Halo attribution is a marketing method that measures the impact of ads beyond direct platform attribution by examining brand connections and product categories. This approach extends the default platform attribution algorithm with additional mechanisms and varied settings, offering a broader understanding of ad impact. Within halo attribution, there are two primary types:

• Click attribution
• Impression view-through attribution

Click attribution

Click attribution is a method that connects purchases to an ad, tracking conversions even if the purchase involves different but related products, provided they occur within the same session.

Impression view-through attribution

Impression view-through attribution tracks whether viewing an ad (without clicking it) leads to a purchase of the same product within the same session. For instance, if you see an ad for 2L Acme Milk and later buy it without viewing any other ads in between, it is considered impression view-through attribution.

How halo attribution process works?

To enable precise halo attribution, ensure that each product catalog entry includes a specified brand value. This is crucial for accurate attribution calculations.

To activate advanced attribution methods, follow these steps:

Activate enhanced attribution

• Work with your Customer Integration Engineer (CIE) to enable advanced attribution methods, including:
  • View-through conversions: Track purchases resulting from ads viewed but not clicked.
  • Halo click conversions: Identify purchases within the same brand halo.

Configure lookback window

• Define a unique lookback period for tracking conversions independent of standard settings, allowing for campaign-specific adjustments.
• Differentiate attribution windows for view-through versus halo click attributions.

Structure product catalog taxonomies

Organize your product catalog into different taxonomy levels to improve tracking and attribution accuracy, considering both brand and category relationships:

Brand level (level 1)

Attributes purchases within the brand. For example, if a user clicks on an ad for 2L Acme Milk and later buys 500g Acme Yogurt, both products fall under the Acme brand, thereby attributing the yogurt purchase to the initial milk ad.

Category level (level 2)

Attributes across a broader category. For example, if a user clicks on an ad for 2L Acme Milk (Dairy > Milk) and buys 500g Acme Yogurt (Dairy > Yogurt), both items are classified under Dairy, supporting attribution at Category Level 2 even though they are different subcategories. The yogurt purchase is attributed to the milk ad because both are dairy products under the brand Acme.

Subcategory level (level 3)

Requires precise subcategory matching. For example, if a user purchases Acme Cheese (taxonomy: Dairy > Cheese) after viewing an ad for Milk (taxonomy: Dairy > Milk), although both belong to the Dairy category, they do not share the same specific subcategory. Thus, under Subcategory Level 3, the cheese purchase would not be attributed to the milk ad, demonstrating the stricter requirement for shared subcategories.