Amazon Advertising Attribution Models Explained - Beyond Last Touch PPC Analytics
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Learn about Amazon advertising attribution models beyond the default last touch attribution in Amazon Campaign Manager. This video explains the four main attribution models available for Amazon PPC campaigns and how to access them through Amazon Marketing Cloud.
I cover the four attribution models you can use to measure your Amazon ads performance. First touch attribution gives all credit to the first ad interaction, which helps you understand how customers discover your brand initially. Last touch attribution is the default setting in Campaign Manager and assigns all credit to the final clicked ad before conversion.
Linear attribution splits credit equally across all touchpoints in the customer journey. If a customer sees your sponsored display ad, streaming TV ad, and sponsored product ad, each campaign gets 33% attribution credit. Position based attribution allows customization where you can assign different percentages to first and last touches, like 70% to first click and 30% to last click.
The video demonstrates how to access these attribution reports through Amazon Marketing Cloud. You can find attribution use cases by searching for attribution in the use cases section. Running these different attribution models helps you understand the true impact of your Amazon advertising campaigns rather than relying solely on last touch data.
This is particularly valuable for measuring brand awareness campaigns versus conversion oriented campaigns. Each attribution model provides different insights into your Amazon PPC performance and customer journey analysis.
Contents: 0:00 Introduction to Attribution Models 0:30 First Touch Attribution Explained 1:12 Last Touch Attribution Details 1:59 Linear Attribution Model 2:48 Position Based Attribution 3:13 How to Access Reports in Amazon Marketing Cloud 3:48 Why Different Attribution Models Matter 4:02 Next Steps and Conclusion
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Transcript
Frequently asked questions
What are the four attribution models available through Amazon Marketing Cloud and how do they differ?
First-touch gives all conversion credit to the first ad a shopper interacted with, making it useful for understanding how customers discover your brand. Last-touch, the default in Campaign Manager, gives all credit to the final clicked ad before purchase, which tends to favor lower-funnel campaigns like branded Sponsored Products. Linear splits credit equally across every touchpoint in the customer journey, so if a shopper saw a Sponsored Display, a Streaming TV ad, and a Sponsored Product ad, each gets 33%. Position-based lets you assign custom weights to the first and last interactions (for example 70% to the first click and 30% to the last), giving you flexibility to reflect what matters most in your specific strategy.
Why does relying only on last-touch attribution in Amazon Campaign Manager give an incomplete picture of campaign performance?
Last-touch ignores everything that happened before the final click, which means upper-funnel campaigns like Sponsored Display, Streaming TV, or DSP get no credit for the role they played in building awareness or nudging a shopper toward the purchase. A customer who saw your DSP ad, then a Sponsored Brands ad, and finally converted through a branded Sponsored Products click would make that branded keyword look like the sole driver of the sale, potentially leading you to cut the awareness campaigns that were doing the real work earlier in the journey.
How do you access multi-touch attribution reports in Amazon Marketing Cloud?
Log in to Amazon Marketing Cloud, navigate to the Use Cases section, and search for "attribution." You will find pre-built instructional query templates for each of the four models: first-touch, last-touch, linear, and position-based. Running these reports alongside your standard Campaign Manager data lets you compare how credit is distributed across your campaigns under different models and identify which ad types are contributing more to conversions than last-touch reporting alone would suggest.
