Function Overview
Analysis in Quick Tracking is divided into two main parts: Behavior Insights and User Insights.
Behavior Insights
This feature provides statistical analysis of event-based metrics. It supports drill-down analysis with grouping and filtering conditions to help you understand how users interact with your product and identify key factors that affect metrics.
This feature helps you understand user conversion or churn across behavioral steps. You can then optimize your product or implement operational strategies to increase conversion rates and achieve business goals.
You can calculate retention based on custom start and subsequent behaviors for different business scenarios and product stages to analyze user stickiness. Use the results to adjust your strategies, guide users to discover product value, retain them, and achieve real user growth.
You can view the distribution of users for a specific event metric within defined intervals. Common analyses include user distribution by the number of event occurrences, days of product use, or a specific property value. You can use distribution analysis to better understand user characteristics.
You can observe the time intervals and data distribution between specified user behaviors. Data metrics, such as maximum, minimum, and median, reflect the user experience within the product path. You can use this information to evaluate the effectiveness of your product's feature settings.
Session analysis links individual user behaviors into a cohesive whole. This helps you perform an in-depth analysis of a user's series of actions within the product.
Path analysis uses a Sankey diagram to visualize user paths within the product. It illustrates the flow of traffic between pages. You can use path analysis to validate operational strategies and refine product design.
User Insights
Audience Insights lets you select users who share common behavioral and attribute features within a specific time period. You can then perform precise analysis and outreach to these selected users.
You can divide users by their lifecycle stage. This helps you focus on, analyze, and manage users who are at risk of churning to increase the number of active users.
Document attachments
Behavior Insights and User Insights share some common features. This section explains these common features in detail.
Attachment 1. Selecting event metrics
Event metrics include preset and property metrics. Preset metrics support calculations for Deduplicated Count, Count, and Count per Capita. Property metrics are divided into string and numeric types. String metrics support Deduplicated Count. Numeric metrics support calculations such as Deduplicated Count, Sum, Maximum, Minimum, Average, Lower Quartile, Median, and Upper Quartile. The following table describes these metrics.
Metric Type | Metric Name | Metric Meaning | Example |
Preset Metric | Deduplicated count of device IDs | The deduplicated count of device IDs for the selected event. | Number of unique OpenIDs that accessed the miniapp. |
Count of device IDs | The number of triggers for the selected event associated with a device ID. | Number of times the miniapp was launched. | |
Count per capita of device IDs | Count of device IDs / Deduplicated count of device IDs | Average number of launches per user for the miniapp. | |
Deduplicated count of account IDs | The deduplicated count of logon account IDs for the selected event. | Number of accounts that paid for orders. | |
Count of account IDs | The total number of times the selected event was triggered by a logged-on account. | Number of times orders were paid. | |
Count per capita of account IDs | Count of account IDs / Deduplicated count of account IDs | Average number of paid orders per person. | |
Deduplicated count of entity IDs | The deduplicated count of entity IDs for the selected event. | Number of active one-ids in a cross-terminal scenario. | |
Count of entity IDs | The number of triggers for the selected event with a specified entity ID | Number of active sessions in a cross-terminal scenario. | |
Count per capita of entity IDs | Count of entity IDs / Deduplicated count of entity IDs | Average number of active sessions per person in a cross-terminal scenario. | |
Property Metric | Deduplicated Count | The deduplicated count of event properties. | Number of unique products exposed. |
Sum | The sum of a numeric property. | Sum of order amounts for paid orders. | |
Maximum | The maximum value of a numeric property. | Maximum order amount for paid orders. | |
Minimum | The minimum value of a numeric property. | Minimum order amount for paid orders. | |
Average | The arithmetic mean of a numeric property. | Average order amount for paid orders. | |
Lower Quartile | The 75th percentile of numeric attributes | The 25th percentile value of the order amount for paid orders. | |
Median | The middle value of a numeric property. | The 50th percentile value of the order amount for paid orders. | |
Upper Quartile | The 25th percentile of values for a numeric property | Data value for the payment of 25% of the order amount | |
P90 | The 90th percentile value of a numeric property. | The 90th percentile value of the order amount for paid orders. |
Attachment 2. Adding filter conditions
You can filter data using system, global, event, channel, and user properties. The following table describes these properties.
Category Name | Category Description | Details |
System property | This property applies to all events and is collected by the software development kit (SDK). | A preset system property, such as the SDK version. |
Global property | This property, such as the region of the Ele.me service, applies to all events and is reported by the user. | A property that you enter in the global properties section of the Collection Management platform. |
Event property | This property applies to all events and represents data reported by users or collected by the system, such as the payment amount for a payment event. | A property that you enter in the event properties section of the Collection Management platform. |
Channel property | This property applies to all events. You can use it to view the in-app behavioral characteristics of users from different channels and their contribution to a specific metric. | A property that you enter in the channel properties section of the Collection Management platform. |
User property | This property applies to all events and is sent through a specific event or a server-side API, such as user membership information. | A property that you enter in the user properties section of the Collection Management platform. |
Share reflow property | This property applies to sharing behavior events. It is a key attribution field used to identify users who return to the app through a share link. Examples include `share_id` or `utm_source=share&ref=xxx`. | A property that you enter in the user properties section of the Collection Management platform. |
Different filter operators are supported based on the property type. The following table describes the available operators and their definitions.
Property Type | Filter Operator | Definition |
String | Equals | Equals one or more specific values. |
Does not equal | Excludes one or more specific values. | |
Contains | Finds values that contain specific characters. | |
Does not contain | Excludes values that contain specific characters. | |
Has no value | Finds data with no property value. | |
Has a value | Finds data with a property value. | |
Numeric | Equals | Equals one or more specific values. |
Does not equal | Does not equal a specific numeric value. | |
Greater than | Is greater than a specific numeric value. | |
Greater than or equal to | Is greater than or equal to a specific numeric value. | |
Less than | Is less than a specific numeric value. | |
Less than or equal to | Is less than or equal to a specific numeric value. | |
Between... and... | Is between two specific numeric values. | |
Has no value | You can find data with no order amount. | |
Has a value | You can find data with an order amount. | |
Boolean | true | The result is true. |
false | The result is false. | |
Has a value | Finds data that has a time value. | |
Has no value | Finds data that has no time value. | |
Timestamp | Absolute time | Equals a fixed time. |
Time period | Within a specific time period. | |
Time relative to the current time | A point in time before or after the current time. | |
Time relative to the event occurrence | Based on the time an event occurred, such as the time of a click event. | |
Time period relative to the event occurrence | A time period relative to an event occurrence. For example, the period from when a click event first occurred to when it occurred again. | |
Has a value | Finds data that has a time value. | |
Has no value | Finds data that has no time value. |
The following table shows how data is categorized and displayed when filtering for 'Has a value' or 'Has no value'.
Actual Situation | Categorization for "Has a value" / "Has no value" filter | Display when showing by property value |
key:'value' | Has a value | 'value' |
key:'' | Has no value | Empty string (preset) |
key:null | Has no value | Null object (preset) |
key does not exist | Has no value | Unknown (preset) |