Funnels

Overview

The Funnels module of MoEngage Analytics helps understand user behavior for web and mobile applications. It provides various insights into how your users interact with your application through different stages of your customer journey.

A Funnel is a series of events that lead to a predefined goal event. For example, a user opens an e-commerce mobile app to purchase products. Funnel analysis is primarily used to calculate conversion on specific user behaviors.

Funnels can be visualized easily, with tabular reports that can be downloaded for deeper analysis. These reports help analyze conversion rates throughout the funnel and gauge the overall performance of your product.

 

Use Cases

Funnels help answer questions like:

  • How many users searched for a hotel on my travel application and booked one?

  • How many times did users view a notification, click on it, and then convert?

  • What percentage of users who visited a particular product on my e-commerce app bought it?

  • How many times do users play a game before moving to the next level?

  • How much time do users take to purchase a product?

  • At what stage of the buying process are most users dropping off? 

Create a Funnel

To create a funnel:

  1. Navigate to MoEngage DashboardAnalyze > Funnels.

  2. Create the Funnel Steps.

  3. Filter users to analyze funnels.

  4. From the Funnel Type drop-down, select one of the funnel types.

  5. In Funnel Split, from the drop-down, select the step and the attribute to divide the funnel to get specific information.
  6. In Holding Attribute, from the drop-down, select the step and the attribute to
  7. Click Strict Order to ensure the analysis in the funnel is applicable only when there is an adherence to the sequence or order of the steps followed.
  8. Select the Funnel Window to define the difference between the first step and last step of the funnel.

  9. In Duration, select one of the following to define the time to perform the funnel analysis.

    • Today

    • Yesterday

    • This month

    • Last month

    • The custom range for last days, weeks, and month format.

  10. Click on Apply. 
  11. View the funnel reports.

Funnel Steps

  1. Select the event of the funnel step from the drop-down menu.
    Funnel_steps__1_.png
  2. Click + Attribute to add attributes to the events in the funnel steps.
      • Select the appropriate attributes that you want to add.
      • Select the Case Sensitive checkbox if you want the attribute to match the exact case of the value you specify.
      • Add multiple attributes, and choose whether to apply all of any one of them. Click on AND or OR to filter further.
  3. Click Add Step to create new funnel steps.

  4. Use the arrow icons to move a step up or down and reorder the funnel steps.
    reorder.png

  5. Click Exclude Events to remove events from funnel steps.

Funnel OR Events

Funnel steps are A → B → C and A → D → C. The funnel starts at A and ends at C but has two different paths. Using OR event these two paths can be combined into one funnel: A → (B OR D) → C. Users who perform at least one of B or D events will qualify for this funnel.
OR_Event.png

For example, if users navigate to the home page and perform a search or if they open a specific category item, they can arrive at the same product. Both of these products can be added to their cart and then purchased. 

The funnel analysis here is performed using the OR functionality. The funnel is defined as:

  • Home Page > (Search OR Category View) > Checkout > Purchase

Exclude Events

Exclude events are used to remove the event sequence from the analysis. Exclude event is a negative funnel step when the user moves forward in the funnel even if the user has not performed the selected event.

Exclude events enables users to move to the next step in the funnel only if they do not perform the specified events between the specified steps. If the user performs the excluded event in between the selected steps, the funnel for the user will end there.

You can:

  • Add excluded events between any two consecutive steps.

  • Add Multiple excluded events.

Exclude.png

For example, the step Not Done is the excluded event:

  • Search → Category Viewed → Not Done Promotion Clicked → Add to Cart → Purchase.

  • App Open → Home Page Viewed → Not Done Playlist clicked → Song clicked → Song Played

  • Payment Initiated → Not Done Coupon applied → Purchased

Filter Users

  1. Click All Users or Filter Users by in Filter Users to sort the set of users for the funnel.

  2. Click Exclude Users to remove users from the funnel.

Custom Segment Analysis

Funnel analysis is performed on all users or a group of users available in your MoEngage system. A group of users or a segment is created using User Properties, User Activity, Custom Segments, or any other combination. For more information, refer to creating user segments and custom segments.

