behind them.

## Typical Use Cases

Funnel Analysis can help you answer the following questions:

**Registration conversion analysis**:
- What is the conversion rate at each step from visiting the homepage to completing registration?
- At which step in the registration flow do users drop off the most?
- How much did the conversion rate improve after optimizing the registration form?

**Purchase conversion analysis**:
- What is the overall purchase conversion rate from browsing products to completing payment?
- How many users drop off between adding to cart and submitting an order?
- Are there differences in success rates across different payment methods?

**Feature usage path analysis**:
- What proportion of new users complete core feature operations?
- What are the key steps from first use to becoming an active user?
- Which step has the highest drop-off rate and needs priority optimization?

**User group comparison**:
- What are the differences in purchase conversion rates between iOS and Android users?
- What are the respective registration conversion rates for paid channel and organic traffic users?
- How do feature usage conversion rates compare between the new and old versions?

**Time trend monitoring**:
- What is the trend of purchase conversion rates over the past 30 days?
- How does the conversion rate during promotional events compare to normal times?
- Has the conversion rate improved significantly after product optimization?

## Prerequisites

Before using Funnel Analysis, make sure you have:

- Completed [SDK integration](../data-integration/client-sdks/javascript.mdx) and started receiving data
- Understood the basic concepts of the [Data Model](../data-integration/data-model.mdx) and [Events and Properties](../data-integration/events-and-properties.mdx)
- Created the [Event Definitions](../08-数据管理/01-事件类型.md) you need to analyze in Data Center
- Instrumented the key step events in your business process
- Have view or analysis permissions for the project, see [Roles and User Permissions](../10-组织与项目/03-角色和用户权限.md)

## Quick Start

### Create a Funnel Analysis

1. Click **Insights** in the left navigation bar
2. Click the **New Analysis** button in the upper right corner
3. Select the **Funnel Analysis** model
4. Configure funnel steps:
   - Add the first step event (funnel entry point)
   - Add subsequent step events in order
   - Set the conversion window
5. Click the **Query** button to view the Funnel Analysis results

### Save and Share

After completing the analysis configuration, you can:

1. Click the **Save** button in the upper right corner to name and save the analysis
2. Click the **Add to Report** button to add the analysis to an existing report
3. Click the **Share** button to generate a share link or export an image

## Core Features in Detail

The Funnel Analysis configuration is divided into four parts: metric configuration, Segment By, Group By, and visualization settings. These features work together to enable comprehensive conversion analysis.

### 1. Metric Configuration

Metric configuration determines "which conversion path to analyze" and is the core of Funnel Analysis. Unlike Event Analysis and Retention Analysis, Funnel Analysis requires configuring a series of ordered conversion steps.

#### Funnel Steps

Funnel steps define the series of behaviors users need to complete in sequence, with each step corresponding to an event.

**Adding steps**:
- Click the "Add Step" button and select an event
- The first step is the funnel's entry point, representing users entering the conversion process
- Subsequent steps are added in the natural order of the business process
- The last step is the funnel's endpoint, representing users who completed the conversion
- Supports up to 10 steps

**Step naming**:
- The system automatically uses the event name as the step name
- You can customize step names to better match business semantics
- Clear step naming helps quickly understand the funnel's meaning

**Step event property filters**:
- Add event property filter conditions for each step to precisely define the scope of that step
- Example: In the "Submit Order" step, filter for "Order Amount" greater than 100
- Example: In the "Page View" step, filter for "Page Type" equals "Product Detail Page"

**Use case examples**:

Scenario 1: E-commerce purchase funnel
- Step 1: Browse Product
- Step 2: Add to Cart
- Step 3: Submit Order
- Step 4: Complete Payment
- Purpose: Analyze the complete conversion path from browsing to purchase

Scenario 2: User registration funnel
- Step 1: Visit Registration Page
- Step 2: Enter Phone Number
- Step 3: Verify Phone Number
- Step 4: Set Password
- Step 5: Complete Registration
- Purpose: Identify drop-off points in the registration flow

Scenario 3: Feature activation funnel
- Step 1: App Launch
- Step 2: Enter Feature Guide
- Step 3: Complete First Core Action
- Step 4: Complete Second Core Action
- Purpose: Measure the feature activation rate for new users

#### Conversion Window

The conversion window defines the time limit for users to complete the entire funnel process — "how long do users have to complete all steps to count as a successful conversion."

