By now, you’ve probably heard from someone at Amazon or your agency that you should consider collaborating your first-party data with the Amazon Marketing Cloud. The impetus to collaborate first party data is for the express reason that it can elevate your advertising strategy for both audience creation and measurement.

We think this is particularly impactful for Streaming TV ads, and so we thought we’d outline the three primary benefits of collaborating first-party data with Amazon Ads.

Exclusion Audiences

Reaching new customers and focusing on driving new to brand sales is a common strategy for awareness campaigns like Streaming TV ads. To achieve this, marketers often exclude past purchasers and past product viewers for specific lookback windows. But without incorporating first-party signals, marketers narrow the characterization of new to brand to Amazon only, excluding new to brand customers across their own first-party channels.

By collaborating first-party data for exclusion audiences, marketers can exclude past purchasers and past product viewers on both Amazon and their first-party sources. This means marketers are able to drive actual new to brand sales, ensuring they reach all new customers - not just new customers limited to Amazon.

Additionally with Gigi, marketers can choose specific purchasing behaviours they may want to exclude. For example, it’s possible the TV campaign is focusing on a newly launched product, and the goal of the campaign is to acquire new customers for this product specifically. With Gigi, a marketer is able to exclude purchasers of this specific product on both Amazon and their first-party channel while allowing for the inclusion of customers who have purchased all other products of a brand.

Inclusion Audiences

When building lookalike audiences, marketers typically want as much signal as possible to create seed audiences—this naturally manifests in extending a lookback window as far as possible. For example, suppose you wanted to build a seed audience of your best customers by lifetime value (LTV). In that case, you’d want to extend the lookback window to capture as many repeat purchases at your brand as possible to determine an accurate LTV. But channel extension can play just as important role in analyzing your entire customer base across Amazon and your first-party sources for seed audiences. First-party data collaboration unlocks channel extension, which provides a vast pool of metadata for building high signal seed audiences for lookalikes within Amazon ads.

Additionally, customers often don't restrict their purchases to only one channel. By collaborating first-party data with Amazon, a marketer is able to build more sophisticated audiences of where people shop and target those audiences for their Streaming TV campaigns. For instance, if the goal of a campaign is to drive purchases on a first-party channel and not Amazon, it’s possible that a marketer would want to create a seed audience of only people who have purchased on their first-party channel and not Amazon. None of this would be possible without first-party data collaboration.

Deterministic Omnichannel Measurement

This is probably the top reason to collaborate first-party data with Amazon. Compared to linear advertising, one of the primary benefits of Streaming TV advertising is the ability to measure outcomes. We’re not here to say that TV should be viewed as a performance channel, nor do we believe that one should be looking at ROAS as a success metric. However, since all Streaming TV viewers are logged in with a distinct profile, marketers can match a cohort of viewers of a Streaming TV ad to a cohort of behaviours (or outcomes) to logged in users on Amazon or first-party channels.

The further up the funnel your advertising strategy, the less control you have over where these outcomes occur. Where an Amazon Sponsored Product ad (at the bottom of the funnel) will be most restricted to sales on Amazon, an Amazon Streaming TV ad may lead to sales across Amazon, first-party channels, or retail. So, as a marketer, if you’re measuring outcomes - like assisted sales (where the TV ad was a touchpoint anywhere on the customer journey leading to a sale) - then it is in your best interest to capture as many potential channels for measuring the efficacy of your Streaming TV ads. By collaborating first-party data with Amazon, one is able to capture outcomes of the channel they control. Together with outcomes on Amazon, a marketer can ascertain a fuller picture of the effect of their awareness efforts.

Let’s use an example: You’re a brand in which 35% of your sales come from Amazon, 15% of your sales come from your DTC site, and the remaining 50% of sales are spread across a variety of retail channels. Assuming your sales post-STV ad exposure are proportionately distributed across all those channels, then by incorporating your first-party channel, your STV campaign immediately improves by 15%. That’s a big deal.

Moreover, if you scope out a little further, you can use this share of sales distribution to build your broader TV budget. Think about it. If, through first-party data collaboration, Amazon Streaming TV ads are uniquely able to deterministically capture 50% of your total sales then why shouldn’t your Amazon Streaming TV budget be roughly 50% of your total TV budget?

At Gigi, we’ve built the Gigi 1P Platform so every advertiser can realize the benefits of first party data collaboration with Amazon. If you’d like to learn more, please check out www.gigico.tv