User properties store information about users such as their personal information (name, birthdate, address, location, and so on), their unique identifiers (mobile number, ID, email address, device ID, and so on), and their preferences. These user properties are crucial in personalizing user experiences, targeting specific audiences, and tailoring content, services, or products to individual needs or preferences. The list of standard user attributes tracked by MoEngage is available in Derived User Attributes.

Analysis of user attributes allows for a comprehensive understanding of user preferences and facilitates the execution of targeted campaigns driven by such preferences.

User Analysis Overview

Use Cases

Here are a few examples where user analysis can be employed:

1. How to analyze user preferences on different devices using device types and conversion rates.
2. How to identify regional spending patterns using the user location and average order value.
3. How to analyze the number of unique users who have abandoned checkout grouped by platform or device.
4. How to analyze the total number of users who have purchased in a store based on device-triggered push notifications.
5. Perform purchasing preference analysis among different income groups by combining income level and product preferences.
6. Grouping users based on their demographic and purchase patterns within the last 'x' days (based on the event retention period for segmentation) in a specific city.
7. Analyzing the effectiveness of targeted marketing campaigns by combining user interests and click-through rate (CTR).
8. Assessing the customer value from different sources using the registration source and customer lifetime value (CLV).
9. How to identify the number of users who have engaged with emails in a specified domain.

How does User Analysis work?

To analyze user properties:

1. Navigate to Analyze -> Users on the MoEngage Dashboard.
2. Select the User Property(ies) for analysis in the User property section.
3. Filter users based on the segments.
4. Select the Analysis type and Split by criteria in the User analysis options section. 
5. Click APPLY to view the User analysis charts and tables for the selected criteria.

Comparing User Properties

You can select user properties to be analyzed in this section. To do so:

1. Select the user property to be analyzed in the user property dropdown selector.
2. Click +Filters to filter the user properties based on specified criteria. For example, if you want to analyze the number of users who have ordered products totaling a specified price or higher based on their last known city, select the user property as Cart Value in the user property dropdown selector. Click +Filter and add the filter conditions as Filter By Last known city is <city_name>. Replace the city_name with the city of your choice.
3. You can add multiple filters using the AND/OR option after adding one filter, as illustrated in the following image. If you want the report to match the exact case of the value specified, select the Case Sensitive checkbox. 
4. You can add up to 5 different properties for analysis by clicking the +Add user property.

Filter Users

User analysis is performed on all users preset in the MoEngage system by default.
User analysis can also be performed for a group of users filtered using User Properties, User Activity, User Affinity or Custom Segments, or any such combination. This is similar to creating a segment and analyzing the users who fall into the segment based on User analysis. For more information, refer to creating user segments and custom segments.
You can also add up to 5 segments to compare your users by clicking +Add segment to compare. For more information, refer to Comparing by Segments.

User Analysis Options

Select an analysis type to calculate and click Apply. The following are the analysis types:

Analysis Type	Description
Total Count	This function provides the total count of available values of the selected user property. For example, the total count analysis can be used to get the number of users who have purchased a product or added a product to their carts in a city or group of cities.
Unique Count	This function provides the unique count of available values of the selected user property. For example, the count of unique products purchased by users over a period of time in a specified geography or demography.
Aggregation	This function provides aggregation options (user count for available values) for the selected user property. The aggregation functions supported are sum, minimum, maximum, average, median, percentile, and distinct count. Aggregation operations can only be performed on numerical attributes. For example, the total number of purchases, the minimum order value, the average order value, the maximum order value, the median value, and the nth percentile value in a given set of users based on geography, recent purchase history, and device or platform used.
Distribution	This function provides distribution (user count for available values) for the selected user property. The distribution buckets can be auto (split based on each individual value in the given user set) or custom (where the buckets are defined and the users from the given set are sorted into each of those buckets). For example, users can be put into various buckets based on a specified salary range. This can help you identify users who would qualify for a specific credit card product or a specific add-on service aimed at a group of users with a specific spending capacity.
Email Domain Analysis	This function provides domain-specific information based on the standard email attribute. For example, the total count of users in various domains, the unique number of users in all available domains in the set of users chosen, domains that have the maximum open rates for email campaigns, and so on. Note: This feature is supported for the Email (Standard) attribute and any other attribute that is configured on the MoEngage Dashboard as being used for storing the Email Address of the user (User attribute that stores the user's email address). For more information, refer to Email General Settings.

 

The following sections describe each of these analysis types and how they can be used.

