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Analysing Funnels

A guide on how to analyse funnels in your WebEngage dashboard

Must Read

Before we deep dive into funnel analysis, please ensure that you have read through, What are Events and Event Attributes? and have a robust understanding of related concepts, as events are the build blocks of funnels.

Once a funnel has been created, WebEngage gives you an in-depth view of its users and their behaviour via the Funnel Analysis section.

First Impression

This section can be accessed by clicking a Funnel’s Name on the Funnels Homepage, as shown below.

Click to enlarge

Click to enlarge

The default view has been designed to give you a comprehensive breakdown of the funnel's performance over the past month against several metrics like;

  • Total conversion rate
  • The Average time that a user takes to exit the funnel
  • Conversion rate between each step
  • A week-wise breakdown of how many people entered the funnel, and how many of these users performed each step of the funnel

A Hands-on Demonstration

But before we deep dive into the features of this section, let's warm you up by analysing a short use-case.

Use-case: Analysing a Search to Checkout Funnel

Let's assume that you're a marketing manager at an online marketplace and would like to analyse the purchase behaviour of users who search for products on your app and website.

For this, you decide to create a funnel, mapping out the following steps:

Step 1: Search

Step 2: Product Page Viewed

Step 3: Product Added to Cart

Step 4: Checkout Started

Step 5: Checkout Completed

Here's what the funnel looks like when analysed for a period of 60 days.

Click to enlarge

Click to enlarge

Key takeaways from the analysis

As you can see above, only 0.87% of users who searched for a product, end up purchasing it. To increase the overall conversion rate, you will have to understand the various bottlenecks in the funnel and take corrective measures.

  1. For instance, we can see that the drop-off rate between Step 2, Product Page Viewed and Step 3, Product Added to Cart is the highest at 87.35%.

But is this the only problem? Let's analyse the funnel further to find out.

  1. We can see that even though a considerable number of users have added products to their cart, a majority of them do not go ahead with purchasing it.

    • This is indicated by the high drop-off rates between Step 3, Product Added to Cart and Step 4, Checkout Started - 28.32%.
  2. Further, a high number of users who have begun the checkout process seem to drop-off mid-way.

    • This is indicated by the high drop-off rates between Step 4, Checkout Started and Step 5, Checkout Completed - 12.44%.
    • This user behaviour seems slightly odd as one would expect the conversion rate between these steps to be considerably higher.

Going by the high drop-off rates between all the crucial steps, it becomes evident that there is a disconnect between the user's needs and the experience offered by the platform. A few common challenges faced by online marketplaces between these steps include;

  • The layout of the cart in their app/website is not optimized well enough to promote purchase as the next most viable step in the user's journey.
  • The users need some external motivation or assurance to go ahead with the purchase after adding the products to their cart.
  • A few elements of the checkout process like payment, selecting shipping method etc. have not been optimized for providing a smooth checkout experience.

But the good news is that we now have a limited pool of problems which can be fixed to optimize the conversion rate!

Similarly, you can leverage funnel analysis as a powerful tool to dig into your users' behaviour, identify problem areas in your product and user lifecycle and take effective measures to optimize growth.

Understanding features of the funnel analysis section

Now that we have a broad idea of how funnel analysis works, here’s a detailed breakdown of all the features of this section:

1. Date Range Filter

Click to enlarge

Click to enlarge

Placed on the top right, using the date range filter you can choose to analyse the funnel over any desired time period.

The following options are included here:

  • Today

  • Yesterday

  • Lasy 7 days

  • Lasy 30 days

  • Lasy 90 days

  • Custom dates

The selected date range determines the period within which Step 1 of the funnel occurs** and does not define the entire duration over which the analysis can be conducted. Hence, all users who have performed Step 1 within the selected date range will be accounted for when calculating its performance, irrespective of whether or not they have exited the funnel within the specified period.

**Understanding the Concept of Occurrence of an Event

Please refer to How are Events Calculated for Analysis? for a detailed understanding of the concept of Occurrences and Uniques, and how it impacts funnel analysis in WebEngage.

2. Completion Time

Click to enlarge

Click to enlarge

Placed on the top left under the funnel's name, Completion Time allows you to analyse the funnel by specifying a time frame within which a user should ideally exit the funnel, after performing the last step.