Segment_Filter.png

 

Funnel Options

Funnel Types

There are six different types of Funnel Types.

Funnel_Types.png

 

Analysis Type Description
Funnel - Unique Users
  • This function provides the count of unique users who have completed all of the funnel steps. 
  • Even if a user has performed the funnel multiple times, only one occurrence of the funnel will be counted.
  • This analysis can be used to see how many users proceeded to checkout an item after clicking on a promotional campaign for it. 
Funnel - All Occurrences
  • This function provides the count of all occurrences of the Funnel. Each occurrence of a funnel step is counted as a funnel.
  • If a user has performed the funnel multiple times, then each occurrence of the funnel will be counted.
  • This analysis can be used to see how many times a product was viewed by the users before it was finally added to cart and then purchased.
Time to Convert - Unique Users
  • This function provides the distribution of time taken by users to complete all of the steps in the funnel.
  • In the case of a single user, the least amount of time taken in completing the steps will be counted.
  • This analysis can be used to see how long users are spending on the landing page for a new show before finally starting it. 
Time to Convert - All Occurrences
  • This function provides the distribution of the time taken by all occurrences of the conversion for the selected steps. 
  • In the case of a single user, the conversion time of all the conversions between the selected steps is considered.
  • This analysis can be used to see how long users are taking from viewing a promotional campaign to adding a product to their cart. 
Frequency - Unique Users
  • This function provides the distribution of the number of times (frequency) a step is performed by each user before moving on to the next step. 
  • In the case of a single user, the lowest frequency is considered for the analysis.
  • This analysis can be used to see how many times a user listens to a song before adding it to a playlist. 
Frequency - All Occurrences
  • This function provides the distribution of the number of times a steps is performed before moving on to the next step.
  • In the case of a single user, all of the frequencies are considered for the analysis.
  • This analysis can be used to see how many times a certain level of a game is played before users move on to the next level. 

 

Funnel - Unique Users

Consider an E-commerce app that wants to know about the conversion rates of its latest promotional campaign. They can use Funnels to solve their use case with the funnel type set to Unique Users.

Suppose the promotional campaign that they ran was an In-App campaign, then:

  1. Set the App/ Site opened event as the first step of the funnel.
  2. Set the In-App clicked event as the second step of the funnel. Add the Campaign ID of the promotional campaign as an Attribute.  
  3. Set the Added to Cart event as the third step of the funnel.
  4. Select Funnel - Unique Users as the Funnel type, and enter the date range and other details.
  5. Click on Apply and view the results.

By running this analysis, the E-commerce app will be able to see exactly how many users added a product to their cart after viewing their promotional campaign. They can also use the Funnel Split or other Funnel options to get more data. 

If they use Funnel Split here and set Platform as the splitting attribute, then they'll be able to differentiate between the performance of the campaign on Android and iOS. 

Example: The following chart shows the number of unique users who opened the app or site, and then proceeded to add a product to their cart. 

 

Funnel - All Occurrences

Consider an E-commerce app that wants to know exactly how many times a particular product was viewed before users finally added it to their cart. They can use Funnels to solve their use case with the funnel type set to Funnel - All Occurrences. 

  1. Set the App/ Site opened event as the first step of the funnel.
  2. Set the productViewed event as the second step of the funnel. Add the Product ID of the product as an Attribute.  
  3. Set the Added to Cart event as the third step of the funnel.
  4. Select Funnel - All Occurrences as the Funnel type, and enter the date range and other details.
  5. Click on Apply and view the results.

By running this analysis, the E-commerce app will be able to see exactly how many times a product was viewed before users added it to their cart. If they use the Funnel Split option here and set Hour of the Day as the splitting attribute, they can see the exact hour of the day when the conversion was highest. 

