Example GA4 reports for preezie journeys

This quick start guide will show you how to view preezie data alongside your other GA4 analytics.

Because custom event values are not automatically shown in standard ecommerce reports, we’ll use Explore.

🖱️ User engagement

This will allow you to calculate some engagement metrics:

• Start rate what % of my users start preezie when seeing it? 20% is a good benchmark to aim for

  • preezie users / preezie loads users

• Completion rate what % complete preezie after starting it? Over 85% is a good benchmark to aim for

  • preezie completed users / preezie users

• Improved engagement is preezie helping grow more engaged users?

  • preezie completed engagement rate vs non-preezie users

  • preezie completed views per user vs non-preezie users

  • preezie completed session duration/bounce rate vs non-preezie users etc.

Set up

First, let’s create a Free form Exploration to analyse some preezie engagement metrics.

Create 4 segments

preezie users Include users when Events > preezie_click
preezie completed Include users when Events > preezie_completed
preezie loads Include users when Events > preezie_load
non-preezie users We’ll create a segment that includes where it was loaded but not clicked Include users when Events > preezie_load Exclude users when Events > preezie_click

Import these metrics

• Total users

• Engagement rate - as defined by Google: Engaged sessions divided by Sessions

• Engaged sessions per user - as defined by Google: The number of sessions that lasted longer than 10 seconds, or had a conversion event, or had 2 or more screen or page views.

• Views per user - as defined by Google: The average number of mobile app screens or web pages viewed per user.

• Average session duration

• Bounce rate - as defined by Google the % of sessions that: lasted longer than 10 seconds, or had a conversion event, or had 2 or more screen or page views.

Create the report

Add the segments and metrics to your report, it should look something like this:

You can now also then add a Dimension of New / established users

Google defines this as: New users first opened your app or visited your website within the last 7 days.
Established users first opened the app or visited the website more than 7 days ago.

Add this Dimension to see a further breakdown of how new and returning users are becoming engaged:

• New user engagement is preezie helping new users stick around?

If you establish how much preezie can help new users then you can use it as a dedicated landing experience for marketing/advertising campaigns, e.g. www.preezie.com/christmas-gift-finder

🛒 Conversions and revenue

Now using your segments you can create an additional report tab to analyse preezie conversions.

This will allow you to calculate some conversion metrics:

• What is the add to cart rate for preezie users? This is a sign they’re entering your purchase funnel
  Add to carts / Total users

• Do preezie users incur higher spend?

• Are they driving first time purchases?
  First time purchase conversion rate by journey

• Are they driving new users to convert?
  First time purchasers per new user by journey

• How do these compare against non-preezie users?

Set up

First, add another Free form tab and call it ‘preezie conversion'

Add segments

Add your preezie users and non-preezie users segments created in step 1 above, this will breakdown allow us to compare performance:

Import these metrics

• User conversion rate - we use this as preezie users drive more sessions

• Add to carts

• Ecommerce purchases

• Purchase revenue

• Average revenue per user

• First-time purchaser conversion - as defined by Google: Percentage of active users that completed their first purchase event for the time period selected.

• First-time purchasers per new user - as defined by Google: Ratio of active users that completed their first purchase event divided by new users for the time period selected. These two populations are exclusive.

Create the report

• Make sure the 2 segments are added to the report

• Change the PIVOT to first row

• Add the imported Metrics from above, plus Total users (already imported)

It should look something like this:

❔ Answer trends

This will allow you to calculate some answer metrics, for example:

• What answers are most/least popular?

• Is there a correlation with certain answers to add to cart events?

Set up

First, add another Free form tab and call it ‘answer trends'.

Add a dimension

Add a dimension of preezie_answer, this will expose the quiz answers:

Create the report

• Add preezie_answer dimension to your rows - note, this also includes the question number as a prefix

• Add preezie_journey to your columns

• Add Total users and Add to carts to your Metrics

• Add Filters of preezie_answer <> (not set) and preezie_journey <> (not set)

It will look something like this:

👚 Product clicks

This will allow you to calculate some result metrics:

• What product result position is most commonly clicked?

• How often are users purchasing the exact recommended product?

• Which product gets the highest clicks?

Set up

Add another Free form tab and call it ‘results clicked'.

Add dimensions

Add dimensions below to expose details of product results that were clicked. Note these parameters only trigger when the product is clicked:

• preezie_product_name

• preezie_product_position

• Product name - this is your preconfigured eCommerce product name, it should ideally match your preezie feed’s product name

Import these metrics

• Event count

Create the report

• Add preezie_product_name, preezie_product_position and Product name dimensions to your rows

• Add preezie_journey to your columns

• Add Event count and Ecommerce purchases to your Metrics

• Add the segment preezie users - this will show us only those who have used preezie, clicked and then bought these products

You should now be able to match your products clicked (event count) with your purchased product names (Ecommerce purchases):