The following formats of time can be define here:

  • Minutes, preceded by a manually entered numerical value

  • Hours, preceded by a manually entered numerical value

  • Days, preceded by a manually entered numerical value

This feature comes in handy, especially when analysing user behaviour against an ideal conversion time frame or understanding the variations in completion time across several flows which lead to the same end goal on your app/website.

How Completion Time works

Let’s say that you have selected a period of 30 days under the Date Range, January 1 to January 30 and have specified a Completion Time of 1 Day.

This would mean that if a user performs Step 1 of the funnel on January 5, then we will track the following steps of the funnel, for the particular user only till January 6, as per the defined completion time.

Similarly, if another user performs Step 1 of the funnel on January 30, then we will track the following steps of the funnel, for the particular user till January 31.

Hence, a sum of the actions of all the users who enter the funnel by performing Step 1, between January 1 to January 30 and exit it within 1 Day will be taken into account by us when calculating;

  • The conversion rate between each step
  • Average completion time
  • The overall conversion rate of the funnel

Users that performed Step 1 in the specified time frame but did not exit the funnel within a day, will be excluded from your analysis.

How a combination of Completion Time and Date Range affect conversion rate

Now that we know how Completion Time and Date Range work, let's show you how a combination of the two can help you analyse user behaviour for specific time frames.

Use-case: Analysing purchase behaviour of high-intent users

Let's say that you are a retention specialist at an e-commerce business and want to analyse the behaviour of high-intent users over a specific time frame. For this, you create a checkout funnel by defining the following steps:

  • Step 1: Shopping Cart Viewed

  • Step 2: Checkout Button Clicked

  • Step 3: Delivery Address Added

  • Step 4: Payment Mode Selected

  • Step 5: Checkout Complete

From the existing data, you know that on an average, high-intent users take a maximum of 10 minutes to exit the funnel. So, let's analyse this funnel to find out how many high-intent users made a purchase over the last 30 days and the last 7 days, drawing a comparison.

Here's what the original funnel looks like, when analysed for a period of 30 days:

Click to enlarge

Click to enlarge

A total of 400 users enter the funnel and 290 users exit it. Recording an overall conversion rate of 72.5% for the month.

Now, let's add a Completion Time of 10 minutes to analyse how many high-intent users made a purchase over the previous month. Here's what the revised funnel looks like:

Click to enlarge

Click to enlarge

The number of users entering the funnel, 400, remains the same, while the number of users exiting the funnel drops to 270. This makes the conversion rate for high-intent purchasers, 67.5% for the month.

Now, let's define the Date Range as, last 7 days, to analyse how many high intent users entered the funnel in the previous week. So, on combining the Completion Time and Date Range, the funnel shows the following results:

Click to enlarge

Click to enlarge

A change in date range changes the number of users entering the funnel to 100 and when combined with the completion time, the number of users exiting the funnel equals 72. This means that over the previous week, the conversion rate for high-intent purchasers was 72% - which is considerably higher than the entire month's conversion rate.

It'll be interesting to dig deeper into who these users are, where they were acquired from and which products they purchased to understand the factors which have contributed to the high conversions.

3. Conversions

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Click to enlarge

The conversion rate displayed on the top left of the visualisation shows you the overall conversion rate of the funnel for the selected Date Range and Completion Time.

4. Average Time to Convert

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Click to enlarge

Placed towards the left of Conversion, this metric shows the average time taken by a user to exit the funnel, i.e. travel from Step 1 to the last Step defined by you. These results change depending on the Completion Time and Date Range selected by you.

Let's analyse a quick use-case to show you how a change in Completion Time affect the Average Time to Convert.

Use-case: Analysing outliers in the checkout funnel

Let's take the example of an e-commerce checkout funnel for the sake of ease of analysis. With reference to the visual below, we can see that the average time taken by a user to exit the funnel is 57 minutes, for a date range of 60 days.

Wouldn't it be interesting to find out how many users made a purchase in a shorter duration?

This will help us analyse the outliers or users who have made a purchase faster than the average. And if this number is considerably higher, then it presents an opportunity to further refine the checkout process on the business's app and website.