**Window settings**:
- Supports setting windows in minutes, hours, or days
- Common settings: 30 minutes, 1 hour, 1 day, 7 days, 30 days
- The window timer starts when the user triggers the first step event

**Window selection principles**:

| Business Scenario | Recommended Window | Reason |
|---------|----------|------|
| **Immediate conversion process** | 30 minutes - 1 hour | Users complete in a single session, e.g., shopping cart checkout |
| **Short-term conversion process** | 1 day - 3 days | Users may need time to consider or compare, e.g., B2C purchase decisions |
| **Mid-term conversion process** | 7 days - 14 days | Users need a longer decision cycle, e.g., enterprise service trials |
| **Long-term conversion process** | 30 days or more | High-value, low-frequency conversions, e.g., B2B procurement |

**How the window affects conversion rates**:
- Shorter windows typically yield lower conversion rates (requiring users to complete faster)
- Longer windows typically yield higher conversion rates (giving users more time)
- The window should match the actual decision cycle of the business scenario

**Examples**:

E-commerce cart checkout:
- Window: 30 minutes
- Reason: Users typically complete within a single shopping session; beyond 30 minutes, they may have left or abandoned

SaaS product trial conversion:
- Window: 14 days
- Reason: Enterprise users need adequate trial time and internal decision-making; 14 days is a common trial period

Mobile app onboarding:
- Window: 1 day
- Reason: Onboarding should be completed on first use; beyond 1 day loses guidance significance

> **Tip**: You can compare conversion rates across different windows to understand the distribution of user decision speeds. For example, comparing 1-day, 3-day, and 7-day windows reveals how many users convert on the same day vs. need more time.

#### Step Order

Step order defines the order requirements for users to complete funnel steps.

**Order modes**:

Funnel Analysis supports two step order modes:

**1. Sequential completion (default)**:
- Users must complete steps in the defined order
- Other events may occur between steps
- As long as all step events are triggered in order within the window, the conversion is successful

Example:
- Step definition: A → B → C
- User behavior: A → X → B → Y → C (conversion successful)
- User behavior: A → C → B (not converted, wrong order)

**2. Strict order**:
- Users must complete steps in the defined order
- No other events may occur between steps
- Requires users to trigger step events consecutively

Example:
- Step definition: A → B → C
- User behavior: A → B → C (conversion successful)
- User behavior: A → X → B → C (not converted, other events in between)

**Selection recommendations**:

(Recommended) **Use sequential completion for**:
- Most business process analysis, such as registration, purchase, etc.
- Allowing users to have other exploratory behaviors during the process
- When you care about whether users ultimately complete the conversion, not the strictness of the process

(Recommended) **Use strict order for**:
- Mandatory operational processes, such as security verification, compliance processes
- Onboarding and other scenarios requiring strict step-by-step completion
- When you want to identify whether users skip or deviate from key steps

#### Conversion Measures

Funnel Analysis supports multiple measurement dimensions to help you understand conversion performance from different angles.

**Overall conversion rate**:
- Users who completed the entire funnel / Users who entered the funnel
- Reflects overall conversion efficiency
- Example: 1000 entered, 200 completed, overall conversion rate = 20%

**Step conversion rate**:
- Users who completed the current step / Users who completed the previous step
- Reflects conversion efficiency at each stage
- Helps identify the step with the most severe drop-off

**Drop-off rate**:
- Users who did not complete the current step / Users who completed the previous step
- Drop-off rate = 1 - Step conversion rate
- Intuitively shows user loss at each stage

**Convert Time**:
- The time from entering the funnel to completing conversion
- Supports viewing average Convert Time, median, and distribution
- Helps identify conversion speed

### 2. Segment By (Segmentation)

Segment By is equivalent to the `WHERE` clause in SQL, used to filter the user groups you care about from massive data. For detailed information about Segment By, see [Common Analysis Features](common-analysis-features.mdx#2-细分筛选-segmentation).