Total Count

Consider an OTT platform whose user information contains the following: user names, subscription end date, user preferences, last watched show, last login time, and preferred genre.

If the brand were to perform the Total Count analysis on the subscription end date, they could get the number of users whose subscription ends on specific dates. For example, consider the following data set:

Subscription End Date	Number of Users
September 20th, 2024	253
October 10th, 2024	314
October 25th, 2024	296
November 1st, 2024	353

By analyzing this information, the OTT platform can gain insights into the subscription expiration dates among its user base and use it to send targeted campaigns for subscription renewal, cross-sell other services provided by the platform, and gather data to plan user retention.

Steps to Perform Total Count Analysis

1. Select the user property for which you want to perform the Total Analysis in the User property section. In the case of the example illustrated below, the user property whose total count is being calculated is the Subscription end date.
2. Select the desired option in the Project <user_property> as a dropdown to project the selected user property in a specific manner in the analysis chart. For example, the Subscription end date is a date attribute and can be projected in various granularities such as Date, Month, Year, and so on.
3. To filter the user property based on any other attribute or property, click +Filter and select the property.
4. You can add up to five user properties to perform the total analysis. Click +Add user property to add more user properties for analysis.
5. To perform user analysis on a specific set or segment of users, choose the segmentation criteria in the Filter users section. By default, all users are considered for analysis, and the all users option is selected. Click the Filter users by radio button to specify the segmentation criteria. For more information, refer to Segmenting users for Analysis.
6. In the User analysis options section, select Total count in the Analysis type.
7. To split the user properties selected by a specific user attribute(s), select the user attribute(s) in the Split by dropdown. For more information, refer to Splitting User Properties.
8. Click APPLY.

The following chart illustrates the Total Count of users having an email address (stored in the Email(Standard) user property) in MoEngage.

Unique Count

Consider a brand looking for the number of subscriptions from distinct users (a user could have purchased more than one subscription) to plan retention strategies. The brand can achieve this by performing a unique count analysis of the users who have subscriptions. This can help the brand view specific trends and linked characteristics, such as:

• What their LTV trend has been for the month
• What their journey has been - what they have added to their carts, what they've purchased
• Which demography and location do they belong to
• Which tier does the user belong to

Such an analysis can help you identify opportunities for uplifting users from one tier to another, understand the demography that is actively engaging with your products, identify the locations they are most active in, and so on. You can drive your engagement and retention strategies with these valuable insights.

Steps to Perform Unique Count Analysis

1. Select the user property for which you want to perform the Total Analysis in the User property section. In the case of the example illustrated below, the user property whose unique count is being calculated is the Subscription active (the number of active subscribers).
2. Select the desired option in the Project <user_property> as a dropdown to project the selected user property in a specific manner in the analysis chart. For example, the Subscription active is a Boolean attribute, and thus the dropdown is disabled. If you were to choose a date or time field, you would be able to select the granularity.
3. To filter the user property based on any other attribute or property, click +Filter and select the property.
4. You can add up to five user properties to perform the total analysis. Click +Add user property to add more user properties for analysis.
5. To perform user analysis on a specific set or segment of users, choose the segmentation criteria in the Filter users section. By default, all users are considered for analysis, and the all users option is selected. Click the Filter users by radio button to specify the segmentation criteria. For more information, refer to Segmenting users for Analysis.
6. In the User analysis options section, select Unique count in the Analysis type.
7. To split the user properties selected by a specific user attribute(s), select the user attribute(s) in the Split by dropdown. For more information, refer to Splitting User Properties.
8. Click APPLY.

The following chart illustrates the number of unique countries (last known country) to which your users belong.

Aggregation

Consider the scenario where a brand wants to analyze information about users in the first percentile and the ninety-ninth percentile based on their engagement in the platform or purchase value. For example, an E-commerce brand can analyze users in the first and ninety-ninth percentile and understand their app usage patterns, campaign engagement scores, and the time it takes to view and purchase a product. This information can help the brand understand the behavior patterns of power users and segments that do not engage with the brand. Also, aggregation helps brands perform other mathematical operations such as finding averages, maximum, minimum, sum, median, percentile, and distinct values.

