if I am creating a cohort with a certain event, and I want that event to only be considered in companies with certain properties. If I filter by an attribute of a company, will it also filter the event?
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To answer your question about creating a cohort with specific event and company property filters in Mixpanel, let's look at the relevant information from the knowledge sources.
Yes, if you filter by an attribute of a company (which is likely implemented as a Group in Mixpanel), it will also filter the event. This is because of how Mixpanel's Group Analytics and cohort creation work together.
According to the Group Analytics documentation:
"Group keys must be event properties. All events need to have a defined group key on them in order to be attributed to a group."
This means that when you're creating a cohort based on an event and you want to filter by a company property, you're essentially creating a multi-step filter:
1. First, it will look at the events that have the group key (company ID) associated with them. 2. Then, it will filter those events based on the company properties you've specified. 3. Finally, it will consider the specific event you're interested in.
When creating your cohort, you'll need to ensure that:
1. You have set up Group Analytics for your companies. 2. The events you're interested in have the appropriate group key (company ID) tracked as an event property. 3. You use the correct filtering options in the cohort builder to specify both the company properties and the event you're interested in.
It's important to note that this filtering process happens at the query level, so it will indeed filter the events based on the company properties before considering them for the cohort.
For more detailed information on creating cohorts and using Group Analytics, you can refer to these Mixpanel documentation pages:
- Define User Cohorts - Group Analytics Implementation
Remember that the specific steps for creating such a cohort may depend on your Mixpanel project setup and the exact implementation of Group Analytics in your tracking.
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To answer your question about how filters are applied in Mixpanel's Insights report, let's look at the relevant information from the knowledge sources.
There are two types of filters in Mixpanel's Insights report: global filters (applied at the bottom of the report) and inline filters (applied at the metric level). These filters are applied differently and can lead to different results.
1. Global Filters (at the bottom of the report):
According to the Mixpanel documentation:
"To filter the results of your report by any property or cohort, click the Filter button."
These global filters are applied to the entire report after the query has been run. They affect all metrics in the report simultaneously.
2. Inline Filters (at the metric level):
The same documentation states:
"An inline filter applies to one particular event in the report. Add a filter to your query by clicking on the … icon beside an event, profile, cohort, or step."
Inline filters are applied specifically to the event or metric they're attached to, and they're processed before the query is run.
The reason you see different results when using filters at the metric level (inline) versus at the bottom of the report (global) is due to when and how these filters are applied in the query process.
As explained in the Funnels documentation (which is applicable to Insights as well):
"Per-step filters are performed pre-query. Thus, these filters will impact your funnel's conversion by removing events that don't qualify from consideration of being included in a funnel calculation."
"Global filters apply your selected filters to the entire report after the Funnels query has been run. Since Funnels will calculate conversions first and then apply your global filters, entire conversions with any part that does not meet these filter requirements will be excluded from the report's results."
While this explanation is specific to Funnels, the concept applies similarly to Insights reports. Inline filters (at the metric level) are applied before the data is queried, potentially changing the dataset that's being analyzed. Global filters (at the bottom) are applied after the initial query, filtering the results rather than the input data.
This difference in application timing can lead to different results, especially when dealing with complex queries or time-based analyses.
For the most accurate and specific analysis, it's generally recommended to use inline filters at the metric level when you want to analyze specific subsets of data for each metric independently. Use global filters when you want to apply a consistent filter across all metrics in your report.
