Back in 2020, Google announced the launch of Google Analytics 4 (GA4), the successor to Universal Analytics that would usher in a new era of privacy-first analytics, powered by machine learning.
The solution was developed in response to shifts in the regulatory and technological landscapes, including the introduction of GDPR (General Data Protection Regulation) in Europe and the UK, CCPA (California Consumer Privacy Act) in California, and Apple’s iOS 14.5 update which first introduced its App Tracking Transparency (ATT) policy.
As the first of two scheduled sunset dates for Universal Analytics users approaches on July 1st, 2023, we sat down with two analytics experts at Reprise – Chris Schimkat, Global Head of Analytics and Felipe Vallejos, Regional Performance Director, Media & Analytics – LATAM – to find out how GA4 can help brands to improve their DTC (direct to consumer) eCommerce operations.
Here’s what we found out…
GA4 introduces a new approach to data collection and analysis that is better suited for today’s multi-device, multi-platform, and privacy-focused digital environment, utilizing cross-device tracking, improved user privacy controls, machine learning-powered insights, and integration with Google Ads and other Google marketing tools.
For eCommerce brands selling DTC, that means a simpler setup with better reporting, and a much greater level of detail in the kinds of data that can be synced with GA4.
It also introduces a new data model that allows businesses to unify their user data across different platforms and devices, making it easier to understand how users interact with their digital properties and make informed decisions about marketing strategies and website optimization.
Sequencing and funnel analysis
GA4’s event-based model enables brands to run much more detailed sequencing analysis than was previously possible under its Category/Action/Label taxonomy.
Brands can now track which products and categories are viewed together in the same session, identify crossovers, and tailor advertising and onsite personalization by recommending products and content that are relevant to each customer.
Crucially, brands can now accurately map customers’ journeys to purchase and use this information to identify product adjacencies, re-order PLPs (Product List Pages) to highlight best-selling or high-margin products, create new product bundles and more.
GA4 also makes it possible to identify customer journeys which did not convert to a purchase, enabling brands to spot areas of their website that are causing frustration or confusion for users. This information can be used to improve website design and UX – such as removing unnecessary steps from the checkout process or improving navigation to make it easier for customers to find what they’re looking for.
Cohort analysis enables brands to divide users into groups based on shared characteristics, and then track user behavior over time.
For eCommerce brands, this is a valuable tool for identifying cohorts of users that return to the website, and the time between visits. For brands selling products with recurring revenue potential, this is a particularly powerful tool, as the customer may return weekly, monthly, or otherwise.
Understanding these patterns enables brands to target consumers with advertising in the right place, at the right time, improving the customer experience whilst creating a leaner advertising operation.
ERP integration and lookalike targeting
By connecting GA4 with an ERP (Enterprise Resource Planning) software, brands can analyze various touchpoints over longer sales cycles, including ones where the purchase takes place offline.
One example is the automotive industry where much of the purchase journey takes place online before an offline purchase. By stitching a customer’s online and offline behaviors we are able to understand the complete customer journey and even create audiences based on offline behaviors, such as lookalike targeting.
If a consumer books a test drive, and then makes a purchase in a dealership with a salesperson, it’s possible to track this purchase all the way back to the initial online touchpoint and start lookalike targeting to find more profiles like the one which made the purchase.
GA4 can also help brands to understand which interactions on their website led to a customer starting the offline aspect of their journey. Taking this one step further, brands can use this data for propensity modelling – looking at which interactions on the site help to predict who will book a test drive, but also who will purchase the car at the end.
RFM (Recency, Frequency and Monetary value) segmentation enables brands to put people into buckets based on how recently they bought, how often they buy, and what the average order value or total lifetime value of that customer is.
RFM segmentation enables brands to identify HVAs (High Value Audiences) and tailor their marketing efforts accordingly. It also makes it possible to suppress audiences that bought recently and are unlikely to buy again soon, ensuring money isn’t wasted on targeting the wrong prospects.
Businesses – such as Quick Service Restaurant brands – with an app and a website, can use GA4 to combine the two datasets, and use loyalty program information to find customers that return daily or weekly and target them with relevant, personalized deals or promotions.
GA4 can also be used to reduce ad spending on audiences that are unlikely to be receptive to further promotion. If a customer has made a purchase with a brand, and they are unlikely to return to site to make another purchase, GA4 can help to identify audiences to be suppressed.
GA4 can also identify defecting customers, who previously purchased from a brand with some regularity and then did not return. These customers can be added to a reengagement sequence to hopefully regain their business.
Using the data import feature within GA4 means that it can be used to conduct margin analysis, whereby the cost-to-brand of each item is inputted, and the gross profit margin generated for every sale on the website can be calculated. Returns data can also be entered into GA4, and as one of the biggest barriers to eCommerce profitability, this is an important feature for brands to make use of.
This data can then be pushed into the Google ads platform, and brands can begin to optimize their paid media strategy towards not only revenue, but also profitability goals.
In conclusion, the advent of Google Analytics 4 (GA4) offers a comprehensive solution to the evolving regulatory and technological landscape in the world of analytics. With its advanced features such as sequencing and funnel analysis, cohort analysis, ERP integration, RFM segmentation, audience suppression, and margin analysis, GA4 is a game-changer for DTC eCommerce brands.
By utilizing these powerful tools, businesses can gain a deeper understanding of customer behavior, optimize marketing strategies, and ultimately improve their overall profitability. As the sunset date for Universal Analytics approaches, brands that adopt GA4 will be better equipped to navigate the complexities of today’s multi-device, multi-platform, and privacy-focused digital environment, while staying ahead of the competition.
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