#### Filter Condition Types

In Funnel Analysis, Segment By applies to the entire funnel, affecting which users are included in the funnel calculation.

**User Property filter**:
- Filter based on inherent user characteristics
- Example: Filter "Registration Channel" equals "TikTok Ads"
- Example: Filter "Membership Level" in "Gold Member", "Platinum Member"

**Cohort filter**:
- Use pre-created Cohorts as filter conditions
- Example: Filter "Belongs to 'New User Cohort'"
- Example: Exclude "Belongs to 'Internal Test Accounts'"

**Use case examples**:

Analyze mobile user purchase conversion:
- Add User Property filter: "Platform" equals "iOS" or "Android"

Analyze paid channel registration conversion:
- Add User Property filter: "Registration Source" in "Baidu Ads", "TikTok Ads"

Exclude internal test users:
- Add User Property filter: "Email" not contains "@sensorswave.com"

#### Comparative Analysis (Multiple Segments)

Similar to Event Analysis and Retention Analysis, Funnel Analysis also supports configuring multiple independent Segment By filters to compare conversion differences across user groups.

**Usage steps**:

1. **Create the first segment**:
   - Click the "Add Segment" button
   - Set filter conditions, e.g., "Platform = iOS"
   - Name this segment, e.g., "iOS Users"

2. **Create the second segment**:
   - Click the "Add Segment" button again
   - Set different filter conditions, e.g., "Platform = Android"
   - Name this segment, e.g., "Android Users"

3. **View comparison results**:
   - The Funnel Graph displays funnels for each segment side by side
   - The conversion data table shows data for each segment separately
   - You can visually compare conversion performance differences across groups

**Use case examples**:

Scenario 1: Compare purchase conversion across platforms
- Segment 1: "Platform = iOS" named "iOS Users"
- Segment 2: "Platform = Android" named "Android Users"
- Observe conversion rate differences and drop-off points across the two platforms

Scenario 2: Compare paid and free users
- Segment 1: "Membership Level in Gold Member, Platinum Member" named "Paid Users"
- Segment 2: "Membership Level = Free Member" named "Free Users"
- Analyze the impact of payment status on conversion

Scenario 3: Compare registration conversion across channels
- Segment 1: "Registration Source = TikTok Ads" named "TikTok Channel"
- Segment 2: "Registration Source = Baidu Search" named "Baidu Channel"
- Segment 3: "Registration Source = Organic Traffic" named "Organic Traffic"
- Evaluate conversion quality across different acquisition channels

### 3. Group By (Grouping)

Group By is equivalent to the `GROUP BY` clause in SQL, splitting funnel conversion data by specified dimensions. For detailed information about Group By, see [Common Analysis Features](common-analysis-features.mdx#3-分组查看-grouping).

#### Supported Group Types

**Group by User Property**:
- Group by inherent user characteristics
- Example: "Group by 'Province' to view conversion rate differences across regions"
- Example: "Group by 'Registration Channel' to compare conversion efficiency across channels"

**Group by first step Event Property**:
- Group by contextual information at the time of the funnel's first step event
- Example: "Group by 'Device Model' to view conversion performance across devices"
- Example: "Group by 'App Version' to compare conversion rates across versions"

> **Tip**: Funnel Analysis supports a single Group By dimension. The system generates independent funnel data for each group value, enabling intuitive comparison of conversion efficiency across groups.