Example: The following chart shows the number of times the app or site was opened, and then a product was added to the cart.  

 

Time to Convert - Unique Users

Consider an OTT website that wants to know exactly how long are users spending on the landing page of a show before finally starting it. They can use Funnels to solve their use case with the funnel type set to Time to Convert - Unique Users.

Suppose the OTT website has a custom event EpisodeWatched with the attribute Episode ID, then:

  1. Set the App/ Site opened event as the first step of the funnel.
  2. Set the Viewed Web Page event as the second step of the funnel. Add the URL of the show's landing page as an Attribute.  
  3. Set the EpisodeWatched event as the third step of the funnel. Add the Episode ID of the show's first episode as an attribute. 
  4. Select Time to Convert - Unique Users as the Funnel type, and enter the date range and other details.
  5. Click on Apply and view the results.

By running this analysis, the E-commerce app will be able to see exactly how long users are spending on the landing page of their show. Moreover, they can use the Funnel Split option here and set Country as the splitting attribute to see how this trend varies in different countries. 

Example: The following chart shows the distribution of the amount of time spent by unique users between the opening of the app or site, and a product being added to the cart.  

Distribution Type

There are two different types of distribution available for this funnel type.

  • Auto: This provides the time taken for each percentile of the users who have completed the selected steps. 
  • Custom Distribution: You can customize the time buckets and this distribution provides the number of users converted for the selected step within the time bucket. A maximum of 25 buckets can be analyzed in a single analysis. Distribution buckets include the lower boundary point and exclude the upper boundary point. 

 

Time to Convert - All Occurences

Consider a song streaming app that wants to know exactly how long are users spending on their app before finally playing a song. They can use Funnels to solve their use case with the funnel type set to Time to Convert - All Occurrences.

Suppose the song streaming app has a custom event called SongPlayed, then: 

  1. Set the App/ Site opened event as the first step of the funnel.
  2. Set the SongPlayed event as the second step of the funnel. 
  3. Select Time to Convert - All Occurences as the Funnel type, and enter the date range and other details.
  4. Click on Apply and view the results.

By running this analysis, the song streaming app will be able to see exactly how long users are spending browsing on their app before playing a song. Moreover, they can use the Funnel Split option here and set the splitting attribute to Day of the Week to see if users browse more on a certain day, and if it coincides with the release dates of new songs.

Example: The following chart shows the distribution of the amount of time spent between all occurences of opening of the app or site, and a product being added to the cart.  

Distribution Type

There are two different types of distribution available for this funnel type.

  • Auto: This provides the time taken for each percentile of the users who have completed the selected steps. 
  • Custom Distribution: You can customize the time buckets and this distribution provides the number of users converted for the selected step within the time bucket. A maximum of 25 buckets can be analyzed in a single analysis. Distribution buckets include the lower boundary point and exclude the upper boundary point. 

 

Frequency - Unique Users

Consider a song streaming app that wants to know exactly how many times a particular song was played before users added it to a playlist. They can use Funnels to solve their use case with the funnel type set to Frequency - Unique Users.

Suppose the song streaming app has custom events called SongPlayed and AddedToPlaylist, then:

  1. Set the App/ Site opened event as the first step of the funnel.
  2. Set the SongPlayed event as the second step of the funnel. Add the Song ID of the song you want to analyse as an attribute.
  3. Set the AddedToPlaylist event as the third step of the funnel. 
  4. Select Frequency - Unique Users as the Funnel type, and enter the date range and other details.
  5. Click on Apply and view the results.

By running this analysis, the song streaming app will be able to see exactly how many times a song was being played by users before they added it to a playlist. Moreover, if they have an attribute called Artist, they can use the Funnel Split to see how this trend varies according to different artists. 

Example: The following chart shows the distribution of the number of times the app or site was opened by unique users before they added a product to the cart. 

