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Instructional Query: Custom Attribution First Touch
Instructional Query: Custom Attribution First Touch

Learn how to use the instructional query, Custom Attribution First Touch, on Intentwise Explore (AMC).

Ankita Goyal avatar
Written by Ankita Goyal
Updated over a week ago

What are instructional queries?

Amazon Marketing Cloud's (AMC) instructional queries provide pre-written SQL code that AMC users can use as is or modify for common measurement and analytics tasks.

Custom Attribution First Touch

The Custom Attribution First Touch instructional query helps advertisers identify customers' first touchpoints in the conversion journey. This model attributes 100% of the credit to the first touchpoint, offering insights into the most effective channels for attracting customers. By analyzing the results, you can understand how your advertising campaigns impact customer conversions and optimize your strategies accordingly.

By analyzing the query results, you'll gain insights into the effectiveness of different advertising channels as initial touchpoints. For example, a high conversion rate for a specific campaign might suggest investing more in that channel for customer engagement. You can also compare different attribution models, like last-touch attribution, for a complete view of the customer journey.

The query provides detailed metrics for each campaign, such as impressions, clicks, user reach, conversions, conversion rate, total units sold, and total product sales. For instance, it might show that a sponsored product campaign had the highest conversion rate (4.1% in this example), indicating its effectiveness as a first touchpoint. These insights can help you make informed decisions to optimize your campaigns.
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To use this query, you'll need data from multiple ad campaigns, including tables like dsp_impressions, dsp_clicks, sponsored_ads_traffic, and conversions_with_relevance. For tailored insights, you can customize the query based on your campaign types, conversion metrics, lookback windows, and traffic qualifications.

For more information, refer to our data model.

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