#### Group Result Sorting

**Sort by entering user count**:
- Sort from highest to lowest or lowest to highest by the number of users entering the first funnel step
- Useful for quickly identifying the groups with the most users

**Sort by conversion rate**:
- Sort by overall conversion rate
- Useful for quickly identifying the best or worst performing groups

**Top N display**:
- Displays the Top 10 group results by default
- Adjustable to show only Top 5 or display all groups

### 4. Visualization Settings

Funnel Analysis provides specialized visualization methods for displaying conversion data.

#### Funnel Graph

The Funnel Graph is the most commonly used visualization in Funnel Analysis, displaying user counts and conversion rates at each step in a funnel shape.

**Chart elements**:
- **Funnel layers**: Each layer represents a step, displayed from top to bottom
- **Layer width**: Reflects the user count at that step — wider means more users
- **Conversion rate labels**: Labeled between layers showing step conversion rates
- **Drop-off rate labels**: Show the number and rate of lost users at each step

**Applicable scenarios**:
- Visually display the overall conversion path
- Quickly identify the step with the most severe drop-off
- Compare conversion performance across funnels

**Color coding**:
- The system uses different colors for different segments or groups
- Helps quickly distinguish and compare different groups

#### Conversion Trend Chart

The conversion trend chart displays conversion rate changes over time as a Line chart.

**Chart elements**:
- **X-axis**: Time (by day, week, month)
- **Y-axis**: Conversion rate (percentage)
- **Lines**: Trends of overall conversion rate or per-step conversion rates

**Applicable scenarios**:
- Monitor long-term conversion rate changes
- Detect fluctuations and anomalies in conversion rates
- Evaluate the effectiveness of product optimizations or campaigns

#### Conversion Data Table

The conversion data table displays detailed conversion data in table format.

**Table structure**:
- **Rows**: Funnel steps or Group By dimensions
- **Columns**: Entering users, completing users, conversion rate, drop-off rate, average Convert Time, etc.
- **Cells**: Precise numeric data

**Applicable scenarios**:
- View precise conversion values
- Drill down to specific user lists
- Export data for secondary analysis

#### Time Settings

**Analysis time range**:
- Select analysis start and end dates
- Determines which users are included in the Funnel Analysis
- Quick options: Today, Yesterday, Last 7 Days, Last 30 Days, etc.
- Supports custom date ranges

**Time granularity**:
- Determines the time aggregation precision of the conversion trend chart
- Supported granularities: Hourly, Daily, Weekly, Monthly
- The system automatically recommends an appropriate granularity based on the selected time range

**Time comparison**:
- Compare with historical data to quickly identify conversion rate changes
- Period-over-period: Compare with the previous period, e.g., this week vs. last week
- Year-over-year: Compare with the same period last year, e.g., this month vs. last year same month

## Data Detail Area

The Data Detail Area is located below the visualization charts, providing detailed data for Funnel Analysis. For detailed information about the Data Detail Area, see [Common Analysis Features](common-analysis-features.mdx#-数据明细区).

### User List Drill-down

In the Funnel Graph or data table, you can click on any step to view the specific user list for that step.

**View converted users**:
1. Click the "Completed Count" for a step
2. The system will pop up a list of users who completed that step
3. The user list shows: user ID, completion time, Convert Time, and other information
4. You can:
   - Click a user ID to jump to [User List](../05-受众计算/01-用户细查.md) to view the user's complete behavior trail
   - Select multiple users and save them as a [Cohort](../05-受众计算/02-用户分群.md)
   - Export the user list as a CSV file

**View lost users**:
1. Click the "Lost Count" for a step
2. The system will pop up a list of users who dropped off at that step
3. Analyze characteristics and behavior patterns of lost users
4. Save lost users as a Cohort for targeted recall or optimization

**Use cases**:
- Analyze common characteristics of high-conversion users to summarize successful paths
- Analyze behavior patterns of lost users to discover reasons for drop-off
- Save lost users at a specific step as a Cohort for targeted operations
- Review anomalous conversion users to check data quality issues

### User Path Analysis

When viewing user lists, you can further analyze users' complete behavior paths.