Distribution Type

There are two different types of distribution available for this funnel type. 

  • Auto: This provides the frequency of performing a step before the next step for each percentile of the users. 
  • Custom Distribution: You can customize the frequency buckets and this distribution provides the number of users converted for the selected step within the frequency bucket. A maximum of 25 buckets can be analyzed in a single analysis. Distribution buckets include the lower boundary point and exclude the upper boundary point. 

 

Frequency - All Occurrences

Consider a gaming app that wants to know exactly how many times are users playing a level of the game before progressing to the next level. They can use Funnels to solve their use case with the funnel type set to Frequency - All Occurrences.

Suppose the gaming app has a custom event called LevelPlayed with an attribute called Level IDthen:

  1. Set the App/ Site opened event as the first step of the funnel.
  2. Set the LevelPlayed event as the second step of the funnel. Add the Level ID of the first level you want to analyse as an attribute.
  3. Set the LevelPlayed event as the third step of the funnel. Add the Level ID of the second level you want to analyse as an attribute.
  4. Select Frequency - All Occurrences as the Funnel type, and enter the date range and other details.
  5. Click on Apply and view the results.

By running this analysis, the gaming app will be able to see exactly how many times users played a level of the game before progressing. Moreover, they can use the Funnel Split and set the splitting attribute to Device Model to see how their users are faring on different devices.

Example: The following chart shows the distribution of the number of times the app or site was opened before a product was added to the cart. 

Distribution Type

There are two different types of distribution available for this funnel type.

  • Auto: This provides the frequency of performing a step before the next step for each percentile of the users. 
  • Custom Distribution: You can customize the frequency buckets and this distribution provides the number of users converted for the selected step within the frequency bucket. A maximum of 25 buckets can be analyzed in a single analysis. Distribution buckets include the lower boundary point and exclude the upper boundary point. 

 

Funnel Split

Funnel split analysis compares the funnel based on the event attribute values. Funnel analysis is compared to the event attribute of any step. The funnel analysis is divided into values of the event attribute and the analysis is represented in tables and charts.

Comparison or funnel split analysis is performed because not all step events are the same and not all attributes in different steps are the same. Three different types of split analysis are available in MoEngage Funnels to perform the analysis. You can compare up to three attributes for all split by analysis. The unique combination of the attribute values is considered one funnel.

Funnel analysis is performed for each value of the attribute and represented in the chart and table.

Following are the funnel split options:

All Steps

In the split analysis for All Steps, an event attribute (name of the attribute) should be part of all step events of the funnel (including OR and exclude events). All funnel events are first filtered for a specific attribute value and then funnel analysis is performed on the steps.

For example:

  • Analyze and compare a campaign funnel based on the campaign name.

  • Analyze and compare app events funnel based on the platform.

First N Steps

In the split analysis for First N Steps, an event attribute (name of the attribute) has to be present in the first N step events of the funnel (including OR and exclude events).
Funnel analysis is performed for each value of the attribute up to the Nth step and for the events after the Nth step system does a forward filling and completes funnel analysis. In forward filling, one event may get attributed in multiple funnels.

For example, analyze the campaign to conversion funnel and compare the campaign name till the step where the campaign name is available.

Nth Step

In the split analysis for the Nth Step, event attributes of the selected Nth step are available for analysis. Ensure that the event attribute is present in all OR events of the step.
For Nth step split funnel analysis, the system performs backward filling, forward filling, or both as required (depending on the step selection). In the analysis, you may find a value Not reached Step N, which means the funnel has not reached the Nth step in the funnel analysis.

For example, compare funnel analysis on any one of the step event attributes.

warning

Compare by or Split by is not available for Predictive Insights (Sherpa) attributes i.e. Best time to send, Most Preferred Channel, and Prediction attributes.

