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Web Analytics Dimension Package Technical Specifications
The Web Analytics Dimension Package Set includes standard dimension packages derived from a standardized data extension–based data model. It brings web analytics data into Audience Builder without needing custom implementation.
The Audience Builder Web Analytics Dimension Package Set contains these components.
- Data extension templates in Email Studio
- Dimension package set in Audience Builder
These data extensions are required. Review the fields and details in the tables.
- Standard Page Browse data extension
- Standard Cart Abandon data extension
- Standard Product Purchase data extension
- Standard Product Catalog 3-Level data extension
Standard Page Browse Data Extension
This data extension acts as a universal page or product browse event repository. While some analytics providers separate this information into two or more output files, we chose to merge them for the sake of simplicity. For ProductID values, we perform a lookup on the Product Catalog Data Extension (STD_PRODUCT_CATALOG_3LEVEL) and use the related product data to populate various dimension packages with the related Product Data.
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STD_PAGE_BROWSE
| Field Name | Description and Comments | Data Type | Primary Key | Required |
|---|---|---|---|---|
| SubscriberKey | The subscriber key includes a client-specific value supplied in your data. Because this value often contains an email address or other PII-type data, some web analytics providers choose to use the Subscriber ID bigint value from Marketing Cloud Engagement. For SubscriberID values in the native data load, either perform a join before loading data into Engagement or after loading data via query activity using Data Views. | Text (100) | Y | Y |
| BrowseDate | Date of browse event | Date - MM/DD/YYYY | Y | Y |
| PageName | Typically the page title or product name browsed | Text (200) | Y | Y |
| PageCategory | Optional page category as supported by some web analytics providers | Text (200) | N | |
| ProductID | This value includes the foreign key to the STD_PRODUCT_CATALOG_3LEVEL data extension. When using the ProductID value, we use the related data contained within the Product Catalog to build product-specific browse dimension packages. | Text (100) | N |
Standard Cart Abandon Data Extension
This data extension acts as an abandoned cart event repository.
| Related Dimension Packages | |
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STD_CART_ABANDON
| Field Name | Description and Comments | Data Type | Primary Key | Required | ||
|---|---|---|---|---|---|---|
| SubscriberKey | The subscriber key includes a client-specific value supplied in your data. Because this value often contains an email address or other PII-type data, some web analytics providers choose to use the Subscriber ID bigint value from Marketing Cloud Engagement. For SubscriberID values in the native data load, either perform a join before loading data into Engagement or after loading data via query activity using Data Views. | Text (100) | Y | Y | ||
| AbandonDate | Date of abandon event | Date - MM/DD/YYYY | Y | Y | ||
| ProductID | This value includes the foreign key to the STD_PRODUCT_CATALOG_3LEVEL data extension. When using the ProductID value, we use the related data contained within the Product Catalog to build product-specific browse dimension packages. | Text (100) | Y | |||
Standard Product Purchase Data Extension
This data extension acts as a product purchase event repository.
| Related Dimension Packages | |
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STD_PRODUCT_PURCHASE
| Field Name | Description and Comments | Data Type | Primary Key | Required |
|---|---|---|---|---|
| SubscriberKey | The subscriber key includes a client-specific value supplied in your data. Because this value often contains an email address or other PII-type data, some web analytics providers choose to use the Subscriber ID bigint value from Marketing Cloud Engagement. For SubscriberID values in the native data load, either perform a join before loading data into Engagement or after loading data via query activity using Data Views. | Text (100) | Y | Y |
| PurchaseDate | Date of purchase event | Date - MM/DD/YYYY | Y | Y |
| ProductID | This value includes the foreign key to the STD_PRODUCT_CATALOG_3LEVEL data extension. When using the ProductID value, we use the related data contained within the Product Catalog to build product-specific browse dimension packages. | Text (100) | Y | Y |
Standard Product Catalog 3-Level Data Extension
This data extension acts as the primary product catalog for all items designated as a product on a website. The data contained in this data extension relates to browse, purchase, and abandon events to enrich that event data with metadata to add value to your implementation.
This data extension contains product catalog data up to three levels deep.
(Parent Category (level 1) > Sub Category1 (level 2)> Sub category2 (level 3)> Product)Assume the following structure for levels one through three.
Clothing > Mens > Outer Wear > [ITEM]| Related Dimension Packages | |
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STD_PRODUCT_CATALOG_3LEVEL
| Field Name | Description and Comments | Data Type | Primary Key | Required |
|---|---|---|---|---|
| ProductID | Product ID | Text (100) | Y | Y |
| SKU | Product SKU | Text (100) | Y | Y |
| ProductDescription | Description of the product – the human-readable name of your product | Text (200) | Y | |
| ProductLevel1Id | ID of product root level | Text (100) | Y | |
| ProductLevel1Description | Text (200) | Y | ||
| ProductLevel2Id | ID of product level 2 | Text (100) | N | |
| ProductLevel2Description | Description of level 2 | Text (200) | N | |
| ProductLevel3Id | ID of product level 3 | Text (100) | N | |
| ProductLevel3Description | Description of level 3 | Text (200) | N | |
| ProductUnitPrice | Selling price of the product | Decimal | N | |
| ProductCost | Cost of the product | Decimal | N |
Hierarchical Dimension Packages Example
The related hierarchical dimension packages, including those dimension packages using the term “…By Product Hierarchy” in their names, rely on these listed ProductLevel IDs. Choose whether you use one, two, or three levels of product hierarchy and adhere to that number of levels with your products.
For example, if you sell shoes and you have three levels for men’s shoes, your product catalog items for men’s shoes can look like this.
| Level 1 | Level 2 | Level 3 | Product |
|---|---|---|---|
| Mens | Shoes | Running | [products…] |
| Mens | Shoes | Dress | [products…] |
If your children’s line of shoes only has two levels, your product catalog items for children’s shoes looks like this.
| Level 1 | Level 2 | Level 3 | Product |
|---|---|---|---|
| Childrens | Shoes | null | [products…] |
| Childrens | Shoes | null | [products…] |
Modify your three-level products to fit into two levels or modify your two-level products to fit into three levels.
| Level 1 | Level 2 | Level 3 | Product |
|---|---|---|---|
| Mens | Running Shoes | null | [products…] |
| or | |||
| Children’s | Boys | Shoes | [products…] |
Data Model