Steps to Perform Aggregation Analysis

1. Select the user property for which you want to perform the Aggregation Analysis in the User property section. In the case of the example illustrated below, the user property whose aggregation analysis is being performed is the No. of Conversions.
2. Select the desired option in the Project <user_property> as a dropdown to project the selected user property in a specific manner in the analysis chart. For example, the No. of Conversions is a Double attribute, and thus, the dropdown is disabled. If you were to choose a date or time field, you would be able to select the granularity.
3. To filter the user property based on any other attribute or property, click +Filter and select the property.
4. You can add up to five user properties to perform the total analysis. Click +Add user property to add more user properties for analysis.
5. To perform user analysis on a specific set or segment of users, choose the segmentation criteria in the Filter users section. By default, all users are considered for analysis, and the all users option is selected. Click the Filter users by radio button to specify the segmentation criteria. For more information, refer to Segmenting users for Analysis.
6. In the User analysis options section, select Aggregation in the Analysis type.
7. To split the user properties selected by a specific user attribute(s), select the user attribute(s) in the Split by dropdown. For more information, refer to Splitting User Properties.
8. Since the Analysis Type is Aggregation, you must choose an aggregation function in the View <user_property> as dropdown. Select the aggregation function desired in the dropdown. In the case of the example illustrated below, the Average is the chosen aggregation function. This will fetch the Average number of conversions split by the Last Known City of the users. This information provides insights into the spending patterns of users based on their location. This can help brands optimize their campaign strategies.
9. Click APPLY.

The following chart illustrates the average lifetime value of users in your MoEngage account.

Distribution

Consider the case where an OTT platform wants to understand the number of subscriptions based on geographic locations. The platform can then perform a distribution analysis based on the subscriptions and the last known city of the subscribers. This option helps brands analyze properties using custom buckets within chosen segments, thus helping them understand the performance of various properties in the designated buckets. (for example, what is the LTV of subscribers in a city who fall into a defined segment, such as those with the maximum number of app opens or purchases in the past year, and so on).

Steps to Perform Distribution Analysis

1. Select the user property for which you want to perform the Aggregation Analysis in the User property section. In the case of the example illustrated below, the user property whose aggregation analysis is being performed is the Newsletter Subscriber.
2. Select the desired option in the Project <user_property> as a dropdown to project the selected user property in a specific manner in the analysis chart. For example, the Newsletter Subscriber is a Double attribute, and thus, the dropdown is disabled. If you were to choose a date or time field, you would be able to select the granularity.
3. To filter the user property based on any other attribute or property, click +Filter and select the property.
4. You can add up to five user properties to perform the total analysis. Click +Add user property to add more user properties for analysis.
5. To perform user analysis on a specific set or segment of users, choose the segmentation criteria in the Filter users section. By default, all users are considered for analysis, and the all users option is selected. Click the Filter users by radio button to specify the segmentation criteria. For more information, refer to Segmenting users for Analysis. 
6. In the User analysis options section, select Distribution in the Analysis type.
7. To split the user properties selected by a specific user attribute(s), select the user attribute(s) in the Split by dropdown. In the case of the example in the illustration below, the split by user property is Last known city. For more information, refer to Splitting User Properties.
8. Select the Distribution bucketing option in the Distribution bucketing toggle. The following options are available:
   
   

   Auto Distribution	Auto Distribution feature allows users to view their data on a self-split column chart.
   Custom Distribution	With Custom Distribution, you can select the intervals and boundaries (the lower and upper limits) for the buckets. The buckets are calculated by dividing the interval size after deducting the upper and lower boundaries. You can use a maximum of ten buckets after deducting the upper and lower boundaries. Note: The Custom distribution option is available only for numerical properties.

9. Click APPLY.

Email Domain Analysis

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. Also, brands can assess the most common domains across geographies and so on. This can help them refine their email marketing strategies and increase open rates.

Steps to Email Domain Analysis

1. Select the user property for which you want to perform the Email Domain Analysis in the User property section. In the case of the example illustrated below, the user property whose aggregation analysis is being performed is the Email (Standard).
2. Select the desired option in the Project <user_property> as a dropdown to project the selected user property in a specific manner in the analysis chart. For example, the Email (Standard) is a String attribute, and thus, the dropdown is disabled. If you were to choose a date or time field, you would be able to select the granularity.
3. To filter the user property based on any other attribute or property, click +Filter and select the property.
4. You can add up to five user properties to perform the total analysis. Click +Add user property to add more user properties for analysis.
5. To perform user analysis on a specific set or segment of users, choose the segmentation criteria in the Filter users section. By default, all users are considered for analysis, and the all users option is selected. Click the Filter users by radio button to specify the segmentation criteria. For more information, refer to Segmenting users for Analysis.
6. In the User analysis options section, select Email domain analysis in the Analysis type. Split by is not supported for Email Domain Analysis.
7. Click APPLY.

