Information that the channel comparison component can provide
The channel comparison component can help you automatically generate the following metrics:
Web-based channel metrics
- Daily number of visits and users on different website channels
- Daily number of new user visits and users on different website channels
- Daily new user retention rate on different website channels
App-related channel metrics
- Daily activation volume of apps from different channels
- Average daily usage frequency and average usage time per user across different app channels
- Daily new user retention rate on different app channels
General Indicators
- Custom metrics such as the number of times an event occurs, the number of users who trigger it, and the amount of revenue generated can be specified for different channels.
The role of channel comparison components
The channel comparison component can quickly provide the following information:
- Based on metrics such as visitor volume and the percentage of new users, it's possible to determine which channel has the most traffic and a high percentage of new users.
- By incorporating revenue-related events, the return on investment (ROI) for each channel can be quickly calculated.
- Based on metrics such as retention and usage time, it's possible to compare which channel has better user engagement and product fit.
By comparing the above indicators, we can gain a more specific understanding of the channels we access, clearly determine which channels are star channels and require increased investment, and which channels require reduced investment.
Example: Channel comparison component example (all data are simulated data)
Information 1:
The data in the chart shows that the product's daily visits mainly come from 36kr, Toutiao, offline promotions, WeChat, Baidu, and the overall market.
Information 2:
Baidu had the largest traffic, with 332 new users; 36kr and Toutiao had the highest proportion of new users, both at 100%. If you are concerned about the volume of traffic, then Baidu is your best channel; if you are concerned about the ability to attract new users, then 36kr and Toutiao are good choices.
Information 3:
We can learn about users' overall experience with the product by analyzing new user retention rates the following day. This also indirectly reflects the relevance of the target audience for our promotion. A high retention rate indicates that users promoted through this channel are more aligned with the product's positioning and are of higher quality.
Information 4:
The target event, selected in this example as payment orders, allows you to observe the number of new users making payment orders from each channel and the total amount paid. If your company is more focused on user engagement, you can set events related to engagement here (e.g., posting, saving); if your company is focused on revenue, you can set events related to revenue (e.g., payments, completed orders).
Configure channel components
Add channel components
Step 1: Select New Component in the overview.
Step 2: In the pop-up window, select the channel comparison component.
Step 3: The system will automatically generate a channel comparison component for you based on your event tracking information.
Configure channel components
Set entry
Click the "More" button in the upper right corner of the channel comparison component to configure or delete the component.
Basic settings
- Supports switching between App and Web channel statistics
- Supports custom switching of channel identifier attributes
Advanced settings
- The target conversion event switch controls whether the target event module is displayed in the entire component.
- When selecting a target conversion event, it is recommended to choose an event related to revenue.
- It supports adding attributes for target conversion events; it is recommended to select revenue amount, which allows for a clear view of the profitability of each channel.
Explanation and Calculation Rules of Channel Component Metrics
The data for the channel components is generated based on the data collected through full-tracking, and the product needs to be integrated with Sensors Analytics' full-tracking system (see: Web full-tracking , Android full-tracking , iOS full-tracking ).
- The data for the web is based on the number of daily visitors and the number of visits per pageview.
- The app's data is based on the app's browsing page ($AppViewScreen) to calculate the daily number of visitors and visits.
- Whether a user is a new user is determined based on whether the user visited on the first day ($is_first_day) in the event attributes.
- The user's registration channel is identified by the campaign source ($utm_source) in the user attributes, which is used for user tagging.
Web-related metrics
- Total number of visits/users:
The number of web page views /users are grouped according to the campaign source ($utm_source) of the user attributes. - Number of new user visits / number of users:
Group users by the number of times/number of people viewed the web page , and whether the visit was on the first day ($is_first_day) is true, based on the campaign source ($utm_source) of the user attribute. - Day 2 retention rate of new users:
Initial event Users who browse the web page will perform the following day Web browsing page The percentage.
App-related metrics
- Total number of visits/users:
App browsing page The frequency/number of occurrences is grouped according to the campaign source ($utm_source) of the user attributes. - Number of new user visits / number of users:
App browsing page Group by the number of occurrences/number of people, and whether the first day of visit ($is_first_day) is true, and group by the campaign source ($utm_source) of the user attribute. - Number of activated users:
App Activation Group by the number of occurrences/number of people, and whether the first day of visit ($is_first_day) is true, and group by the campaign source ($utm_source) of the user attribute. - User usage time:
Exit the app ($AppEnd) Average startup duration ($event_duration) per user, categorized by user attributes. Group the campaign sources ($utm_source). - Day 2 retention rate of new users:
Initial event App browsing page Users browsed the App page the following day. The percentage.
Query condition configuration in event analysis
Channel Comparison Component
User scale
New user target conversion event
General Indicators
- Number of times/number of new users completed custom events:
Customize the number of times/number of people to occur, and Is it a first-day visit? ($is_first_day) is true, according to user attributes Advertising campaign source ($utm_source) is used for grouping. - Custom metrics for new user target events:
Customize the value of the indicator, and Whether it's a first-day visit ($is_first_day) is true depends on the user's attributes. Group the campaign sources ($utm_source).
Data bias
Since the channel source uses user attributes (defaulting to registration source), once a user registers through channel A, they will be marked as a channel A user regardless of which channel they access from later, unless a manual channel update is performed. Therefore, there may be some data deviation in the grouping of each channel. However, since the main function of the channel comparison component is to help filter and compare high-quality channels, the deviation in channel distribution is within an acceptable range. If you want to accurately obtain the daily visits for each channel, you can manually configure the analysis in event analysis.