**View user paths**:
1. In the user list, click the "View Path" button
2. The system displays the user's complete behavioral sequence before and after funnel steps
3. Helps you understand how users entered the funnel and how they completed or dropped off

**Path insights**:
- Discover other behaviors users performed between funnel steps
- Identify key behaviors that impact conversion
- Understand what lost users did before and after dropping off

### Data Export

Click the "Export" button in the upper right corner of the data table to export Funnel Analysis results as:
- **CSV format**: Contains complete conversion data, suitable for secondary analysis
- **Excel format**: Suitable for creating presentation materials
- **Image format**: Save the Funnel Graph directly as a PNG image

## Tips and Best Practices

### Designing Effective Funnel Steps

Funnel step design directly impacts analysis value.

**Recommended practices**:

(Recommended) **Steps should have business meaning**:
- Each step should correspond to a key node in the business process
- Avoid adding too many granular intermediate steps
- Focus on the core conversion path

(Recommended) **Moderate number of steps**:
- Recommended: 3-7 steps
- Too few: Cannot identify specific drop-off points
- Too many: Analysis becomes overly complex, conversion rates become too low

(Recommended) **Step order should be logical**:
- Ensure natural causal relationships between steps
- Step order should match the actual user behavior path
- Avoid impossible step combinations

(Recommended) **Consider required vs. optional steps**:
- Clarify which steps must be completed
- If there are alternative paths, consider creating multiple funnels for comparison

**Practices to avoid**:

(Not recommended) Adding too many steps, resulting in extremely low overall conversion (e.g., below 1%)
(Not recommended) Steps that are too far apart, lacking insight into intermediate stages
(Not recommended) Unclear step definitions, making data interpretation difficult
(Not recommended) Mixing steps from different business processes into a single funnel

### Choosing an Appropriate Conversion Window

The conversion window selection should match the business scenario.

**Methods for determining the window**:

1. **Based on business experience**:
   - Reference industry standards and business common sense
   - Consult business teams to understand typical decision cycles

2. **Data-driven**:
   - Start with a longer window (e.g., 30 days)
   - Review the Convert Time distribution
   - Adjust the window based on the majority of users' Convert Time

3. **A/B comparison**:
   - Create multiple identical funnels with different windows
   - Compare conversion rate differences
   - Choose a window that covers most conversions without being too lenient

**Common mistakes**:
- (Not recommended) Window too short: Misses many conversions that need longer decision time
- (Not recommended) Window too long: Includes too many abandoned users, inflating conversion rates
- (Not recommended) Using the same window for all funnels: Ignores differences across business scenarios

### Effectively Identifying Drop-off Points

The core value of Funnel Analysis lies in identifying the steps with the most severe drop-off.

**Identification methods**:

**1. Review step conversion rates**:
- Find the step with the lowest conversion rate
- This is typically the step with the most severe drop-off

**2. Compare drop-off rates**:
- View the drop-off rate at each step (= 1 - conversion rate)
- Steps where the drop-off rate suddenly increases need priority attention

**3. Analyze lost users**:
- Drill down to the lost user list
- Analyze their common characteristics
- View their behavior paths to understand reasons for drop-off

**4. Group comparison**:
- View conversion rates by different dimensions
- Identify which user groups experience more severe drop-off at which steps
- Optimize accordingly

**Optimization recommendations**:

Based on drop-off points, take action:
- **Process optimization**: Simplify complex steps, reduce user effort
- **Experience optimization**: Improve page design, enhance loading speed
- **Guidance optimization**: Add or improve user guidance, reduce comprehension barriers
- **Incentive optimization**: Add discounts or incentives at drop-off points
- **Technical optimization**: Fix technical issues that may cause drop-off

### Using Group By and Comparison Wisely

Group By and Segment By comparisons are effective methods for discovering conversion differences.

