Insights is a powerful and flexible tool designed to visualize trends and compositions within your data. You can analyze events, cohorts, and user profiles, and display the data in a wide variety of chart types.
Advanced Insights features also allow you to create formulas, compare current data to past data, and generate custom events and properties for deeper analysis.
Sample Questions you can Answer in Insights
Imagine your product is a B2B messaging application. You might use Insights to answer these sample questions:
- How many messages were sent in the US in the past 30 days? (total events, filtered)
- How many users had a mobile app session yesterday? (unique events)
- How many messages are sent per session? (formulas)
- Which advertising campaigns generate the most signups? (property breakdown)
- How much revenue was generated on plans purchased in the past year? (property aggregation)
- How has the power users cohort grown over the past 6 months? (cohort trends)
Building your First Report
Building a report in Insights takes just a few clicks, and results arrive in seconds. Let's build a simple report together. Continuing the B2B messaging example, imagine you wanted to answer the following question:
Which cities in the United States have the most users who sent messages via the iOS platform?
Step 1: Choose Events
Events, cohorts, or profiles can be the basic building block of an Insights report. In this case, we want to know about users who sent messages, so within the "Events and Cohorts" section, add the "Send Message" event. At this point, your query should look like this:
Step 2: Choose Count Type
Next to your selected event, you can choose how to count that event. By default, Insights will count Total events, which, as the name implies, will count every occurrence of the event. In this case, we want to know how many users sent messages, so choose "Unique." Unique counts one event per user. At this point, your query should look like this:
Step 3: Choose Filters
Filters exclude unwanted data. In this case, we only care about events performed on the iOS platform. Therefore, add a "Platform" filter, where Platform equals "iOS Native". At this point, your query should look like this:
Step 4: Choose Breakdowns
Breakdowns segment data into groups. In this case, we want to count message sending users in different cities. Therefore, add a "City" breakdown. At this point, your query should look like this:
Congratulations, you've constructed your first Insights query! Now, it's time to examine the results.
Insights features multiple visualizations to help you view the results of your query in the clearest chart type. By default, Insights displays line charts, which help you understand how metrics trend over time. However, another chart type might present the results with more clarity.
In Insights, you can either choose to get a metric calculated across the entire time period selected in the date picker, or get a time-segmented view of the metric (e.g. daily breakdown).
- Metric calculated across the entire time period
- Bar chart
- Stacked bar chart
- Pie chart
- Metric time-segmented
- Line chart
- Stacked line chart
You can easily resize the columns in the bar chart in order to see more or remove detail.
When breaking down results, click on a bar in the chart to either filter or exclude that property value. Filter zooms in on that property value, filtering the entire report to that property value. Exclude filters out that property value from the results.
Analysis & Value Settings
You can switch between Absolute and Relative totals by selecting the # dropdown in the top right of the chart and selecting either # Absolute or % Relative.
You can only select Absolute or Relative values for the Table, Stacked Line, Stacked Bar, and Bar charts.
The Absolute view will show you, in numbers, your totals for different event counts. Relative view will display these counts as a percentage of the whole.
The Analysis options will determine the way the chart is calculated and visualized. The options are:
- Linear: This is the standard view for the chart.
- Rolling: Rolling analysis calculates the rolling average of the data set. A rolling average curve is a series of averages from subsets of data. Use rolling average analysis to remove noise or spikes from data and smooth out trends over time. Mixpanel calculates the rolling average based on the selected time interval (hour, day, week, month or quarter) for each data point in the graph.
Default Rolling Time Range
Hour Last 12 hours Day Last 7 days Week Last 5 weeks Month Last 3 months Quarter Last 2 quarters
- Logarithmic: A nonlinear scale based on orders of magnitude, rather than a standard linear scale, so the value represented by each equidistant mark on the scale is the value at the previous mark multiplied by a constant.
- Cumulative: Adds up the values of each point on the graph as it goes along, so the height of the line will increase over time.
When you are viewing a bar chart, you have four different sorting options: A-Z Ascending, Z-A Descending, Value Ascending, or Value Descending. To switch sorting views, select the Events icon in the upper left hand of the report and select which view you would like to see.
Line charts in Insights are accompanied with a table of values to give users another way to consume the trends information. This data table can also be sorted by clicking column headers.
Click on a "data column" header to sort by that column. Click the header again to reverse the sort order. The table below is sorted by event counts on August 2nd:
Results that are segmented (from one or more “group by” clauses in your query) have four different sorting options when you click on the "segment column" headers:
- Segment Ascending: sort by segment name in ascending order.
- Segment Descending: sort by segment name in descending order.
- Value Ascending: sort by segment value in ascending order.
- Value Descending. sort by segment value in descending order.
When sorting by segments, the sort is carried out left to right.
Clicking on the "Average" data column performs a flat sort across all segments:
Use Cases for Insights Reports
Here's a video that shows a set of use-cases with Mixpanel's Insights report:
Here's another common use-case: Jenny is a Marketing Manager for an online shoes marketplace. and she wants to know which utm source is getting the maximum number of purchases to the platform.
In Insights, Jenny looks at purchase activity by selecting the "Complete Purchase" event and analyzing the activity over the last 1 month. Mixpanel returns an aggregate number of the total times the event was performed, but Jenny wants to dig deeper.
She elects to break down the data by the event property "UTM_source", which categorizes the results into the different UTM_source values of the "Complete Purchase" event.
Based on the data from the last 30 days, the Insights report shows that LinkedIn is the highest source of paid conversions.
It's important to know what's the natural frequency at which your users use your product / experience the core value proposition of your product - do majority of your users use your product daily? weekly ? monthly? A16Z wrote a great article about the Power User Curve, and this video below shows how you can reproduce that within Mixpanel:
While this article covers the basics of the Insights report, you can learn more about its capabilities in the following additional articles:
- Advanced Insights Functionality - learn about the report's more advanced capabilities and modes