Step 1: Define User Property

User properties store information about individuals, including personal details such as name, birthdate, address, and location. They also include unique identifiers like mobile numbers, ID numbers, email addresses, device IDs, and user preferences. You can select multiple user properties to start your analysis. To learn more about the properties, refer to Derived User Attributes.

You can filter each selected user property by assigning attributes. After choosing the user property, click + Filters to add attributes. Adding filters will exclude unwanted data for your analysis. You can apply multiple filters using AND/OR, as demonstrated below:

You can add multiple user properties for your analysis using + Add user property.

Duplicate the added property by clicking the Duplicate step icon in the right corner.

Step 2: Filter Users

By default, you can perform user analysis on all users. However, you can also query for a group of users filtered using User Properties, User Activity, User Affinity or Custom Segments. Filtering users is similar to creating a segment. For more information, refer to Create Segment.

Step 3: Choose an Analysis Option

After defining your users, you can choose how to calculate the data. The following options are available:

• Total count: This option provides the total count of available values for the selected user property.
  For example, with total count analysis, you can determine how many users have purchased or added a product to their carts within a specific city or group of cities.
• Unique count: This option provides the unique count of available values for the selected user property.
  For example, you can identify the number of unique products purchased by users over a specific period in a given geography or demographic.
• Distribution: This option provides distribution for the selected user property. In a simple way, this is a self-split of data.
  
  You can also configure Custom Distribution bucketing. With custom distribution, you will enter the boundaries in the From and to fields (the lower and upper limits) and an interval size to divide each distribution. This option is available only for numerical properties.
  
  For example, you can use distribution to identify users who would qualify for a specific credit card product or an add-on service based on different spending habits.
• Aggregation: This option aggregates the count of the selected user property. The supported aggregation functions are sum, minimum, maximum, average, median, percentile, and distinct count. The aggregation option is available only for numerical attributes.
  
  For example, aggregation can be used to calculate the average screen-on duration of users on your platform.
• Email domain analysis: This option provides domain-specific visualization based on the standard email attribute. The email domain is the part after @, for example, gmail.com, yahoo.com, etc.
  

  

  For example, consider a bank that wants to analyze its user base using the domains of its users. By performing Email Domain analysis, they can see the number of spam complaints and bounces bucketed on a domain basis.

Step 4: Choose a Split

The Split by option allows you to group and compare the data in a report based on a specific attribute.

For example, you can see the data of users on Android OS in different cities. Here, Android OS is the User property, and the Split by condition is their Last known city.

Step 5: Choose Charts

After your query is generated, choose your preferred visualization type. You can change between Line, Bar, Column, and Pie charts. Every chart holds its own significance.

• Line charts are widely used for analyzing data trends, changes, or patterns. They are frequently used to display and compare data over time.
  
• Bar and column charts present aggregated data, such as sales figures for various products, population comparisons of different cities, or analysis of survey responses across different categories.
  
  Column Chart Bar Chart

  

  

• Pie charts are commonly used to visualize categorical data that can be divided into distinct parts. They are helpful for displaying the relative proportions or contributions of various categories within a whole.

  

• Choropleth charts offer a quick and easy way to visually represent the distribution of attributes or variables geographically and help identify patterns or disparities among different regions.
  

  
  This chart is available only if the Split by condition is Last known country.

You can switch between Numerical and Percentage views for Line, Bar, and Column charts.

Step 6: Tables

In addition to charts, the data is also available for analysis in a tabular format. You can sort the table or change its view using the transpose feature. Additionally, you can download the data in CSV format. For more information, refer here.

Save to My Studio or Custom Dashboard

Your analysis is now ready. To access this later, you can save the analysis.

To save:

1. Click the icon in the upper-right corner and click Save to My Studio.
2. Additionally, you can save the analysis to a custom dashboard. Choose the required option from the list.
   
3. Select the dashboard where you want to save the analysis. You can choose an existing dashboard or create a new one.
   
4. Enter a name for your analysis to help you identify.
5. Enter a brief description of the analysis.
6. Click Save.

You can access the analysis by navigating to Analyze > My Studio. To access the custom dashboard, navigate to Dashboards.

Next Steps

• My Studio
• Dashboards