Introducing the Ayima Data Studio Funnel chart

Monika Mesnage
Reading time: 5 minutes
30th January 2020

Discovering how many pairs of glittery unicorn socks were added to a shopping basket and then how many were successfully purchased is a pretty important part of our job as Digital Analysts; especially when it comes to the issues those customers might face in their journey to unicorn sock heaven.

Of course, fabulous socks are just an example. Ultimately it’s our job to help clients increase their revenue – be that ramping up sock sales or getting more subscriptions – and a small increase in the proportion of customers moving from ‘begin checkout’ through to ‘purchase’ can lead to a substantial revenue increase. That’s why we love a funnel.

Purchase funnels are a core tool to help visualise a customer journey when creating reports for key stakeholders. They help us locate where the main issues lie and where we should look for potential obstacles for customers.

But if you’re a Digital Analyst, you might understand the frustrations of spending time creating these purchase funnels manually, often using blended data in Data Studio . That’s why we created this simple funnel visualisation: it can be added to your dashboard in just four clicks. And adding the data for each stage using relevant Goal Completions can be done in no more than ten clicks. Reducing the time to create a funnel frees up more time for analysis, giving us the opportunity to easily spot any changes in a customer’s journey to a successful purchase.

What is Data Studio?

Data Studio is Google’s visualisation tool. It seamlessly links with Google products, but you can also connect it to your own database. It connects to Google Analytics, allowing you to visualise your web data in any format you want, and it’s fantastic for creating dashboards.

What are Data Studio Community Visualisations?

This newly launched Data Studio feature allows you to add a funnel visualisation to your dashboard in just a few clicks. There’s a whole gallery of community-generated visualisations which you can explore, including the Ayima Funnel chart!

Add a funnel to your dashboard in four clicks!

  1. In your Data Studio dashboard, click the ‘community visualisations and components’ icon located next to ‘add a chart’ dropdown
  2. Click ‘explore more’ to see all visualisations available
  3. Click the ‘Funnel Chart’ by Ayima and drop it onto your dashboard
  4. Drop in your goals for each stage of the funnel. The initial metrics are ‘sessions’ and ‘pageviews’ – adjust those to create the funnel you require.
  5. Check out our example below, where we visualise all standard stages of an Ayima Ecommerce funnel.

adding funnel to dashboard

adding stages to the funnel

Want to make it look pretty? You can customise the bars in the same way you can customise all other Data Studio Community Visualisations components. Change the colour of bars, the text and how many decimal places are shown on the chart. The width of the bars corresponds to the funnel step size.

personalising the funnel

Do I have community visualisations enabled?

If you just created a new dashboard, the community visualisations should be enabled by default. If it’s an old one, you might have to turn this option on.

To check if your report has ‘community visualisation accesses’ enabled:

  1. Select ‘resource’ and ‘manage added data sources’ in the header
  2. Then select ‘edit’ on the data source
  3. In the top right-hand corner, next to ‘field editing in reports’ option option, turn the community visualisations on
  4. Remember to click ‘finished’ when you are done, to save this change in the data source.

Top tip: If you can’t see the ‘edit’ option for the data source, this means you have no access to add this permission and have to ask the data source owner to turn on this setting.

check if vis is enabled

How to read this funnel

The ‘% vs Previous’ is the percentage of the most recent stage vs the previous stage.’% vs Initial’ calculates each step as a percentage of the first step.
For example, in our funnel below, the ‘% vs Previous’ should be interpreted as follows:

We had 2,241 sessions on the example website. 84% (1,889) of all sessions are Ecommerce: those are the sessions which showed some interest in purchasing a product, by visiting a product-related page, pop-up, basket, or checkout. The idea is to discount sessions which, for example, read a single blog post and never visited a product page. They were unlikely to convert, therefore we don’t want to class those as an ‘Ecommerce session’.

90% (1,698) of those with an ‘Ecommerce session’ viewed a product, and 57% of those that viewed a product, created a basket. Of those who created a basket (967), 70% (678) clicked on ‘commence checkout’ but only 23 (3%) completed a purchase. The last stage has a very significant drop off – we would immediately look into this to understand what the issue is with the user’s check out!

Whereas the ‘% vs Initial’ should be interpreted as follows:

We had 2,241 sessions on the example website. Of those sessions, 84% (1,889) of all sessions are ecommerce. 76% of all sessions (again, vs 2,241) viewed a product, and 43% of all sessions created a basket. 30% of all sessions commenced checkout and 1% transacted.

The last % in this case (1%) is the Google Analytics metric of ‘Conversion Rate’: the number of transactions divided by the total number of sessions.

Top tip: Create this funnel using goals, or session-level metrics, otherwise you might be comparing the number of interactions to sessions. For example, if your starting point is the number of sessions (just like in our example) you should use goals for all other stages. Goals are recorded only once per session, therefore you will be comparing sessions to those where the interactions occurred.

If you use the number of events to count the ‘add to basket’ stage instead of a goal, you might artificially inflate the funnel: someone might have one session, but ‘add to basket’ three times. If you use an event, you will over count the number of ‘adds to basket’. This could make the funnel highly unstable over time.

If you would like to know more about reporting on the customer journey using purchase funnels, and how we could help your analytics function, we’d love to hear from you.

Written By Monika Mesnage
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