Better data, better decisions: Data enrichment in Server-Side Tag Manager

Success in digital marketing largely depends on the quality of available data. Server-Side Tracking and the associated data enrichment offer the opportunity to gain deeper insights, make more precise decisions, and ultimately use advertising budgets more efficiently.

Optimization based on POAS instead of ROAS

A decisive advantage for companies is the optimization of POAS (Profit on Advertising Spend), which allows for a more precise evaluation of the profitability of marketing measures than the conventional ROAS (Return on Advertising Spend) model. This can be a decisive competitive advantage, especially when there are strong margin fluctuations in the product range.

Event Value Attribution

Business models with longer decision-making processes can particularly benefit from Event Value Attribution between various e-commerce signals such as (add_to_cart purchase). This provides a more accurate capture of the customer journey and helps maximize early purchase signals.

Business data

Furthermore, the integration of external business data, such as customer groups or scoring values from other systems, allows for improving the data situation and, for example, moving customers into specific target groups in real-time.

A Server-Side Tag Manager (SSTM) is a solution for managing and controlling tags that are executed on a server instead of on the client (browser). Unlike traditional client-side tag managers, SSTM offers numerous advantages, including improved performance, increased security, and better data control.

• Improved performance: As tags are processed on the server, the loading time of the website is reduced.
• Security: Sensitive data is handled more securely as it is not processed directly in the browser.
• Data control: Better control over collected data and its forwarding to various tools and platforms.
• Data enrichment: from other sources

Server Side Tag Managers are thus advantageous for various reasons. However, this article will focus on the topic of data enrichment in the following.

To enrich data in the Server Side Tag Manager, we need a data basis that is available in real-time. Firestore in Google Cloud provides this. The data is transferred from a data source like BigQuery to Firestore and later retrieved through a Firestore variable in the Server Side Tag Manager.

While ROAS (Revenue on Ad Spend) is a common metric in online marketing that measures revenue in relation to advertising expenses, POAS (Profit on Ad Spend) takes a crucial step further. POAS focuses on actual profit and considers not only revenue but also the direct costs and margins of a product or service. This results in normalizing income in relation to expenses.

Target audience: Google Ads e-commerce customers with highly variable margins in their product range.

Example:

• ROAS: If a company spends 100 euros on advertising and generates 500 euros in revenue, the ROAS is 5 (500 / 100).
• POAS: Here, the profit from the 500 euros revenue is considered after deducting costs (e.g., production, storage, or shipping costs). If the profit after costs is 200 euros, the POAS is 2 (200 / 100).

Calculation of POAS:

• Profit = (Selling price – VAT – Purchase price of goods * (1 – Return rate))

Data sources:

• Selling price: From the customer’s shopping cart
• VAT: From the customer’s shopping cart
• Purchase price: From the shop system
• Return rate: Calculated from the shop system

In this specific case, the customer transfers the purchase prices of the products into their shop system. Since we have an internal data transfer from the shop system to BigQuery, it was easy to use the product data field from Shopware to perform the calculation.

Once the profit data is available in Firestore, it must be retrieved through a variable. There are already ready-made solutions for this, such as Sorteria from sgtm-pantheon.

According to the Git repo, the naming is derived as follows:

Sorteria – Goddess of safety, and deliverance and preservation from harm. Like the goddess, this project provides safety from end users to your sensitive value data (e.g. profit).

Among other things, Sorteria addresses the problem that profit values must be calculated per shopping cart item and then output as a total amount. Details can be found here.

Example:

• Article 1: Revenue 100€, Profit 50€, Quantity 1
• Article 2: Revenue 20€, Profit 13€, Quantity 2
• Total: Revenue 140€, Profit 76€

Once the value is fully calculated, it can be passed on to any conversion tag so that the modified value is then recorded. For analysis purposes, however, it is always recommended to track the normal revenue value in a parallel event, so that direct comparisons are always possible.

For the client, this creates a comfortable situation where their POAS data, unlike revenue, is normalized. This means the result value always represents the same amount of business value and this result does not fluctuate between different products. This also makes life easier for the SEA manager, as the client’s requirements can be formulated more clearly in the future.

Event Value Attribution is a process where certain events are assigned a value even before the purchase. It attempts to distribute the sales value ‘fairly’ to important pre-purchase events.

Goal of the process: To reward the bidding system for early events leading to purchase accordingly, with approximately similar ROAS.

Target audience: Google Ads customers with particularly long or complex purchasing processes

Example calculation:

An online shop sells a product for 100 €. Over a period of 90 days, Google Analytics measured 10 purchase and 100 add_to_cart events for the product. In the conversion measurement, the sales value should now be divided between add_to_cart and purchase so that the sales value of 100€ is approximately reached.

10 (purchase) / 100 (add_to_cart) = 0.1 add_to_cart_rate

(add_to_cart_rate * value) / 2 (number of event names) = 5 € add_to_cart_value

value / 2 (number of event names) = 50 purchase_value

The illustration shows how GA4 data from BigQuery can be used to make it available for Firestore. The data transfer to Firestore generally works with this Cloud Function.

To determine the add_to_cart_rate, it should be calculated for several levels. This is easiest in BigQuery with a SUM() OVER() window function. This way, we get not only the add_to_cart_ratio at the product level for each row, but also for the levels item_group_id, product_type, brand, and total.

In order to get the most relevant number as the final add_to_cart_ratio, we select it using a CASE WHEN statement.

The CASE statement ensures that a level is only used if more than 10 add_to_cart actions are available. If the action is not sufficiently available for the levels product, item_group_id, product_type, brand, total is taken, which then represents the shop level.

The method is useful for customers when there is a longer customer journey before the purchase or when a device change without login is likely.

This allows e-commerce companies to incorporate early purchase signals as relevant rewards into the bidding system without having to live with the fear of setting wrong incentives on a large scale, as the value distribution takes place dynamically based on the old movement data.

Data enrichment in the Server-Side Tag Manager offers significant advantages for companies in digital marketing. By focusing on POAS (Profit on Ad Spend) instead of ROAS (Return on Ad Spend), companies can more accurately capture the actual profitability of their marketing efforts, which is particularly crucial for product ranges with highly varying margins. Event Value Attribution allows for appropriate consideration of pre-purchase events such as “add_to_cart”, enabling better representation of longer and more complex customer journeys.

Technically speaking, the use of a Server-Side Tag Manager offers improved performance, increased security, and better control over collected data. The integration of real-time data sources such as Firestore and BigQuery allows complex calculations to be performed directly in the Tag Manager and efficiently incorporates external business data. Tools like Sorteria facilitate the calculation and aggregation of profit values at the product level.

For SEA managers and marketing teams, this means a more precise data foundation on which to base informed decisions. Advertising budgets can be used more efficiently, as campaign performance evaluation is based on actual profits rather than just revenues. The dynamic value distribution minimizes the risk of misaligned incentives and maximizes the benefit from early purchase signals.

• Google Cloud Function to push to Firestore
• Sorteria – Value Based Bidding Cloud Function from Google
• Server Side Tag Manager Pantheon of Gods