Optimize Campaigns with Multi-Touch Attribution in Hector

We meticulously planned the presentation of the Multi-Touch Attribution report from Amazon Marketing Cloud and we are excited to share the final look at how you can evaluate the performance of a campaign on Hector.

Every campaign is designed for a specific goal, and each one deserves its fair share of credit before being evaluated.

So, we decided to show all four attribution results for each campaign you run. This way, you can assess whether your broad keyword campaign contributed to the final conversion. If it did, you can determine the ROAS for the campaign based on the first-touch attribution. 

Why should you consider evaluating multi-touch attribution?

Customer acquisition is not linear.
Shoppers engage with multiple ads before making a purchase.
Each ad influences the customer’s buying decision.
Amazon attributes 100% of the purchase credit to the last touch.
The last keyword/search term/campaign takes all the credit for a purchase.
Customers search for multiple keywords before buying.
They view/click on multiple ads before making a purchase.
Each view/click deserves a portion of the credit.
Performance should be evaluated using the right touchpoint model.

Let’s take an example to understand campaign optimization and the different types of attribution models:

You notice that a long-tail keyword has a better ROAS than a single-word keyword, so you decide to spend less on the single-word keyword.

You lower bids and even reduce budgets for those keywords.

This is because last-touch attribution gives credit to the long-tail keyword and its campaign.

But we know that customers search multiple keywords before making a purchase.

So, how do we find out which keywords they searched before landing on that long-tail keyword and completing the purchase?

In AMC, there’s an option to view the first and last search terms a customer used before making a purchase; we’ll cover this later.

Now, what if Amazon gave full credit to the first keyword searched and clicked? In that case, the single-word keyword and campaign would show a better ROAS than the last-touch keyword or campaign.

What if you don’t want to give all the credit to either the first or last touch?

You can choose to distribute equal credit to both first- and last-touch keywords, this is called the linear attribution model.

What if you prefer to assign 30% credit to the first touch and 70% to the last touch? In that case, you can use the position-based attribution model.

Hope this helps you understand why evaluating different attribution models is crucial for campaign optimization.

With Hector, this is now possible. All your SP, SB, SD, and DSP campaigns can be evaluated using different attribution models.

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