Digital Analytics and Measurement Tools Evaluationv1.0

1. Introduction

Digital analytics covers a growing set of use cases as more and more of our lives are mediated by digital platforms. This includes:

• Marketing Analytics: Measuring the return on marketing spend, especially from digital channels.
• Product Analytics: Helping product teams understand the impact of their product developments on revenue, customer lifetime value, conversion rates, and retention rates and churn rates.
• Merchandising Analytics: Relevant for retailers that want to optimize their online offer by understanding the performance of different stock keeping units (SKU).

As digital analytics has become more sophisticated, there has been a move to performing more analytics in the warehouse. Google has supported and driven this trend with the native BigQuery integration with GA4. Snowplow has done something similar. It is a warehouse-first analytics tool, delivering all the data into the data warehouse (i.e., BigQuery, Snowflake, Databricks, Redshift) in near real time.

With these differences in mind, we performed a field test to assess how Snowplow compares to GA4 for a retail organization that realizes the mandate for digital analytics. To make the test as fair as possible, we used both Google Analytics and Snowplow in vanilla e-commerce implementations. This means that for both solutions, we used the out-of-the-box e-commerce events.

Both tools support the definition of custom events, but to make the comparison like-for-like, we stuck to the implementation that a retailer is most likely to deploy. We utilized the Snowplow out-of-the-box e-commerce accelerator. Snowplow Accelerators are recipes/templates that enable Snowplow users to execute specific use cases rapidly. Snowplow E-commerce Accelerators allow online retailers to get started with Snowplow quickly, delivering data to power a wide range of e-commerce analytics out-of-the-box. The accelerators provide a standard way to set up e-commerce tracking (including tracking product views, add-to baskets, and transactions), and data models that optimize delivery of the data for analytics and AI.

Google Analytics has standard out-of-the-box e-commerce events (schemas), which are comparable to those that are part of the Snowplow accelerator. The Snowplow accelerator also includes dbt models that process the data in the data warehouse to make it AI and BI ready, and Google Analytics lacks an equivalent to this. But this is half of what the accelerator is—the other half is the schemas, which Google does have.