Splitting User Properties

To compare the data in a report based on a specific attribute:

1. Click on the Split By dropdown selector.
2. Select the user property to be used for comparison.
3. You can select up to 10 user properties for breakdown using split-by.

For example, if you want to find the total count of users who have added products to their cart since they have registered on the platform, select the User Property as Add to Cart, Analysis Type as Total Count, and the Split By user property as Registration Date.

This analysis will generate a breakdown of the users who have added products to their carts based on their registration date. This will allow you to identify any patterns or trends related to the registration dates and the users' engagement with the "Add to Cart" action.

Note: Date attributes can be compared on various date attributes such as - Date ( DD MMM YYYY), Day of the month, day of the week, Day of the year, Hour of the day, Month of the year, Week of the month, Week of the year, and Year in addition to the actual date.

Visualizing and Analyzing Results

User analysis charts are available when you click APPLY after selecting the user properties, filters, segments, and analysis type. You can view Analysis Charts and Tables to visualize the results of your analysis.

User Analysis Charts

This section contains the charts that show which buckets your properties fall into based on the split conditions and the analysis option chosen. The following chart options are available:

Line Charts

• Line charts are popular for analyzing trends, changes, or patterns in data. They are commonly used in various fields for displaying and comparing data over time.
• For example, a line chart can be used to compare the LTV for customers acquired from different registration sources.

Bar and Column Charts

• Bar and Column charts display the aggregated data, such as showing sales figures for different products, comparing population sizes of different cities, or analyzing survey responses for different categories.
• These charts are particularly effective for making comparisons between different data points and identifying the magnitude or proportion of each category.

• For example,

  • A bar chart can be used to compare the purchase history of different age groups and genders.

  • A column chart can be used to compare the conversion rates across device types.

Pie Chart

• Pie charts are commonly used to visualize categorical data that can be easily divided into distinct parts. They are handy for showing the relative proportions or contributions of different categories within a whole.
• For example, a pie chart can be used to compare the conversion rates across device types.

Choropleth Chart

• 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.
• For example, it can be used to compare sales figures in different regions or cities.

Switching Views in Graphs

The following actions are supported in the User Analysis chart section:

1. Switching to different views:
   1. Line - Click on the Line view icon to view the data as a line chart. This option is available only for data that is projected as a timestamp. You can view the data in the following granularities: Daily, Weekly, Monthly, and Yearly. You can also specify the date range for the chart.
   2. Horizontal - Click on the Horizontal view icon to view the bar chart.
   3. Vertical - Click on the Vertical view icon to view the column chart. This is the default option for most user properties.
   4. Numerical View - Click on # to have a numeric view for line, bar, and column charts. This is the default option.
   5. Percentage View - Click on % to have a percentage view for line, bar, and column charts.
   6. Pie Charts - Click on the Pie chart icon to view the pie chart.
2. Download Chart - Click on the Download Chart option to download a chart. The chart gets downloaded as a .png file.

User Analysis Tables

The User Analysis Tables section contains the analysis data in a tabular format. The following actions are available:

• Transpose table - this helps you view the information in your preferred format by shifting the vertical and horizontal orientation of the table. 
• Download CSV - this option lets you download a CSV file that contains the information in the tables.

Actionable Analytics

You can create segments and analyze them based on distinct chart elements. For example, you can click on a bar in a bar chart to create a segment out of it. To create a segment:

1. 1. Click on any data point, column or line and select Create Segment. The Create Segment popup opens.

   2. Add a name for the Segment in the Segment name and click Save to save this custom segment.
   3. Check the Take Action on the Custom Segment checkbox to get the following options:
      1. Create campaign from this segment - Select the channel and delivery type in the dropdowns and click Create to create a campaign with the specified custom segment.
      2. Analyze this segment - Select the analysis type from the dropdown and click Analyze to analyze the custom segment.
      Creating a Campaign from the Custom Segment Analyzing the Custom Segment

      

      

      info

      Note Static Segments are available only in the Create Segment screen when the time interval is well-defined, such as a specified date range. For all other cases, only dynamic segments are available for creation. Charts display a maximum of 20 different entities. The remaining entities are available in tabular format. Distribution is available as a bar/column chart. User Analysis Charts cannot be pinned to Custom Dashboards.

If you need further assistance, contact our support team or get in touch with your Customer Success Manager(CSM).