Checkout funnel analysed for 60 days with default completion time of 7 days. (click to enlarge)

Checkout funnel analysed for 60 days with default completion time of 7 days. (click to enlarge)

Now let's change the Completion Time to 50 minutes to conduct our analysis.

Click to enlarge

Click to enlarge

As you can see in the visual above, the Average Time to Convert drops to 8 minutes!

That's a massive drop from the average time taken by users to make a purchase currently. Further, we can see that the conversion rate has dropped by just 0.75% (from 2.81% to 2.06%) suggesting that maximum users complete checkout in 8 minutes, barring a few, due to which there's a hike in average time to convert. This is a clear indicator of the fact that a considerable improvement can be made in the checkout experience for all users if the existing process were optimized for these outliers.

5. Understanding the Funnel Visualisation

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Click to enlarge

Apart from the overall conversion rate and the average time taken to convert, the funnel visualisation indicates several other performance metrics including:

  • Conversion rate between each step (indicated in grey between steps)
  • Conversion rate from the first step to a step lying in-between the funnel (indicated in bold black towards the left of each step)
    • Total number of people entering each step of the funnel (indicated in grey towards extreme left of each step)

These metrics are strong indicators of how your users interact with your app and website at each stage of their lifecycle and play a crucial role in optimizing the overall user experience.

6. Analyzing Funnel Over

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Click to enlarge

Moving on to the second section of Funnel Analysis, this section allows you to analyze the funnel against an event attribute of the event defined by you under Step 1.

As discussed under What are Events and Event Attributes?, each event in WebEngage can be further defined by adding event attributes to them. This helps narrow down the scope of occurrence of the event, including only those users who have performed the event, in the context of the attribute, in the table here.

While the default attribute has been set to Weeks, you can change this by selecting an attribute from the drop-down placed at the top of the table.

The following event attributes are included in the dropdown here:

  • Time (Days, Weeks, Months)

  • Time Block (Hours of Day, Days of Week, Months of Year)

  • Location (City, Country)

  • Technology (Browser Name, OS Name, Device Manufacturer, Device Model, Carrier, App Version, App ID, Platform)

  • UTM (Channel, Campaign Name, Campaign Source, Campaign Medium, Referrer Host, Referrer URL, Landing Page)

  • Screen (Page URL, Screen Name)

  • Engagement (Campaign ID, Journey ID)

This section has been designed to give you an in-depth view of the funnel against the selected attribute, broken down by each step. Let's get you acquainted with all the details shown here:

1. Understanding how the selected attribute and its values are displayed

The heading of the first column in the table indicates the event attribute selected by you and each subsequent cell under the header indicates a value of the attribute. These cells also double up as headings for each row of the table. Thus, depending on the number of values, this section may span into several pages.

Click to enlarge

Click to enlarge

For example, if you were to select Country as an attribute, then this table will show you details of all users who have entered the funnel, in the defined date range, against the various countries they are located in.

Depending on the Date Range and Completion Time defined by you, the users included in the funnel change, and so will the data points being shown here.

2. Stepwise breakdown of the funnel

Starting from the second column, this table will include a breakdown of all the steps of the funnel, with each subsequent column representing details of users who performed the various steps of the funnel.

This means that if you have added 5 steps in your funnel, then each step will double up as a heading for each column. For example, the table below shows conversions for all users located in different countries, against each step of the funnel - giving you a complete picture of their behaviour.

Click to enlarge

Click to enlarge

As shown in the visual above, against the Country, Algeria, you can see a numerical value under the second column of the table, Users Entering Step 1, while the subsequent columns indicate percentages.

Why is this so?

  1. The values being shown under the column, Users Entering Step 1, will always be shown as a numerical value as it represents the total number of users that have entered the funnel, spilt by the attribute.

  2. The percentage values being shown under the subsequent columns, post Users Entering Step 1, are calculated against the total number of users shown under Users Entering Step 1.

3. Change the format of data

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Click to enlarge

Using the overflow menu placed on the top right of this section, you can choose to view the data being shown here as numerical values or percentage.

We hope this has given you a good idea of how you can make the most of this section to analyse user behaviour via funnels.


What's Next

Let's show you how to modify funnels, next.

Modifying Funnels