 

Hold Attribute

Holding Attribute Constant funnel analysis is performed for each attribute value of an event not on the funnel at the event level. Funnel analysis is performed for each attribute value:

  • When an attribute is carried through multiple steps of the funnel.
    For example, product id in checkout funnel or campaign id in campaign funnel.

  • All step events have the same attribute present.
    For example, campaign id or product id. 

For example, if a user converts two times in a funnel with two different product ids, then

  • The funnel option Funnel - Unique Users displays only one conversion,

  • The funnel option Funnel - Unique Users with holding attribute constant on product id displays two conversions, one for each product id.

You can select up to three attributes for all Hold Attribute analyses. The unique combination of three attribute values is considered an entity.

Funnel Window

The funnel window provides the time taken to complete all the steps. By default, the funnel window is set as one day.

For example,

  • Calculate how many users opened the app (first step) and then made a purchase (last step) within five minutes by setting the funnel window to five minutes.

  • Use Funnel Window to set the attribution time window for any funnel.

  • Calculate how many users are converting within 1 day, 1 hour, and so on.

Strict Order

Strict Order considers user events or activities based on the sequence defined in the funnel steps. The strict order funnel provides the number of users who have performed all the steps in the funnel in provided order without performing any of the steps out of order.

The funnel option, Funnel - Unique Users, provides the number of users who have performed all the steps in the funnel in the defined sequence. A user is counted in the funnel, if the funnel steps sequence matches with the user event or activity order, regardless of the event or activity order performed.

Funnel with steps as A → B → C will count users who have performed steps in the following order:

  • A → B → C or

  • A → Z → B → C or

  • A → X → B → Y→ Z → C  
    X, Y, and Z are other events or activities not specified in funnel steps and do not affect funnel calculation, even if they are performed in between steps. 

A user performing A → C → B → C is removed from the strict funnel after step 1 (A) as the user has performed step 3 (C) before step 2 (B).

Example for the strict funnel 

  1. Remove users from analysis when the user clicks on the second campaign before completing the first campaign funnel.

  2. Remove users searching for different products after viewing a product.

The strict order is also applicable to all other funnel types.

 

Funnel Reports and Visualization

Chart Types

Switch the chart view for better visualization depending on the type of data. You can choose between a Line ChartArea Chart, or Bar Chart to view progress, compare volumes over a time period, or compare absolute numbers respectively.

Charts show a maximum of 20 different entities. The remaining entities are available in tabular format. By default, Funnels is shown in a bar chart for the selected time period. To view the funnel with different granularity, select Line/Area Chart and select the granularity.

 

Granularity_2.png

 

Granularity

You can also break down the report into the granularity of Hourly, Daily, Weekly, or Monthly for easy visualization. Funnel users will be displayed based on the granularity you select.

Funels_Report_Graphs.gif


Add to Custom Dashboard

Funnel analysis reports can be pinned to any desired dashboard. Click on the Pin to Dashboard button to save this funnel chart in custom dashboards. You can see the analysis with one click on the custom dashboards without inputting the steps and filters every time. For more information, refer to Custom Dashboards

Granularity.png


Downloading Reports

Download the report after viewing the report. The charts are downloaded in PNG format and the table is exported in CSV format.

Transpose Table

You can transpose the table, it allows you to view and download the table in your preferred format by shifting the vertical and horizontal orientation of the table.

Actionable Analytics

Funnel analysis is transformed from just informative to actionable through actionable analytics. The insights provided by Funnels can be saved as custom segments, and you can take the following actions on custom segments:

  • Create Campaign
  • Analyze Segments

Click on any column to engage the respective users. On the Create Segment popup, select Dynamic segments to have the relative date range, or select Static segment to have a fixed date range for your segment. Provide the Segment name to save the insights (thus users) as a custom segment.

 

Click on Create a campaign for this segment to use this segment in the desired campaigns.

 

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Click on Analyze this segment to directly use this segment in the desired analysis types.

 

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Actionable insights are not available for hourly granularity at this point.

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