Documentation > Dashboard User Guide

Analyzing Users

User Actions

User actions or events are activities that a user does in your mobile app or website, like viewing a product or making a purchase. You’ll have to integrate the CleverTap SDKs (available for all major platforms) to record events from your app or website. To record historical events or offline events, you can use the CleverTap Server API.

See Recording Events to read up on how to record events from your apps and website

Using the DashboardAnalyzeUser Actions View, you can filter and segment (i.e. group) users based on the events they have taken in your application.

You accomplish this by:

  • Selecting the event to segment by – you can also filter that event by the properties recorded for that event.
  • Optionally applying filters based on user attributes, including specific geographic, demographic data, or device information.

Once constructed, the Dashboard provides deep insights into the user group defined by your event query, including geographic, demographic and device information.

You can then save this event query and use it later to send targeted messages to those users.



A funnel is a way of grouping users who take a specific event in your app and comparing how many of those users go on to take a second event in your app in a specified period of time. For example, you might construct a funnel analyzing the users in your app who added an item to cart and then went on to complete the cart checkout process.

Funnels help you see where people drop off so you can work on a way to reduce drop-off rates.

You can use the DashboardAnalyzeFunnels View to construct user funnels. You can also save your constructed funnels for later use.


How Funnels are Calculated

By definition Funnels are counts of users who perform a specified sequence of events in order. Take for example the following conversion funnel:


The count of users and the percentage shown at each step are the total users who progressed from the first event to the subsequent, in that order.

So the 14,190 users in the last step (Charged) performed Searched, Product Viewed and Added to Cart before Charging.

In contrast users who performed Searched —> Added to Cart —> Charged and DID NOT perform Product Viewed are not counted since they did not complete all the steps. Nor are the users who performed Product Viewed —> Added to Cart —> Searched —> Charged since they did not complete the steps in the specified order.

Of course most users will perform multiple steps in between the specified funnel steps.

Take for example the following groups of users who:

Searched —> Product Viewed —> Favorited —> Added to Cart —> Charged
Searched —> Favorited —> Add to Wish list —> Product Viewed —> Added to Cart —> Charged

Both of these groups of users performed the funnel steps in order. They are counted.

Default Behavior vs. Enforcing Strict Ordering

By default Funnels will count all users who perform all the specified events in order regardless of whether they “loop-back” to an earlier step or move forward to a later step in the funnel.

For example users who follow both of these paths are counted by default.

Searched —> Product Viewed —> Searched —> Added to Cart —> Charged
Searched —> Product Viewed —> Added to Cart —-> Searched —> Charged

In contrast if you select the option to “Enforce Strict Ordering,” then in order to be counted the user cannot perform any of the specified events out of sequence.

When we enforce strict order, the above groups of users will NOT be counted since they did not adhere to the strict ordering of the events in the sequence.

Enforce Strict Ordering


Cohort Analysis is a way of grouping users who perform a certain event in your app and tracking their behavior over time. Cohorts are commonly used to understand user retention or churn by measuring how long it takes users who have launched your application to come back and launch it in a subsequent time. With the flexibility we provide, you are able to create a cohort of users based on any event in your system and measure the time it takes them to perform any other subsequent event – for example:

  • First App Launch to next App Launch (retention)
  • First App Launch to Purchase (initial conversion)
  • Video Played to next Video Played (engagement)

You can use the DashboardAnalyzeCohorts View to construct user cohorts. You can also save your cohorts for later use.


Using Cohorts

Let’s say we want to analyze users who have launched your app and then went on to make a purchase. Under Cohorts you’ll select a First Event (App Launched) and a Return Event (Charged).

Cohort Picker

The corresponding Cohort Table for a Daily View looks like:

Cohort Chart

Each Row in the Table represents a cohort of users with the average of all the rows shown at the top labeled “All.”

  • Cohort of Apr 01 represents all the users who performed the First Event (App Launched) on Apr 01. (Note this can be a weekly or monthly analysis as well).
  • Each column represents the % of users in that cohort who performed the Return Event on that day.

For example:

  • 32,048 users launched the app on Apr 01.
  • 5.6% of these users came back and Purchased a day later on (Day 1)
  • 3.5% of the 32,048 users came back and purchased two days later on (Day 2).
  • etc

Trending and Comparing Cohorts (Row Analysis)

You can view cohorts in either the Table View or in a Trend View that lets you visualize the data graphically and compare individual cohorts (rows) side by side.
The Trend View defaults to the Average (All) of all the cohorts for the selected date range. Click on the dropdown to select any other cohort you want to superimpose on the graph. You can plot up to four cohorts at a time.

Cohort Picker

Cohort Trends

As you select the cohorts to plot, they’ll be highlighted in the Table View.

Cohort Highlights

Analyzing Data ACROSS Cohorts (Column Analysis)

Another way to analyze your data is to look down the columns in the Cohort Table. Each column signifies how many users came back and did the Return Event on that particular Day, Week or Month for the respective cohort.

Analyzing across cohorts let’s you answer questions such as,

  • “Is my Day 2 Retention better for certain cohorts than others?”
  • “Is my Day 7 Retention better than my day 3 Retention for all cohorts?”

Take the following Cohort Table for Initial App Launch → Charged. On the column labeled Day 1 (1 day after the user launched the app) we see:

  • 5.6% of users made a purchase for my first cohort (Apr 01)
  • 3.0% of users made a purchase for my second cohort (Apr 02)
  • 2.0% of users made a purchase for my 5th cohort (Apr 05)
  • etc.

So my Day 1 purchases following an initial App Launch are decreasing with each new cohort of users.

Cohort Chart

Trending and Comparing Days, Weeks or Months (Column Analysis)

As with cohort trending above, you can trend the Days (or Weeks or Months in the columns) graphically. Just select which Days you wish to plot from the dropdown. You can plot up to four.

Column Picker

In the Trend View you can more readily see patterns in your data. For Day 1 and Day 2 below, we readily see that our retention rate is gradually improving across the Apr 06, 07 and 08 cohorts of users. So with each subsequent day (a new cohort of users) we’re improving our retention.

Column Trends

The Table View highlights which Days you’ve selected to plot.

Column Highlights


Trend analysis is the process of comparing events over time to identify patterns.

For example, identifying and analyzing event patterns in your app can help you assess how well you are doing in encouraging specific user behavior over time.

You can use the DashboardAnalyzeTrends View to see trend analysis on specific events in your application. You can also filter by specific properties of those events, user attributes or device models.