**Recommended practices**:

(Recommended) **Comparison dimensions should have clear hypotheses**:
- Hypothesize that different platforms, channels, or versions have different conversion rates
- Validate hypotheses and discover optimization directions

(Recommended) **Ensure sufficient sample sizes**:
- Each group should have at least 100+ samples
- Avoid conclusions based on small sample sizes

(Recommended) **Compare 2-5 key groups first**:
- Too many groups lead to confused analysis
- Focus on the most important comparison dimensions

(Recommended) **Combine Funnel Graph and data table**:
- Use the Funnel Graph for overall trends
- Use the data table for precise values
- Use user lists for specific behaviors

**Practices to avoid**:

(Not recommended) Comparing groups with vastly different sample sizes (e.g., 10,000 vs. 10 users)
(Not recommended) Comparing too many dimensions simultaneously, losing focus
(Not recommended) Only looking at overall conversion rate, ignoring per-step conversion rate differences
(Not recommended) Discovering differences without investigating the underlying reasons

### Monitoring Conversion Trends

Regularly monitor conversion rate trends to promptly detect issues and opportunities.

**Monitoring methods**:

**1. Create core funnel monitoring reports**:
- Add key business process funnels to reports
- Regularly review conversion rate changes
- Set conversion rate anomaly alerts (if supported)

**2. Use time comparison**:
- Compare this week vs. last week, this month vs. last month
- Quickly detect significant conversion rate changes

**3. Analyze fluctuation causes**:
- Conversion rate increase: Summarize successful experiences and apply to other scenarios
- Conversion rate decrease: Quickly investigate causes and take timely action

**Common fluctuation causes**:
- Product feature changes or optimizations
- Marketing campaign or promotion strategy adjustments
- Seasonal factors or external events
- User composition changes (e.g., increasing proportion of new users)
- Technical issues or data quality problems

### Combining Funnel Analysis with Other Models

Funnel Analysis should not be used in isolation — it should be combined with other analysis methods.

**Funnel Analysis + Event Analysis**:
- Funnel Analysis reveals a step with low conversion
- Use Event Analysis to investigate user behavior at that step in depth
- Understand the detailed behavior patterns at that step

**Funnel Analysis + Retention Analysis**:
- Funnel Analysis identifies converted users
- Use Retention Analysis to view the subsequent retention performance of these users
- Assess the long-term value of converted users

**Funnel Analysis + User List**:
- Funnel Analysis reveals anomalous drop-offs
- Drill down to User List to view specific users' complete behavior trails
- Discover the deep-rooted causes of drop-off

**Funnel Analysis + Cohorts**:
- Save lost users as a Cohort
- Conduct targeted recall campaigns
- Save high-conversion users as a Cohort and analyze their characteristics

## Important Notes

### Data Accuracy

**Event tracking quality**:
- The accuracy of Funnel Analysis depends on the completeness and correctness of tracking data
- Ensure every step event in the funnel is correctly instrumented and reported
- Before making important decisions, we recommend checking event trigger volumes and property completeness in Data Center
- If you find data anomalies, see [Event Definitions](../08-数据管理/01-事件类型.md) for data quality checks

**User identification**:
- Funnel Analysis relies on accurate user identification
- Ensure the same user is correctly identified across different steps
- If a user switches devices or clears cache and is identified as a new user, conversion rates will be underestimated
- See [How to Properly Identify Users](../data-integration/user-identification.mdx)

**Accuracy of event ordering**:
- Ensure client and server time synchronization
- Avoid step order confusion caused by incorrect timestamps
- Timestamp accuracy is especially important for strict order mode

### Correct Interpretation of Conversion Rates

**Avoid over-interpreting a single metric**:
- Conversion rate is just one dimension for measuring process efficiency
- Consider it alongside converted user quality, long-term value, etc. for comprehensive judgment
- A high conversion rate doesn't necessarily mean good results — it could mean filter conditions are too lenient

**Consider base size effects**:
- Conversion rates with small sample sizes fluctuate significantly and lack statistical significance
- When comparing different groups, ensure each group has a sufficiently large sample
- We recommend at least 100+ samples for meaningful reference

