Server-Side Tracking Metrics for e-commerce
This add-on provides anonymous default device metrics (such as screen resolution and network bandwidth), along with e-commerce-specific metrics. With this enriched data in your BigQuery dataset from server-side Google Tag Manager, you'll be able to:
- Enhance Personalization: Utilize the data to create more personalized shopping experiences. By understanding user interactions and device metrics, you can tailor product recommendations and promotions based on user behavior and preferences.
- Optimize Ad Campaigns: Analyze how different metrics correlate with purchase behaviors and ad engagement. This helps in creating targeted ads that resonate with users' shopping patterns and device setups.
- Improve Product Recommendations: Use interaction and device data to recommend products that match users' shopping behaviors, leading to higher conversion rates and customer satisfaction.
- Segment Customers Effectively: Perform cohort analysis to understand how different user segments interact with your site and respond to marketing efforts over time.
| Field Name | Data Type | Description | Source |
|---|---|---|---|
session_id | STRING | Unique identifier for each shopping session. | Custom |
timestamp | TIMESTAMP | The time when the data was collected. | Default |
screen_resolution | STRING | Screen resolution in the format width x height. | Default |
viewport_size | STRING | Viewport size in the format width x height. | Default |
pixel_density | FLOAT | Device pixel ratio (DPR). | Default |
browser_name | STRING | Browser name and version. | Default |
operating_system | STRING | Operating system and version. | Default |
source | STRING | Origin of the traffic (e.g., Direct, Google, Social). | Default |
medium | STRING | Medium of the traffic (e.g., Organic, Ads, Email). | Default |
add_to_cart | BOOLEAN | Indicates if a product was added to the cart. | Custom |
purchase | BOOLEAN | Indicates if a purchase was completed. | Custom |
product_id | STRING | Unique identifier for the product viewed or purchased. | Custom |
product_category | STRING | Category of the product viewed or purchased. | Custom |
cart_value | FLOAT | The total value of items in the cart. | Custom |
page_view | STRING | The page or section of the website viewed. | Default |
checkout_start | BOOLEAN | Indicates if the user started the checkout process. | Custom |
checkout_complete | BOOLEAN | Indicates if the user completed the checkout process. | Custom |
Explanation:
-
Custom Metrics: These metrics, such as
add_to_cart,purchase, andcart_value, are specific to e-commerce and will need to be set up via your own JavaScript or tagging system like client Google Tag Manager. -
Default Metrics: These metrics, like
screen_resolution,browser_name, andsource, are automatically collected using standard JavaScript APIs. These provide insights into the user's device and browsing environment.
Case Study: Enhancing e-commerce Performance with Server-Side Metrics
In the competitive world of e-commerce, understanding user interactions and device capabilities can provide valuable insights for optimizing your website and marketing strategies. By collecting anonymous metrics using server-side Google Tag Manager, you can:
Optimize Ad Campaigns
Target users with ads for products that align with their shopping behaviors and device capabilities. For example, users who frequently add items to their cart but don’t purchase might receive ads with special offers or discounts to encourage conversion.
Improve Product Recommendations
Use metrics like add_to_cart and product_category to recommend products relevant to users' interests. Personalizing recommendations based on past interactions and device performance can lead to higher conversion rates and increased sales.
Enhance Checkout Experience
Analyze metrics such as checkout_start and checkout_complete to identify and address any friction points in the checkout process. Improving the user experience during checkout can reduce cart abandonment rates and increase completed purchases.
Segment Your Audience
Create detailed audience segments based on e-commerce behaviors, such as high-value customers or frequent browsers. Tailor your marketing campaigns and product recommendations to these segments to improve engagement and drive sales.
By leveraging these metrics, you can enhance user engagement, optimize marketing efforts, and drive higher sales through a more personalized and responsive e-commerce experience.