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Certainly! I'd be happy to provide some numeric examples to illustrate the difference between global filters and inline filters in Mixpanel's Insights report. Let's use a hypothetical scenario to demonstrate this. Scenario: Let's say we're tracking a "Login" event for a mobile app that's available on both iOS and Android platforms. Example 1: Using Inline Filters (at the metric level) Let's say we have the following data for the "Login" event over a week: - Total Login events: 1000 - iOS Logins: 600 - Android Logins: 400 If we apply an inline filter for "Platform is iOS" to the Login event metric, our query would look like this: Metric: Login event (with inline filter: Platform is iOS) Result: 600 Login events In this case, the filter is applied before the data is queried, so only iOS logins are considered. Example 2: Using Global Filters (at the bottom of the report) Now, let's use the same data but apply the filter globally: Metric: Login event Global Filter: Platform is iOS Result: 600 Login events In this case, we first query all 1000 Login events, and then the global filter is applied, resulting in 600 iOS logins. While in this simple example the results are the same, let's look at a more complex scenario to see where differences can occur. Example 3: Multiple Metrics with Global vs Inline Filters Let's add a "Purchase" event to our analysis: - Total Purchase events: 200 - Purchases on iOS: 150 - Purchases on Android: 50 Scenario A: Using Inline Filters Metric 1: Login event (with inline filter: Platform is iOS) Metric 2: Purchase event (with inline filter: Platform is iOS) Results: - Login events: 600 - Purchase events: 150 Scenario B: Using Global Filters Metric 1: Login event Metric 2: Purchase event Global Filter: Platform is iOS Results: - Login events: 600 - Purchase events: 150 In this case, both approaches yield the same results. However, the difference becomes apparent when we introduce time-based or user-based analysis. Example 4: User-based Analysis Let's say we want to know how many unique users logged in and made a purchase: Total unique users who logged in: 800 Total unique users who made a purchase: 180 Unique users who both logged in and made a purchase: 160 Scenario A: Using Inline Filters Metric 1: Unique users who Login (with inline filter: Platform is iOS) Metric 2: Unique users who Purchase (with inline filter: Platform is iOS) Results: - Unique users who Login on iOS: 500 - Unique users who Purchase on iOS: 140 Scenario B: Using Global Filters Metric 1: Unique users who Login Metric 2: Unique users who Purchase Global Filter: Platform is iOS Results: - Unique users who Login: 500 - Unique users who Purchase: 140 Again, in this simple example, the results are the same. However, if we were to add a more complex analysis, such as looking at the overlap between these users, we might see differences. For instance, if we wanted to know how many users both logged in and made a purchase:
Scenario A (Inline Filters): This would require a separate query, as each metric is filtered independently.
Scenario B (Global Filters): This information is readily available, as the global filter is applied after the relationship between events is established. In this case, it might show that 130 unique users both logged in and made a purchase on iOS.
These examples demonstrate how the timing and application of filters can affect your results, especially in more complex analyses. The key is to understand whether you want to filter your input data (inline filters) or your results (global filters) based on your specific analysis needs.
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I apologize for the confusion in my previous explanation. You're right to point out that the last scenario wasn't clear about when the results would be different. Let me provide a clearer example that shows a difference between using inline filters and global filters.
Let's consider a scenario where we're tracking "Login" and "Purchase" events, and we're interested in iOS users:
Example: Complex User Journey Analysis
Data: - Total unique users: 1000 - Users who logged in on iOS: 600 - Users who logged in on Android: 400 - Users who made a purchase on iOS: 300 - Users who made a purchase on Android: 200 - Users who logged in on iOS and then made a purchase on Android: 50
Scenario A: Using Inline Filters
Metric 1: Unique users who Login (with inline filter: Platform is iOS) Metric 2: Unique users who Purchase (with inline filter: Platform is iOS)
Results: - Unique users who Login on iOS: 600 - Unique users who Purchase on iOS: 300
Scenario B: Using Global Filters
Metric 1: Unique users who Login Metric 2: Unique users who Purchase Global Filter: Platform is iOS
Results: - Unique users who Login: 600 - Unique users who Purchase: 250
In this scenario, we see a difference in the "Purchase" metric. Here's why:
1. With inline filters (Scenario A), we're specifically querying for purchases made on iOS, which gives us 300 users.
2. With global filters (Scenario B), we're first looking at all users who logged in and made a purchase, and then applying the iOS filter afterwards. This excludes the 50 users who logged in on iOS but made their purchase on Android, resulting in 250 users.
The key difference is that global filters are applied to the entire user journey, while inline filters are applied to each event independently. In complex user journeys where users might switch platforms or have mixed behaviors, this can lead to different results.
This example demonstrates how the choice between inline and global filters can affect your analysis, especially when dealing with cross-platform user behavior or complex user journeys.
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