Although Littledata works with all eCommerce platforms, they’re well-known for their Shopify app which automates a large part of the Google Analytics setup process, including the on-page tagging, creation of the Google Tag Manager containers, triggers etc, the various requirements for enhanced eCommerce and the creation of the Data Layer.
So, having been impressed with their services and their Shopify solution, I asked Littledata’s Founder, Ed Upton, to answer some questions about setting up Google Analytics for Shopify Plus stores – which can be seen below.
What’s your recommended approach for integrating Shopify and Google Analytics? What are some of the disadvantages of just adding your tracking code?
Shopify added in-built ‘enhanced ecommerce’ tracking for Google Analytics (GA) back in 2016. Unfortunately, they missed a few important details of the data setup, which has driven larger stores – and those with subscriptions or other add-ons – to seek a better solution, often with lots of expensive manual tagging. Littledata’s app is the first to provide a stable, complete connection from Shopify to GA and audit the data for ongoing accuracy.
I know Littledata also adds a number of custom dimensions, what are these and which other custom dimensions do you generally recommend Shopify users push into Google Analytics?
Littledata adds custom dimensions, so stores can better segment their data to spot patterns. The dimensions are:
- Shopify customer ID – so you can see web behaviour leading to a particular purchase
- Date of last purchase – so you can build cohort reports to look at retention or post-purchase behaviour
- Customer lifetime value (CLV) – to look at true Return on Advertising Spend
- Purchase count – so you can build an audience of users who have purchased before (for retargeting) or who have not purchased before (for better targeting prospecting ads)
- Payment gateway – to examine how payment methods affects checkout completion
What are the biggest things people miss when they set up Google Analytics with Shopify?
- 100% accuracy on order volumes and revenue, including revenues: the standard setup misses up to 10% of orders, making it hard to compare GA reports with the actual sales figures.
- Marketing attribution of orders: many stores cannot see the real contribution of marketing channels, since the online sales are not attributed back to the campaigns that brought them.
- Product list views and clicks
How does Littledata ensure 100% accuracy around transactional data?
The reason most analytics setups miss transactions is that they rely on a tracking code firing on the ‘thank you’ page. This might never trigger, maybe due to another slow running script on that page, or just that the customer navigates away after payment (knowing they will get an email confirmation).
What’s unusual about our solution is that we set up server-side tracking, rather than rely on any page load. So if the order is picked up by Shopify’s servers, and you get the payment, then we also send that on to GA – in a way that it can be linked with the pre-purchase behaviour.
I know you’ve done a lot of work with subscription-based Shopify merchants – how should Google Analytics be setup to track repeat orders in your opinion?
Subscription ecommerce is growing rapidly on the Shopify platform, and we connect with ReCharge and Bold (two of the biggest platforms) to correctly attribute repeat orders in GA. In our view, the ideal data setup is to:
- Capture all the recurring orders and link them back with the original user who signed up in GA.
- Analyze lifetime value of subscribers by looking at the history of transactions, or with an additional custom dimension in GA of their lifetime value.
- Set up a filtered view of the data to only show first-time orders, so that the ecommerce conversion rate can be measured like for like.
And that’s exactly how Littledata’s app for Shopify works!
Tell us about the Littledata app, what it does, where’s it going etc.
So our app provides Shopify merchants with instant setup and configuration of Google Analytics, without getting a developer involved. Stores can choose to test their new data setup in parallel, or go live immediately.
Once the data is GA is complete, stores can then benchmark their online performance against their industry sector and launch missions to improve that performance – based on a collection of expert conversion rate optimization tips.
We’ve also launched connectors for a number of other marketing platforms (Facebook Ads, Google Ads) in the last 6 months, and we’re building out more marketing data connectors this year to fulfil our tagline of helping stores get a ‘complete picture of their ecommerce performance’.
How do you handle situations where there are multiple checkouts (e.g. global-e, other subscription services etc) from a Google Analytics perspective?
Third party checkouts present two more problems: the checkout process happens on another domain where you don’t have GA or GTM tracking scripts, and the attribution of the sale is typically lost when the user crosses between your website and another domain.
Littledata’s app gets around both of these by listening to the checkout updates and orders as they get passed back to Shopify – and linking these orders back to the original customer journey on your website.
What do you think is the most under-utilised feature or report within Google Analytics (for eCommerce)?
Product list performance. Being able to see which products get browsed but not clicked on, or rarely browsed but frequently brought allows you to tune your search results and lists to present the better performing products. Who doesn’t want to present their top performing or highest margin products first??
What are some of the other things you recommend tracking that retailers often ignore?
There’s so much you could look at! I’d start with some of the simple stuff we set up for Shopify:
- Search usage: both what searches are driving purchases – and can you highlight those popular products – and also what searches lead to no results or further page views
- Usage of navigation: visualising the user journey with a Chrome extension can be a powerful way to check assumptions about how users want to navigate your site, and maybe clean up unused navigation
- Cross-device usage: our tracking code opts you into using Google Signals by default, which can link journeys across different devices (e.g. views ad on mobile, then purchases on laptop). It’s important to optimize what content is shown on which screen size.
This piece was written alongside Ed Upton from Littledata – if you have any questions about Shopify and Google Analytics, please feel free to leave them in comments below or contact Littledata directly.