**Understand the business meaning of conversion rates**:
- Conversion rate benchmarks vary significantly across industries and product types
- Don't blindly compare conversion rates across different businesses
- It's more important to compare with your own historical data and identify change trends

### Window Impact

**Window impact on results**:
- Different windows produce different conversion rates
- When comparing conversion rates, ensure the same window is used
- When reporting conversion rates, always state the window setting

**Incompleteness of recent data**:
- For longer windows (e.g., 30 days), recent conversion data may be incomplete
- Example: A user who entered the funnel yesterday may need 30 days to complete conversion
- We recommend focusing on data with sufficient observation periods

### Performance and Limits

**Query performance**:
- Larger time ranges and more users result in longer query times
- We recommend using shorter time ranges (e.g., within 30 days) for exploratory analysis
- For long time ranges, use coarser time granularities

**Feature limits**:
- A single funnel supports up to 10 steps
- Up to 10 Segment By filters for comparison
- Only 1 Group By dimension is supported
- Maximum conversion window is 365 days

### Common Misconceptions

**Misconception 1: More steps are better**:
- Too many steps lead to extremely low conversion rates, losing reference value
- Recommended: Focus on core steps, keeping 3-7 steps

**Misconception 2: Only focus on overall conversion rate**:
- Overall conversion rate can't tell you which step has problems
- Focus on per-step conversion rates to identify drop-off points

**Misconception 3: Only look at conversions, not drop-offs**:
- Lost users often have more optimization value than converted users
- Analyze drop-off reasons and optimize accordingly

**Misconception 4: Setting an unreasonable window**:
- Too short a window misses many conversions
- Too long a window inflates conversion rates
- Set based on actual business conditions

## Related Documentation

**Analysis models**:
- [Choosing the Right Analysis Model](choosing-analysis-model.mdx): Learn how to choose an analysis model
- [Common Analysis Features](common-analysis-features.mdx): Learn about features shared across all analysis models
- [Event Analysis](event-analysis.mdx): Analyze user behavior trends and distributions
- [Retention Analysis](retention-analysis.mdx): Analyze user retention and return visits

**Data integration**:
- [Data Model](../data-integration/data-model.mdx): Understand the Sensors Wave data model
- [Events and Properties](../data-integration/events-and-properties.mdx): Learn about event and property concepts
- [How to Properly Identify Users](../data-integration/user-identification.mdx): Ensure user identification accuracy

**Data Center**:
- [Event Definitions](../08-数据管理/01-事件类型.md): Manage and maintain event metadata
- [Event Properties](../08-数据管理/02-事件属性.md): Manage event property information
- [User Properties](../08-数据管理/03-用户属性.md): Manage user property information

**Users**:
- [User List](../05-受众计算/01-用户细查.md): View detailed behavior trails for individual users
- [Cohorts](../05-受众计算/02-用户分群.md): Create and manage user groups

**Reports and visualization**:
- [Chart Management](../04-可视化与报表/01-图表管理.md): Save and manage analysis charts
- [Report Management](../04-可视化与报表/02-报表管理.md): Create Dashboards and reports

## Next Steps

After completing Funnel Analysis, you can:

1. **Analyze lost users in depth**: Use [User List](../05-受众计算/01-用户细查.md) to view the detailed behavior trails of lost users and discover reasons for drop-off
2. **Analyze converted user retention**: Use [Retention Analysis](retention-analysis.mdx) to understand the subsequent retention performance of converted users
3. **Create Cohorts**: Save lost or high-conversion users from the funnel as [Cohorts](../05-受众计算/02-用户分群.md) for targeted operations
4. **Monitor core conversion metrics**: Add key funnels to [Reports](../04-可视化与报表/02-报表管理.md) for daily conversion monitoring
5. **Optimize business processes**: Based on Funnel Analysis insights, optimize product features, user experience, and operational strategies
6. **View best practices**: See [Best Practices](best-practices.mdx) to improve analysis efficiency
7. **Resolve common issues**: If you encounter problems, check the [FAQ](faq.mdx)