Google Analytics Remarketing Audiences for E-Commerce

If you’re advertising your products through Google AdWords, it’s likely you are using remarketing as part of your overall strategy. However, if you are only building audiences through AdWords you are missing out on a lot of great targeting features available in Google Analytics.

In Google AdWords, you are limited to creating audiences based on pages people did or did not visit. In Google Analytics, you can create audiences around all of the user data in your account. Audiences are not all created equal, and some will be much more valuable than others, so you don’t want to have a one-size fits all remarketing campaign.

Segmentation of remarketing audiences is critical so you can prioritize your remarketing efforts. Let’s take a look at some audiences e-commerce advertisers should be building in Google Analytics.

Cart Abandoners

Cart abandoners are people who have added products to the shopping cart but have not completed the purchase process. Now, you can define this with URLs in AdWords but Analytics allows you to segment based on what types of products were in the cart, making your remarketing efforts even that much more effective.

If your audience size is large enough, it’s also a good idea to breakdown cart abandoners based on how far they got in the checkout process. For example, you can have a separate audience for people who added items to the cart vs people who added items to the cart and entered payment information before exiting.

New Customers

New customers are people who have only made 1 purchase on your site. You can define this audience in Google Analytics using the transactions per user filter and setting it equal to 1.

Since it’s typically cheaper to retain customers than it is to acquire new ones, you’ll want to try and turn these new customers into repeat purchasers.

Repeat Purchasers

Repeat purchasers are people who have made more than 1 transaction on your site. You’ll build this the same way as New Users, but set the transactions per user filter to greater than 1.

You can also segment this further by different frequencies. Other than frequency, you can also find customers who spend more than the average on your site. These are your most profitable customers.

High-Spend Purchasers

If you know that the average revenue per customer is $500, you can use this create an audience for those customers who spend more than the average. You can use the revenue per user filter and set the threshold that makes sense for your business.

You should also create an audience for lower spending customers. They aren’t as profitable but are still important to retarget to.

Customers Who Purchased Certain Products

Just like you can target people who added certain items to their cart, you can also create audiences for people who purchased certain products or product categories.

You can use these audiences to target ads for complimentary products you carry. For example, if someone has purchased a dress you might want to show them ads for heels or clutches.

Audience Demographics

Another great thing about Google Analytics is that it allows you to use demographic data when creating audiences. You can target people based on age, gender, location, etc.

For example, if you have an upcoming promotion for women’s clothing you can use demographic targeting to build an audience for female purchasers.

Layering Audiences

Each of these audiences it great on its own, but can become even more powerful when you start to layer targeting. For example, you can target repeat purchasers whose average revenue per session is in the top 10% of all orders. This would be an incredibly high-value audience as these people shop often and spend a lot.

I always advocate for segmenting as much as possible to have the best control over your marketing. However, you’ll need to keep in mind the size of the audience you’re working with. You need at least 100 active users in the last 30 days to retargeting to an audience on the Display network.

What are some other audiences you’ve found to be valuable in Google Analytics? Let us know in the comments below!