Using Funnel Analysis

Funnel Analysis Usage Tips

With User Funnel Analysis, you can analyze step by step the process from when users enter your service to when they achieve a specific goal.

1. Product Acquisition

I'm curious about the conversion and drop-off rate for each segment from service entry to adding to cart.

First, define the Critical Path that is central to your service, and use Funnel Analysis when you want to see the conversion/drop-off rate for that path.

Select events to configure the behavioral flow path.
- Funnel > Configure Analysis Items
- view_home > view_product_list > view_product_detail > add_to_cart

Set the analysis type and period to view according to your goal.

Type > Direct Order The path from entry to adding to cart as provided in the example typically proceeds sequentially. For behavioral flows that must occur in strict sequential order like this, you can set that path and view it as a [Direct Order Funnel].

View and interpret data

You can see the number of converted users and the conversion rate for each segment, and also confirm drop-off segments at a glance.

You can also check the 'final conversion rate' (from step 1 to the final step) that is set as a KPI or OKR.

Drop-off point on the landing page for users who came in via a specific marketing channel

In this case, you can break down by marketing channel (utm_source) and compare and analyze the conversion rate at each step.

Select events to configure the behavioral flow path.

Funnel > Analysis Steps
- view_landingpage > view_landingpage_impression_1 > view_landingpage_impression_2 > view_landingpage_impression_3

Select Split by Property, then choose the property to split by.

Split by utm_source

View and interpret data

As shown below, you can see the conversion rate differences for each utm_source path (naver, tiktok, facebook, etc.) in different colors.

2. Product Usability

At which step of the sign-up entry path did users drop off the most?
What is the final conversion rate from a user's service entry to making a purchase?
Through which path do users convert to making a payment?
Which part of the service is the most efficient to improve to increase revenue?

Can I see the activation difference between users who signed up in June and July?

For example, if you ran a marketing campaign such as coupons or the service went viral, users may have been acquired at different times. In that case, you can also use a funnel to compare how those users behave within the service or compare their degree of activation.

Set the events to analyze in the funnel steps
Set the events you want to view in the analysis steps. For logins, you can see the degree of activation. If you provided a payment-related coupon, you can also set payment events. In this example, let's look at how activated users are via [visit - login].
Configure user groups
Set the sign-up completion event and configure the period as June and July. This creates 2 user segments as shown in the attached image.

Query and analyze data
As shown below, you can see the conversion rate (%) from service visit to login for users who newly signed up in June and July, displayed in two colors, along with the difference between them.
Users acquired in July show a relatively 6% higher conversion rate, and by the same principle, you can extend the funnel further or create more user groups for comparison.

How did the purchase conversion rate change before and after a major update?

During service development, you may have conducted major updates such as updates to payment modules or algorithms.

In that case, since users are using different versions, you can compare conversion rates by version using a funnel.

Set the service's Critical Path.
If a payment-related update was conducted, set the key paths (paths) affected by it as analysis steps.
Split users by OsVersion or BrowserVersion, etc. in the user groups.
As shown in the image below, split users by version from their attributes.

Query and analyze funnel data
You can check the conversion/drop-off rate for each version along the funnel path. The example below shows that v.16.0 has the highest payment conversion rate. Looking at performance by version like this lets you check what impact was made and to what extent.

3. Payment/Revenue

How did the payment funnel conversion rate change for a specific region (e.g., Japan, USA)?

You can create segments from users who triggered a specific event, but you can also segment by users' region/language/OS/version, etc.

For example, to analyze the conversion rate from entry to payment for each user region, you can proceed as follows.

Set the funnel steps to analyze
Select events like [entry - product click - add to wishlist - add to cart - payment complete] to configure the behavioral flow path.
Select and query by user attributes in User Groups.
Query and select attributes such as language, country, etc.

View and interpret data
As shown below, the selected user segments are shown in the graph. While the final conversion rates are similar, through comparison you can analyze that English-speaking users passed through add_to_wish relatively more.
For example, you can use this kind of graph to set a target value for the final conversion rate, or to get ideas for enhancing the add_to_wish screen for English-speaking users.